Breaking the shackles : zombie firms, weak banks and depressed restructuring in Europe.
Date: 2019-04-17
Institutions: European Central Bank
Subjects: bank, banking policy, company growth, economic growth, financial institution, work productivity

Dan Andrews, Filippos Petroulakis 

Working Paper Series 

Breaking the shackles: 
Zombie firms, weak banks and 
depressed restructuring in Europe 

No 2240 / February 2019 

Disclaimer: This paper should not be reported as representing the views of the European Central Bank 
(ECB). The views expressed are those of the authors and do not necessarily reflect those of the ECB. 

 
 
 
Abstract

This paper explores the connection between ”zombie” ﬁrms (ﬁrms that would typically exit
in a competitive market) and bank health and the consequences for aggregate productivity
in 11 European countries. Controlling for cyclical effects, the results show that zombie ﬁrms
are more likely to be connected to weak banks, suggesting that the zombie ﬁrm problem in
Europe may at least partly stem from bank forbearance. The increasing survival of zombie
ﬁrms congests markets and constrains the growth of more productive ﬁrms, to the detriment
of aggregate productivity growth. Our results suggest that around one-third of the impact of
zombie congestion on capital misallocation can be directly attributed to bank health and addi-
tional analysis suggests that this may partly be due to reduced availability of credit to healthy
ﬁrms. Finally, improvements in bank health are more likely to be associated with a reduc-
tion in the prevalence of zombie ﬁrms in countries where insolvency regimes do not unduly
inhibit corporate restructuring. Thus, leveraging the important complementarities between
bank strengthening efforts and insolvency regime reform would contribute to breaking the
shackles on potential growth in Europe.

Keywords: Zombie Firms; Factor Reallocation; Productivity; Credit Constraints
JEL codes: D24, G21, L25, O47.

ECB Working Paper Series No 2240 / February 20191Non-Technical Summary

The collapse in potential output growth over the past two decades brings into focus the struc-
tural barriers to productivity growth in Europe and the scope for policy to break these shackles.
Firm-level research has linked the productivity slowdown to widening productivity dispersion
across ﬁrms (Andrews et al. 2016), rising capital misallocation (Gopinath et al. 2017) and declin-
ing business dynamism (Decker et al. 2016). A related source of concern is that ﬁrms that would
typically exit or be forced to restructure in a competitive market – ”zombie ﬁrms” – seem to in-
creasingly survive during the crisis, which may weigh on average productivity and crowd-out
growth opportunities for more productive ﬁrms (Adalet McGowan et al. 2018). It has been ar-
gued (Caballero et al. 2008) that the presence of zombies imposes a tax on healthy ﬁrms, who
suffer a loss in proﬁtability and hence growth prospects, impacting aggregate growth. These
problems may be symptomatic of impaired banks, which may be reluctant to recognize non-
performing loans (NPLs) and realize losses on their balance sheets, and may rather “evergreen”
loans to zombie ﬁrms (i.e. bank forbearance). Outside of the Japanese experience of the 1990s we
know little about why zombies exist, and to what extent the zombie problem is a manifestation
of banking sector weakness. We also know very little about the role of insolvency frameworks:
lengthy procedures and barriers to restructuring may result in low enough recovery rates that
even healthy banks have little incentive to force a default and may instead forbear on zombies.

Accordingly, this paper studies the connection between zombie ﬁrms, bank health and insolvency
frameworks and the consequences for productivity growth. Using a large cross-country ﬁrm-
level database, information on bank balance sheets, credit availability perception indicators by
ﬁrms, and a new indicator on insolvency regimes, our paper produces four concrete results.

First, our estimates show that zombie ﬁrms have a higher likelihood of being connected to a
weak bank. After controlling for cyclical inﬂuences at the industry-country level, our estimates
imply that weak banks are between 1.2 and 1.8 percentage points more likely of being connected
with a zombie ﬁrm compared to healthy banks. This translates into a 13% to 19% difference in
zombie incidence between healthy and weak banks. These results are consistent with the idea
that the zombie ﬁrm problem in Europe may at least partly stem from bank forbearance.

Second, we ﬁnd an important role for the insolvency framework in shaping the link between
zombie ﬁrms and bank health. Even when banks are healthy, an insolvency framework which
impedes corporate restructuring and reduces recovery rates for creditors may reduce incentives
for banks to commence the process of recovery, liquidation, or restructuring. We show that im-
provements in bank health are more likely to be associated with a reduction in the prevalence
of zombie ﬁrms in countries where insolvency regimes do not unduly inhibit corporate restruc-

ECB Working Paper Series No 2240 / February 20192turing. Put differently, the effectiveness of efforts to reduce NPLs will be limited if insolvency
regimes remain hostile to efﬁcient restructuring. These ﬁndings carry strong policy implications
and imply that leveraging the important complementarities between efforts to restore bank health
and insolvency regime reform will be important for breaking the shackles on potential growth.

Third, we explore the aggregate consequences of the zombie ﬁrm-bank connection through
the channel of capital reallocation. In a well-functioning economy, capital should ﬂow towards
more productive ﬁrms at the expense of less productive ﬁrms (productivity-enhancing capital
reallocation). We show that, on average, capital reallocation is productivity-enhancing, but the
extent to which this is true diminishes as zombie congestion (i.e. the share of the industry capital
stock sunk in zombie ﬁrms) rises. Consistent with the zombie ﬁrm- weak bank link uncovered
above, we then show that the capital reallocation process is less (more) productivity-enhancing
in industries exposed to weaker (healthier) banks. Our results suggest that around one-third
of the impact of zombie congestion on capital misallocation can be directly attributed to bank
health. These ﬁndings are signiﬁcant given that rising capital misallocation is emerging as a key
explanation of the productivity slowdown in some countries (Gopinath et al. 2017).

Finally, we provide new evidence on an understudied channel: the potential for zombie ﬁrms
to crowd-out credit to healthier and more productive ﬁrms. If zombie congestion crowds-out
investment opportunities for healthier and more productive ﬁrms, one would expect that this re-
lationship is also reﬂected in access to credit. We ﬁnd a statistically signiﬁcant negative relation-
ship at the sectoral level between the share of capital sunk in zombie ﬁrms and credit availability
for healthy ﬁrms. This could reﬂect direct crowd-out – banks cannot lend to healthy ﬁrms be-
cause their balance sheets are weakened due to zombie exposure – or reduced borrowing capacity
because zombie congestion has reduced industry proﬁtability and thus the returns on potential
projects of healthy ﬁrms. Assuming banks engage in sectoral diversiﬁcation, we hypothesize that
the latter is more likely. These results should be interpreted with caution, given that the sample
size is very small and the economic magnitude of the estimated coefﬁcient is modest.

The results of this paper imply that in order to facilitate the unwinding of the zombie problem it
is essential that bank balance sheets are strong, underlining the need for fast recapitalizations af-
ter crises and measures to reduce NPLs. But strengthening banks is insufﬁcient while insolvency
regimes remain hostile to the orderly restructuring and resolution of weak ﬁrms. Thus, in coun-
tries with weak banks and weak insolvency regimes, efforts to improve banks’ health should be
accompanied by appropriate insolvency reforms to reduce impediments to corporate restructur-
ing. At the same time, countries with strong ﬁrms and banks should view the establishment of
an efﬁcient insolvency framework as equivalent to a ﬁnancial stability-enhancing measure.

ECB Working Paper Series No 2240 / February 201931

Introduction

The collapse in potential output growth over the past two decades brings into closer focus the
structural barriers to productivity growth in Europe and the scope for policy to break these
shackles. Firm-level research is increasingly linking the aggregate productivity slowdown to the
widening productivity dispersion across ﬁrms (Andrews et al. 2016), rising capital misallocation
(Gopinath et al. 2017) and declining business dynamism (Decker et al. 2016). A related source of
concern is that ﬁrms that would typically exit or be forced to restructure in a competitive market
– i.e. ”zombie ﬁrms” – are increasingly surviving, which may weigh on average productivity and
crowd-out growth opportunities for more productive ﬁrms (Adalet McGowan et al. 2018). The
zombie problem is a particular manifestation of inefﬁciencies on the (ﬁrm) exit margin. When
zombie ﬁrms remain in the market, they trap scarce resources, which imposes an implicit tax on
healthy ﬁrms, reducing their proﬁt and hence their investment and growth opportunities (zom-
bie congestion). A rich literature dating back to Hopenhayn (1992) has posited that the exit of low
productivity ﬁrms and the reallocation of their resources to more productive ﬁrms are key for ag-
gregate MFP (multi-factor productivity) growth, and ultimately, economic growth. Inefﬁciencies
in the exit margin can thus have severe aggregate consequences.

In some countries, these problems may be symptomatic of impaired banking sectors.
In this
view, banks’ reluctance or lack of incentives to deal with non-performing loans (NPLs) and re-
alize losses on their balance sheets that may arise from corporate insolvencies, may lead to ”ev-
ergreening” of loans to zombie ﬁrms (i.e. bank forbearance). But almost everything we know
about this issue is based on the Japanese experience of the 1990s (Caballero et al. 2008; Peek &
Rosengren 2005) and cross-country research on the connection between zombie ﬁrms and weak
banks is scarce.

Accordingly, this paper explores the connection between zombie ﬁrms and bank health and the
resulting consequences for aggregate productivity growth. To this end, we assemble a cross-
country micro dataset by linking ORBIS, the largest commercially available ﬁrm-level source of
cross-country data, with bank-level information from Bankscope and microdata on ﬁrms’ access
to ﬁnance from the Survey on the Access to Finance for Enterprises (SAFE), run by the European
Central Bank (ECB). We use principal components analysis to construct a multi-dimensional in-
dicator of bank health, which takes into account tangible equity, net income, NPLs, return on
assets, and measures of maturity mismatch and asset riskiness. Our ﬁnal dataset covers 11 Eu-
ropean countries, from 2001 to 2014. In turn, cross-country empirical analysis delivers four main
policy ﬁndings.

ECB Working Paper Series No 2240 / February 20194First, after controlling for cyclical inﬂuences at the industry-country level, our baseline (within-
industry) estimates show that zombie ﬁrms have a higher likelihood of being connected to a
weak bank. These results survive a battery of robustness tests. Assuming a causal relationship,
our baseline estimates imply that weak banks (i.e. those one standard deviation below the mean
of the bank health distribution) are between 1.2 and1.8 percentage points more likely of being
those one standard deviation
connected with a zombie ﬁrm compared to healthy banks (i.e.
above the mean of the bank health distribution), using the deﬁnition of Adalet McGowan et al.
(2018). This translates into a 13% to 19% difference in zombie incidence between healthy and
weak banks relative to the mean. These results are consistent with the idea that the zombie ﬁrm
problem in Europe may at least partly stem from bank forbearance.

We then explore the potential for the design of insolvency regimes to condition the link between
zombie ﬁrms and bank health. Even when banks are healthy, an insolvency framework which
creates impediments to corporate restructuring and reduces recovery rates for creditors may re-
duce the economic incentives for banks to commence the process of recovery, liquidation, or
restructuring. Consistent with this idea, we exploit new OECD insolvency regime indicators (see
Adalet McGowan & Andrews 2018) to show that improvements in bank health are more likely to
be associated with a reduction in the prevalence of zombie ﬁrms in countries where insolvency
regimes do not unduly inhibit corporate restructuring. Put differently, the effectiveness of efforts
to reduce NPLs will be limited if insolvency frameworks remain hostile to efﬁcient restructuring.
Taken together, these ﬁndings carry strong policy implications and imply that leveraging the im-
portant complementarities between efforts to restore bank health and insolvency regime reform
will be crucial for breaking the shackles on potential growth in Europe.

Third, we explore the aggregate consequences of the zombie ﬁrm-weak bank connection through
the channel of capital reallocation. We ﬁrst replicate the key ﬁndings of Adalet McGowan et al.
(2018), and show that on average, more productive ﬁrms attract more capital (i.e. capital realloca-
tion is productivity-enhancing) but the extent to which this is true diminishes as zombie conges-
tion (i.e. the share of the industry capital stock sunk in zombie ﬁrms) rises. Consistent with the
empirical link between zombie ﬁrms and bank health uncovered above, we then show that the
capital reallocation process is less (more) productivity-enhancing in industries exposed to weaker
(healthier) banks. For example, a one standard deviation increase in bank health would raise our
preferred measure of the efﬁciency of capital reallocation (the difference in capital growth be-
tween ﬁrms one standard deviation above and below the sectoral mean of productivity) by 23%.
Moreover, our results suggest that around one-third of the impact of zombie congestion on cap-
ital misallocation can be directly attributed to bank health. These ﬁndings are signiﬁcant given
that rising capital misallocation is emerging as a key explanation of the productivity slowdown

ECB Working Paper Series No 2240 / February 20195in some countries (Gopinath et al. 2017).

Finally, we study the channels through which zombie congestion may adversely affect the growth
opportunities of healthier and more productive ﬁrms. If zombie congestion crowds-out invest-
ment opportunities for healthier and more productive ﬁrms, then one would expect that this
relationship is also reﬂected in access to credit. Using data from the Survey on the Access to
Finance of Enterprises (SAFE) from the European Central Bank (ECB), we ﬁnd a statistically sig-
niﬁcant negative relationship at the sectoral level between the share of capital sunk in zombie
ﬁrms and credit availability for healthy ﬁrms. Intuitively, this could reﬂect direct crowd-out ef-
fects – i.e. banks cannot lend to healthy ﬁrms because their balance sheets are weakened due to
their zombie exposure – or reduced borrowing capacity because zombie congestion has reduced
industry proﬁtability and thus the returns on potential projects of healthy ﬁrms. Assuming banks
engage in sectoral diversiﬁcation, we hypothesize that this is more likely to reﬂect the latter and
less so direct crowd-out. These results should be interpreted with caution, however, given that
the sample size is very small and the economic magnitude of the estimated coefﬁcient is modest.

The paper is structured as follows. The following section illustrates the link between zombies and
productivity weakness and then reviews existing literature between zombie ﬁrms, weak banks,
and insolvency regimes. Section 3 outlines the empirical strategy while Section 4 explains the
data sources. Section 5 then provides new empirical evidence on four issues: i) the connection be-
tween zombie ﬁrms and weak banks; ii) how this link is conditioned by the design of insolvency
regimes; iii) the aggregate consequences of the zombie ﬁrm-weak bank connection through the
channel of capital reallocation; and iv) channels through which zombie congestion adversely af-
fects growth, with a particular focus on healthy ﬁrms access to ﬁnance. Section 6 provides some
concluding remarks.

2 Productivity, Zombie ﬁrms, and Public Policy

2.1 The exit margin as a source of productivity weakness

Potential output growth has slowed by about one percentage point per annum across the EU since
the late 1990s, which is entirely accounted for by a collapse in labor productivity growth. Digging
deeper, this reﬂects a pre-crisis slowing in multi-factor productivity (MFP)1 growth (ﬁgure 1), and
more recently a weakness in capital deepening (Ollivaud et al. 2016). This has raised questions

1MFP is equivalent to TFP, Total Factor Productivity, but we use the MFP term throughout the text.

ECB Working Paper Series No 2240 / February 20196about our societies’ ability to make good on promises to current and future generations.

A key lesson from recent research is that using aggregate data to understand the proximate
drivers of productivity weakness only gets one so far. In fact, cross-country differences in aggre-
gate productivity are increasingly being linked to the widespread asymmetry and heterogeneity
in ﬁrm performance within sectors (Bartelsman et al. 2013, Hsieh & Klenow 2009). The distribu-
tion of ﬁrm productivity is typically not clustered around the mean but is instead characterized
by many below-average performers and a smaller number of star performers. Moreover, the de-
gree of heterogeneity is striking: even within narrowly deﬁned industries in the United States,
ﬁrms at the 90th percentile of the MFP distribution are twice as productive as ﬁrms at the 10th
percentile (Syverson 2011).

This heterogeneity in ﬁrm performance is important as it creates scope for growth-enhancing
resource reallocation. A theoretical literature dating back to Hopenhayn (1992) has posited that
the exit of low productivity ﬁrms and the reallocation of their resources to more productive ﬁrms
are key for aggregate MFP growth. Indeed, ﬁrm exit creates space for new varieties to emerge
and new entrants productively recycle the assets of defunct ﬁrms across a range of activities.2
While this typically reﬂects the reallocation of tangible inputs, there is also scope for the post-exit
diffusion of codiﬁed knowledge to new entrants via employee mobility and the sales of patents
(Hoetker & Agarwal 2007). More generally, these predictions have been borne out in a host of
empirical studies3, which document that within-industry resource reallocation is a signiﬁcant
contributor to – if not the predominant driver of – aggregate productivity growth. While the
creative-destruction process in a well-functioning market economy compels poorly performing
ﬁrms to improve their efﬁciency or exit the market, there are signs from recent ﬁrm-level studies
that this process may be slowing down.

First, the level of productivity dispersion within industries has risen over time, implying a widen-
ing gap between more productive and less productive ﬁrms (Andrews et al. 2016). This ﬁnding
of rising productivity dispersion is conﬁrmed by a range of studies using alternate data sources
(Decker et al. 2016, Berlingieri et al. 2017, Gamberoni et al. 2016) and it appears that it is not
just driven by frontier ﬁrms pushing the boundary outward. Instead, it has been attributed to
stagnating laggard ﬁrm productivity related to the declining ability or incentives of such ﬁrms to
adopt best practices from the frontier (Andrews et al. 2016). This raises serious questions about
what allows weak ﬁrms that do not adopt the latest technologies to remain in the market.

2For the productive recycling of assets of exiting ﬁrms, see Australian Productivity Commission (2015) for the

airline industry, Hiatt et al. (2009) for beverages, and Hoetker & Agarwal (2007) for the disk drive industry.

3See Baily et al. 1992, Foster et al. 2006, Baldwin & Gu 2006, Disney et al. 2003.

ECB Working Paper Series No 2240 / February 20197Second, the contribution of resource reallocation to aggregate productivity growth has declined
over time. This is reﬂected in a decline in the responsiveness of ﬁrm growth (investment) to
productivity over recent decades in Europe (Adalet McGowan et al. 2018) and the United States
(Decker et al. 2017), which implies that the propensity of high productivity ﬁrms to expand and
low productivity ﬁrms to downsize (or exit) has fallen (Decker et al. 2017). Similarly, Gopinath
et al. (2017) document a rising dispersion of return to capital in the manufacturing sector in Spain,
Italy, and Portugal in the run-up to the crisis, which they attribute to the misallocation of credit-
driven capital inﬂows towards high net worth, but not necessarily productive, ﬁrms. Evidence
of a decline in productivity-enhancing reallocation is particularly signiﬁcant in light of rising
productivity dispersion, which would ordinarily imply stronger incentives for productive ﬁrms
to aggressively expand and drive out less productive ﬁrms.

Finally, rising productivity dispersion and declining productivity-enhancing reallocation have
coincided with a decline in a variety of measures of business dynamism. A well-documented
aspect of this development is that ﬁrm entry has declined in many countries (Criscuolo et al.
2014). Clearly, this does not augur well for productivity growth given that young ﬁrms possess a
comparative advantage in commercializing radical innovations (Acemoglu et al. 2013) and place
indirect pressure on incumbent ﬁrms to improve their productivity via technology adoption. But
a less widely understood fact is that declining ﬁrm entry has also been accompanied by a rising
survival probability of marginal ﬁrms that would typically exit in a competitive market (Andrews
et al. 2016).

The prolonged survival of such ﬁrms not only weighs on average productivity but potentially
crowds-out growth opportunities for more productive ﬁrms. Until recently, almost everything
we knew about this issue was conﬁned to Japan in the 1990s. In this regard, Caballero et al.
(2008) argue that the survival of zombie ﬁrms congests markets and lowers industry proﬁtability
– by inﬂating wages relative to productivity and depressing market prices – which deters the
expansion of healthier ﬁrms. Using ﬁrm-level data for the Japanese episode, Caballero et al.
(2008) infer whether a ﬁrm is a zombie by comparing its interest payments to prime lending
rates. They conﬁrm that in industries where a higher share of capital is sunk in zombie ﬁrms,
healthy ﬁrms experience lower investment and employment growth, and that the productivity
gap between non-zombie and zombie ﬁrms is higher, implying that new ﬁrms need to clear a
wide productivity threshold in order to enter the market.

Outside of the Japanese episode, Adalet McGowan et al. (2018) apply the same framework to a
broad sample of OECD countries. After controlling for cyclical inﬂuences at the industry-country
level, a higher share of industry capital sunk in zombie ﬁrms is found to crowd-out the growth –

ECB Working Paper Series No 2240 / February 20198in terms of investment and employment – of healthy ﬁrms within an industry. Moreover, young
ﬁrms are particularly affected. They ﬁnd that up to one-quarter of the actual decline in aggregate
business investment in Italy between 2008 and 2013 can be linked to the rise in zombie conges-
tion. But the story does not end there since zombie congestion disproportionately crowds-out the
growth of more productive ﬁrms, thus slowing aggregate MFP growth via less efﬁcient capital
reallocation. In Spain, for example, perhaps one-half of the decline in the efﬁciency of capital
reallocation can be accounted for by the rise in zombie congestion.

2.2 Zombie ﬁrms and weak banks

Despite the renewed research interest in zombie ﬁrms and their aggregate consequences, the liter-
ature on the causes of the problem is slimmer. The seminal paper on the emergence of zombies in
Japan is Peek & Rosengren (2005), who conﬁrm the widely suspected phenomenon of ”evergreen-
ing”, whereby banks engage in zombie lending by continuing to ﬁnance weak or even insolvent
ﬁrms (”unnatural selection”). This was attributed to perverse institutional incentives, such as
the reluctance of banks to write-off loans, which increased the closer was the reported capital to
regulatory requirements (”balance sheet cosmetics”). Cultural incentives also mattered: banks
had an implicit duty to support troubled ﬁrms, and relationship-lending forces were particularly
important. Overall, 14 to 36% of all ﬁrms were zombies at some point, according to Kwon et al.
(2015).4

These studies from Japan provide insights into the proximate causes of zombie congestion in
Europe. The most obvious account would emphasize the damage to the banking sector incurred
from the ﬁnancial crisis, which undermined its ability to channel scarce credit to the most efﬁcient
uses. In this regard, Acharya et al. (2017) provide some evidence that undercapitalized banks
in Europe have directed loans to zombie ﬁrms in order to avoid incurring losses on their loan
portfolios, which – instead of raising real activity – crowded-out the growth of non-zombie ﬁrms
via zombie congestion. At the same time, recent evidence shows that bank stress hinders the
deleveraging process of zombie ﬁrms in the euro periphery (Storz et al. 2017).

4Subsequent work has shed light on the nuances of the Japanese episode. Sakai et al. (2010)found that small
ﬁrms that eventually defaulted performed worse and paid higher interest rates than surviving ﬁrms. This ﬁnding
– which contradicts Peek and Rosengren – may reﬂect differences in sample composition, particularly the focus on
listed ﬁrms only in the seminal study. Against this, Akiyoshi & Kobayashi (2010)directly test for the effect of bank
distress on ﬁrm productivity and conﬁrm the negative link. Nishimura et al. (2005) ﬁnd that ﬁrms with relatively
high MFP exited, while ﬁrms with relatively low MFP stayed in the market, an indication of unnatural selection.
Okada & Horioka (2008) argued that the selection mechanism was disruptive, with banks giving preference to high
and low MFP ﬁrms, at the expense of medium MFP ﬁrms, who presumably could survive even with rationed credit.

ECB Working Paper Series No 2240 / February 20199Against this, Schivardi et al. (2018) study the universe of loans in Italy in 2003-2014, matched
with the universe of ﬁrms. While their results conﬁrm both zombie lending and negative ef-
fects on healthy ﬁrms, they claim that the aggregate effects on MFP and the growth of healthy
ﬁrms are minimal. 5 They also ﬁnd no evidence that bank capitalization affects credit supply to
non-zombies. While this paper is closely related to our own, it differs in two key respects. First,
Schivardi et al. (2018) focus on new lending. Under the stricter supervision landscape in Europe,
however, new lending to zombies may only constitute a small part of the forbearance strategy of
banks. Instead, indirect means of forbearance – i.e. beneﬁcial restructuring, maturity extension
or conversion of the payment structure (i.e. from regular installments to a lump-sum payment at
maturity) – may be more prevalent. For this reason, we implicitly focus on forbearance strategies
by considering indebtedness when constructing zombie ﬁrm indicators, and not only new lend-
ing. Second, Schivardi et al. (2018) consider the bank-ﬁrm relationship, and therefore abstract
from the possibility that lending is restricted because healthy ﬁrms lack the proﬁtability required
to get good credit, due to zombie congestion. By contrast, we employ a catch-all approach and
thus consider all types of zombie-friendly behavior.

2.3 Zombie ﬁrms, weak banks, and insolvency regimes

The fact that zombies appear after large shocks in some countries but not others suggests that
there is a potentially important role for structural policies, particularly the extent to which in-
solvency regimes foster the timely initiation and resolution of insolvency proceedings.6 In this
regard, new evidence assembled in Adalet McGowan & Andrews (2018) reveal signiﬁcant dif-
ferences across European countries in barriers to corporate restructuring and the personal costs
associated with entrepreneurial failure implied by national insolvency regimes. Moreover, they
demonstrate the potential for reforms to insolvency regimes to reduce the share of capital sunk in
zombie ﬁrms. For example, reducing barriers to restructuring to best practices could reduce the
zombie capital share by one-half and one-third in Italy and Greece, respectively. These gains are
partly realized via the restructuring of weak ﬁrms, which in turn spurs the reallocation of capital
to more productive ﬁrms (Adalet McGowan et al. 2018) and generates MFP gains within laggard
ﬁrms via more efﬁcient technological adoption (Adalet McGowan et al. 2017).

5Note, however, that Schivardi et al. (2018) ﬁnd that a bank recapitalization of 4 billion euros in 2012 (0.25% of

GDP), would lead to additional GDP growth of 0.2-0.35% annually for ﬁve years, which is arguably sizable.

6For instance, we know little about why zombie ﬁrms emerged as a widespread phenomenon in 1990s Japan in
contrast to other episodes. As Peek and Rosengren point out, this stands in contrast to the S&L crisis, where banks
quickly shrank their loan portfolios after the shock. Indeed, that was the case even though loose lending standards
from the previous decade were also a large part of the problem, and also in contrast to the last crisis, where debt
overhang was a major concern several years after the shock.

ECB Working Paper Series No 2240 / February 201910In a frictionless model, when a ﬁrm has trouble meeting its obligations to its debtor it will re-
linquish ownership of its collateral. If this is not sufﬁcient to repay the value of the debt, it will
enter into bankruptcy proceedings to determine the value of its assets, which are then sold-off
to pay the remainder of the debt. The faster this process is completed, the sooner will the re-
sources tied to the failing ﬁrm be able to be reallocated to more productive uses. In practice,
there can be a number of frictions that emanate from the inability of the insolvency framework
to address the existence of incomplete contracts, which can severely delay the orderly realloca-
tion process. Smith & Stromberg (2005) argue that the principal role of an orderly bankruptcy
process is to mitigate bargaining frictions between the debtor and its creditor(s). By preventing
a ”rush to the exit” motive, a well-designed insolvency regime veriﬁes the value of assets and
liabilities, it maintains the value of assets during bargaining, and improves coordination among
creditors, among others.7 Consolo et al. (forthcoming) consider how the insolvency framework
affects deleveraging and NPLs in OECD countries, and ﬁnd that a more efﬁcient framework is a
strong predictor of both the level of NPLs, but also the speed of NPL reduction.

In the context of zombie lending, poorly designed insolvency frameworks are likely to interact
with evergreening motives. Recall that a bank will be willing to evergreen loans to a troubled
ﬁrm (either by extending new loans or allowing non-payment of existing loans via restructuring
of debt) because it does not want to write-off losses on its balance sheet and increase provisions. It
will do so to the extent to which the amount recovered is not high enough to cover the increase in
provisions or, even if expected recovery is complete, it is expected to be delayed to such an extent
that the bank prefers to evergreen. Thus, a poorly-designed insolvency framework ultimately
implies a lower expected recovery value for the amounts owed to the bank and it follows that
evergreening motives should be higher than under a more appropriate regime. This leads us
to suspect that improvements in bank health are more likely to be associated with a reduction
in the prevalence of zombie ﬁrms in countries where insolvency regimes do not unduly inhibit
corporate restructuring.

2.4 Four key issues for investigation

Against this background, this paper addresses four key issues. First, it explores whether zombie
ﬁrms are more likely to be connected to weak banks, in order to gauge the extent to which bank
forbearance is a relevant policy concern. Second, it examines whether the aforementioned link
between zombie ﬁrms and weak banks is conditioned by the design of insolvency regimes, with a

7Similar arguments are made by Marinc & Vlahu (2012), who also underline the role of the bankruptcy regime

in preventing hold-out problems.

ECB Working Paper Series No 2240 / February 201911view to highlighting potential policy complementarities between ﬁnancial sector and insolvency
regime reform. Third, the paper explores consequences of the zombie ﬁrm-weak bank connection
for aggregate productivity via the channel of capital reallocation. Finally, it contributes to the rel-
atively scarce literature on the channels through which zombie ﬁrms affect growth, by examining
whether zombie congestion crowds-out healthy ﬁrms’ access to credit.

3 Empirical framework

3.1 Zombie ﬁrms and weak banks

Our ﬁrst aim is to establish a robust connection between zombie ﬁrms and weak banks. To this
end, we estimate the following baseline model for 11 countries8, over the period 2003-2014:

zombieisc,t = α0 + α1BankHealthisc,t−2 + Xisc,t−2Θ + γsc,t + (cid:15)isc,t.

(1)

The dependent variable takes the value of 1 if the ﬁrm is classiﬁed as a zombie in a given year
and 0 otherwise. BankHealthisc,t denotes the health of the bank associated with ﬁrm i in sector s
in country c at time t), and is increasing in bank health.9 We enter bank health in the model with
a two-year lag (t-2) in order to observe it at beginning of the time window used to deﬁne zombie
ﬁrms (recall that ﬁrms are classiﬁed as zombies if they demonstrate persistent ﬁnancial weakness
for three consecutive years) but the results are also highly robust to using a three-year lag.10 The
matrix Xisc,t is composed of relevant ﬁrm-level controls, such as ﬁrm size, ﬁrm age, and labor
productivity.11 We use a rich ﬁxed effect structure, denoted by γsc,t. In our baseline speciﬁcation,
we use a triple-interacted country-sector-year ﬁxed effect, meaning that we control for the effects
of time-varying shocks at the country-industry level, and we thus exploit ﬁrm-level variation

8These are Austria, Denmark, Estonia, France, Germany, Greece, Latvia, Slovenia, Spain, Portugal, United King-

dom

9Although we mainly rely on the holistic bank health measure, we also brieﬂy explore the role of different bank

balance sheet variables.

10We do this as we cannot know whether a troubled ﬁrm at time t is allowed to survive by its bank because the
bank judges it to have growth potential, or because the bank engages in zombie forbearance (for balance sheet or
other reasons). As such, we need to observe the ﬁrm as being troubled for some time in order to be more conﬁdent
classifying it as a zombie. At the same time, the timing relationship between bank health and the incentive to allow
zombie ﬁrms to survive (”bad” forbearance), or simply engage in temporary forbearance for struggling ﬁrms with
good growth prospects (”good” forbearance), is unclear.

11We do this as we want to control for possible selection on size or age (which have been shown to be important)
or for cases where productive ﬁrms are struggling and their banks forbear as they expect to see proﬁts in the future
(in which case forbearance is efﬁcient), and isolate the effect of bank health.

ECB Working Paper Series No 2240 / February 201912within country-sector-year cells. Given our ﬁxed effects structure, we are unable to use logit or
probit models, and so we estimate all our models with OLS (via linear probability models). Since
our working hypothesis is that healthier banks have less of an incentive to allow zombie ﬁrms to
stay alive (or remain zombies, without restructuring their debt), we expect α1 < 0.

One implication of our high-dimensional ﬁxed effects structure is that since we identify off de-
viations from within-group (i.e. country-industry-year) means, we potentially underestimate
aggregate effects (which is indeed the case as we show in section 5). Intuitively, over the period
we study, there was a worsening of bank health as a result of the ﬁnancial crisis initially, and the
sovereign debt crisis subsequently, for each country and sector. By imposing a triple-interacted
ﬁxed effect framework, we are effectively purging a lot of this variation. Thus, after establishing
the robustness of our results to controlling for country-sector-year ﬁxed effects, we also apply
a less burdensome ﬁxed effects structure, in order to produce a less conservative bound for the
magnitude of our results. The latter involves controlling for time ﬁxed effects (to absorb global
shocks) and country-industry ﬁxed effects to control for time-invariant country-speciﬁc industry
characteristics. We also report an upper bound effect that includes country-sector ﬁxed effects
but omits time effects.

3.2 Zombie ﬁrms, bank health, and insolvency regimes

Next, we incorporate the insolvency framework indicators. We focus on four indicators, denoted
by IN SOLc, as they vary only at the country level and pertain to a single point in time.12 Due to
the lack of temporal variation, the level effect of the indicator variable is absorbed by the country
ﬁxed effect, and so we identify its effect from the interaction with bank health.

We augment (1) as follows:

zombieisc,t = α0 + α1BankHealthisc,t−2 + α2BankHealthisc,t−2 × IN SOLc

+ Xisc,t−2Θ + γsc,t + (cid:15)isc,t.

(2)

The insolvency variables are deﬁned as deviations from best practices, so a rising value of the
indicator denotes a framework that is more likely to delay the initiation and resolution of insol-
vency proceedings. More speciﬁcally, our working assumption is that higher barriers to corporate

12The indicators pertain to two separate years - 2010 and 2016 - and were collected in a single wave, in 2016 and
2017. As our sample covers the period 2003-2014, we consider regressions with both years separately. Unfortunately,
it is not possible to exploit time series variation in the indicator (see Adalet McGowan & Andrews 2018.

ECB Working Paper Series No 2240 / February 201913restructuring, for example, will reduce the extent to which improvements in bank health translate
into a reduction in zombie ﬁrm prevalence. Thus, our prediction is that α1 < 0, as before, and
α2 > 0.

3.3 Weak banks and capital misallocation

To the extent that there is a connection between bank health and zombie status, then one would
also expect there to be a connection between bank health and the efﬁciency of capital alloca-
tion; that is, capital should ﬂow more readily to more productive ﬁrms in industries exposed to
healthier banks. To test this hypothesis, we employ the same framework as Adalet McGowan
et al. (2018), augmenting a canonical ﬁrm dynamics model (Foster et al. 2016, Decker et al. 2016)
which predicts that conditional on initial size, more productive ﬁrms should have higher growth
potential and thus grow more quickly if reallocation is efﬁcient.13 More speciﬁcally, the regression
takes the following form:

Yisc,t = β0+β1M F Pisc,t−1 + β2M F Pisc,t−1 × BankHealthsc,t−1

+ β3M F Pisc,t−1 × ZombieCapitalsc,t−1 + Xisc,t−1Θ + γsc,t + (cid:15)isc,t,

(3)

where Yisc,t is the growth in the capital stock for ﬁrm i, in sector s, country c, and time t, MFP
denotes ﬁrm-level multi-factor productivity measured as a deviation from the country-industry-
year average to control for MFP differences across industries and countries, and the share of
industry capital sunk in zombie ﬁrms is denoted by ZombieCapitalsc,t−1. We expect β1 to be posi-
tive, consistent with the prediction that more productive ﬁrms should grow faster relative to their
peers. A positive β2 would indicate that in sectors exposed to healthy banks, this effect is mag-
niﬁed, and the efﬁciency of capital reallocation is strengthened. Conversely, sectors connected to
more stressed banks should be characterized by less efﬁcient capital reallocation. Finally, β3 is
expected to be negative, to the extent that a higher share of industry capital sunk in zombie ﬁrms
crowds-out the growth of more productive ﬁrms (see Adalet McGowan et al. 2018). The extent
to which weak banks can account for the adverse effect of zombie congestion on capital reallo-
cation can be quantiﬁed by comparing the estimated magnitude of β3 from equation 3 with the
estimated coefﬁcient on MFP×Zombie Capital term from a version of equation 3 that excludes
the MFP×BankHealth term.

13We prefer this approach to others used in the literature (e.g. Hsieh & Klenow 2009) since ﬁrm growth is disci-
plined on MFP, which implies that we are isolating the component of capital reallocation that is truly productivity-
enhancing.

ECB Working Paper Series No 2240 / February 201914We use bank health at the sector level, not at the ﬁrm-level, for two reasons. First, because it
is sectoral bank health that matters for zombie congestion. According to our hypothesis, weak
banks may prop up zombie ﬁrms, which in turn crowds out growth opportunities even for pro-
ductive ﬁrms that are connected to healthy banks.14 Second, by adding sectoral bank health we
address one criticism for the reallocation regression by Schivardi et al. (2018). They argue that
an aggregate shock - which increases the zombie share in a sector - may also affect the relative
performance of ﬁrms, even in the absence of zombie congestion effects. They instead examine the
effect of sectoral bank health on the relative performance of ﬁrms, claiming that it does not suffer
from the same issues, and implicitly treating negative reallocation through zombie congestion or
bank health as equivalent. The identifying assumption necessary in this case is that bank health
is exogenous to sectoral business cycle conditions (a test we directly carry out); if this is satisﬁed,
then our model gives a direct estimate of the reallocation effects of bank health.

For the reallocation models, we focus on 7 countries from our sample which give us the broadest
estimates of MFP at the ﬁrm level. These are Austria, France, Germany, Slovenia, Spain, Portugal,
and the United Kingdom. See Adalet McGowan et al. (2018) for more details.

3.4 Zombie ﬁrms and credit crowd-out

Finally, we explore the question of whether the survival of zombie ﬁrms crowds-out credit access
to healthier ﬁrms, by combining data from ORBIS with SAFE, in a sample of 4 euro area coun-
tries (France, Germany, Italy and Spain). More speciﬁcally, we examine whether healthy ﬁrms
operating in industries where a higher share of industry capital is sunk in zombie ﬁrms have
more difﬁculty obtaining bank credit than ﬁrms in sectors where zombie congestion is lower.
The sample chosen is representative and we utilize the rolling panel component of the survey,
which contains ﬁrm-level balance sheet information. The period covered is from 2009 to 2013.

The model is as follows:

CreditAvailabilityisc,t = δ0 + δ1ZombieCapitalsc,t−1 + Xisc,t−1Θ + γc,t + (cid:15)isc,t,

(4)

where: CreditAvailability is increasing in the extent to which bank loan (or credit line) availability
for ﬁrms has improved over time (see section 4.2). The remaining variables are deﬁned above.
Contrary to the other models, the bulk of the variation in the zombie capital share is at the sectoral

14In addition, this strategy allows us to increase the sample size, allowing our sample to capture the underlying

distribution of ﬁrms, which is crucial for the validity of the exercises.

ECB Working Paper Series No 2240 / February 201915level, since zombie shares are highly persistent over time (85% autocorrelation). As such, if we
add sectoral effects, there is little variation left. Instead, we use country-year ﬁxed effects – which
controls for time-varying country-speciﬁc shocks – and thus our variation comes from sectoral
differences within each country in a speciﬁc year. In other words, we purge the industry zombie
shares in each country of the country average in a given year and then exploit sectoral variation.

We also consider a subtler form of credit crowd-out, arising from credit misallocation.15 As ar-
gued by Gopinath et al. (2017), a key factor behind the productivity slowdown in South Europe
was that credit ﬂew to ﬁrms with higher net worth, but not necessarily higher productivity, which
implied signiﬁcant productivity losses. A crucial element for this mechanism to match the model
to the data is a size-contingent borrowing constraint, as larger ﬁrms are more likely to overcome
this constraint. We can directly test this mechanism by updating the model above as follows:

CreditAvailabilityisc,t = δ0+δ1LPisc,t−1 + δ2LPisc,t−1 × ZombieCapitalsc,t−1+

δ3N etW orthisc,t−1 + γsc,t + (cid:15)isc,t.

(5)

A negative effect on the LP × ZombieCapital interaction, where LP stands for labor productivity,
would indicate misallocation. Unlike the baseline model, we do not need to rely on cross-sectoral
differences in the zombie capital for variation, but instead exploit variation between high- and
low-LP ﬁrms in the same sector, which is where congestion is expected to occur. As such, we
employ a country-sector-year ﬁxed effects structure, and so the level ZombieCapital term drops.16

We run both models for both the full ﬁrm sample, and the sub-sample of small ﬁrms (less than
50 employees), and our hypothesis is that results will be stronger for small ﬁrms.

4 Data and deﬁnitions

4.1 Data sources

Our ﬁrm-level data come from ORBIS – compiled by Bureau Van Dijk – the largest cross-country
panel database on the balance sheet and output data for ﬁrms. We adopt the same data cleaning
and harmonization procedures as Adalet McGowan et al. (2018), who closely follow the sugges-
tions of Gal (2013), Kalemli-Ozcan et al. (2018) and Andrews et al. (2016). The data is cleaned

15We thank Sebnem Kalemli-Ozcan for pointing out this channel.
16Note that we use LP instead of MFP as our sample is relatively small, and would diminish even more with the

more data-demanding MFP.

ECB Working Paper Series No 2240 / February 201916in a common manner across all countries, harmonizing balance sheets in terms of consolidation
level and time horizon (calendar year). Firm-level nominal variables are deﬂated using 2-digit
industry deﬂators from the OECD STAN database (as ﬁrm-level prices are not available, all pro-
ductivity measures are revenue-based) and prices are expressed in industry purchasing power
parities. We use the methods identiﬁed in Gal (2013) to construct capital stock and productivity
variables (see below).17 Finally, as is standard practice, we consider only non-farm, non-ﬁnancial
ﬁrms, and end up with a sample based on 61 sectors (between NACE Rev 2. 10-82, excluding
64-66 – i.e. ﬁnancials).

Bank balance sheet data is sourced from Bankscope, which contains data for over 30,000 banks
as far back as 2001. We match ORBIS and Bankscope based on the banker variable in ORBIS,
and we follow the literature and assume that the stated banker relationship also implies a bor-
rowing relationship. There is ample evidence in the recent literature (Kalemli-Ozcan et al. 2018,
Chodorow-Reich 2014) that lending relationships are highly sticky. Consistent with other papers,
we are able to match a very large fraction of our ﬁrms to their main bank (Kalemli-Ozcan et al.
2018, Storz et al. 2017), with match rates exceeding 90% in most countries. For cases where the
ﬁrm mentions more than one bank, we assume that the ﬁrst bank mentioned is the main one.
Summary statistics are given in Table I. Finally, we exploit restricted microdata on ﬁrms’ access
to ﬁnance from the Survey on the Access to Finance for Enterprises (SAFE), run by the European
Central Bank (ECB).

4.2 Key variable deﬁnitions

Productivity For productivity, we use two different methods, depending on the application,
with a goal of striking a balance between maximizing our sample and using a robust estimate.
As a control variable in the zombie ﬁrm regression analysis, we simply use gross output per
employee, deﬂated using the 2-digit OECD STAN industry deﬂators mentioned above, which
we denote as labor productivity (LP). While this allows us to maximize data coverage, it should
be noted that Bartelsman et al. (2013) ﬁnd that LP captures systematic variations in value-added
based labor productivity. For the reallocation regressions, we follow Adalet McGowan et al.
(2017b) and use a productivity measure based on a Solow residual (MFPR).

Zombie ﬁrms We employ two different deﬁnitions of zombie ﬁrms, each with the view of
maximizing our sample, but our regression estimates are insensitive to the choice of measure. In

17See Gal (2013) and Andrews et al. (2016) for more details.

ECB Working Paper Series No 2240 / February 201917each case, we only consider ﬁrms aged ten years or more, as start-ups are in general not expected
to be proﬁtable. First, we deﬁne as zombies - following Adalet McGowan et al. (2018) - ﬁrms
that register an interest coverage ratio (the ratio of proﬁt to interest payments) below 1 for three
years in a row. This measure intends to capture ﬁrms that are not proﬁtable enough to cover debt
payments – and thus on the margin of exit in a competitive market. We presume that these ﬁrms
are potentially beneﬁting from creditor forbearance.

One drawback of the interest coverage (IC) measure is that it requires data on interest payments,
which are often sparsely reported in the countries for which we can successfully match ﬁrms to
banks. As a complementary measure, we thus follow Storz et al. (2017) and classify the ﬁrm as a
zombie if it has: i) low debt service capacity for three years in a row; and ii) either negative return
on assets or negative investment for three years in a row.18 Again, the logic is that ﬁrms that have
chronically low debt service capacity and have negative returns on assets or negative investment
are likely to be on the brink of exit unless their creditors allow them to remain operational. This
measure, which we call NRI, is highly correlated with the IC measure (correlation around 0.7) and
allows for a larger sample of ﬁrms from all countries, plus the addition of Denmark and Estonia
(where interest rate data are scarcely reported). Accordingly, we adopt this NRI deﬁnition when
our dependent variable is a binary indicator of zombie ﬁrm status in order to maximize sample
coverage, but are careful to show that our results are also robust to using the IC measure. In
sections 5.3 and 5.4, however, where we focus on the zombie capital share and country coverage
in more restricted, we employ the IC measure in order to ensure consistency and comparability
with Adalet McGowan et al. (2018), whose baseline model we adopt.

Bank Health Since the aim is to link bank health as a whole to zombie ﬁrms and capital real-
location, and not to characterize the relevant drivers of a bank balance sheet in detail, we rely
on a holistic measure of bank health. Accordingly, we construct a bank health index by Principal
Components Analysis of the following seven measures: capital (TCE), NPLs, return on average
assets (ROAA), Retail Funding, Z-Score, Net Income, and Net Interest Income. Retail Funding
is given by the ratio of retail deposits and total assets and is intended to measure the extent to
which the bank relies on sticky retail deposits, as opposed to more volatile wholesale funding
(such as money market funds, whose dry-up was a key driver of bank stress in the euro crisis).
The Z-Score is given by the sum of ROAA and TCE, divided by the standard deviation of ROAA,
and captures the riskiness of bank assets. 19 Ideally, we would also want to include sovereign

18Here, we set the limit for low debt service capacity as a ratio of EBIT to ﬁnancial debt (sum of loans and long-

term debt) lower than 20%.

19Principal Components Analysis has become fairly standard in economics, for applications where the precise
nature and number of the relevant variables are unknown and the precise magnitude of each component variable

ECB Working Paper Series No 2240 / February 201918bond holding, as sovereign exposure has been shown to be particularly important during the
European crisis. Adding sovereign bond holdings to our indicator reduces the size of our sample
by over 30% and provides almost no new information, as the correlation of the indicators with
and without sovereign bond holdings is over 99%. We conﬁrm in the empirical part that our
results are unchanged by using the broader indicator and maintain the indicator based on the
seven measures (without sovereign bond holdings) throughout our analysis.

Figures 3 and 4 show the evolution of the average value of the bank health indicator for the whole
sample and for each country in our sample separately. Overall the indicator for each country
aligns well with the evolution of the crisis. The earlier parts of the crisis saw sharp declines in
the health of Baltic banks followed by a relatively swift recovery, whereas the hit came later for
Greece (the sharpest as expected), as well as Portugal, Spain, and Slovenia. Austria, Denmark,
France, Germany, and the United Kingdom exhibit much smaller declines in bank health, but
typically do not recover to reach pre-crisis levels of health (partly due to low proﬁtability).

Before proceeding, it is important to note that the ex-ante relationship between bank balance
sheet variables and bank health, and by extension lending, is unclear.20 All things equal, a higher
level of regulatory capital is considered a sign of a healthy balance sheet, but it could also be
the result of low risk-taking, and little lending activity. At the same time, banks possessing high
quality, high return assets, which generate a steady ﬂow of income with limited risk, may afford
to have relatively lower levels of capital adequacy. As such, capital may be a misleading metric
of bank health. Risk-weighted (or regulatory) capital could be a potentially better measure but
data availability issues force us to use Tangible Common Equity (TCE) as our measure of bank
capital, which is typically the most stringent of all regulatory deﬁnitions and is not adjusted for
risk.21 The same concerns hold for alternative indicators of bank health, over and above capital, as
they are likely to measure different vulnerabilities. For instance, a high return on average assets
(ROAA) and Net Interest Income indicate that a bank can withstand shocks by drawing down
cash buffers, while a low level of maturity mismatch implies that a bank may be less vulnerable
to funding shock. Finally, we cannot observe exactly what determines bank health from the
perspective of internal bank management.22

not important (see Bernanke et al. 2005). Storz et al. (2017) take a similar approach.

20Strictly speaking, bank balance sheets refer to asset, liabilities, and equity capital. Here, we use the term to refer
also to variables typically belonging to the bank’s proﬁt and loss (P&L) statement, such as net income or return on
assets.

21TCE is the owner equity after removing preferred stock, goodwill, and intangible assets (the latter two generally
refer to difﬁcult to value and highly illiquid assets, such as trademarks). As such, it is typically thought of as the
loss-absorbing capacity of the owners of the bank.

22The health of the bank as judged by its internal risk management framework will determine whether the man-

agers deem the bank to be sound, and hence inﬂuence the forbearance strategy.

ECB Working Paper Series No 2240 / February 201919Access to ﬁnance for healthy ﬁrms
To measure healthy ﬁrms’ access to ﬁnance, we use a re-
stricted microdata version of the Survey on the Access to Finance for Enterprises (SAFE), designed
and run by the ECB to provide insight into the ﬁrms’ perception of credit availability. The rele-
vant question the survey asks ﬁrms is whether, compared to the previous six months, it is more
difﬁcult to obtain different types of bank credit; we focus on loans or credit lines. In each wave,
we rescale the response variable as follows: -1 indicates that access to bank loan availability has
worsened over the last six months; 0 indicates no change; and 1 indicates an improvement. We
cumulate the access to bank credit variable across time for the duration of the presence of the
ﬁrm in the sample, to implicitly create an index of credit availability across time. For instance, if
access improves for both semesters of 2012 and 2013, then the value for 2013 will be 4.23

We also use a composite ﬁnancing gap indicator (see Ferrando et al. 2013) to take into account
credit demand (and not just supply), which makes use of the self-reported change in ﬁnancing
needs for the ﬁrm (increased, decreased, or unchanged). The (rescaled) composite indicator takes
a value of -1 for increased needs and deteriorating availability, -0.5 for either increased needs
or deteriorating availability (but not both), and so forth. Again, responses are cumulated across
time.24

Insolvency Framework To test the effect of the insolvency framework on the relationship be-
tween bank health and zombie ﬁrms, we make use of a novel set of indicators developed by the
OECD (see Adalet McGowan & Andrews 2018). The set covers 13 different distinct features of the
insolvency framework which – based on international best practice and existing research – may
carry adverse consequences for productivity growth by delaying the initiation of and increasing
the length of insolvency proceedings. We focus our attention on three composite indices – the
aggregate index (insol13 – an unweighted average of the 13 components) plus two sub-indices:
barriers to corporate restructuring and the lack of preventative and streamlining measures.

According to these metrics, cross-country differences in the design of insolvency regimes are
signiﬁcant. For example, the United Kingdom’s low value on the aggregate composite indicators
in Figure 5 reﬂects the fact that the personal costs associated with entrepreneurial failure and
barriers to restructuring are low, while there is also a number of provisions to aid prevention and
streamlining. In Estonia, however, the reverse is true and our working hypothesis is that this is

23When we cumulate the semester-level data, we truncate the value to ±3, as less than 1% of observations are

outside this set. Results are very similar if instead we use the semester-level variables.

24In detail for the other values: 0 for unchanged situation (increased need and improved availability, decreased
need and deteriorated availability, or both unchanged); 0.5 for one-sided decreasing ﬁnancing gap (decreased needs
or improved availability, but not both); and 1 for two-sided decrease in ﬁnancing gap (decreased needs and improved
availability).

ECB Working Paper Series No 2240 / February 201920likely to result in an insolvency regime which delays the timely restructuring of weak ﬁrms and
thus slow down the reallocation of scarce resources to their most productive use.25

5 Results

5.1 Are zombie ﬁrms connected to weak banks?

Figure 6 presents graphical evidence of the relationship between bank health and zombie ﬁrms.
In each graph, we show the share of ﬁrms at the country-industry-year level associated with
each bank that are deﬁned as zombies (y variable), plotted against the two-year lags of the bank
health composite index and its seven constituent components (x variable). The red line is a linear
regression ﬁt of y on x, purged of country-industry-year ﬁxed effects to control for cyclical shocks
at the country-industry level. For ease of observation, we split the sample of the x variable into
50 bins of equal size, and each point in the scatter plot gives the sample mean of y for each bin
(after controlling for country-industry-year ﬁxed effects). A strong negative relationship emerges
between bank health and the zombie ﬁrm share, which we interpret as evidence that zombie ﬁrms
are more likely to be connected to weak banks.

Table II shows the results from our baseline model for zombies and banks (equation 1). Panel A
shows the results from regressions with country-industry and year ﬁxed effects, which remove
time-invariant country-industry effects, and also time-varying global shocks. The model in col-
umn 1 includes only bank capital and NPLs, to focus on the asset side. Both are highly signiﬁcant
and have the expected signs. Columns 2 and 3 progressively include more balance sheet vari-
ables, to account for the role of income, asset riskiness, and the risk coming from the liability side
in the form of maturity mismatch. While the coefﬁcients always have the expected signs, they are
not always signiﬁcant, illustrating the multicollinearity problem mentioned previously. As such,
column 4 shows results from a model containing only the composite index, which is an increas-
ing function of bank health. The estimated coefﬁcient is negative, as expected, and statistically
signiﬁcant at the 1% level.

Panel B shows results from the more restrictive model that contains country-industry-year ﬁxed
effects, so variation comes from differences across ﬁrms operating in the same country-industry-

25As discussed in Adalet McGowan & Andrews (2018), an inability of creditors to initiate restructuring, a lack
of priority given to new ﬁnancing over unsecured creditors and an indeﬁnite stay on assets translate into signiﬁ-
cant barriers to corporate restructuring in Estonia. Similarly, a lack of early warning mechanisms, pre-insolvency
regimes and special insolvency procedures for Small and Medium-Sized Enterprises (SMEs) imply that prevention
and streamlining is weak in Estonia.

ECB Working Paper Series No 2240 / February 201921year cell. Results are similar for most variables. Although coefﬁcients are for the most part
smaller in absolute value, as we now control for a substantially larger part of the variation, their
signs remain unchanged.26 Crucially, the composite index of bank health remains highly signif-
icant. The last column of Table II shows results from using a bank health indicator which also
includes sovereign bond holdings. The estimates are virtually indistinguishable, at the cost of a
big reduction in our sample; as such we will rely on the main indicator throughout the paper.

Table III explores the relationship further. Column 1 shows the full results of the baseline regres-
sion, with the previously omitted ﬁrm-level controls shown for exposition purposes. 27 In turn,
columns 2-4 demonstrate that the relationship between zombie status and bank health is virtually
unchanged before and after the crisis. This indicates that the forbearance incentives of banks are
not simply a cyclical phenomenon. Of course, the aggregate size of the zombie population and
the incentives of banks to forbear are cyclical, but the marginal incentive does not seem to vary.

We can gauge the economic magnitude of the effect by comparing the banks at high and low
levels of health. With the NRI deﬁnition (Table III, column 1) of zombies, healthy banks (deﬁned
as those one standard deviation above the bank health variable mean) have a 1 percentage point
lower probability of being associated with zombies compared to weak banks (deﬁned as those
one standard deviation below the bank health variable mean). At a mean zombie rate of 13%
in the sample, this translates to roughly 8% difference in zombie incidence between healthy and
weak banks relative to the mean. The magnitude of the effect is slightly larger using the IC
deﬁnition (Table IV, column 4), where the difference in probability is 1.2 percentage points, which
given a mean zombie rate of 9.4%, implies a difference of 13% in zombie incidence between
healthy and weak banks. It should be noted that this is most likely a conservative estimate, as it
removes country-industry-year ﬁxed effects. If, for instance, we include country-sector and year
ﬁxed effects (i.e. column 4 of Table II, Panel A), then healthy banks are 2.5 percentage points less
likely to be associated with zombies with the NRI deﬁnition and 1.8 percentage points with the
IC deﬁnition, a difference in relative incidence of around 19% in both cases.

Table IV explores the robustness of our baseline estimates. Columns 1-3 show that the relation-
ship between zombie ﬁrms and bank health is evident in manufacturing, services, and construc-
tion. Column 4 shows that the coefﬁcient of bank health is still negative and highly signiﬁcant,
when we apply the interest coverage deﬁnition of a zombie ﬁrm, instead of NRI (Section 4). Col-

26The coefﬁcients on NPLs are now much smaller in magnitude and insigniﬁcant, and the opposite holds for net

interest income.

27Older ﬁrms are more likely to be zombies while ﬁrms with high (lagged) productivity are less likely to be
zombies. Micro ﬁrms (less than ten employees) appear to be the most likely to be zombies, as expected, as ﬁrms that
do not grow are expected to be more problematic.

ECB Working Paper Series No 2240 / February 201922umn 5 then shows that the results are robust to excluding ﬁrms with less than 20 employees,
which tend to be under-represented in ORBIS (see Gal 2013, Kalemli-Ozcan et al. 2018).

The ﬁnal columns of Table IV provide further evidence that our estimated relationship between
zombie ﬁrms and weak banks is not simply a product of reverse causality, whereby troubled ﬁrms
would adversely affect the balance sheets of their banks. While we partly mitigate these concerns
above by entering bank health with a two-year lag, controlling for cyclical shocks and running
separate pre and post-crisis regressions, an alternate approach is to directly remove potential
sources of such reverse causality. Accordingly, Column 6 shows that the estimated relationship
is robust to excluding larger ﬁrms (i.e. ﬁrms with 50 or more employees). This is signiﬁcant since
bank balance sheets are likely to be more sensitive to large ﬁrms in distress than smaller ﬁrms.

Removing large ﬁrms, however, may not adequately address endogeneity concerns if a given
bank has high indirect exposure, via its connections with a cluster of small ﬁrms which together
account for a signiﬁcant share of sectoral activity. Accordingly, column 7 shows that the results
are robust to removing ﬁrm-year observations for cases with high (above median) bank exposure
to speciﬁc sectors, deﬁned as the sum of tangible ﬁxed assets of all the ﬁrms in a country-sector-
year cell with which a bank is linked, as a fraction of bank assets. This is important in the face of
a new literature on the role of collateral in amplifying sectoral shocks, either through collateral
ﬁresales (real estate or equipment) or through agglomeration effects (Benmelech & Bergman 2011,
Benmelech et al. 2014), and particularly housing in the case of small ﬁrms (Banerjee & Blicke
2016). Jaskowski (2015) makes the theoretical argument that zombie lending may in fact, be a
rational strategy on the part of banks, who engage in zombie lending as a way of preventing
a string of bankruptcies and forced ﬁre sales. With our approach, we can thus control for a
speciﬁc form of this externality (operating speciﬁcally through sectoral effects), and we conﬁrm
the robustness of our results.

Finally, if a bank was particularly exposed to a sector badly hit by the crisis, then it is possible that
the country-industry-year ﬁxed effect will not fully remove any correlation between bank health
and the error term, thus inducing reverse causality. As a robustness check, we also run an IV
regression for both the NRI and IC deﬁnitions (see the ﬁnal two columns of Table IV), using the
pre-crisis lag of bank health as an instrument. Speciﬁcally, we instrument for the second lag of
bank health using its ﬁfth lag (and so the seventh lag of bank health). As we also control for bank
size, we have to instrument for it as well (also using its ﬁfth lag), since bank size is correlated
with health, and so failing to instrument for it would violate the exclusion restriction.28 The IV
coefﬁcient is actually more negative than OLS, suggesting that, if anything, the OLS estimate may

28See Acemoglu & Angrist (2000) for a similar argument.

ECB Working Paper Series No 2240 / February 201923be biased downwards.

5.2 Insolvency Frameworks

Table V shows the estimates of equation (2), where we use insolvency regime indicators for two
separate years: 2016 (Panel A) and 2010 (Panel B). As predicted, the BankHealth × IN SOL inter-
action is positive and highly signiﬁcant (column 1 of Panel A). Column 2 shows that this result
is mainly driven by the Barriers to Restructuring (BTR) sub-component and this result is robust
to an alternative deﬁnition of zombie ﬁrms based on the interest coverage ratio. This suggests
that in countries with high barriers to restructuring weak ﬁrms, improvements in bank health are
less likely to translate into fewer zombie ﬁrms. This is a central result of the paper: policy initia-
tives to improve bank health following a ﬁnancial crisis are more likely to be associated with a
decline in zombie congestion in countries where insolvency regimes do not unduly inhibit corpo-
rate restructuring since such regimes increase the economic incentives for banks to recognize the
loans to zombie ﬁrms as non-performing and commence restructuring or foreclosure proceed-
ings. Thus, there are important complementarities between policy initiatives to improve bank
health and insolvency reform.

Column 3 shows that the interaction term is not statistically signiﬁcant for the indicator relating
to prevention and streamlining (LPS), which broadly refer to mechanisms that allow ﬁrms under
temporary distress to avoid entering into insolvency. This should not be surprising: zombie
ﬁrm problems mostly manifest themselves during slow recoveries after deep recessions brought
about by ﬁnancial crises when temporary distress is not expected to be of ﬁrst-order importance.
Finally, column 4 repeats the exercise with barriers to restructuring, except now augmenting the
model to include two additional interaction terms with GDP growth (lagged by two years, as with
all other controls). The concern is that, although we include country-sector-year ﬁxed effects, our
model could be capturing (accidental) correlation between the insolvency framework and the
size of the downturn.29 The main result is robust to this test. In Panel B, we repeat the same
exercise for 2010 values of the insolvency framework. The results for barriers to restructuring
remain highly statistically signiﬁcant, although the coefﬁcients for the aggregate measures now
become statistically insigniﬁcant (though they do maintain the correct sign).30 We repeat the

29If, for instance, Denmark, which was one of the countries least affected by the crisis, had an insolvency frame-
work that entailed low barriers to restructuring, it is possible that the effect we capture is purely driven by such
noise.

30It is unclear why this is the case, but regardless, our focus and intuitive framework relates to barriers to re-
structuring mostly, and less so to other features of the framework. The changes in the sample occur because of data
availability issues for Denmark. In the 2016 wave, there are no data for insol13, and for the 2010 wave we only have

ECB Working Paper Series No 2240 / February 201924exercise using the IC deﬁnition of status, instead of the baseline NRI deﬁnition, and the results,
shown in Table VI, are very similar. 31

One way to illustrate the importance of the insolvency regime is to estimate how much more
potent the role of a healthy bank would be if the regime were at the level of best practices. In
ﬁgure 9, we plot, for each country in our sample, how the percentage point difference between the
zombie-reducing effect of a healthy bank (one standard deviation above the mean) versus a weak
bank (one standard deviation below the mean) varies according to the design of the insolvency
regime, as measured by the barriers to restructuring indicator (BTR) in 2010. The results in Panel
A pertain to the baseline NRI deﬁnition of zombie ﬁrms which allows us to include Denmark
in the exercise, while Panel B shows the corresponding exercise based on the interest coverage
deﬁnition of zombie ﬁrms. As the framework performance is measured in terms of distance from
best practice, the blue bars can be interpreted as ”room for improvement” in terms of reducing
zombie prevalence through reforming BTR. By deﬁnition, the best performer in the sample, the
United Kingdom, has no room for improvement. For example, Panel B shows that if Estonia –
where barriers to corporate restructuring are high – reformed its insolvency regime to the sample
minimum (i.e. the United Kingdom), then the reduction in the zombie ﬁrm share associated with
a two standard deviation improvement in bank health could be around 2.6 percentage points
higher than otherwise.

We also show, with red diamonds, the position of the country using the value of BTR in 2016,
to capture the effect of reforms. Several countries, particularly in the euro periphery, have im-
plemented important reforms, and have gone some way into reducing the incentives of banks to
allow zombies to operate. Reforms to insolvency regimes since 2010 in Greece, Slovenia, and Por-
tugal have managed to exploit roughly half of the possibilities offered by improving their BTR,
with smaller improvements in Spain and Latvia. Germany has been the most successful in that
regard, fully capturing reform opportunities.

data for LPS.

31We check the robustness of the estimates by repeating the analysis for the IC zombie deﬁnition (table VI). More-
over, table VII shows results from estimating the zombie and insolvency regressions for both the IC and NRI indica-
tors with the triple ﬁxed effects speciﬁcation now at the 3-digit NACE sector. This further enhances the robustness
of our estimates by controlling for time-varying shocks at a more granular level.

ECB Working Paper Series No 2240 / February 2019255.3 Weak banks and capital misallocation

Table VIII shows results from the baseline reallocation regression.32 Row 1 shows that more
productive ﬁrms grow at a statistically signiﬁcantly higher pace, suggesting that the process of
capital reallocation enhances aggregate productivity. Moreover, row 2 shows that bank health
acts as an ampliﬁer to the reallocation process: that is, productive ﬁrms in industries exposed to
healthy banks grow faster than those in industries exposed to weak banks. The other columns of
Panel A show that the relationship between bank health and capital reallocation is robust across
sectors, and was not materially affected by the crisis (Panel C).33 The economic magnitude of
this effect is signiﬁcant: an increase in bank health by one standard deviation above its mean
would raise the difference in the capital growth between high and low productivity ﬁrms (i.e.
those one standard deviation above and below the sectoral mean) by 23%. As such, the health
of the banking system is important in channeling scarce capital to productive ﬁrms and thus
underpinning their growth.

Next, we jointly examine bank health and zombie congestion (table IX). We do this in steps. First,
we estimate a capital reallocation model that separately includes a zombie capital×MFP inter-
action (which is expected to have a negative coefﬁcient; see Adalet McGowan et al. 2018); then
we include also a bank health×MFP interaction (which is expected to have a positive coefﬁcient).
Column 3 reports the estimates of the model that includes both interaction terms. Regarding
the latter, our expectation is that the inclusion of the bank health×MFP interaction will reduce
the estimated absolute magnitude of the coefﬁcient on the zombie capital×MFP interaction, as
bank health is relevant to both zombie congestion and capital reallocation. Finally, Column 4 re-
peats the baseline exercise with the MFP×Bank Health interaction, but we now add a ﬁrm ﬁxed
effect, and qualitatively the results are very similar. Removing time-invariant ﬁrm-level hetero-
geneity helps to shore-up the robustness of our estimates, ensuring that they are not driven by
unobserved ﬁrm characteristics.

As expected from the results of Adalet McGowan et al. (2018), a higher zombie capital share
dampens the efﬁciency of capital reallocation, by moderating the responsiveness of ﬁrm capital
growth to (lagged) MFP; moreover, when both interactions are included, they remain highly sig-
niﬁcant but their absolute magnitude falls, indicating that they are correlated. The coefﬁcient on

32The regressions in this section use a deﬁnition of capital using only tangible ﬁxed assets, but if we use total ﬁxed

assets instead (including intangibles), the results are broadly similar.

33There is evidence that reallocation intensity fell during the crisis – which is consistent with the ﬁndings of Foster
et al. (2016) for the United States - although this result is driven by the construction sector (Table VIII, Panel B). If
we broaden the deﬁnition of capital to include intangible assets, however, the estimated effects are larger, and the
MFP×Crisis interaction becomes signiﬁcant for services as well.

ECB Working Paper Series No 2240 / February 201926the zombie capital×MFP interaction changes from -0.1340 to -0.0894, a fall in absolute magnitude
of around 33%. To the extent that causation runs from bank health to zombie ﬁrms, this suggests
that around one-third of the distortionary effects of zombie congestion on capital reallocation can
be directly attributed to weak banks which allow zombie ﬁrms to fester.

The estimated coefﬁcients in column 2 (Table IX) imply that if Spain were able to reduce its zom-
bie capital share to the sample minimum (Slovenia), then the difference in the capital growth
between high and low productivity ﬁrms (those one standard deviation above and one stan-
dard deviation below sectoral mean) would be 2.2 percentage points higher. Similarly, if bank
health in Spain were at the level of France in 2012, the gap in capital growth between high and
low productivity ﬁrms in 2013 would have been roughly 1.2 percentage points higher (based on
the estimated coefﬁcient in column 1, Table VIII). These estimated effects are economically sig-
niﬁcant, since the difference in capital growth between high and low productivity ﬁrms fell by
2.4 percentage points in Spain from 2004 to 2013 (Adalet McGowan et al. 2018), consistent with
the idea that rising capital misallocation is a key driver of the productivity slowdown in Spain
(Gopinath et al. 2017), and more generally (see Section 2).

5.4 Zombie ﬁrms and Credit Crowd-Out

Figure 8 shows the binned scatter plot of the sectoral average of the cumulated credit availability
variable for healthy ﬁrms against the (lagged) share of zombie capital in the same sector, control-
ling for country-year ﬁxed effects. The left panel shows the plot for bank loans and the right panel
for bank credit lines. We see a clear negative effect of zombie capital on bank credit availability
for healthy ﬁrms: sectors with a high share of capital tied to zombie ﬁrms, within country-year
clusters, are associated with more restricted access to bank loans.

Regression results in Table X conﬁrm the graphical evidence, for both the simple and the com-
posite indicator (which accounts for credit demand).34 Columns 1-2 give the results of running
different versions of (4). Looking at Panel A, which shows results for loan availability for all ﬁrms,
we see that controlling for lagged ﬁrm productivity, lagged ﬁrm net worth (given as sharehold-
ers’ funds as a fraction of total assets, as in Gopinath et al. (2017)) and country-year ﬁxed effects,
the coefﬁcient for lagged zombie capital share is negative and signiﬁcant for either the simple
or the composite indicator. This conﬁrms the hypothesis that in sectors with a higher share of

34An alternative way is to directly control for credit demand, using the self-reported indicator of changing credit
needs. Doing so is similar in spirit to the Khwaja & Mian (2008) approach of using ﬁrm ﬁxed-effects in lending
regressions. Running the basic model with the credit availability indicator with dummies for ﬁnancing needs gives
very similar results.

ECB Working Paper Series No 2240 / February 201927zombie capital, healthy ﬁrms have a harder time obtaining credit. The coefﬁcient on net worth is
positive and highly signiﬁcant, indicating that ﬁrms with high net worth (an indicator of credit
constraints) have easier access to credit, even controlling for productivity. Panel B then restricts
the analysis to small ﬁrms only (those with less than 50 employees), and the zombie capital coefﬁ-
cient is more negative, implying that smaller ﬁrms are disproportionately affected by crowd-out.
Both of these conﬁrm the results of Gopinath et al. (2017). Finally, Columns 5-6 present results of
model (4) for an ordered logit model. Results are very similar but less signiﬁcant for credit lines.

Columns 3-4 present results from regressions of model (5). Recall that, in this case, we directly
test for misallocation, namely whether more productive ﬁrms suffered more from crowd-out in
sectors with high levels of capital tied to zombie ﬁrms, controlling for net worth. As variation
here comes from differences in productivity within-sector, we employ country-sector-year ﬁxed
effects. The hypothesis is not borne for the sample including all ﬁrms (Panel A), but it is con-
ﬁrmed for the small ﬁrm subsample in Panel B. The coefﬁcient of the zombie share - productivity
interaction is negative and signiﬁcant for the simple indicator. It also has the correct sign for
the composite indicator, but the errors are higher in this case, giving a p-value of 22%. Again,
these results conﬁrm those of Gopinath et al. (2017), who posit the existence of a size-contingent
borrowing constraint to rationalize misallocation.

Panels C and D repeat the analysis for availability of credit lines. Results are by and large similar,
albeit at lower signiﬁcance levels. We conﬁrm the existence of a crowd-out for both the simple
and the composite indicator, and, for the simple indicator, we obtain a statistically signiﬁcant
estimate of misallocation.

It should be noted that, given that our sample size is relatively small (up to 7,613 observations
for loans, 6,944 for credit lines) and the panel short, the results in this section should be viewed
as indicative.

To gauge the magnitude of the effect, it is useful to restrict attention to values at 0 or 1 of the
credit availability indicators, since the sample period is one of gradually improving ﬁnancing
conditions.35 A logistic regression of this transformed variable on the zombie capital share and
various control variables indicates that a one standard deviation increase in the zombie capital
share is associated with a reduction in the probability of improved loan availability by 1.9 per-
centage points (with a similar magnitude for credit lines). Compared to an average probability
of improvement in loan availability of 25% at the mean of the distribution, the overall effect is
modest, but not trivial.

35Over the sample period (i.e. 2009 to 2013), we observe a general improvement in lending conditions – save for

2011H2-2012H1 – which are likely tied to the cycle and accommodative monetary policy.

ECB Working Paper Series No 2240 / February 201928The negative relationship between credit availability to healthy ﬁrms and zombie capital in a
sector could either be the result of lower proﬁtability of potential investments due to zombie
congestion, or limited lending capacity of banks exposed to this particular sector due to zombie
forbearance. However, our sense is that it is more likely to reﬂect the former. Banks are unlikely,
on average, to be overly exposed to a speciﬁc sector so much so that exposure to zombie ﬁrms in
that sector substantially hurts their balance sheet. For instance, the median bank exposure to a
speciﬁc country-sector-year cell, given by the total tangible capital of the ﬁrms in that particular
sector associated with any given bank, is approximately 0.12% of total bank assets. This makes
it unlikely that losses from loans to zombies in a given sector would affect lending capacity to
a healthy ﬁrm in the same sector. Of course, this is not to imply that crowd-out cannot occur
through the bank balance sheet. In fact, contrary to the zombie congestion case, credit crowd-out
may spillover to other sectors, but this hypothesis is difﬁcult to address with the data at hand.

Finally, it is useful to discuss how our results relate to those of Schivardi et al. (2018). Although
they do not ﬁnd evidence of crowd-out, we believe that their research strategy examines differ-
ent dimensions to ours, and so our papers are rather complementary in that regard. First, we
show that in sectors with a high zombie share, healthy ﬁrms ﬁnd it more difﬁcult to borrow. By
contrast, Schivardi et al. consider the bank-ﬁrm relationship only, and do not study the mar-
gin where lending is restricted because healthy ﬁrms lack the proﬁtability required to get good
credit, due to zombie congestion. Second, Schivardi et al. focus on new credit, and do not con-
sider restructuring/forbearance. As already argued, while new lending to zombie was clearly a
concern in Japan, in the European supervisory context such behavior would be rare, and indirect
forbearance methods more likely. While they do examine the classiﬁcation of NPLs, they do not
check to see whether weak banks have different strategies in how they treat problematic loans,
which would be indicative evidence of forbearance. We have a catchall approach so we consider
all types of zombie-friendly behavior.

In addition, crowd-out occurs when lending to the zombie reduces credit to the healthy ﬁrm.
Schivardi et al. show that the likelihood of lending to healthy ﬁrms during the crisis for weak
banks is not lower than for zombies. However, the crucial question is whether they would have
received more credit had it not been for zombie lending or forbearance, a counterfactual that
these authors cannot capture with their setup. Our approach cannot capture it either, but we can
more concretely capture a different dimension: namely, that healthy ﬁrms in industries with a
large number of zombies are disadvantaged.

ECB Working Paper Series No 2240 / February 2019296 Conclusion

This paper presents robust evidence that zombie ﬁrms are more likely to be connected to weak
banks. We then show that the effect of bank health on zombie status is ampliﬁed under insolvency
regimes that do not unduly inhibit corporate restructuring. Thus efﬁcient insolvency policies,
together with healthier banks, would appear to be important in resolving the zombie problem,
thereby raising aggregate productivity growth.

Next, we show that healthy banks foster productivity-enhancing capital reallocation, the process
by which productive ﬁrms grow relatively more. We also show that about one-third of the nega-
tive effects of zombie congestion on the efﬁciency of reallocation, identiﬁed in earlier work, could
be attributed to weak banks.

Furthermore, we offer some evidence on the mechanism behind zombie congestion. The neg-
ative zombie effects on reallocation and productivity have been attributed to congestion effects
(competition) or the credit crowd-out. However, little is known about the crowd-out channel. We
provide some evidence of the existence of credit crowd-out, using SAFE. Results are indicative
due to the small sample, but we document a modest but statistically signiﬁcant effect: healthy
ﬁrms report smaller improvements in access to ﬁnance in sectors with a high share of capital
tied to zombie ﬁrms. This would suggest that zombie congestion works mostly through reduced
proﬁts for healthy ﬁrms, which reduce expected return on projects, and as a result lead to worse
borrowing opportunities.

Set in the broader context of the productivity slowdown and the aftermath of the global ﬁnancial
crisis, which has hurt bank and ﬁrm balance sheets, our results can carry important implications.
As rising capital misallocation has been shown to be a key driver of the aggregate productivity
slowdown (Gopinath et al. 2017), while zombie congestion is intimately linked to the other key
micro dimensions to the slowdown, such as rising productivity dispersion (Andrews et al. 2016)
and declining business dynamism (Decker et al. 2016), identifying and correcting the distortions
which disrupt the natural market selection mechanism is vital to reviving productivity growth.

Future research could look to dig deeper into the recent debate about the link between zombie
congestion and monetary policy. It has been argued that accommodative policy increases the
incentives of banks to bet on the resurrection of zombie ﬁrms (White 2012) and that ”too low
for too long” policies make funding cheap and fuel the survival of weak ﬁrms, increasing mis-
allocation and harming productivity growth (Borio 2018). Conversely, others have argued that
misallocation did not increase during the crisis and is a country-speciﬁc than a global problem.
At the same time, monetary accommodation facilitated access to credit for viable but vulnerable

ECB Working Paper Series No 2240 / February 201930ﬁrms and allowed them to ﬁnance working capital/invest, hence alleviating an even larger drop
in aggregate demand, investment, and productivity (Obstfeld & Duval 2018).

While this question remains unresolved, it is important that future research takes into account
two ideas. First, it is necessary to distinguish between stocks and ﬂows of credit to zombie ﬁrms
and the impact of monetary policy on these two margins. Unlike in earlier zombie episodes, it is
improbable that weak and indebted ﬁrms receive new lending to roll over existing debt, as mod-
ern supervisory practices would capture and prohibit it. Instead, banks could allow forbearance
on the stock of existing debt, and help ﬁrms to alleviate payment problems. However, monetary
policy intends to inﬂuence the ﬂow of credit and is hence unlikely to be a direct factor in the
decision to forbear.

One implication that future research should reﬂect on is how monetary policy affects the oppor-
tunity cost of holding bad loans on bank balance sheets. In principle, banks will recognize bad
loans if the net return to recognition (proﬁts from outside projects net of the cost of provisions) is
higher than the return to forbearance (assumed zero). If this net return is an increasing function
of interest rates, then low rates may imply a negative net return to recognition, and hence an
inaction region where the return to forbearance dominates. If the low policy rate is a response
to a lack of proﬁtable investment opportunities, then a substantial interest rate increase may be
needed to materially alter the incentives of banks. This fundamental trade-off is at the heart of
this debate. At the same time, this discussion highlights the key role of an efﬁcient insolvency
framework, which would reduce the cost of recognition and hence narrow the inaction region, at
any policy rate.

Second, our research suggests that it is important to take into account the behavior of banks,
which have an incentive to forbear when their balance sheets are weak and their proﬁtability is
low. Accordingly, monetary policy may affect forbearance decisions through its balance sheet
effects. To the extent that expansionary policies also have positive effects on bank balance sheets,
indirect effects may work in the same direction. At the same time, forbearing on a bad loan
implies an increase in the expected riskiness of the bank’s balance sheet; Dell’Ariccia et al. (2017)
show that, in line with theoretical predictions by Dell’Ariccia et al. (2014), while indeed risk-
taking in new loans is higher when real policy interest rates are low, this effect is lower for weakly
capitalized banks.

Nevertheless, the connection between monetary policy and zombies should not distract from the
contribution of bank recapitalization and insolvency regimes. The results of this paper imply
that in order to facilitate the unwinding of the zombie problem it is essential that bank balance
sheets are strong, underlining the need for fast recapitalizations after crises and measures to re-

ECB Working Paper Series No 2240 / February 201931duce NPLs. But strengthening banks is an insufﬁcient policy response while insolvency regimes
remain hostile to the orderly restructuring and resolution of weak ﬁrms. Thus, in countries with
weak banks and weak insolvency regimes, efforts to improve banks’ health should be accompa-
nied by appropriate insolvency reforms to reduce impediments to corporate restructuring. At the
same time, countries with strong ﬁrms and banks should view the establishment of an efﬁcient
insolvency framework as equivalent to a ﬁnancial stability-enhancing measure.

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ECB Working Paper Series No 2240 / February 201936Figure 1: Potential Output Growth in OECD countries

The graph shows the evolution of potential output growth in OECD countries, and the break-
down to the contributions. Data for 2016 and 2017 are based on projections. Data come from
the OECD Economic Outlook Database, and the chart from the OECD June 2016 Economic
Outlook.

ECB Working Paper Series No 2240 / February 201937Figure 2: Insolvency Regimes in OECD countries, 2016

The stacked bars correspond to three subcomponents of the insolvency indicator in 2016. The diamond corresponds
to the value of the aggregate insolvency indicator based on these three subcomponents in 2010. Only countries for
which data are available for the three sub-components in 2016 are included. Source: Adalet McGowan et al. (2017).

ECB Working Paper Series No 2240 / February 201938Figure 3: Bank Health Composite Indicator - Part A

The chart gives average bank health for banks in the dataset (11 countries), weighted by the number of ﬁrms for
which a bank is considered to be their main bank. Bank health is given by the ﬁrst principal component (i.e. the one
associated with the largest eigenvalue) from a principal component analysis of seven core balance sheet and ﬁnancial
statement variables of banks. These are tangible common equity, net income, net interest income, NPLs, Z-score (a
measure of riskiness of assets), return on average assets, and retail funding, all as a share of total assets.

ECB Working Paper Series No 2240 / February 201939Figure 4: Bank Health Composite Indicator - Part B

The chart gives average bank health for banks in the dataset (11 countries), weighted by the number of ﬁrms for
which a bank is considered to be their main bank. Bank health is given by the ﬁrst principal component (i.e. the one
associated with the largest eigenvalue) from a principal component analysis of seven core balance sheet and ﬁnancial
statement variables of banks. These are tangible common equity, net income, net interest income, NPLs, Z-score (a
measure of riskiness of assets), return on average assets, and retail funding, all as a share of total assets.

ECB Working Paper Series No 2240 / February 201940Figure 5: Insolvency Framework Sub-Indicators in OECD countries

Panel A - Personal Costs for Failed Entrepreneurs

Panel B - Barriers to Restructuring

Panel C - Lack of prevention and streamlining

0.2.4.6.81TRCAUSRUAUUKJPCHNZCRLVNOFIIEATFRITSKSIDKPLCLGRESNLBEILKRDEPTHUSEEECZLTMX201020160.2.4.6.81CRCZUKRUUSSKDEJPPTFICHILMXIEFRPLNZESSEKRATBECNSIAUGRCLNOLTHUITLVNLEECATRDK201020160.2.4.6.81FRUKCNATESDKRUNZILKRFICHITDEIEUSTRAUCRPLBENOSELVSKNLSIPTJPCLGRMXHUEECZLTCA20102016ECB Working Paper Series No 2240 / February 201941Figure 6: Bank Zombie Shares

The plot is a ﬁtted line from regressing the share of ﬁrms by bank that are zombies on bank health, with
country-industry-year ﬁxed effects. The scatters show the mean of y for each bin of x.

5.566.577.58Zombie Share by Bank (%)-4-2024Bank Health Index (t-2)ECB Working Paper Series No 2240 / February 201942Figure 7: Bank Zombie Shares - Components

The plot is a ﬁtted line from regressing the share of ﬁrms by bank that are zombies on bank
health, with country-industry-year ﬁxed effects. The scatters show the mean of y for each bin
of x.

5.566.577.58Zombie Share by Bank (%)-4-2024Bank Health Index (t-2)ECB Working Paper Series No 2240 / February 201943Figure 8: Credit Availability

The plot is a ﬁtted line from regressing the cumulated credit availability variable (sectoral
mean) on lagged zombie capital share in the sector, with country-year ﬁxed effects. The
scatters show the mean of y for each bin of x.

ECB Working Paper Series No 2240 / February 201944Figure 9: Zombie ﬁrm share reduction associated with an improvement in bank health
according to the insolvency regimes

Panel A - NRI deﬁnition

Panel B - IC deﬁnition

The graph shows the percentage point difference between the zombie-reducing effect of a healthy bank (one standard
deviation above the mean) versus a weak bank (one standard deviation below the mean) under the barriers to
restructuring performance (BTR) in 2010. As the BTR indicator is deﬁned as distance from the frontier, the bars
indicate the gains from reducing BTR to sample minimum. The red diamonds show the difference in 2016. Given
that Denmark lacks insolvency regime indicators for 2010, we assume that the 2010 value is equivalent to the 2016
value and as such do not simulate potential gains from reform. The estimates are based on the coefﬁcients in Table
V.

ECB Working Paper Series No 2240 / February 201945Table I: Summary Statistics

Firms

Tangible Fixed Assets (1000s)
Tangibility
Number of employees
Zombie (IC)
Zombie (NRI)

N

Mean Median

S.D.

Min

Max

2141170
2,140,963
2,202,818
1,854,147
2,093,000

8504
.217
111
.073
.098

135
.132
11

298754
.229
1860
.260
.298

0
0
1
0
0

1.28e+08
11.523
477100
1
1

Number of employees

Below 10
Between 10 and 19
Between 20 and 49
Between 50 and 99
Between 100 and 249
Over 250

Banks

1,031,694
385,345
384,261
168,819
129,083
103,616

Bank Health
Tangible Common Equity (% assets)
NPLs (% assets)
ROAA
Z-Score
Net Income (% assets)
Net Interest Income (% assets)
Deposits (% liabilities)

2,202,818
2,202,818
2,202,818
2,202,818
2,202,818
2,202,818
2,202,818
2,202,818

-.139
5.38
1.60
.275
37.94
.252
1.617
53.431

.020
4.81
.831
.36
14.95
.356
1.58
51.68

1.649
3.86
2.48
1.32
83.04
1.373
.820
19.20

-8.964
-72.8
0
-58.11
-34.89
-82.240
-3.13
0

5.592
98.96
90.61
29.89
3891.32
25
29.29
100

ECB Working Paper Series No 2240 / February 201946Table II: Zombie regression

(1)

(2)

(3)

(4)

(5)

Panel A - Country-Industry and Year Fixed Effects

Capital

NPLs

Net Income

Net Interest Income

Maturity Mismatch
(x10)
Z-Score
(x10)

Bank Health

Bank Health
inc Gov. Bonds
Firm Controls
N
R2

-0.0022∗∗∗
(-2.71)
0.0055∗∗∗
(4.11)

-0.0020∗∗
(-2.27)
0.0056∗∗∗
(3.59)
-0.0008
(-0.27)
-0.0042
(-1.30)

-0.0020∗
(-1.94)
0.0049∗∗∗
(3.40)
-0.0022
(-0.75)
-0.0021
(-0.58)

-0.0017
(-1.20)
-0.0004∗
(-1.61)

-0.0073∗∗∗
(-4.95)

Yes
1534841
0.0784

Yes
1534011
0.0784

Yes
1508501
0.0789

Yes
1508501
0.0786

-0.0075∗∗∗
(-3.48)
Yes
1,61284
0.0784

Panel B - Country-Industry-Year Fixed Effects

Capital

NPLs

Net Income

Net Interest Income

Maturity Mismatch
(x10)
Z-Score
(x10)

Bank Health

Bank Health
inc Gov. Bonds
Firm Controls
N
R2

-0.0008∗∗∗
(-3.30)
-0.0003
(-0.52)

-0.0006∗∗∗
(-2.49)
-0.0000
(-0.05)
-0.0006
(-0.66)
-0.0046∗∗∗
(-4.38)

-0.0004∗∗∗
(-1.87)
-0.0002
(-0.42)
-0.0011
(-1.07)
-0.0037∗∗∗
(-3.58)

-0.0003
(-0.69)
-0.0004∗∗∗
(-3.52)

-0.0026∗∗∗
(-5.31)

Yes
1534412
0.1014

Yes
1533582
0.1014

Yes
1508076
0.1015

Yes
1508076
0.1014

-0.0027∗∗∗
(-4.59)
Yes
1160665
0.1005

The dependent variable is a binary indicator of zombie status, using the NRI deﬁnition. All
bank balance sheet variables are expressed in two years lags. All regressions include a control
for bank size. Firm controls include ﬁrm age, lagged productivity, and a set of dummies for
ﬁrm employment.
Standard Errors clustered at the bank level. t statistics in parentheses.
∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

ECB Working Paper Series No 2240 / February 201947Bank Health

Crisis × Bank Health

Age

Labor Productivity

10<Employment<24

25<Employment<49

49<Employment<99

99<Employment<249

Employment≥250

Table III: Zombie regression - Crisis

(1)

(2)

Full Sample Pre-Crisis
-0.0022∗∗
-0.0026∗∗∗
(-2.43)
(-5.31)

(3)
Crisis
-0.0028∗∗∗
(-4.75)

(4)
Full Sample
-0.0028∗∗∗
(-3.52)

0.0005∗∗∗
(5.60)

-0.0377∗∗∗
(-10.41)

-0.0095∗∗∗
(-3.73)

-0.0048∗
(-1.65)

0.0104∗∗
(2.42)

0.0166∗∗∗
(3.01)

0.0138∗∗
(2.45)

0.0004∗∗∗
(4.72)

0.0005∗∗∗
(5.69)

-0.0282∗∗∗
(-15.52)

-0.0432∗∗∗
(-9.24)

-0.0074∗∗
(-2.15)

-0.0104∗∗∗
(-4.37)

-0.0015
(-0.58)

0.0149∗∗∗
(5.23)

0.0210∗∗∗
(4.09)

0.0183∗∗∗
(4.41)

Yes

578756
0.0442

-0.0067∗
(-1.83)

0.0074
(1.20)

0.0139∗
(1.86)

0.0111
(1.45)

Yes

929320
0.1043

0.0002
(0.29)

0.0005∗∗∗
(5.60)

-0.0377∗∗∗
(-10.41)

-0.0095∗∗∗
(-3.73)

-0.0048∗
(-1.65)

0.0104∗∗
(2.42)

0.0166∗∗∗
(3.01)

0.0138∗∗
(2.45)

Yes

1508076
0.1014

Country-Industry-Year FE

Yes

N
R2

1508076
0.1014

The dependent variable is a binary indicator of zombie status, using the NRI deﬁnition.
All bank balance sheet variables are expressed in two years lags. All regressions include
a control for bank size. Firm controls include ﬁrm age, lagged productivity, and a set of
dummies for ﬁrm employment.
Standard Errors clustered at the bank level. t statistics in parentheses.
∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

ECB Working Paper Series No 2240 / February 201948∗
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ECB Working Paper Series No 2240 / February 201949Table V: Insolvency Framework - NRI deﬁnition

(1)
insol13

(2)
Barriers

(3)
Lack of

to restructuring prevention

Panel A - 2016 indicators

Bank Health

Bank Health × INSOL

Bank Health × GDP growth

Bank Health × INSOL ×GDP growth

R2
N

Panel B - 2010 indicators

Bank Health

Bank Health ×INSOL

Bank Health × GDP growth

-0.0069∗∗∗
(-3.52)
0.0141∗∗
(2.39)

-0.0049∗∗∗
(-6.46)
0.0116∗∗∗
(4.56)

-0.0035∗∗∗
(-4.40)

0.0028
(1.46)

0.1011
1487846

0.1014
1508076

0.1014
1508076

-0.0052∗∗
(-2.54)

0.0061
(1.31)

-0.0053∗∗∗
(-4.17)
0.0082∗∗
(2.41)

-0.0028∗∗∗
(-3.57)

0.0004
(0.30)

Bank Health × INSOL × GDP growth

R2
N

Country-Industry-Year FE

0.1011
1487846

Yes

0.1011
1487846

Yes

0.1014
1508076

Yes

(4)
Barriers
to restructuring

-0.0049∗∗∗
(-6.29)
0.0113∗∗∗
(4.35)
-0.0003∗∗
(-2.53)
0.0005∗
(1.91)
0.1015
1508076

-0.0051∗∗∗
(-4.11)
0.0075∗∗
(2.29)
-0.0004∗
(-1.86)

0.0006
(1.44)
0.1011
1487846

Yes

The dependent variable is a binary indicator of zombie status, using the NRI deﬁnition. All regressors
are expressed in two years lags. All regressions include a control for bank size. Firm controls include
ﬁrm age, lagged productivity, and a set of dummies for ﬁrm employment. INSOL12 is the aggregate
measure without rights of employees, and INSOL13 includes rights of employees.
Standard Errors clustered at the bank level. t statistics in parentheses.
∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

ECB Working Paper Series No 2240 / February 201950Table VI: Insolvency Framework - IC deﬁnition

(1)
insol13

(2)
Barriers

(3)
Lack of

to restructuring prevention

Panel A - 2016 indicators

Bank Health

Bank Health × INSOL

Bank Health × GDP growth

Bank Health × INSOL ×GDP growth

R2
N

Panel B - 2010 indicators

Bank Health

Bank Health ×INSOL

Bank Health × GDP growth

-0.0067∗∗∗
(-3.45)
0.0141∗∗
(2.39)

-0.0053∗∗∗
(-5.48)
0.0144∗∗∗
(4.43)

-0.0033∗∗∗
(-3.57)

0.0030
(1.17)

0.0583
1387620

0.0585
1389844

0.0585
1389844

-0.0038∗∗
(-2.19)

0.0031
(0.83)

-0.0049∗∗∗
(-3.89)
0.0076∗∗
(2.38)

-0.0024∗∗∗
(-2.56)

-0.0003
(-0.23)

Bank Health × INSOL × GDP growth

R2
N

Country-Industry-Year FE

0.0583
1387620

Yes

0.0583
1387620

Yes

0.0584
1387620

Yes

(4)
Barriers
to restructuring

-0.0053∗∗∗
(-5.42)
0.0141∗∗∗
(4.28)
-0.0003∗∗∗
(-3.67)
0.0007∗
(2.76)
0.0585
1389844

-0.0046∗∗∗
(-3.82)
0.0068∗∗
(2.17)
-0.0004∗∗∗
(-2.67)
0.0007 ∗∗
(2.04)
0.0584
1387620

Yes

The dependent variable is a binary indicator of zombie status, using the IC deﬁnition. All regressors are
expressed in two years lags. All regressions include a control for bank size. Firm controls include ﬁrm
age, lagged productivity, and a set of dummies for ﬁrm employment. INSOL12 is the aggregate measure
without rights of employees, and INSOL13 includes rights of employees.
Standard Errors clustered at the bank level. t statistics in parentheses.
∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

ECB Working Paper Series No 2240 / February 201951Table VII: Zombie regressions - 3 digit sectors

Bank Health

Bank Health ×Barriers

Bank Health ×Barriers 2010

Age

Labor Productivity

10<Employment<24

25<Employment<49

49<Employment<99

99<Employment<249

Employment>250

R2
Observations

(1)
NRI
-0.0025∗∗∗
(-5.04)

0.0004∗∗∗
(5.19)
-0.0403∗∗∗
(-10.10)
-0.0088∗∗∗
(-3.57)
-0.0048∗
(-1.75)
0.0103∗∗
(2.47)
0.0159∗∗∗
(2.92)
0.0134∗∗
(2.35)
0.1154
1505815

(2)
NRI
-0.0048∗∗∗
(-6.23)
0.0118∗∗∗
(4.48)

0.0004∗∗∗
(5.22)
-0.0404∗∗∗
(-10.13)
-0.0088∗∗∗
(-3.57)
-0.0049∗
(-1.75)
0.0103∗∗
(2.46)
0.0157∗∗∗
(2.91)
0.0131∗∗
(2.32)
0.1155
1505815

(3)
NRI
-0.0051∗∗∗
(-3.93)

(4)
IC
-0.0024∗∗∗
(-4.16)

(5)
IC
-0.0051∗∗∗
(-5.28)
0.0141∗∗∗
(4.29)

0.0080∗∗
(2.27)
0.0004∗∗∗
(5.16)
-0.0405∗∗∗
(-10.04)
-0.0089∗∗∗
(-3.60)
-0.0051∗
(-1.82)
0.0104∗∗
(2.44)
0.0159∗∗∗
(2.87)
0.0138∗∗
(2.35)
0.1149
1486032

0.0006∗∗∗
(8.11)
-0.0291∗∗∗
(-10.25)

0.0006∗∗∗
(8.16)
-0.0292∗∗∗
(-10.29)

0.0006
(0.27)
0.0058∗∗
(2.14)
0.0247∗∗∗
(6.69)
0.0332∗∗∗
(7.65)
0.0336∗∗∗
(6.26)
0.0746
1387779

0.0006
(0.26)
0.0058∗∗
(2.14)
0.0246∗∗∗
(6.67)
0.0330∗∗∗
(7.64)
0.0333∗∗∗
(6.21)
0.0747
1387779

(6)
IC
-0.0045∗∗∗
(-3.60)

0.0069∗∗
(2.13)
0.0006∗∗∗
(8.11)
-0.0292∗∗∗
(-10.26)

0.0006
(0.25)
0.0058∗∗
(2.12)
0.0246∗∗∗
(6.67)
0.0332∗∗∗
(7.64)
0.0336∗∗∗
(6.25)
0.0745
1385652

Country-Industry-Year FE

Yes

Yes

Yes

Yes

Yes

Yes

The dependent variable is a binary indicator of zombie status, according to the deﬁnition indicated in each
columns. All regressors are expressed in two years lags. All regressions include a control for bank size. ”Bar-
riers” indicates the value of the insolvency subindicator for barriers to restructuring for the 2016 wave, and
”Barriers 2010” the respective value for the 2010 wave. The country-industry-year ﬁxed effects are at the 3-
digit NACE classiﬁcation.
Standard Errors clustered at the bank level. t statistics in parentheses.
∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

ECB Working Paper Series No 2240 / February 201952Table VIII: Capital reallocation and bank health

(1)

(2)

Full Sample Manufacturing

(3)
Services

(4)
Construction

Panel A

MFP

MFP × Bank Health

R2
N

Panel B

lag MFP

MFP × Crisis

R2
N

Panel C

MFP

MFP × Bank Health

MFP × Crisis

MFP × Bank Health ×Crisis

R2
N

0.0547∗∗∗
(11.63)

0.0165∗∗∗
(5.35)

0.0612
6063141

0.0610∗∗∗
(13.20)

-0.0115∗∗
(-2.58)

0.0606
6063141

0.0516∗∗∗
(9.40)

0.0203∗∗∗
(3.60)

0.0047
(0.75)

-0.0035
(-0.48)

0.0612
6063141

Country-Industry-Year FE

Yes

0.0675∗∗∗
(10.90)

0.0192∗∗∗
(3.42)

0.0709
1080686

0.0564∗∗∗
(9.77)

0.0134∗∗∗
(3.86)

0.0613
3678234

0.0715∗∗∗
(8.06)

0.0590∗∗∗
(11.61)

-0.0058
(-0.72)

0.0705
1080686

0.0672∗∗∗
(4.89)

0.00980
(0.63)

0.0075
(0.53)

0.0185
(1.15)

0.0710
1080686

Yes

-0.0045
(-0.69)

0.0609
3678234

0.0502∗∗∗
(8.17)

0.0194∗∗∗
(3.07)

0.0108
(1.28)

-0.0041
(-0.47)

0.0613
3678234

Yes

0.0504∗∗∗
(5.87)

0.0220∗∗∗
(3.61)

0.0571
1304221

0.0649∗∗∗
(5.97)

-0.0262∗∗∗
(-3.08)

0.0562
1304221

0.0536∗∗∗
(4.20)

0.0228∗
(1.90)

-0.0083
(-0.73)

-0.0052
(-0.38)

0.0573
1304221

Yes

The dependent variable is log real capital growth. The zombie capital share is constructed using the IC
deﬁnition of zombie ﬁrms. All regressions include lagged ﬁrm age and dummies for size.
Standard Errors clustered at the country-sector level t statistics in parentheses.
∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

ECB Working Paper Series No 2240 / February 201953Table IX: Capital reallocation, bank health and zombie congestion

MFP

MFP× Bank Health

MFP × Industry Zombie Share

Age

10<Employment<24

25<Employment<49

49<Employment<99

99<Employment<249

Employment>=250

Country-Industry-Year FE
Firm FE
R2
N

(1)

(2)

(3)

(4)

Full Sample Full Sample Full Sample Full Sample

0.0547∗∗∗
(11.63)

0.0165∗∗∗
(5.35)

-0.0019 ∗∗∗
(-6.53)

-0.0212∗∗∗
(-4.25)

-0.0154∗∗
(-2.57)

-0.0204∗
(-1.81)

-0.0339∗∗∗
(-2.91)

-0.0306∗∗
(-2.31)

Yes
No

0.0655∗∗∗
(9.97)

-0.1340∗∗∗
(-3.16)

-0.0018∗∗∗
(-6.42)

-0.0213∗∗∗
(-4.27)

-0.0154∗∗
(-2.55)

-0.0201∗
(-1.74)

-0.0344∗∗∗
(-2.93)

-0.0312∗∗
(-2.33)

Yes
No

0.0616∗∗∗
(9.88)

0.0147∗∗∗
(4.76)

-0.0894∗∗
(-2.29)

-0.0018 ∗∗∗
(-6.43)

-0.0213∗∗∗
(-4.29)

-0.0155∗∗∗
(-2.59)

-0.0205∗
(-1.81)

-0.0344∗∗∗
(-2.95)

-0.0310∗∗
(-2.32)

Yes
No

0.1826∗∗∗
(17.06)

0.0171∗∗∗
(5.85)

-0.0237
(-0.01)

-0.0484∗∗∗
(-8.83)

-0.0887∗∗∗
(-13.38)

-0.1223∗∗∗
(-13.88)

-0.1723∗∗∗
(-16.64)

-0.1967∗∗∗
(-10.98)

Yes
Yes

0.0612
6063141

0.0608
6063141

0.0613
6063141

0.3998
5774378

The dependent variable is log real capital growth. The zombie capital share is constructed
using the IC deﬁnition of zombie ﬁrms. All regressors are lagged.
Standard Errors clustered at the country-sector level. t statistics in parentheses
∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

ECB Working Paper Series No 2240 / February 201954Table X: Credit Availability

OLS

(1)
Simple

(2)
Composite

(3)
Simple

(4)
Composite

Ordered Logit
(6)
Composite

(5)
Simple

Panel A - Loans, all ﬁrms

Zombie Share

LP

Net Worth

Zombie Share × LP

R2
N

Panel B - Loans, small ﬁrms

Zombie Share

LP

Net Worth

Zombie Share × LP

R2
N

Panel C - Credit Lines, all ﬁrms

Zombie Share

LP

Net Worth

Zombie Share × LP

R2
N

Panel D - Credit Lines, small ﬁrms

Zombie Share

LP

Net Worth

Zombie Share × LP

-0.0053∗
(-1.94)
0.0145
(0.48)
0.3272∗∗∗
(4.03)

-0.0101∗∗∗
(-2.89)
0.0168
(0.40)
0.4912∗∗∗
(3.62)

0.0524
7613

0.0542
6111

-0.0089∗∗
(-2.38)
0.0447
(1.54)
0.2701∗∗∗
(3.21)

-0.0163∗∗∗
(-3.31)
0.0359
(0.88)
0.3170∗∗∗
(3.02)

0.0449
3815

0.0416
2996

-0.0052∗
(-1.80)
0.0277
(1.17)
0.3916∗∗∗
(6.03)

-0.0107∗∗
(-2.18)
0.0362
(1.05)
0.5027∗∗∗
(4.86)

0.0688
6944

0.0715
5270

-0.0040
(-1.31)
0.0378
(1.58)
0.3624∗∗∗
(4.49)

-0.0093∗∗
(-2.06)
0.0762∗∗
(2.03)
0.4381∗∗∗
(3.89)

-0.0118∗
(-1.87)
0.0504
(0.79)
0.7500∗∗∗
(4.18)

-0.0123∗∗
(-2.08)
0.0479
(0.71)
0.9214∗∗∗
(4.11)

7613

6111

-0.0184∗∗
(-2.40)
0.1064
(1.64)
0.6166∗∗∗
(3.48)

-0.0269∗∗∗
(-3.22)
0.0454
(0.62)
0.6172∗∗∗
(3.53)

3815

2996

-0.0116
(-1.61)
0.0709
(1.26)
0.9457∗∗∗
(5.88)

-0.0159∗
(-1.88)
0.0668
(1.15)
0.8290∗∗∗
(4.32)

6944

5270

-0.0080
(-1.13)
0.1028∗
(1.77)
0.8654∗∗∗
(4.96)

-0.0128
(-1.64)
0.1287∗∗
(2.06)
0.7680∗∗∗
(4.16)

0.0336
(0.84)
0.3772∗∗∗
(4.64)
-0.0033
(-1.03)
0.2424
7466

-0.0509
(-0.78)
0.5761∗∗∗
(4.26)
0.0022
(0.41)
0.2683
5995

0.1355∗∗
(2.50)
0.3190∗∗∗
(3.69)
-0.0095∗∗
(-2.00)
0.2581
3615

0.0697
(0.88)
0.3858∗∗∗
(3.46)
-0.0078
(-1.23)
0.2754
2837

0.0051
(0.12)
0.3930∗∗∗
(6.25)
0.0012
(0.31)
0.2438
6852

0.0800
(1.19)
0.4646∗∗∗
(4.65)
-0.0004
(-0.06)
0.2952
5146

0.1251∗∗
(2.36)
0.3788∗∗∗
(4.07)
-0.0103∗∗
(-2.09)
0.2186
3443

0.1279∗
(1.67)
0.5432∗∗∗
(4.35)
-0.0068
(-0.93)
0.2593
2543

0.0650
2715

0.0512
3593
Yes

R2
N
Country-Year FE
Country-Sector-Year FE
The dependent variable is the cumulated indicator of bank credit availability (loans or credit lines) as described
in the text, truncated at ±3, where 0 indicates no change, and higher values improvement in availability. Small
ﬁrms have less than 50 employees.
Standard Errors clustered at the country-sector level. The logit coefﬁcients are expressed in log-odds ratio.
∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01, t statistics in parentheses

3593

2715

Yes

Yes

Yes

Yes

Yes

No

No

No

No

No

No

ECB Working Paper Series No 2240 / February 201955Acknowledgements 

The views expressed in this paper are those of the authors alone, and may not necessarily reflect those of the European Central Bank 
(ECB), the Organization for Economic Co-operation and Development (OECD), or the Australian Treasury. This work was undertaken 
while Andrews was at the OECD. We would like to thank Sebnem-Kalemli Ozcan, Romain Duval, Garry Young, Enrique Moral-Benito, 
Bob Anderton, Giuseppe Nicoletti, Isabel Vanksteenkiste, Beatrice Pierluigi, Muge Adalet McGowan, Agostino Consolo, Bjorn Fischer, 
Peter Gal, Valentine Millot, Ralph Setzer, Manuela Storz, Livio Stracca, seminar participants at the ECB, the OECD, the Joint OECD-
IMF-BIS conference on weak productivity, Bruegel, Peterson Institute, Bank of Spain, and EARIE 2018. All errors are our own. 

Dan Andrews 
Australian Treasury, Canberra, Australia; OECD; email: dan.andrews@treasury.gov.au 

Filippos Petroulakis 
European Central Bank, Frankfurt am Main, Germany; email: filippos.petroulakis@ecb.europa.eu 

© European Central Bank, 2019 

Postal address  60640 Frankfurt am Main, Germany 
Telephone 
Website 

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All rights reserved. Any reproduction, publication and reprint in the form of a different publication, whether printed or produced 
electronically, in whole or in part, is permitted only with the explicit written authorisation of the ECB or the authors.  

This paper can be downloaded without charge from www.ecb.europa.eu, from the Social Science Research Network electronic library or 
from RePEc: Research Papers in Economics. Information on all of the papers published in the ECB Working Paper Series can be found 
on the ECB’s website. 

PDF 

ISBN 978-92-899-3502-9 

ISSN 1725-2806 

doi:10.2866/192858 

QB-AR-19-021-EN-N