Fear the Walking Dead? : incidence and effects of zombie firms in Europe.
Date: 2018-08-30
Institutions: Joint Research Centre
Subjects: Europe, branch of activity, business policy, cost of capital, debt, economic growth, investment policy, redemption, research report

Fear the Walking Dead? 
Incidence and Effects of Zombie 
Firms in Europe 

Hallak, Issam 

Harasztosi, Péter 

Schich, Sebastian 

2018 

1 

EUR  29238 EN 

This  publication  is  a  Technical  report  by  the  Joint  Research  Centre  (JRC),  the  European  Commission’s  science 
and  knowledge  service.  It  aims  to  provide  evidence-based  scientific  support  to  the  European  policymaking 
process. The scientific output expressed does not imply a policy position of the European Commission. Neither 
the  European  Commission  nor  any  person  acting  on  behalf  of  the  Commission  is  responsible  for  the  use  that 
might  be  made  of  this  publication.  Issam  Hallak  and  Péter  Harasztosi  are  senior  economist  and  economist, 
respectively  at  the  JRC  and  Sebastian  Schich  is  a  senior  economist  at  the  OECD.  The  views  expressed  in  this 
report  do  not  necessarily  reflect  the  views  of  the  European  Commission,  the  OECD  or  any  of  their  member 
countries. 

Contact information 
Name: Issam Hallak 
Address: Via E. Fermi 2749, 21027 Ispra (VA), Italy 
Email: issam.hallak@ec.europa.eu 

JRC Science Hub 
https://ec.europa.eu/jrc 

JRC111915 

EUR 29238 EN 

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ISBN 978-92-79-85969-4 

ISSN 1831-9424 

doi:10.2760/314636 

Luxembourg: Publications Office of the European Union, 2018 

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How to cite this report: Hallak Issam, Péter Harasztosi, and Sebastian Schich, Fear the Walking Dead? Incidence 
and Effects of Zombie Firms in Europe, EUR 29238 EN, Publications Office of the European Union, Luxembourg, 
2018, ISBN 978-92-79-85969-4, doi:10.2760/314636, JRC111915 

All images © European Union 2018. 

i 

Contents 

Abstract .......................................................................................................................................................... 1 

1 

2 

3 

4 

Introduction ........................................................................................................................................... 2 

Related literature .................................................................................................................................. 3 

Data ........................................................................................................................................................ 6 

Incidence of zombie firms..................................................................................................................... 7 

4.1 

4.2 

Share of zombie firms by age and size categories ...................................................................... 7 

Share of zombie firms by country ................................................................................................ 8 

5 

Effects of Zombies on Non-Zombie Firms .......................................................................................... 10 

5.1 

5.2 

5.3 

Effects of zombie firms on investment and growth of non-zombies ....................................... 12 

Effects of zombie firms on labour and capital productivity of non-zombies ........................... 13 

Effects of zombie firms on young non-zombie firms ................................................................ 14 

6 

7 

Persistence of zombie status .............................................................................................................. 16 

Conclusions .......................................................................................................................................... 19 

REFERENCES ................................................................................................................................................. 21 

Annex A – Sample Selection and Zombie Definitions ................................................................................ 22 

Annex B – Definitions of Variables .............................................................................................................. 23 

Annex C – Results using sample close to AAM ........................................................................................... 24 

Annex D – Persistence of zombie status .................................................................................................... 26 

ii 

List of Tables 

Table 1. Number of observations by country and year ............................................................................... 7 
Table 2. Effects of Zombie Firms on Non-Zombie Firms – Growth. .......................................................... 12 
Table 3. Effects of Zombie Firms on Non-Zombie Firms – Productivity ................................................... 13 
Table 4. Effects of Zombie Firms on Young Non-Zombie Firms – Growth. .............................................. 15 
Table 5. Effects of Zombie Firms on Young Non-Zombie Firms – Productivity. ....................................... 16 

List of Figures 

Figure 1. Zombie firms by size and age category, 2013 .............................................................................. 8 
Figure 2. Zombie firms incidence by country (number of zombie firms as of total firms). ....................... 9 
Figure 3. Zombie firm incidence by country (Share of fixed assets of zombie firms as of total assets). 10 
Figure 4. Zombie status persistence ........................................................................................................... 17 
Figure 5. Zombie Firms by Sector ............................................................................................................... 19 

iii 

 
 
 
 
 
 
 
 
Abstract 

The European corporate sector currently lacks investment dynamism and one explanation is that so-
called  zombie  firms  are  spreading  and  that  they  crowd  out  the  growth  of  other,  potentially  more 
“lively”, companies. Zombie firms are firms that apparently are unable to repay their debt and yet, they 
continue operating. The note describes estimates for 2010 and 2013 of the incidence of zombie firms 
across  19  European  countries,  using  firm-level  data  for  more  than  one  million  companies  and 
considering three alternative definitions to ensure robustness of estimates. The note finds that zombie 
firms are spreading in Europe, judged by estimates for 2013 compared to those for 2010. It also finds 
that  the  aggregate  figures  hide  considerable  differences  across  countries.  Zombie  firm  shares  as  of 
overall corporate capital are particularly high in Greece and Spain, while low in the Czech Republic and 
Slovakia. The estimates distinguish between size and age of firms and suggest that larger and older 
firms are more likely to be zombie firms than relatively smaller and younger firms (although very young 
firms of less than three years of age are not included in the sample). The report also finds that the 
growth of zombie firms in terms of employment crowds out the growth of other, non-zombie firms, 
especially young ones. 

1 

1 

Introduction 

There is growing concern among policy makers about the observed lack of investment dynamism in the 
corporate sector. Some recent research has drawn attention in this context to the potential role of so-
called zombie firms. Zombie firms are firms that are not reporting any profits (net of interest paid) for 
an extensive period of time, and yet manage to continue operating. Such firms, if financial textbook 
suggestions were literally applied, are “distressed” and should be considered candidates for liquidation. 
But in practice, they are rarely liquidated. Moreover, their continued existence seems to create some 
negative externalities for the remaining corporate sector in that the former crowd out the growth of 
other, non-Zombie, firms. As a result, countries with a large share of zombie firms would be expected 
to  experience  substantially  more  sluggish  growth  and  investment  dynamics  than  they  would  have 
experienced in the absence of sizeable shares of zombie firms.  

The incidence of zombie firms may be an undesirable effect of the incentives that banking regulation 
creates on the part of banks. Faced with the alternative of restructuring or extending a loan, a bank 
may choose to refinance zombie firms rather than restructuring their loans because the latter would 
reveal substantial losses on existing loans, which would be bad news for shareholders. If the bank’s 
capital is already close to its regulatory threshold, then a large-scale restructuring of its loans might 
trigger the need for costly recapitalization, which would dilute the value of existing shareholders. There 
is in fact evidence that more weakly capitalised banks are more forbearing to their creditors than better 
capitalised banks are (Caballero, Hoshi, Kashyap, 2008; Acharya et al., 2017). Hence, it is possible that 
“zombie banks make zombie firms” (Okamura, 2011). 

The  present  report provides estimates  of  the  incidence  of  zombie firms in Europe and  analyses  the 
potential effects of the presence of such zombie firms on other firms in the same country and sector. 
The report follows Adalet McGowan, Andrews and Millot (2017, henceforth AAM) and Bank of Korea 
(2013),  although  it  goes  beyond  these  studies  in  that  it  considers  several  alternative  definitions  of 
zombie firms to ensure robustness of estimation results. In particular, it alternatively defines zombie 
companies  as  i)  firms  of  any  age  with a  coverage  ratio  (operational earnings,  that  is  earnings  after 
depreciation over interest payments) below one during three consecutive years; ii) mature firms (ten 
years or older) with a coverage ratio below one during three consecutive years; and iii) mature firms 
with a coverage ratio below one during five consecutive years. Young firms, defined as firms that are 
nine years old or younger, are excluded in the latter two definitions as the lack of profits might be a 
temporary (and natural) feature of such firms. In fact, firms often incur losses in the first years of their 
life  for  extended  periods  of  time  because  they  must  invest  until  they  reach  sufficient  operating 
capacities and customer bases, which then allows them to eventually report profits. All three definitions 
aim to capture indebted companies whose debt servicing eats up their profits during a considerable 
stretch of time and yet continue to operate; perhaps because the firm is being kept alive “artificially” 
by its lenders?  

Our main empirical results are as follows: 

•

The share of zombie firms is larger among larger and older firms, thus confirming the results by
AAM.  The  latter  authors consider definition i),  according  to  which even young firms can be
identified as zombie firms. Using that definition, we find that about 3% of 3 to 6 year old firms
are zombies, versus 7% in the case of firms that are between 21 and 40 years old. For large

2 

firms with more than 250 employees, the share is nearly 6%, while it is less than 4% for firms 
with 5-9 employees firms. 

•  Zombies are spreading: There is an increase between 2010 and 2013 in the share of zombie 
firms in Europe and in particular in Greece, Spain, and Portugal. Ireland is characterised by a 
high share as well, although it is decreasing. Slovenia, Belgium and Sweden have comparatively 
lower shares of zombie companies and Hungary and Bulgaria have the lowest shares among 
the sample countries.  

•  Zombies are crowding out the growth of non-zombie forms. To assess the potential interaction 
between  zombies  and  non-zombies,  we  construct  a  measure  for  zombie  congestion  by 
calculating the share of sunk capital by country and sector. We find that the share of zombies 
affects non zombies. In particular, the larger the share of zombies is, the less non-zombie firms 
are likely to grow, although they seem to become more productive to overcome the constraints 
they  seem  to  be  facing.  Zombie  firms  thus  creates  a  “congestion”  Focusing  on  young  non-
zombie firms, we find that they are more strongly affected by the “zombie congestion” than 
older  non-zombies  are.  These  findings  are  in  line  with  AAM  and  they  suggest  that  greater 
incidence of zombie firms tends to hamper the growth of young and potentially fast growing 
firms. As regards the future of zombie firms, we find that smaller and younger zombies stand a 
greater chance of becoming “alive” again and report positive operating profits compared to 
larger and older firms. 

•  Zombies are fairly rarely turning healthy again, especially when they are old. We find that close 
to 90% of zombie firms remain zombies or exit the market in the next two years after they were 
identified as zombies, by any of the three zombie definitions.  

The report is organized as follows. The next section discusses selected previous studies and provides 
some theoretical background. Section 3 describes the data used for the empirical analysis. Section 4 
reports the results of the analysis of the incidence of zombie firms distinguishing between countries, 
firm age and firm size. Section 5 reports the results from estimates of multivariate models assessing the 
effects of the existence of zombie on non-zombie firms. The final section concludes and raises questions 
that could be addressed in follow-up work. 

2  Related literature 

Takeo Hoshi highlighted the issue of zombie firms in Japan in a series of contributions, including Hoshi 
(2006). He observes  that a  large  share  of  Japanese  firms  was  under financial distress but could still 
continue to operate. His analysis focuses on the interest paid by Japanese firms in the 1990’s at the 
level of the individual firm, which he then compares with an estimate of what should be the lowest 
payable rate – as derived from the term structure of (government bond) risk-free rates, taking into 
account the debt maturity structure of the firm under consideration. The analysis reveals that a large 
share of firms made interest payments that were lower than the minimum estimates. Also, those firms 
that  made  such  “abnormally”  low  interest  rate  payments  were  firms  that  were  performing  rather 
poorly, which seems at odds with such low rates being charged. These observations can however be 
reconciled by assuming that banks were financially supporting these firms by lowering nominal interest 

3 

 
 
rates to effectively provide a form of debt forgiveness; Hoshi argues banks were subsidizing firms. While 
such a strategy to refinance loans in the expectation to obtain repayment in the future might in principle 
be more profitable for the bank  than  plainly reducing the reported  value of the loan through  asset 
liquidation, such practises were concentrated in particularly weak corporate sectors with little upside 
risk. In fact, such practises were concentrated in sectors less subject to international competition such 
as services and construction. This observation casts doubts on the ultimate rationale on the part of the 
banks for keeping the firms artificially further alive as an eventual improvement in the debtor situation 
appears unlikely. 

Peek and Rosengren (2005) argue that banking regulation is part of the mechanics behind the zombie 
phenomenon. Using a sample of loans extended to Japanese listed firms during the Japanese 1990’s 
recession, the authors find that banks with capital ratios close to regulatory thresholds were more likely 
to refinance loans to weakly performing companies at unusually low interest rates than were banks 
with stronger capital ratios. The authors refer to the phenomenon as bank forbearance, whereby banks 
rollover debt in order to avoid to have to report losses, which in turn might trigger costly regulatory 
recapitalization.  This  phenomenon  is  often  considered  to  be  a  main  factor  explaining  the  Japanese 
economic stagnation of the 1990’s, i.e. the growing incidence of so-called zombie firms. 

Caballero,  Hoshi,  and  Kashyap  (2008)  go  beyond  the  analysis  of  the  incidence  of  zombie  firms  and 
investigate theoretically and empirically the effects of the incidence of zombie on non-zombie firms. 
Following  Peek and Rosengren (2005),  the  former  authors  assume  that banks, in  order  to  maintain 
required  regulatory  capital  ratios,  opt  to  support  existing  borrowers  in  economic  downturns. 
Unproductive  zombie  firms  are therefore subsidised through abnormally low  interest  rates  because 
banks  are  unwilling  to  book  loan  losses.  Healthy  and  productive  non-zombie  firms  must  compete 
against zombie firms on loanable amounts. Banks’ subsidies to zombies distort competition in credit 
markets in favour of the latter; this adds on financial constraints to healthy non-zombies and slows 
down their growth.  Moreover, zombies are likely to distort prices in product and labour markets as a 
result  of  the  subsidies,  as  zombies  are  likely  to  gamble  for  their  survival.  As  a  result  of  the  fierce 
competition  from  zombies,  incumbent  healthy  non-zombie  firms  must  retrench  from  relatively  less 
productive projects and instead renew or select more productive projects. As a result, the growth of 
non-zombies is adversely affected, while their measured productivity increases.  The authors construct 
a measure of interest gap similar to Hoshi (2006), that is the difference between an estimate of the 
interest rate a firm does pay in practise and the estimated lowest possible market interest rate that it 
might secure in theory (if it managed to finance itself at the rates applicable to government borrowing). 
Negative values of that measure is taken as an indication that the firm is subsidised by the bank (zombie 
firm).1  The authors show that the share of total assets held by zombie firms assets in Japan grew from 
about 2% in the early 1980s to about 12% in 2002. Zombie firms have hindered the growth of non-

1 Authors use short-term debt (less than a year), long-term debt (more than a year), outstanding bond and commercial papers.  
They relate financial expenses of the firm (including interest payments and fee payments) to the estimated amounts. The 
number is compared with the equivalent lowest market rate the firm should pay controlling for maturities and bond amounts. 

4 

zombie firms. Among different sectors, construction was most severely affected with a share assets 
held by zombie firms that attained 30% in 2001.  

Adalet  McGowan,  Andrews  and  Millot  (2017)  conduct  a  somewhat  similar  empirical  analysis  for 
selected OECD countries, although the latter authors consider an alternative definition of zombie firms. 
Their suggested definition is that zombie firms are ten years or older and are reporting coverage ratios 
below one in three consecutive years, where coverage ratio is the ratio of earnings before interest and 
tax  payments  but  after  depreciation  (EBIT)  to  interest  payments.  The  coverage  ratio  is  a  standard 
measure for assessing loan repayment capacity and is widely used by credit analysts. Repeated records 
of the coverage ratio falling below one is seen as indicating serious financial difficulties. The authors 
show that the share of zombie companies varies substantially across the 13 sample countries. They 
compute the share of total capital (fixed assets) held by zombie firms, which they call sunk capital. In 
their sample, sunk capital ranges in 2013 from 4% in Slovenia to 15% in Spain and almost 20% in Italy. 
This  estimate  suggests  that  the  empirical  relevance  of  the  zombie  firm  phenomenon  can  be 
considerable in practise for countries other than Japan during the 1990s.  

That the phenomenon of zombie firms can be practically relevant in Europe is also suggested by Acharya 
et al. (2016). The authors study the effects of the European Central Bank (ECB)’s Outright Monetary 
Transactions (OMT) program, which was launched in 2012. The program is interpreted by the authors 
as an indirect subsidisation of European banks that substantial amounts of sovereign bonds issued by 
European  periphery  countries.  The  authors  find  that  banks  that  thus  benefited  from  the  program 
increased their overall loan supply, but that this supply was mostly targeted towards low-quality firms 
with pre-existing lending relationships with these banks. Loans to unproductive firms increased in the 
period shortly after the start of the OMT program in particular in Italy, Spain and Portugal. Following 
Caballero, Hoshi, and Kashyap (2008), the authors show that banks extended loans to borrowers at 
interest rates that were below those paid by the most creditworthy European sovereign borrowers. 
Moreover, non-zombie firms suffered from the presence of zombie firms. According to the authors, 
both  investment  and  employment  growth  rates of non-zombie  firms  were significantly lower if the 
fraction of zombie firms in their respective industry sector increased compared to non-zombie firms 
that were active in sectors without a high incidence of zombie firms. This finding suggests that, in the 
presence of a considerable incidence of zombie firms, indirect subsidiation of banks can have undesired 
effects on employment and investment operating through a misallocation of loan supply.  

Aggregate  estimates  for  Europe  can  hide  considerable  cross-country  differences.  For  example, 
Deutsche Bundesbank (2017) consider firm- level microdata from the Deutsche Bundesbank Financial 
Statement Data Pool for German firms and shows that zombie firms zombie firms only make up a small 
percentage of all firms in Germany and that this share has not increased as of recently compared to the 
situation  eight  years  before.  That  said,  that  result  is  based  on  the  widely  used  zombie  company 
definition  of  a  firm  with  interest  rate  coverage  not  exceeding  one  for  three  years.  Considering  an 
alternative definition of zombie firms that classifies as a zombie firm one whose cash flow has been 
negative  for  three  consecutive  years,  a  slight  increase  in  zombie  firm  incidence  can  be  observed, 
although at much lower levels than considering the more widely used definition.   

5 

 
 
3  Data 

We obtain micro-level firm data from Orbis, an electronic databank of corporate financial statements 
published by Moody’s Bureau van Dijk. We extract unconsolidated accounts of all non-financial firms in 
both manufacturing and services sectors registered in European countries and retain firms with at least 
five employees and reporting assets in at least two years.2  Our sample includes nineteen European 
Union  countries  for  which  we  have  sufficient  information  so  to  identify  the  incidence  and  share  of 
zombie  companies.  The  countries  represented  in  our  sample  are  Austria,  Belgium,  Bulgaria,  Czech 
Republic,  Finland,  France,  Germany,  Greece,  Hungary,  Ireland,  Italy,  Poland,  Portugal,  Romania, 
Slovenia, Slovakia, Spain, Sweden, and the United Kingdom.3 

Table 1 reports the number of sample firms by year and country. It illustrates that the number of firms 
covered varies over time and across countries. One issue in Orbis is a sudden increase (or drop) in the 
number  of  firms,  which  might  just  reflect  that  data  from  specific  providers  to  Orbis  is  added  or 
withdrawn. Fortunately, we do not observe such variations in our sample. As a general rule, the most 
recent years of data available from Orbis tends to contain fewer companies simply because firms may 
delay their reporting. To avoid this issue, we choose 2013 as the last year for our sample. The table 
shows that we have available data for only about 5,000 and a little more than 10,000 firms for Ireland 
and Slovenia, respectively, while we have data for more than 300,000 and 250,000 firms for Germany 
and Spain, respectively.  

We consider three definitions for zombie firms and refer to the firms identified by them as zombie A, 
zombie B, and zombie C, respectively.  

•  The first definition is zombie A and defines a firms as a zombie firm, any firm – regardless of 
age  –  with a  coverage  ratio  equals  to  one  or  less; coverage  ratio  is  defined  as  the  ratio  of 
operational earnings – EBIT, earnings net of depreciation – to interest payments. We substitute 
missing  interest  payments  with  financial  expenses.  Financial  expenses  typically  includes 
interest payments plus fees on loans.  

•  Zombie B definition restricts zombie A definition to ten years or older firms.  
•  Zombie C definition restrict zombie B definition to firms with a coverage ratio below one during 
five consecutive years – instead of three. Table A1 in Annex A summarizes zombie definitions. 

Each of the three definitions of zombie firms requires that firms report at least three consecutive years 
of  coverage  ratio.  This  constraint  limits  the  extent  to  which  we  are  able  to  produce  estimates  for 
different points in time for the given sample. As regards zombie A and zombie B definitions, data for 
the three years from 2008 to 2010 are considered to obtain estimates of zombie firm incidence in 2010, 
although such data is not available for all sample countries. Data from 2011 to 2013 is used to identify 

2 Selected NACE Rev. 2 sectors (code in brackets) are: Manufacturing (10-33); Electricity and Water supply services (35-39); 
Construction (40-43); Wholesale and retail services (45-47); Transportation (49-53); Accommodation and Food services (55-
56); Information and Communication services (58-63); Real estate services (68); Professional Services (77-83). 

3  AAM  sample  consists  of  the  thirteen  OECD  countries:  Austria,  Belgium,  Finland,  France,  Germany,  Italy,  Luxembourg, 
Portugal, Spain, Slovenia, South Korea, Sweden, and the United Kingdom. Compared with AAM we exclude South Korea (non-
EU member) and Luxembourg, and add Bulgaria, Czech Republic, Greece, Hungary, Ireland, Poland, Romania, and Slovakia. 

6 

 
 
                                                             
 
incidence of zombie firms in 2013, and this data is available for all sample countries. As regards zombie 
C definition, estimates are produced for year 2013 only, and for a subset of countries. Orbis reports 
corporate  profits unevenly  across  countries.  Typically,  only 20% of firms report  profits in  Germany, 
Austria,  the  Netherlands,  and  Belgium  (see,  e.g.,  Kalemli-Ozcan  et  al.,  2015,  and  Gal,  2013),  which 
accordingly reduces the number of firms available in our sample in the case of these countries. 

Table 1. Number of observations by country and year 

2008 

2009 

2010 

Austria 
Bulgaria 
Belgium 
Czech Republic 
Finland 
France 
Germany 
Greece 
Hungary 
Ireland 
Italy 
Poland 
Portugal 
Romania 
Slovakia 
Slovenia 
Spain 
Sweden 
United Kingdom 
TOTAL 

45,771 
38,643 
35,435 
20,784 
189,535 

48,471 
39,557 
36,684 
21,696 
194,889 

22,373 

22,687 

185,044 

78,236 
20,042 
10,846 
234,837 
41,817 
40,071 
963,434 

194,370 
74,820 
77,256 
81,617 
20,891 
11,060 
239,484 
43,354 
41,017 
1,147,853 

50,952 
40,608 
37,714 
22,572 
200,039 

22,909 
44,917 
4,948 
204,112 
76,679 
79,581 
85,656 
21,756 
11,287 
244,115 
45,114 
41,886 
1,234,845 

2011 
36,611 
54,203 
41,481 
38,074 
23,385 
204,149 
308,962 
23,098 
48,139 
5,024 
212,470 
79,994 
81,929 
89,957 
22,555 
11,501 
248,119 
45,832 
42,641 
1,618,124 

2012 
37,203 
57,077 
41,667 
38,101 
23,990 
206,600 
309,313 
23,198 
50,324 
5,052 
216,524 
80,031 
84,137 
94,176 
22,998 
11,662 
251,562 
45,934 
43,227 
1,642,776 

2013 
37,253 
59,127 
41,769 
38,101 
24,221 
207,205 
310,413 
23,206 
52,311 
5,152 
217,574 
82,031 
86,037 
97,692 
23,041 
11,729 
252,951 
46,132 
43,415 
1,654,760 

Notes: This table reports the distribution of firms in Orbis by country and year which report non-missing total assets. 

4 

Incidence of zombie firms  

4.1  Share of zombie firms by age and size categories 

This  section  reports  estimates  for  2013  of  the  share  of  zombies,  by  age  and  by  size  in  number  of 
employees,  considering all three  zombie  definitions.   The  share  of  zombie  firms  increases  with age, 
although  also  exhibiting  a  slight  U-shape  at  the  lower  end,  regardless  of  the  zombie  definition  we 
consider (Figure 1). Considering zombie B definition, the share of zombie firms among 5-9 employee 
firms is about 5.2% and drops to about 3.8% for 10-19 employee firms, representing a drop of 26%. The 
number  then  gradually  increases  up  to  6.45%  for  larger  companies  (250  employees  or  more);  for 
example,  the  share  of  zombies  is  56%  higher  among  250+  firms  than  among  firms  with  10-19 
employees.  

The relation between age and zombies is also monotonically increasing for the most part, although it 
slightly drops at the higher end, that is considering firms with 41 years or older. Considering zombie A 
definition, which does not restrict companies to be at least 10 years old unlike zombie B definition, 
young companies (2-5 years) display the lowest share of zombies (4.5%) while 21-40 years old firms 
display the largest share (nearly 7.7%); the latter is considerably higher than the former (70%). The 
share of zombies then drops to 7.2% among oldest companies (41+). 

7 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 1. Zombie firms by size and age category, 2013 

Notes: The figures report the shares of zombie firms by category. The left-hand figure reports the share of zombie firms by size 
category; the right-hand figure reports the share of zombie firms by age category. We use three definitions of zombies. 

The patterns of zombie incidence as a function of firm age and size described above are consistent with 
Hoshi  (2006)  and  AAM  (Figure  4),  although  the  latter  estimates  are  higher  in  absolute  values.  For 
instance, AAM find that the highest share of zombie in age categories is 11.5% at the higher end (41+).  
In  most  categories,  our  estimates  are  within  a  +/-  0.5  percentage  points  range  compared  to  the 
estimates by AAM, but estimates for the oldest firms differ considerably. As regards size in terms of 
number of employees, the overall patterns of estimates are similar although our point estimates are 
about 2-4 percentage points smaller than those reported in AAM.  

Thus, our results confirm that the share of zombie firms increases with age and size.  These patterns 
can be explained in various ways. First, larger and older firms are least opaque: they have longer track 
records,  provide  more  reliable  financial  reports,  and  may  have  established  longer  term  lending 
relationships with banks. Such firms may have built a reputation as regards their ability to cope with 
temporary spells of profitless years, which might justify refinancing viewed from the point of the bank. 
Moreover, the sunk costs of loan restructuring and potential need for additional capital tend  to  be 
higher for large firms. Young companies are less likely to benefit from such considerations.  

4.2  Share of zombie firms by country 

This section reports estimates of the total share of zombie firms by country for 2010 and 2013. Two 
measures are considered. First we calculate the share as the ratio of the number of zombie firms in a 
country to the total number of firms in this country in our sample. Then we compute the ratio of the 
total amount of fixed assets (capital stock) held by zombie firms to the total fixed assets in the same 
country in our sample. The latter measures the so-called sunk capital; sunk capital is the figure we will 
use for the analysis of the spill-overs of zombies on non-zombies.  

The results for the first measure are shown in Figure 2.  The share of zombie firms increases between 
2011 and  2013, except in Great Britain  and  Ireland.  The  jump  in the  share  of zombies  is  striking  in 
Greece, Spain, Portugal, and Italy, which are countries that were particularly affected by the European 
sovereign  debt  crisis.  Ireland  which  was  affected  by  the  global  financial  crisis,  but  less  so  by  the 
subsequent European sovereign debt crisis, experiences a decline from 2010 to 2013, although at high 
levels. By contrast, estimates for Bulgaria suggest a jump, although at a comparatively low level. The 
direction of changes over time and the absolute numbers are broadly similar to AAM, although the 

8 

 
 
 
estimates in  absolute  numbers  differ  for  Sweden  (our  estimates  slightly  lower  than  AAM),  Slovenia 
(slightly higher), Belgium (much lower), France (much higher), and Italy (highest share of sunk capital in 
AAM,  but  ranked  only  average  here).  Annex  C  reports  more  details  on  the  differences  between 
estimates; these could facilitate reconciliation of differences in results, although such reconciliation is 
beyond the scope of the present report. 

Figure 2. Zombie firms incidence by country (number of zombie firms as of total firms). 

Notes:  This  figure  shows  the  share  of  zombie  firms  by  country  in 2013,  identified  by  bars  and calculated  as the  number  of 
zombie firms over total number of firms. Change in percentage points of zombie shares between 2010 and 2013 shown as 
circles.  Note  that  change  in  Zombie  incidence  for  definition  cannot  be  calculated  for  all  countries  due  to  data  limitations, 
therefore panel C does not have indications for e.g., IE or DE. 

The results for the second measure are shown in Figure 3. As background, this measure is obtained as 
the  total  amount  of  funding  allocated  to  zombies  which  thus  cannot  be  allocated  to  other  firms. 
Following AAM, it is referred to as sunk capital and obtained by using the book value of fixed assets as 
reported  in  Orbis.  Unlike  AAM  though,  we  did  not  calculate  the  real  stock  of  capital.4    The  Figure 
highlights substantial discrepancies across countries. More than a fourth of Greek corporate capital is 
estimated to be held by zombie firms, while (zombie) firms in Slovakia, Czech Republic, and Finland hold 
less than 5% sunk capital.  Countries that were most affected by the European crisis are not necessarily 
those  displaying  the  largest  share.  For  instance,  Belgium,  France  and  Great  Britain  have  greater  or 
similar shares of sunk capital as do Italy and Portugal. In general, new entrants to the European Union 
(e.g., Slovakia, Czech Republic, Hungary, Romania and Poland) are less affected by the issue of sunk 
capital than other EU members. While Belgium, Bulgaria and Germany are estimated to have a low 
number of zombies, they rank less favourably in terms of the share of sunk capital. By contrast, the sunk 

4  The  real  stock  of  capital  is  calculated  using  the  Perpetual  Inventory  Method:  for  each  firm  the  deflated  values  of  past 
deprecations and changes in fixed assets are added up. This allows to compare the value of fixed assets acquired in say, 2005, 
with new fixed assets purchased in 2013.  Fixed assets deflation factors are sector and country specific. Similar to AAM, we 
removed outliers defined as observations for fixed assets that are equivalent to 100 times the 99th percentile value. 

9 

 
 
 
 
 
 
                                                             
 
capital  share  is  low  in  Ireland,  but  the  number  of  zombie  firms  relatively  high.    Comparing  these 
estimates with AAM, the overall magnitudes are fairly similar, with estimates ranging from somewhat 
less than 5% to 20%. Considering country-specific estimates, results are similar for Spain, Belgium and 
Portugal. Higher results are obtained here for Great Britain (about 12 instead of 7), France (13 instead 
of 8). Lower result are obtained for Italy (13 instead of 20), Austria (7 instead of 9) Finland (4 instead of 
7). 

Figure 3. Zombie firm incidence by country (Share of fixed assets of zombie firms as of total assets). 

Notes: Estimates of the share of sunk capital in 2013. Sunk capital is the share of capital held by zombie firms as of fixed assets 
held by all firms in the respective country. 

5  Effects of Zombies on Non-Zombie Firms 

The incidence of zombie firms, discussed in the previous section, may affect the development of non-
zombie firms in the same economy or sector. At least three potential channels for such spill-overs can 
be distinguished. First, by “absorbing” bank capital at prices unfairly low given their riskiness, zombies 
add to pressures facing healthy non-zombie firms to obtain bank financing.  Healthy non-zombies might 
be forced to contract loans at higher rates, and at one point the rates might be so high that the firms 
will decide to rely on internal financing (i.e., reduce dividends and reinvest earnings) as opposed to 
external financing, which might penalize overall investment and subsequently growth. Second, zombies 
compete  with  non-zombies  on  product  markets.  Given  their  low  profitability  and  financially  weak 
situation,  zombie  firms  may  increase  risk  in  an  attempt  to  gamble  for  redemption (Caballero  et  al., 
2009). Any financial subsidies that zombie firms receive from banks allows the former to exert such 
product market competitive pressure on healthy firms for longer periods than would have otherwise 
been feasible.  In a way, unhealthy zombie firms keep a share of products sales away from healthy non-
zombie firms, which would need to reduce prices to recuperate “lost” product sales. Third, a similar 

10 

 
 
 
 
 
mechanism might operate in labour markets – zombie firms increase salaries so as to retain workforce, 
and are thus “crowding out” the labour market for healthy zombie firms.  As a result of the various 
types of competitive pressures, healthy non-zombies face a lower growth potential. In response to this 
situation, healthy non-zombies will renew only relative more productive incumbent projects, which will 
have the effect that the latter gradually increase their overall productivity (Caballero et al., 2017).  

There is evidence that these various conceptual considerations are reflected in the data. For example, 
Caballero et al. (2008) and AAM find that the incidence of zombie firms penalizes the growth of non-
zombie firms. In fact, they find that the share of assets (total assets or fixed assets) held by zombie firms 
in a sector affects the growth in employment and investment of healthy zombie firms in that sector. 
There is also evidence that non-zombie firms become more productive in response. 

To investigate the effects of the incidence of zombie firms on the performance of non-zombies, we 
estimate the following empirical model of firm performance: 

Firm performancei,y = non-zombiei,y +  

Non-zombiei,y x zombie sharec,y + 
agei,y +  
sizei,y +  
industry-year-country fixed effects 

[1] 

The  firm  performance  model  [1]  is  estimated  using  all  firms  in  our  sample,  i.e.  zombies  and  non-
zombies, and its specification follows AAM. The dependent variable is one of four indicators capturing 
the performance of firm i in year y.  

•  The  first performance  indicator  is  Ln(I/K)I,y,  the ratio  of  capital  stock investment  of  the  firm 

between year t-1 and t; it is equal to ln[FAt/FAt-1] = ln(FAt)-ln(FAt-1). 

•  The second indicator is Δ ln(Employment)i,y, the log difference in the number of employees; it 
measures the employment growth of the firm. An alternative is to use growth of sales, but we 
consider that the number of employees is preferable as it is less volatile than financial indicators 
such as sales. Unfortunately, the number of employees is reported by fewer firms in the Orbis 
dataset than sales, implying that we lose some observations.  

•  The third indicator is labour productivity, here measured by the natural logarithm of the ratio 
of the value added by the firm compared to the number of its employees. In particular, we 
proxy  value  added  by  adding  profits  (EBIT  =  after  depreciation,  before  interest  and  tax 
payments) to the cost of salaries. This estimate is frequently used in firm-specific productivity 
analysis using Orbis and avoids the issue of limited availability of data on added value per firm 
in Orbis.  

•  The fourth indicator is MFP*, an estimate of the multifactor productivity gains of the firm. We 
estimate a Cobb-Douglas productivity function of added value determined by the total fixed 
assets  and  the  number  of  employees.  The  estimated  model  of  the  production  function  is 
0.9*Labour + 0.2*Capital. The residuals provide with the productivity gain of each firm. 

Explanatory variables are Non-Zombiei,y, a dummy that takes the value one if firm i is a zombie firm in 
year y, using alternatively the three zombie firm definitions; Zombie Sharec,y, the share of total capital 
in sector c and country y held by zombie firms; Non-Zombiei,y X Zombie Sharec,y, an interaction variable 

11 

 
 
between  Non-Zombiei,y  and  Zombie  Sharec,y;  Age,  the  age  of  the  firm  which  equals  the  difference 
between the year of report and the year of incorporation; Size the number of employees. We add an 
interacted industry-year-country fixed effects, where industry is the firm’s two digits NACE Rev. 2 code. 
All variables are defined in Appendix A.  

The analysis focuses on the variables Non-Zombiei,y  and Non-Zombiei,y X Zombie Sharec,y. The former 
captures the difference between non-zombie and zombie firms; the latter captures the effects of the 
share  of  zombies  on  non-zombie  firms.  Given  that  they  show  less  financial  issues,  we  expect  non-
zombie firms to have superior growth and productivity. 

5.1  Effects of zombie firms on investment and growth of non-zombies 

We  first  look  at  the  investment  and  growth  of  non-zombie  firms.  The  results  of  the  estimates  are 
reported in  

Table 2 in three panels, which take into account the three definitions of zombie.  We find that non-
zombie firms invest and grow more than zombie firms at high significance standards, regardless of the 
definition of zombies we consider. Considering zombie B definition, a firm switching from zombie to 
non-zombie would gain 0.072/0.469 = 15.4% of the standard deviation in terms of ln(I/K).  

The growth of non-zombies is negatively affected by the existence of zombie firms in their respective 
countries  and  sectors,  and  significantly  so  at  the  1%  level.  The  economic  significance  is  also  high; 
considering the zombie B definition, a non-zombie firm that would switch from an environment with 
no  zombies  to  one  entirely  crowded  by  zombie  firms  would  lose  0.114/0.469  =  24.3%  standard 
deviation of ln(I/K). As a thought experiment for the most extreme case, non-zombie firms switching 
from the most extreme sunk capital environment of 27.8% in Greece (definition B) to the lowest sunk 
capital of 2.7% in Czech republic, would increase ln(I/K) by (27.8%-2.7%)*.114=0.0286 more than if the 
switch  happened  to  a  zombie.  This  increase  represents 6.10% of the standard  deviation increase  in 
investment. Thus, the growth of non-zombies is substantially affected by the existence of zombies in 
their respective countries. 

Table 2. Effects of Zombie Firms on Non-Zombie Firms – Growth. 

Non-Zombie Firm 

Non-Zombie   

  X Zombie Share 

Mean 
Standard Deviation 
Observations 
R-squared 

Zombie A 

Zombie B 

Zombie C 

ln[I/K] 
0.089*** 
[0.0025] 
-0.119*** 
[0.0120] 
0.017 
0.469 
5,733,207 
0.019 

Δln(Employ) 
0.064*** 
[0.0018] 
-0.036*** 
[0.0095] 
0.049 
0.386 
5,977,604 
0.052 

ln[I/K] 
0.072*** 
[0.0023] 
-0.114*** 
[0.0138] 
0.017 
0.469 
5,733,207 
0.019 

Δln(Employ) 
0.056*** 
[0.0017] 
-0.019 
[0.0117] 
0.049 
0.386 
5,977,604 
0.052 

ln[I/K] 
0.068*** 
[0.0025] 
-0.138*** 
[0.0234] 
0.017 
0.469 
5,733,207 
0.019 

Δln(Employ) 
0.052*** 
[0.0020] 
-0.03 
[0.0196] 
0.049 
0.386 
5,977,604 
0.051 

Notes: Estimates of Model [1] of firm performance. Zombie A defines zombies as any firm with coverage ratio less than one in 
three consecutive years. Zombie B restricts Zombie A definition to ten years or older firms. Zombie C restricts Zombie B definition 
to coverage ratio less than one in five consecutive years (see Annex A). The definitions of the variables are reported in Annex B. 
All specifications control for firm age and size categories, and industry-year-country fixed effects. The last line reports the mean 
and standard deviation (in square brackets) of the dependent variables. ***, **, * respectively indicates statistical significance 
at 1%, 5%, and 10% levels. 

12 

The significance of the impact on employment growth varies according of the zombie definition, but is 
negative in all instances. Considering zombie A definition, the share of zombies bears negative effects 
on non-zombies at one standard level. The economic significance is also high yet lower than the effects 
on capital growth; switching from zombie free sector to a fully crowded zombie sector implies a loss in 
standard deviation of -0.036/0.386=-9.3%. Considering Zombie B and C definitions, losses are 4.9% and 
7.8% respectively, but are insignificant at the 10% level. 

5.2  Effects of zombie firms on labour and capital productivity of non-zombies 

We next look at the effects of zombie firms on the productivity of non-zombies. We estimate Model [1] 
of firm performance and substitute the performance dependent variables with productivity measures. 
Similar to Caballero et al. (2008) and AAM we use a proxy of multi-factor productivity MFP.  We further 
investigate the effect on productivity by alternatively considering a proxy for labour productivity, which 
we construct by scaling the added value by the number of employees, both variables being available in 
Orbis.  Again,  The  headers  zombie  A,  B,  and  C  columns  indicate  that  we  alternatively  used  zombie 
definitions A, B, and C. 

As  expected  and  regardless  of  the  specific  definition  of  zombies,  healthy  non-zombie  firms  display 
higher productivity than zombie firms whichever productivity measure we use. Moreover, consistently 
with Caballero et al. (2008) and AAM, non-zombie firms tend to have a higher labour productivity than 
zombie firms, but the effects of the incidence of zombies on the labour productivity of non-zombies are 
negative and significant at the 1% level, regardless of the definition of zombies considered.  Besides, 
the effect of the incidence of zombie firms on the MFP of non-zombie firms is found to be positive, 
similar to earlier studies. That said, the results are only strongly significant when considering the most 
restrictive zombie company definition, requiring firms to be unprofitable for at least five consecutive 
years.  

Table 3. Effects of Zombie Firms on Non-Zombie Firms – Productivity 

Non-Zombie Firm 

Non-Zombie   
        X Zombie Share 
Mean 
Standard Deviation 
Observations 
R-squared 

Zombie A 

Zombie B 

Zombie C 

MFP 
0.107*** 
[0.0049] 
0.055* 
[0.0307] 
9.079 
0.881 
4,663,474 
0.669 

Labour 
0.046*** 
[0.0043] 
-0.260*** 
[0.0243] 
9.951 
0.940 
4,758,161 
0.739 

MFP 
0.109*** 
[0.0055] 
0.026 
[0.0444] 
9.079 
0.881 
4,663,474 
0.669 

Labour 
0.043*** 
[0.0051] 
-0.529*** 
[0.0512] 
9.951 
0.940 
4,758,161 
0.739 

MFP 
0.122*** 
[0.0055] 
0.168*** 
[0.0633] 
9.079 
0.881 
4,663,474 
0.668 

Labour 
0.043*** 
[0.0051] 
-0.529*** 
[0.0512] 
9.951 
0.940 
4,758,161 
0.739 

Notes: Estimates of Model [1] of firm performance. Zombie A defines zombies as any firm with coverage ratio less than one in 
three consecutive years. Zombie B restricts Zombie A definition to ten years or older firms. Zombie C restricts Zombie B definition 
to coverage ratio less than one in five consecutive years (see Annex A). The definitions of the variables are reported in Annex B. 
All specifications control for firm age and size categories, and industry-year-country fixed effects. The last line reports the mean 
and standard deviation (in square brackets) of the dependent variables. ***, **, * respectively indicates statistical significance 
at 1%, 5%, and 10% levels. 

The  difference  in  results  obtained  here  for  labour  productivity  on  the  one  hand  and  multi-factor 
productivity  on  the  other  might  be  explained  by  the  use  of  book  value  of  capital  (fixed  assets)  to 
calculate multi-factor productivity rather than real capital.  An alternative plausible explanation is the 
observation that it takes firms more time to adjust labour productivity than multi-factor productivity. 
The immediate effects on productivity of non-zombie firms of a greater incidence of zombie firm are 

13 

 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
negative but tends to be more positive once the response of non-zombie firms to the situation has 
more fully played out. Caballero et al. (2008) focus on the ability of firms to implement new and more 
productive projects. These projects are likely to require the substitution of fixed assets which is likely 
to be easier than substituting labour force. Therefore, all-in-all firms may be more productive, but other 
factors of productivity make up for the drop in labour productivity. 

5.3  Effects of zombie firms on young non-zombie firms 

Finally,  we  investigate  to  what  extent  the  effects  on  growth  and  productivity  differ  for  young  non-
zombie  firms  as  opposed  to  non-zombie  firms  of  any  age.  Caballero  et  al.  (2008)  argue  that  new 
entrants are most affected by the presence of zombies in their market. As a result of the more intense 
competition, new entrants would need to be propose more productive project than those pursued by 
incumbents. Therefore, new entrants are more severely impacted by the share of funds sunk in zombie 
firms. In order to investigate the hypothesis, we modify model [1] to obtain Model [2] that controls for 
young firms: 

Firm Performancei,y = Non-Zombiei,y +  

Non-Zombiei,y x Youngi,y 
Non-Zombiei,y x Zombie Sharec,y  
Non-Zombiei,y x Zombie Sharec,y x Youngi,y 
Agei,y +  
Sizei,y +  
Industry-Year-Country Fixed Effects 

[2] 

In particular, compared to Model [1], we add the dummy variable Youngi,y  that indicates that firm i is 
young – i.e., six years or younger – in year y.  We also introduce two interaction variables: Non-Zombiei,y 
x Youngi,y, a dummy that captures young non-zombie firms; Non-Zombiei,y x Zombie Sharec,y x Youngi,y, 
non-zombie young firms interacted with the share of zombie firms in their respective industries (in a 
specific country). The estimates of Model [2] are reported in Table 4 (growth) and Table 5 (productivity). 
Specifically,  we  are  interested  in the impact of the share of zombies in a sector on the growth and 
productivity of young non-zombie firms. Notice that considering zombie definitions B and C imply that 
all  young  firms are  non-zombies,  while  zombie  company  definition allows  young  firms  to  be  either 
zombies or non-zombies (see also Table A2 in Annex 1). 

14 

 
 
Table 4. Effects of Zombie Firms on Young Non-Zombie Firms – Growth. 

Non-Zombie 

Non-Zombie X Young 

Non-Zombie 
        X Zombie Share 
Non Zombie X Young 
       X Zombie Share 
Mean 
Standard deviation 
Observations 
R-squared 

Zombie A 

Zombie B 

Zombie C 

ln[I/K] 
0.082*** 

[0.0026] 
0.062*** 
[0.0022] 
-0.113*** 
[0.0121] 
0.037*** 
[0.0140] 
0.033 
0.492 
6,028,707 
0.029 

Δln(Employ) 
0.053*** 
[0.0019] 
0.083*** 
[0.0033] 
-0.012 
[0.0098] 
-0.038* 
[0.0197] 
0.070 
0.441 
6,286,636 
0.079 

ln[I/K] 
0.065*** 
[0.0024] 
0.060*** 
[0.0022] 
-0.107*** 
[0.0139] 
0 
[0.0170] 
0.033 
0.492 
6,028,707 
0.028 

Δln(Employ) 
0.046*** 
[0.0018] 
0.083*** 
[0.0031] 
0.011 
[0.0117] 
-0.092*** 
[0.0250] 
0.070 
0.441 
6,286,636 
0.078 

ln[I/K] 
0.059*** 
[0.0026] 
0.061*** 
[0.0019] 
-0.115*** 
[0.0237] 
-0.024 
[0.0260] 
0.033 
0.492 

  6,028,707 

0.028 

Δln(Employ) 
0.041*** 
[0.0021] 
0.081*** 
[0.0027] 
0.042** 
[0.0214] 
-0.148*** 
[0.0369] 
0.070 
0.441 
6,286,636 
0.078 

Notes: Estimates of model [2] controlling for young firms. Young is a dummy variable that takes the value one if the firm is six 
years or younger. Variables are defined in Annex A. ***, **, * respectively indicates statistical significance at 1%, 5%, and 10% 
level. 

We find evidence that non-zombie young firms grow faster than the average firm in the economy, at 
1% significance levels, regardless of the zombie definition we use. Interestingly, they add on growth 
compared to non-young (non-zombie) firms; young non-zombie firms grow about twice as fast as old 
non-zombie firms, as reflected in the observation that the estimated coefficient of the variable “Non-
zombie * young” in Table 4 is also positive and about the same size as that of “Non-zombie”. The signs 
and magnitudes of the estimated coefficients are similar to AAM (See AAM, Table A5).  

The impact of the share of zombies in the sector of the firm on the performance of non-zombie firms 
is negative overall. Non-zombies are “penalized” in their growth in fixed assets investment, although 
not in terms of employment growth. Young non-zombie firms are negatively affected by the presence 
of  zombie  firms  in  their  sector  in  terms  of  employment  growth,  regardless  of  the  type  of  zombie 
company definition chose. By contrast, no significant adverse effect is identified for these firms in terms 
of their investment. Similarly, no significant adverse effects are identified on the growth in terms of 
employment of non-zombie firms of any age, and one specification even suggests a significant positive 
effect. These results differ from AAM.  

Table 5 shows the effects of the share of (zombie firm) sunk cost on productivity of young non-zombie 
firms.  Young  non-zombie  firms  have  higher  productivity,  regardless  of  the  zombie  definition  and 
productivity indicator (labour or asset) chosen. The results are significant at the 1% level. The estimates 
of the effects of the share of zombies follows the same pattern as in the estimates of model [1], i.e., 
MFP* is positively affected at the 1% significance level (except in Zombie B definition model), while 
labour productivity is negatively affected. Young non-zombie firms are not much affected by the zombie 
firm  share,  at  least  not at  standard  levels  of  significance  (with  the  exception  of  the effect  on MFP* 
considering the zombie C definition). 

15 

 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 5. Effects of Zombie Firms on Young Non-Zombie Firms – Productivity. 

Zombie A 

Zombie B 

Zombie C 

Non-Zombie 

Non-Zombie X Young 

Non-Zombie 
        X Zombie Share 
Non Zombie X Young 
   X Zombie Share 

Mean 
Standard deviation 
Observations 
R-squared 

Labour 
0.029*** 
[0.0043] 
0.023*** 
[0.0041] 
-0.222*** 
[0.0244] 
-0.021 
[0.0222] 
9.900 
0.981 
5,205,765 
0.734 

MFP 
0.094*** 
[0.0050] 
0.034*** 
[0.0039] 
0.086*** 
[0.0307] 
0.036 
[0.0239] 
9.055 
0.901 
5,064,025 
0.658 

Labour 
0.062*** 
[0.0043] 
0.038*** 
[0.0040] 
-0.281*** 
[0.0298] 
-0.025 
[0.0281] 
9.900 
0.981 
5,205,765 
0.734 

MFP 
0.115*** 
[0.0056] 
0.040*** 
[0.0038] 
0.056 
[0.0449] 
0.034 
[0.0309] 
9.055 
0.901 
5,064,025 
0.658 

Labour 
0.049*** 
[0.0051] 
0.038*** 
[0.0034] 
-0.499*** 
[0.0505] 
-0.041 
[0.0422] 
9.900 
0.981 
5,205,765 
0.734 

MFP 
0.131*** 
[0.0057] 
0.041*** 
[0.0032] 
0.214*** 
[0.0641] 
0.069* 
[0.0396] 
9.055 
0.901 
5,064,025 
0.658 

Notes: Estimates of model [2] controlling for young firms. Young is a dummy variable that takes the value one if the firm is six 
years or younger. Variables are defined in Annex A. ***, **, * respectively indicates statistical significance at 1%, 5%, and 10% 
level. 

6  Persistence of zombie status 

One possibility is that bank forbearance is an optimal response by the bank to what will eventually turn 
out  to  be  temporary  pressure  on  corporate  operating  profits.  For  example,  banks  may  support 
distressed  firms  as  part  of  considerations  regarding  the  longer-term  benefits  of  the  relationship 
between  bank  and  borrower  (e.g.  so-called  hausbank  relationship).  It  is  feasible  that  banks  hold 
additional “private” information about their existing customers, e.g., debt payments records, existing 
projects,  checking  accounts,  existing  suppliers  and  customers,  which  enables  them  to  assess  the 
creditworthiness of the borrower with more precision than any outsider.  Under those circumstances, 
banks can effectively provide their existing customers with liquidity insurance (e.g., Sette and Gobbi, 
2015). 

To see to what extent there might be evidence for such effects, we investigate what are the odds that 
a zombie firm remains a zombie in the next two years, or alternatively, becomes “healthy” again.  We 
refer to “successful” zombies as those firms that are classified as zombies in 2010 or 2013 but that 
report a coverage ratio above one in the next two years, i.e. making profits again after an extended 
period without profits.  

We compute the share of successful zombies by age and size category considering each of the three 
zombie definitions. The results are reported in Figure 44; Panel A reports success rates by age category, 
while  Panel  B  reports  success  rates  by  size  category.  Red  part  of  the  bars  represents  the  share  of 
successful zombie firms in the next two years. All numbers are reported in Annex D. 

16 

Figure 4. Zombie status persistence 

PANEL A: Share by Age 

PANEL B: Share by Number of Employees 

Note: This figure reports the share of successful zombies. We define successful zombies, zombie firms in 2010 that have positive 
profits in the next two years. We use the three definitions Zombie A, B, and C, and split by age (Panel A) and employee size 
category  (Panel  B).  The  bar  represents  the  share  of  zombies  in  2010  (left  axis),  with  the  red  (blue)  colour  part  of  the  bar 
representing the successful (unsuccessful) zombies. Circles represent the share of successful zombies (right axis), 

We find that the share of successful zombies is 9.73% on average across all categories, varying from 
5.47%  (Zombie  C  definition,  41+  category)  to  11.96%  (Zombie  A  definition,  50-99  employee  firm 
category). In other words, about 90% of zombies remain zombies in the next two years, or exit from 
the market and our sample.  

Patterns regarding the likelihood of success as a function of firm size are hard to detect.  Zombie  A 
definition implies an inverse U-shape, with large firms 250+ employee firms having the lowest rate of 
success (10.46%), while Zombie B and C definitions suggest an upward trend with respect to size (11.5% 
Zombie B, 8.71% Zombie C). By contrast, firm age correlates negatively with the success rate. Zombie 

17 

 
 
 
 
 
A definition shows that the success rate falls from 10.29% for firms aged 2-5 years to 8.71% for firms 
aged 41+; the rate is thus 15.4% lower. Considering definitions of zombie B and C firms, estimates drop 
from 10.35% and 7.80%to 8.71% and 5.47%, respectively (thus equivalent to declines by 15.9% and 
29.9%, respectively). Older companies are thus particularly likely to remain zombies once they enter 
such status.  

One question is what policy intervention might play in this context.5 For example, numerous financial 
support arrangements exist for firms with up to 250 employees and, in principle, the availability of such 
support programmes might explain why some firms can continue to operate even if they fail to generate 
profits for extended periods of time.  One way to look at this issue is to see whether there is a break in 
the series of the cumulative incidence of zombie company rate around the threshold of 250 employees, 
e.g. to detect whether there are more zombie companies among companies that are just below that 
threshold than there are among companies that are just above that threshold and thus do not qualify 
for any financial support.  

Another issue that could be addressed in future work is that of the incidence of zombie firms across 
different  sectors.  Caballero  et  al.  (2008)  argue  that  the  less  a  sector  is  subject  to  international 
competition the larger the share  of zombie firms, given that banks tend to find it  more difficult to 
subsidize  firms  in  sectors  characterised  by  tight  international  competition.  The  authors  find  that 
manufacturing  is  the  sector  that  is  most  subject  to  international  competition,  and  construction the 
least.  In  fact,  it  turns  out  that  manufacturing  displays  the  lowest  share  of  zombies  in  Japan,  while 
construction the largest. Figure 5 reports the share of zombies by sector in 2013 in our sample. It shows 
that while the construction sector is characterised by above-average rates of incidence of zombie firms, 
the relative incidence of zombie firms is even higher in the manufacturing sector. This result contrasts 
with the estimates in Caballero et al. (2008).  

5 This question was raised plainly by an OECD Secretariat presentation (which drew on collaborative work on financial support 
programmes for SMEs undertaken in collaboration with the EC JRC) at a meeting of the OECD Committee on Financial Markets 
in October 2016. The presentation made explicit reference to the preliminary report produced by AAM and, subsequently, the 
latter  authors  kindly  agreed  to  present  their  findings  at  the  EC  JRC.  The  question  regarding  the  potential  role  of  policy 
intervention remains relevant. Another report prepared by the OECD Secretariat in collaboration with the JRC (Schich et. al., 
2017) suggests that the potential economic costs of such interventions (which would include artificially keeping alive zombie 
firms through subsidised credit) are often not considered as part of performance assessments of publicly supported credit 
guarantee programmes, owing in particular to the lack of data that would allow a rigorous analysis including the construction 
of a proper counterfactual.  

18 

 
 
                                                             
 
Figure 5. Zombie Firms by Sector 

Note: Zombie company incidence by sectors in 2013, full sample. 

7  Conclusions 

The report identifies the incidence of zombie firms across European countries and the effects of such 
incidence on the asset growth, employment growth and productivity of non-zombie firms. Using the 
example  of  Japan,  Peek  and  Rosenberg  (2005)  and  Caballero  et  al.  (2008),  each  using  different 
definitions of zombie firms, find that the larger the share of zombies in a sector, the lower the growth 
and  investment  decisions  of  non-zombies,  and  the  larger  the  productivity  of  the  latter.  Adalet 
McGowan, Andrews, and Millot (2017), using yet another definition of zombie firms, identify similar 
results  for  a  sample  of  twelve  European  countries  and  Korea.  We  extend  the  analysis  of  Adalet 
McGowan, Andrews, and Millot (2017) and include additional European countries and consider three 
different zombie firm definitions to ensure robustness of estimation results. We confirm that the larger 
the share of zombie firms in a country the lower the growth of non-zombies in that country, and the 
larger tends to be the productivity of the latter. Young non-zombies are most affected by such zombie 
“congestion”  effects.  In  addition,  we  find  that  zombie  firms  are  spreading  in  Europe.  Against  this 
background, the results of the present report suggest that one should indeed fear the spread of zombie 
firms in Europe. 

The limits of the present analysis need to be recognised. Data coverage by Orbis is uneven across years 
and countries and sample changes cannot be clearly attributed to exit and entry of firms from and to 
the market, but often reflects artefacts of the way data is reported and collected. Another limitation is 
that it is not clear what an appropriate share of zombie companies for an economy might be, although 
a priori, lower shares would appear to be preferable than higher shares. But what might be a natural 
level of zombie companies and how does that level vary as a function of the conjunctural environment? 
It should be noted that the zombie companies identified here do not ultimately fail with certainty. In 

19 

 
 
 
 
fact,  our  results  suggest  that  especially  young  and  small  firms  might  ultimately  (or  again)  achieve 
profitability.  Such  cases  can  be  seen  as  evidence  of  the  desirable  effects  of  so-called  Hausbank 
relationships between banks and their customers, whereby banks provide financing through difficult 
periods  when  its  borrowers  have  low  or  no  firm  operating  profits  before  they  ultimately  return  to 
profitability. Obviously, to the extent that they never become healthy again and are kept alive artificially 
through  forbearance  on  the  part  of  banks  or  through  subsidies  provided  through  public  financial 
support programmes, resources are inefficiently trapped among zombie firms. Follow-up work could 
investigate under what circumstances distorted incentives on the parts of banks and publicly supported 
financial support programmes are contributing to the incidence and spread of zombie firms.  

20 

REFERENCES 
Acharya,  Viral  V.,  Tim  Eisert,  Christian  Eufinger,  Christian  Hirsch,  2017.  Whatever  it  takes:  The  real 
Effects of Unconventional Monetary Policy. SAFE Working Paper No. 152. 

Adalet McGowan, Müge, Dan Andrews, and Valentine Millot, 2017. The Walking Dead? Zombie Firms 
and  Productivity  Performance  in  OECD  Countries.  OECD  Economics  Department  Working  Paper  No. 
1372. 

Bank of Korea, 2013. Financial Stability Report. Bank of Korea, Seoul. 

Caballero, R., T. Hoshi and A.K. Kashyap, 2008. Zombie Lending and Depressed Restructuring in Japan. 
American Economic Review, 98(5), pp. 1943-1977.  

Deutsche Bundesbank (2017), “The Emergence of Zombie Firms in Germany in the Low-interest-rate 
Environment”, Deutsche Bundesbank Monthly Report, December 2017, pp. 37-40. 

Foster, Lucia, Cheryl Grim, John Haltiwanger, 2016. Reallocation in the Great Recession: Cleansing or 
Not? Journal of Labour Economics 34(1), 293-331. 

Gal,  Peter,  2013.  Measuring  Total  Factor  Productivity  at  the  Firm  Level  using  OECD-ORBIS.  OECD 
Economics Department Working Papers No. 1049. 

Hoshi, Takeo, 2006. Economics of the Living Dead. The Japanese Economic Review, Vol. 57(1), 30-49. 

Hoshi, Takeo and Anil K. Kashyap, 2011. Why Did Japan Stop Growing? National Institute for Research 
Advancement (NIRA) Report. 

Hoshi, Takeo and Anil K. Kashyap, 2004. Japan’s Financial Crisis and Economic Stagnation. Journal of 
Economic Perspectives 18(1), 3-26. 

Kalemli-Ozcan, S., B. Sorensen, C. Villegas-Sanchez, V. Volosovych, S. Yesiltas, 2015. How to Construct 
Nationally  Representative  Firm  Level  Data  from  Orbis  Global  Database.  NBER  Working  Papers,  No. 
21558. 

OECD, 2016. Financing SMEs and Entrepreneurs 2016: An OECD Scoreboard. OECD Publishing, Paris. 

Okamura, Ken, 2011. “Zombie” Banks Make “Zombie” Firms. Said Business School Working Paper. 

Peek, Joe and Eric S. Rosengren, 2005. Unnatural Selection: Perverse Incentives and the Misallocation 
of Credit in Japan. American Economic Review 95(4), 1144-66. 

Schich,  Sebastian,  Anna  Naszodi,  Jessica  Cariboni,  and  Sara  Maccaferri  (2017),  “Evaluating  publicly 
supported credit guarantee programmes for SME: Results of OECD/EC survey and literature review", 
forthcoming in OECD Financial Market Trends.  

Sette, Enrico and Giorgio Gobbi, 2015. Relationship Lending during the Financial Crisis. Journal of the 
European Economic Association 13(3), 453-481. 

21 

Annex A – Sample Selection and Zombie Definitions 

Annex Table A1 presents the three zombie definitions. The first one (zombie A) defines zombies as firms 
with a coverage ratio below one in three consecutive years, regardless of age. Coverage ratio is defined 
as the ratio of EBIT – earnings before interest and taxes, after depreciation – to interest payments. The 
second definition (zombie B) defines zombies as firms that are ten years or older and have a coverage 
ratio below one for three consecutive years. The third definition (zombie C) defines as zombies those 
firms that are ten years or older and have a coverage ratio below one for five consecutive years. 

Table A1. Zombie firms definitions 

Zombie A 
Zombie B 
Zombie C 

Age Restriction 

None 
≥10 years 
≥10 years 

Coverage ratio ≤ 1 

At least 3 consecutive years 
At least 3 consecutive years 
At least 5 consecutive years 

Notes: Age is the difference between year of report and year of incorporation. Coverage ratio is the ratio of profits to interest 
payments;  earnings  are  earnings  after  depreciation,  and  before  interest  and  tax  payments  (EBIT).  We  substitute  interest 
payments with financial expenses whenever missing. 

Table A2 shows what the definitions imply in terms of estimates of zombie and non-zombie firms across 
different  age  groups.  Most  notably,  under  definitions  B  and  C,  all  firms  younger  than  10  years  are 
considered non-zombies.  

Table A2. Firm Age and Zombie Definitions 

3 years < age ≤ 6 years 
Zombie 

Non Zombie 

7 years ≤ age ≤ 9 years 
Zombie 

Non Zombie 

10 years ≤ age 

Zombie 

Non Zombie 

Zombie A 

X 

Zombie B 

Zombie C 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

X 

Notes: This table reports the three definitions of zombies that we use, and whether it may be classified or not as a zombie and 
non-zombie in function of age. An X denotes that estimates for the respective category of firms indicated as column headers 
are available according to the definition shown as the row headers. 

22 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex B – Definitions of Variables 

This annex provides the definitions of the variables used, which are obtained from Moody’s Bureau van 
Dijk Orbis dataset of firm-level financial statements. 

Non-Zombie 

Dummy  variable  that  takes  the  value  one  if  the  firm  is  not  a  zombie  company.  A 
company  is  classified  as  a  zombie  company  in  a  year  if  its  Coverage  Ratio  = 
EBIT/Interest Payments is below one in the previous three years. Zombie companies 
are ten years or older, unless otherwise specified. 

Zombie Share 

Share of fixed assets held by zombie firms at country and sector 2-digit NACE rev 2 
level. 

ln(I/K) 

Investment ratio; equals the difference in the natural logarithm of total fixed assets 
between two years. 

Δ ln(Employment) 

Employment growth; equals the percentage change in natural logarithm of number 
of employees between two years. 

Labour Productivity 

Labour productivity equals the natural logarithm of the ratio of the sum of profits 
(EBIT) and wage-cost to employment. 

MFP* 

Age 

Young 

Multifactor  productivity;  equals  residuals  ε  from  the  OLS  estimate  of  the  Cobb-
Douglas function  
ln(Profit + Wage Cost) = - α * ln(Fixed assets) - β * ln(employees) + ε. 
where  the  equation  includes  country  and  year  fixed  effects.  Profit  is  operational 
profit  EBIT.  Wage  Cost  is  cost  of  employees.  Fixed  assets  are  total  fixed  assets 
(including tangible and intangible assets). 
We obtain α= 0.9, and β = 0.2. We use these coefficients to predict MFP. 

Number of years since year of incorporation. 

Dummy variables that takes the value one if the firm is six years or younger. 

Ln(Employment) 

Natural logarithm of the number of employees. 

Industry 

Two-digit industry NACE rev 2. Classification code. 

23 

Annex C – Results using sample close to AAM 

To allow comparison of our results with those reported by AAM, this Annex reports additional estimates 
that use a sample of firms that is as close as possible to the one considered by AAM. We thus cut down 
our  country  sample  to  11  countries,  which  are  Austria,  Belgium,  Finland,  France,  Germany,  Italy, 
Portugal, Slovenia, Spain, Sweden, and the United Kingdom (note that AAM also include Luxembourg 
and South Korea for which we do unfortunately have no data). We limit the analysis to the zombie firm 
definition considered by AAM, i.e., ten years or older firms and three consecutive years with coverage 
less than one (referred to as zombie B definition in the main text). 

Figure C 1. Rise of Zombie Firms, Full AAM Country Set. 

Note: This figure reproduces Figure 3 with AAM set of countries only. 

As regards the effects of the incidence of zombie firms on non-zombie firms, the signs and magnitudes 
of  estimates  are  similar  to  AAM  for  the  investment  model.  Results  are  less  comparable  for  the 
employment  model.  While  the  estimated  coefficient  for  non-zombie  coefficient  is  positive  in  our 
regressions, as it is in AAM, the magnitude of our estimates is only about half that of AAM (which is 7 
percent). Moreover, our results do not suggest a significant effect (Table C 2). Although we use a simple 
as opposed to a more elaborate proxy for multi-factor productivity unlike AAM, our results are close to 
the latter for the productivity models.  

24 

Table C 1. Number of Observations by Country and Year (sample restricted to resemble AAM) 

2008 

2009 

2010 

38,643 
20,784 
189,535 

39,557 
21,696 
194,889 

40,608 
22,572 
200,039 

Restricted AAM Country Set 
Austria 
Belgium 
Finland 
France 
Germany 
Italy 
Portugal 
Slovenia 
Sweden 
Spain 
United Kingdom 
Subtotal 

code 
AT 
BE 
FI 
FR 
DE 
IT 
PT 
SI 
SE 
ES 
GB 

2011 
36,611 
41,481 
23,385 
204,149 
308,962 
212,470 
81,929 
11,501 
45,832 
248,119 
42,641 
1,257,080 

2012 
37,203 
41,667 
23,990 
206,600 
309,313 
216,524 
84,137 
11,662 
45,934 
251,562 
43,227 
1,271,819 

2013 
37,203 
41,667 
24,221 
207,205 
309,313 
216,524 
86,037 
11,729 
45,934 
252,951 
43,415 
1,276,199 

185,044 

194,370 
77,256 
11,060 
43,354 
239,484 
41,017 
862,683 
Note: This table reports the distribution of firms when the sample is restricted to contain only the countries considered by AAM. 

204,112 
79,581 
11,287 
45,114 
244,115 
41,886 
889,314 

10,846 
41,817 
234,837 
40,071 
761,577 

Table C 2. The Impact of Zombie Firms on Non-Zombie Firms – AAM country set 

Non-ZombieB 

Non-ZombieB X ZombieB Share 

[1] 
ln (I/K) 

[2] 
Δ ln(Employment) 

0.064*** 
[0.0029] 
-0.097*** 
[0.0171] 
Yes 
Yes 
2,837,475 
0.03 

0.043*** 
[0.0020] 
0.019 
[0.0147] 
Yes 
Yes 
2,844,864 
0.068 

[3] 
Labour 
Productivity 
0.049*** 
[0.0041] 
-0.230*** 
[0.0315] 
Yes 
Yes 
2,834,569 
0.455 

[4] 
MFP 

0.064*** 
[0.0061] 
0.155*** 
[0.0529] 
Yes 
Yes 
2,810,461 
0.361 

Firm Age and Size controls 
Industry-Year-Country Fixed Effects 
Observations 
R-squared 
Dependent Variable: 
Mean and [Standard Deviation] 
Note: Estimates of Model [1] of firm performance. We restrict the sample to the countries considered in AAM and use Zombie 
B definition only. Zombie B defines zombies as any firm aged ten years or older and with coverage ratio less than one in three 
consecutive years.  The  definitions  of the  variables  are  reported  in Annex  B.   All  specifications  control  for  firm  age  and  size 
categories, and industry-year-country fixed effects. The last line reports the mean and standard deviation (in square brackets) 
of the dependent variables. ***, **, * respectively indicates statistical significance at 1%, 5%, and 10% level. 

10.251 [0.568] 

0.036 [0.452] 

0.045 [0.379] 

9.298 [0.574] 

Table C 3. The Impact of Zombie Firms on Young Non-Zombie Firms – AAM country set. 

Non-ZombieB 

Non-ZombieB X Young 

Non-ZombieB X ZombieB Share 

Non ZombieB X ZombieB Share X Young 

Firm Age and Size controls 
Industry-Year-Country Fixed Effects 
Observations 
R-squared 
Dependent Variable 
Mean and [Standard Deviation) 

[1] 

ln (I/K) 

0.067*** 
[0.0028] 
0.075*** 
[0.0035] 
-0.108*** 
[0.0173] 
0.109*** 
[0.0251] 
yes 
yes 
2,837,475 
0.032 

[2] 

Δ ln(Employ) 

0.046*** 
[0.0019] 
0.086*** 
[0.0042] 
0.011 
[0.0131] 
0.089*** 
[0.0323] 
yes 
yes 
2,844,864 
0.072 

[3] 
Labour 
Productivity 
0.049*** 
[0.0040] 
0.034*** 
[0.0039] 
-0.219*** 
[0.0299] 
-0.067** 
[0.0281] 
yes 
yes 
2,834,569 
0.456 

[4] 

MFP 

0.067*** 
[0.0057] 
0.015*** 
[0.0056] 
0.132*** 
[0.0487] 
0.159*** 
[0.0489] 
yes 
yes 
2,810,461 
0.361 

0.036 [0.452] 

0.045 [0.379] 

10.251 [0.568] 

9.298 [0.574] 

Note: Estimates of Model [1] of firm performance. We add Young, a dummy variable that takes the value 1 if the firm is 6 years 
old or younger. We restrict the sample to the countries considered in AAM and use Zombie B definition only. Zombie B defines 
zombies as any firm aged ten years or older and with coverage ratio less than one in three consecutive years. The definitions of 
the variables are reported in Annex B.  All specifications control for firm age and size categories, and industry-year-country fixed 
effects.  The  last  line  reports the  mean  and  standard  deviation  (in square  brackets)  of the  dependent  variables. ***, **,  * 
respectively indicates statistical significance at 1%, 5%, and 10% level. 

25 

Annex D – Persistence of zombie status 

The following tables report the figures we used in Figure 1 and Figure 4. Notice that firms aged less 
than 10 years cannot be identified as zombies under Zombie B and Zombie C definitions (See Annex A). 

Table D 1. Zombie firms and “success” rate 

Zombie A 

Zombie B 

Zombie C 

% zombies 

% success 

% zombies 

% success 

% zombies 

% success 

Number of Employees 
5-9 
10-19 
20-49 
50-99 
100-249 
250+ 

4.96 
4.43 
4.90 
5.89 
6.50 
7.13 

Firm Age in Years 
2-5 
6-10 
11-20 
21-40 
41+ 

4.65 
5.18 
5.50 
6.45 
5.96 

10.58 
11.47 
11.45 
11.96 
10.97 
10.46 

10.29 
09.92 
10.09 
10.02 
08.71 

3.52 
3.26 
3.85 
4.67 
5.17 
5.75 

1.05 
5.50 
6.45 
5.96 

10.13 
11.40 
11.25 
11.86 
11.25 
11.50 

10.35 
10.09 
10.02 
08.71 

0.95 
0.87 
1.16 
1.48 
1.79 
2.21 

0.29 
1.46 
1.95 
2.18 

8.03 
8.43 
7.85 
9.36 
8.14 
8.71 

7.80 
7.61 
7.92 
5.47 

Note: “Success” is defined as a situation, where a firm identified as a “zombie” either in 2010 or in 2013 and generates profits 
that outweigh interest rate expenses during the subsequent two years. The “success rate” measures the proportion of such 
successful companies as of all zombie companies in the respective category. 

26 

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