Corporate debt and investment : a firm level analysis for stressed euro area countries.
Date: 2017-09-20
Institutions: European Central Bank
Subjects: corporate finance, debt, euro area, international finance, investment

Stefan Gebauer, Ralph Setzer, 
Andreas Westphal 

Working Paper Series 

Corporate debt and investment:  
a firm level analysis for stressed 
euro area countries 

No 2101 / September 2017 

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

Abstract

This paper investigates the link between corporate debt and investment for a group of
ﬁve peripheral euro area countries. Using ﬁrm-level data from 2005-2014, we postulate a
non-linear corporate leverage-investment relationship and derive thresholds beyond which
leverage has a negative and signiﬁcant impact on investment. The investment sensitivity of
debt increased after 2008 when ﬁnancial distress intensiﬁed and ﬁrms had a lower capacity
to ﬁnance investment from internal sources of funds. Our results also suggest that even
moderate levels of debt can exert a negative inﬂuence on investment for smaller ﬁrms or
when proﬁtability is low.

JEL Classiﬁcation: E22, F34, G31, G32

Keywords: Corporate debt, leverage, debt overhang, investment, threshold model

ECB Working Paper 2101, September 20171Non-Technical Summary

This paper investigates the relationship between corporate debt and investment. It uses ﬁrm-

level data for ﬁve peripheral euro area countries (Italy, Spain, Greece, Portugal, and Slovenia)

over the 2005-2014 period and explicitly accounts for non-linearities in the debt-investment

link. Our paper builds on the existing literature which ﬁnds evidence that high debt distorts

investment due to higher default risks and higher costs of ﬁnancing, while low leverage levels do

not negatively aﬀect investment. One concern of this literature is that the threshold between

high and low leverage has often been set exogenously and in an ad-hoc manner. In contrast, our

empirical approach allows us to endogenously estimate debt thresholds.

Our estimations suggest that there are signiﬁcant non-linearities in the debt-investment link:

We identify leverage thresholds in the range of a debt-to-asset ratio of 80-85 percent. For ﬁrms

with a leverage ratio above this threshold, we ﬁnd strong evidence consistent with debt holding

back investment.

The impact of leverage on investment is economically meaningful. In normal periods, the

negative investment eﬀect materialises only for high levels of debt. A ﬁrm with a debt-to-asset

ratio of 90 percent (i.e. an overleveraged ﬁrm) has, ceteris paribus, 0.7 percentage point of

forgone investment per year compared with a ﬁrm with a debt-to-asset ratio of 80 percent. For

certain periods and subsamples the investment eﬀects are, however, also signiﬁcant for lower

levels of debt. For example, during the post-2008 period, a ﬁrm with a debt-to-asset ratio of 30

percent had additional annual investment of 1.4 percentage points compared with a ﬁrm with a

debt ratio of 60 percent, and 2.4 percentage points compared with a ﬁrm with a debt ratio of

80 percent.

Both the leverage threshold and the investment-dampening impact of high leverage are robust

across various speciﬁcations. In particular, for highly leveraged ﬁrms we ﬁnd a negative debt-

investment relationship in all major sectors and for both proﬁtable and unproﬁtable ﬁrms. There

is, however, evidence for some heterogeneity across ﬁrm size. The negative investment impact

of high debt is observed for micro, small and medium-sized ﬁrms (which amount to 99 percent

of ﬁrms in our sample), but not for large ﬁrms. This suggests that ﬁnancial constraints related

to a debt overhang play a less important role for large ﬁrms.

For ﬁrms with debt levels below the threshold, the relationship between debt and investment

is less robust and depends on a number of ﬁrm characteristics and the macroeconomic environ-

ECB Working Paper 2101, September 20172ment. While our full-sample estimation suggests a slight (and insigniﬁcant) positive eﬀect of

debt on investment, during the ﬁnancial crisis period even low debt seems to constitute a drag

on investment, though the negative investment eﬀect is still smaller than for high-debt ﬁrms.

Similarly, for smaller and less productive ﬁrms, even low levels of debt have a negative impact

on investment.

Overall, these results suggest still substantial deleveraging needs in peripheral countries to

support a stronger investment recovery. The economic environment in peripheral countries is

characterised by a large number of small ﬁrms, low productivity and still relatively high ﬁnancial

uncertainty – factors which reduce the capacity to tolerate high levels of debt and lead to a more

negative debt-investment relationship. Moreover, our results show that a debt overhang is not

only reﬂected in a high stock of debt but also in a low capacity to service the debt (as measured

by the EBITDA-to-debt ratio and the interest expenses-to-EBITDA ratio), which implies that

even ﬁrms with a debt-to-asset ratio below the identiﬁed threshold may be in need of deleveraging

if they face high debt service obligations.

ECB Working Paper 2101, September 201731

Introduction

Corporate debt increased rapidly in many peripheral euro area countries in the years preceding

the ﬁnancial crisis (Figure 1). The build-up of debt was largely driven by easy access to credit

and coincided with strong increases in investment (Figure 2), but did little to create conditions

for sustainable growth. As the ﬁnancial crisis unfolded, a legacy of high debt remained on

corporate balance sheets, rendering ﬁrms vulnerable to the shift in risk sentiment and the fall

in asset prices and proﬁts .

This paper builds on these considerations and investigates the link between indebtedness and

investment at the ﬁrm level. In particular, we highlight the non-linear aspect of this relationship.

The literature on corporate ﬁnance identiﬁes both potentially positive and negative eﬀects of debt

on investment. On the positive side, debt ﬁnancing may give rise to tax advantages compared

to alternative forms of ﬁnancing (Modigliani and Miller, 1963), and it can reduce agency costs

between managers and shareholders (Ross, 1977; Grossman and Hart, 1982). On the negative

side, high corporate indebtedness implies higher interest expenses and thus lowers funds available

for investment. Firms with high debt also ﬁnd it harder to obtain new funds from external ﬁnance

sources due to higher default risk when only a small share of assets is ﬁnanced with equity.

The desire to repair weak balance sheets to lower external ﬁnance costs leads ﬁrms to increase

savings and to forgo otherwise possibly proﬁtable investment opportunities (Myers, 1977). In

combination, these factors suggest a non-linear relationship between debt and investment, with

debt levels below a certain threshold – if at all – positively aﬀecting investment to the extent that

the costs of holding debt are lower than marginal returns from further investment. However,

high debt levels on ﬁrms’ balance sheets presumably exert a negative eﬀect on investment, as

costs associated with high debt holdings increase signiﬁcantly and thus reduce marginal returns

on investment. Many economists have thus pointed out that corporate indebtedness in euro

area countries inhibits investment spending and economic recovery whenever debt levels are
“excessively” high (see e.g. Cecchetti et al., 2011; ECB, 2013; Kalemli- ¨Ozcan et al., 2015a).

In our empirical approach, we exploit a large-scale panel data set to identify empirical thresh-

old levels of leverage beyond which debt holdings pose a drag on corporate investment activity.

Such estimated thresholds could be interpreted as midpoints of “vulnerability regions” around

the estimated debt cut-oﬀ level (Reinhart and Rogoﬀ, 2010) beyond which the structural relation

ECB Working Paper 2101, September 20174Figure 1: NFC debt, in percent of ﬁnancial assets

Source: Eurostat.

Figure 2: Gross ﬁxed capital formation, in percent of GDP

Source: Eurostat.

ECB Working Paper 2101, September 20175between debt and investment changes.1 Our sample includes more than three million ﬁrm-year

observations for ﬁve peripheral euro area countries (Italy, Spain, Greece, Portugal and Slove-

nia) over the 2005-2014 period. The choice of countries is motivated by data coverage and the

strong increase in corporate indebtedness in these ﬁve countries during the pre-crisis period.2

We explain ﬁrms’ investment decision by leverage while controlling for other ﬁrm, sector, and

macroeconomic characteristics.

In contrast to national account data, ﬁrm-level data provide added value to the analysis of

the debt-investment nexus for the following reasons (ECB, 2013): First, the impact of debt on

investment depends not on the aggregate leverage ratio of the corporate sector, but is subject to

the distribution of debt across ﬁrms. This implies that if there are non-linearities in the debt-

investment nexus, the analysis of national account data may not be possible due to aggregation

bias. More precisely, if there are leverage thresholds, combined total investment of a high- and a

low-indebted ﬁrm is diﬀerent than the total investment in the case where the same level of debt

is split equally across the two ﬁrms. Second, ﬁrm-level data allows for more speciﬁc statements

regarding the required deleveraging needs, by taking into account additional ﬁrm characteristics

(such as ﬁrm proﬁtability) and evaluating their role in ﬁrms’ investment decisions. Third, in

particular for smaller and more open economies, macro data are often inﬂuenced by a few

internationally exposed larger ﬁrms, while information on the large number of small ﬁrms is

hidden. Firm-level data also adequately cover leverage and investment decisions of small ﬁrms

and can thus provide a more representative picture of the heterogeneity of ﬁrms in the economy.

Whereas the notion of debt overhang is justiﬁed by theoretical work on corporate ﬁnances,

there is no single, commonly agreed method to measure over-indebtedness and the size of delever-

aging needs.3 In our empirical analysis, we distinguish between two debt overhang concepts:

a) the leverage ratio, which we deﬁne as the stock of debt in relation to ﬁrms’ assets and for

which we hypothesize negative eﬀects on investment above a certain leverage threshold due to

the higher costs of ﬁnancing amid rising default risk; and b) debt service obligations, a con-

1As Reinhart and Rogoﬀ (2010) put it in the context of their analysis on public debt and GDP growth: “We
do not pretend to argue that growth will be normal at 89 percent and subpar . . . at 91 percent debt/GDP any
more than a car crash is unlikely at 54mph and near certain at 56mph. However, mapping the theoretical notion
of ‘vulnerability regions’ to bad outcomes by necessity involves deﬁning thresholds, just as traﬃc signs in the US
specify 55mph.”

2Insuﬃcient availability of data prevented us from including further peripheral euro area countries such as

Ireland and Cyprus.

3See Consolo et al. (2017), Cuerpo et al. (2013), and Bornhorst and Ruiz-Arranz (2015) for approaches put

forward by the ECB, the European Commission, and the IMF, respectively.

ECB Working Paper 2101, September 20176cept emphasising liquidity risk and the capacity to serve outstanding debt with current income

streams. For the latter, we assume a linear negative eﬀect on investment, as, ceteris paribus,

higher liquidity risk should dampen investment activity, independent from the current levels

of debt holdings. We deﬁne liquidity risk in terms of the EBITDA-to-debt and the interest

expenses-to-EBITDA ratio, where EBITDA stands for earnings before interest, taxes, depreci-

ation and amortisation. In most of our speciﬁcations, we include both the leverage ratio and

the debt service obligations to control for diﬀerent aspects of the debt-investment link (see also
Coluzzi et al., 2015; Kalemli- ¨Ozcan et al., 2015a or Antonecchia and Ferrari, 2016).

Our paper adds to the empirical literature on non-linear eﬀects of corporate debt on invest-

ment (see, e.g., J¨ager, 2003; Goretti and Souto, 2013). Leverage thresholds in these studies

are typically determined in an exogenous and ad-hoc manner, instead of being derived from

statistical inference. To the best of our knowledge, this study is the ﬁrst to provide econometric

evidence on the link between corporate indebtedness and investment in peripheral euro area

countries by using panel threshold regression techniques (Hansen, 1999, 2000). We show that

results are sensitive to the identiﬁed thresholds and that ad-hoc threshold speciﬁcations can lead

to inconclusive results. With our model, we take an agnostic approach without any priors on

potential thresholds and estimate threshold candidates directly from the data.

We ﬁnd evidence of a non-linear relationship between leverage and investment. For highly

indebted ﬁrms, i.e. ﬁrms above a debt-to-asset ratio of 80-85 percent (which account for 10 to 15

percent of our observations), we ﬁnd a signiﬁcantly negative leverage-investment link, suggesting

that ﬁrms with weak balance sheets forgo some valuable investment opportunities. By contrast,

leverage does not seem to depress investment for moderately indebted corporates implying a

lower degree of ﬁnancial constraints for these ﬁrms. However, for the ﬁnancial crisis period,

we ﬁnd a negative eﬀect of low debt on investment, suggesting that the capacity to tolerate

even low levels of debt is negatively aﬀected by stronger ﬁnancial constraints and higher risk

aversion. On top of the leverage channel of investment, we also identify an independent debt

service channel, suggesting that not only the strength of the balance sheet but also the ratio of

earnings relative to interest payments and amortisations has an impact on investment.

The remainder of the paper is organised as follows. Section 2 reviews the related empirical

and theoretical literature. Section 3 provides some illustration of the data, followed by a descrip-

tion of the econometric model in section 4. In section 5, we report results of the baseline model

estimations on the debt-investment nexus and then provide evidence on structural diﬀerences

ECB Working Paper 2101, September 20177according to ﬁrm size, sector, and proﬁtability. Section 6 presents robustness tests, including an

instrumental variable approach based on dynamic panel estimations. We conclude in section 7.

2 Related Literature

On the theoretical side, our study is linked to the branch of the ﬁnance literature investigating the

determinants of corporate balance sheets and the link between corporate ﬁnance and investment.

This literature has shown that, in the presence of ﬁnancial market frictions, the Modigliani and

Miller (1958) theorem does not hold and ﬁrms’ net worth, largely determined by investment

decisions, depends on their ﬁnancial structure.

According to the trade-oﬀ theory of capital structure, ﬁrms set a target leverage ratio by

balancing the costs and beneﬁts of debt. The beneﬁts of debt include, inter alia, the tax

deductibility of interest (Modigliani and Miller, 1963), the disciplining eﬀect of debt in case of

agency problems between ﬁrm managers and shareholders (Jensen and Meckling, 1976; Grossman

and Hart, 1982), and the signalling role of debt regarding ﬁrm productivity, for instance if

managers possess inside information about the future productivity gains of the ﬁrm (Leland and

Pyle, 1977; Ross, 1977). The costs of debt relate to potential bankruptcy costs. Increasing debt

holdings compared to equity raises default probabilities as the fraction of asset holdings backed

by equity is decreasing. Higher default probabilities lead to ﬁnancial distress, which is reﬂected

in higher external ﬁnancing premia or the rationing of credit (Myers, 1977; Stiglitz and Weiss,

1981).

The key implication of the trade-oﬀ paradigm is that ﬁrms decide on an optimal leverage ratio

that solves the trade-oﬀ.4 If their actual ﬁnancial structure deviates from the targeted leverage

(e.g. due to shocks to ﬁrm value), balance sheet restructuring aims at gradually moving actual

leverage back to target. A ﬁrm with a leverage ratio below the target will therefore follow a

diﬀerent investment decision rule than one with high leverage. While low-leveraged ﬁrms face

low ﬁnancial constraints and can draw on “reserves of untapped borrowing power” (Modigliani

and Miller, 1963) if proﬁtable investment opportunities arise, high-debt ﬁrms are more concerned

about default risks and their ﬁnancial status (e.g., due to the risk of losing investment grade

status). They will focus on restoring leverage targets and may give up valuable investment

opportunities when internal sources of funds are not suﬃcient (Myers, 1984), especially in times

4For survey studies on corporate debt targets and ﬁnancing structures see for instance Graham and Harvey

(2001), Bancel and Mittoo (2004), or Brounen et al. (2004).

ECB Working Paper 2101, September 20178of heightened uncertainty and ﬁnancial distress.5 In our empirical analysis, we therefore expect

a non-linear debt-investment relation for ﬁrms operating below and above a leverage target or

threshold.

The leverage target is inﬂuenced by several factors that have an impact on the costs and

beneﬁts of debt ﬁnancing. For example, ﬁrms with a lower probability of being distressed, such

as proﬁtable or large (more diversiﬁed) ﬁrms, can be expected to be able to borrow more before

the expected costs of ﬁnancial distress oﬀset the beneﬁts of debt. Moreover, changes in general

risk sentiment may impact on the leverage target through both the supply of and demand for

funds (see e.g. Amador and Nagengast, 2016; Buca and Vermeulen, 2017; Cingano et al., 2016;

Storz et al., 2017). We will come back to these aspects in the empirical section.

A number of empirical studies ﬁnd evidence that high corporate leverage can have negative

eﬀects on investment (Vermeulen, 2002; Benito and Hernando, 2007; Martinez-Carrascal and
Ferrando, 2008; Pal and Ferrando, 2010; Kalemli- ¨Ozcan et al., 2015a; Barbiero et al., 2016).

These studies typically ﬁnd that the investment impact of high corporate indebtedness is not

uniform. Using industry level data, Vermeulen (2002) shows that leverage is more important

in explaining investment during downturns and for small ﬁrms. Barbosa et al. (2007) ﬁnd for

Portuguese ﬁrms that the impact of corporate indebtedness on investment depends on ﬁrm size,

the number of bank lending relationships, and credit default history. In contrast to Vermeulen

(2002), they do not ﬁnd diﬀerent sensitivities of investment to debt between economic booms

and busts.

In line with the trade-oﬀ theory, some studies have also argued that low leverage levels do

not negatively impact on investment but that there is a threshold level of corporate debt beyond

which leverage and investment are negatively associated. Using ﬂow of funds data for the US

and Germany, J¨ager (2003) ﬁnds that the negative impact of corporate indebtedness on invest-

ment is stronger in years of above-average debt holdings than in years of below-average leverage.

Hernando and Martinez-Carrascal (2008) provide ﬁrm-level evidence on threshold eﬀects; their

results for Spanish ﬁrms indicate that a negative impact of indebtedness on investment is only

present for ﬁrms with high ﬁnancial pressure, i.e.

for ﬁrms above the 75th percentile of in-

debtedness. This threshold is above the one identiﬁed by Goretti and Souto (2013) who ﬁnd

strongly negative eﬀects of debt on investment once the debt to equity threshold exceeds the

5Bernanke et al. (1999) show that the sequence of events between ﬁrm net worth, collateral and investment
can lead to larger and more persistent cyclical ﬂuctuations in the economy (“ﬁnancial accelerator mechanism”).

ECB Working Paper 2101, September 2017925th percentile of the ﬁrms in their sample of euro area ﬁrms. While these studies have assumed

potential debt thresholds rather arbitrarily, Ferrando et al. (2017) estimate a leverage equation

from which they calculate the target level of debt. They show that ﬁrms with a conservative

leverage policy (deﬁned by a negative deviation between the actual level of leverage and the

target leverage) invest more in the years following the conservative ﬁnancial policy.

We add to the literature by conducting threshold analyses applying the method by Hansen

(1999, 2000) to a large ﬁrm level data set and analysing the investment sensitivity of leverage

above and beyond these thresholds. The study coming closest to our approach in terms of

method is Coricelli et al. (2012) who apply the panel threshold model by Hansen (1999) to a

subsample of Orbis data for Eastern European countries. In their study, they focus on the eﬀect

of ﬁrm leverage on productivity and leave corporate investment aside.

3 Data

We rely on Bureau van Dijk’s Amadeus database, which reports information on a large set of

European nonﬁnancial corporates. Our dataset includes both listed and unlisted companies and

contains information on ﬁnancial data of about 920 thousand ﬁrms operating in ﬁve peripheral

euro area countries (Italy, Spain, Portugal, Greece and Slovenia) for the 2005-2014 period. We
pursue a standard cleaning procedure to account for data irregularities (Kalemli- ¨Ozcan et al.,

2015b). In particular, we take care of double entries, ﬁrm-year observations with inconsistent

balance sheet or income statement relations, including those with negative debt or asset holdings.

Whenever available, we use consolidated annual accounts to account for potential cross-funding

of investment activity among ﬁrms with subsidiaries. We furthermore exclude ﬁrms operating

in speciﬁc sectors such as agriculture and mining, insurance services, public administration, as

well as ﬁnancial ﬁrms given their speciﬁc leverage characteristics. Furthermore, only ﬁrms with

at least three consecutive years of reporting in the data set are included. Finally, we apply a

trimming on all variables at the 1st and 99th percentile of the distribution to remove outliers.6

Our ﬁnal panel consists of 618 thousand ﬁrms and three million ﬁrm-year observations and

includes ﬁrms from ﬁfteen NACE sectors, with most ﬁrms operating in manufacturing, wholesale

and retail trade, real estate and construction services, as well as food and accommodation

services.

6This implies a maximum debt-to-asset ratio of 3.1. We also performed sensitivity tests with a threshold for

the debt-asset ratio of 1.0, without implications for the main results.

ECB Working Paper 2101, September 201710Our key variables of interest are investment and leverage. We use both net and gross in-

vestment since they capture diﬀerent dimensions of the debt-investment nexus. Net investment

(deﬁned as the annual percentage change in tangible ﬁxed assets) is more relevant from a policy
point of view given its close link to an economy’s level of productivity (Kalemli- ¨Ozcan et al.,

2015a). Gross investment (net investment plus depreciation) has a stronger theoretical moti-

vation since ﬁnancial constraints should aﬀect both investment that replaces depreciated assets

and new investment (Ferrando et al., 2017). We therefore show results both for net and gross

investment. In our baseline regression, we deﬁne leverage as debt holdings relative to total as-

sets, with debt including ﬁnancial debt (loans and bonds) plus other current ﬁnancial liabilities

(mainly pension liabilities and accounts payable). We exclude trade credit as it typically serves

for transaction purposes and not for ﬁnancing. Tables 1 and 2 present the deﬁnitions and de-

scriptive statistics of all variables, including our control variables. Note that all variables are

expressed in real terms. Overall, there is signiﬁcant heterogeneity in the variables of interest

across ﬁrms. While the mean ﬁrm in our sample has a net investment rate of 12 percent, the

median ﬁrms net investment rate is a negative six percent. On average, ﬁrms had debt holdings

of about 48 percent of their assets. The ﬁrms in the highest decile of debt holdings reported 86

percent of debt relative to assets.

Variable
Investment (net)
Investment (gross)
Leverage

Table 1: Firm variable deﬁnition

Deﬁnition
Annual change in net total ﬁxed assets (in %)
Annual change in net total ﬁxed assets plus depreciation (in %)
Ratio of ﬁnancial debt to total assets, with ﬁnancial debt including
loans, securities and other current liabilities.

Debt service capacity Ratio of earnings before interest, taxes, depreciation and

Interest burden

Proﬁtability

Tangibility
Sales growth
Size

amortisations (EBITDA) to ﬁnancial debt (deﬁned as above)
Ratio of interest payments to earnings before interest, taxes,
depreciation and amortisations (EBITDA)
Ratio of earnings before interest, taxes, depreciation and
amortisations (EBITDA) to total assets
Tangible ﬁxed assets divided by total assets
Annual growth rate of sales
Natural log of assets

ECB Working Paper 2101, September 201711Table 2: Descriptive Statistics

Net Investment
Gross Investment
Debt/Assets
∆Debt/Assets
EBITDA/Debt
Interest Burden
EBITDA/Assets
Sales Growth
Log(Assets)

Mean
0.12
0.51
0.48
-0.01
0.37
0.18
0.09
0.11
13.58

Total
N = 3,006,374

SD
1.09
1.56
0.3
0.16
0.73
0.43
0.11
0.75
1.45

p10
-0.34
-0.01
0.12
-0.16
-0.02
-0.01
-0.01
-0.32
11.76

Median
-0.06
0.09
0.45
-0.01
0.17
0.12
0.07
0
13.51

p90
0.46
1.12
0.86
0.14
0.95
0.56
0.21
0.47
15.55

Pre-Crisis Period (2005-2008)
N = 590,224

Post-Crisis Period (2009-2014)
N = 2,416,150

SD
1.46
1.93
0.29
0.15
0.66
0.39
0.11
0.71
1.38

Mean
0.34
0.77
0.53
-0.01
0.38
0.2
0.11
0.18
13.78

Net Investment
Gross Investment
Debt/Assets
∆Debt/Assets
EBITDA/Debt
Interest Burden
EBITDA/Assets
Sales Growth
Log(Assets)
Note: All variables are expressed in real terms (using the GDP deﬂator) and trimmed at the 1st and 99th percentile
of the distribution (including net investment). The sample composition is only slightly diﬀerent when the trimming
procedure is applied with respect to gross rather than net investment. N is number of observations. EBITDA is
earnings before interest, taxes, depreciations and amortisations.

p10 Median
-0.35
-0.01
0.11
-0.17
-0.04
-0.04
-0.02
-0.34
11.7

Median
-0.04
0.21
0.52
-0.01
0.19
0.142
0.09
0.05
13.71

Mean
0.06
0.45
0.47
-0.01
0.37
0.18
0.08
0.1
13.53

p10
-0.31
-0.01
0.15
-0.14
0.03
0.01
0.02
-0.21
12.04

p90
1.09
1.85
0.91
0.13
0.89
0.54
0.23
0.54
15.67

-0.06
0.07
0.44
-0.01
0.17
0.12
0.07
-0.02
13.46

SD
0.97
1.45
0.3
0.16
0.74
0.44
0.12
0.75
1.46

p90
0.32
0.98
0.84
0.15
0.96
0.56
0.2
0.45
15.51

ECB Working Paper 2101, September 201712Figure 3: Investment rate in 2007, 2011 and 2014,by leverage group

Source: Amadeus, own calculations.

We also report descriptive statistics for the periods before and after the outbreak of the

global ﬁnancial crisis in 2008.7 Firms in our sample reported a substantially higher investment

activity prior to the global ﬁnancial crisis, with average net investment rates dropping from 34

percent in the pre-crisis years to only six percent thereafter. However, high average investment

rates in the pre-crisis period appear to be driven by a subgroup of strongly investing ﬁrms, as

the median investment rate is much lower. Even more, the collapse in investment rates after the

outbreak of the ﬁnancial crisis appears to be strongest for the formerly high-investment ﬁrms,

as the median investment rate does not change signiﬁcantly between the two periods. The

substantial drop in investment is furthermore associated with a slight reduction in debt-to-asset

ratios. However, as asset values dropped due to revaluations, the reduction in absolute debt

holdings has been stronger in relative terms.

To elaborate further on the change in debt-investment behaviour over time, Figure 3 shows

the net investment rate in 2007, 2011 and 2014 by leverage group. On the eve of the ﬁnancial

crisis there have not been strong diﬀerences in investment between high and low leveraged ﬁrms.

However, during the crisis years, high-leveraged ﬁrms invested signiﬁcantly less than their low-

leveraged peers. This provides a ﬁrst indication that leverage became more of a constraint for

investment when risk aversion and ﬁnancial uncertainty increased.

7We deﬁne the post-crisis period from 2009 onwards, as we expect potential changes in ﬁrm ﬁnancing and
investment decisions to be initiated shortly after the collapse of global ﬁnancial markets. We also evaluated
descriptive statistics with 2010 as the ﬁrst crisis year, indicating the start of the European sovereign debt crisis.
The main conclusions did not change by shifting the break point in such a way.

ECB Working Paper 2101, September 2017134 Estimation Design

To identify non-linearities in the relationship between corporate debt and investment activity, we

estimate panel threshold regression models following Hansen (1999, 2000). These models allow

for an endogenous identiﬁcation of threshold levels that split the sample in two regimes for which

the linear relationship between the dependent variable and one or more variables of interest

is expected to diﬀer. Furthermore, the procedure tests for the signiﬁcance of the identiﬁed

thresholds, relying on conﬁdence intervals, derived with appropriately designed likelihood-ratio

tests (Hansen, 1999, 2000).

Speciﬁcally, we estimate the following threshold model for diﬀerent subsets of the data:

Iit = β1Dit−1I(Dit−1 ≤ γ) + β2Dit−1I(Dit−1 > γ) + φzit−1 + δ1I(Dit−1 ≤ γ) + εit

(1)

with Iit referring to either net or gross investment, as deﬁned above. The leverage measure

Dit denotes the indicator variable for which the regime-separating threshold γ is estimated.

Based on our theoretical motivation, we use the leverage ratio as both the regime-indicating

variable and the regime-dependent regressor. The impact of leverage on investment is repre-

sented by the slope coeﬃcients β1 and β2 which are allowed to diﬀer in the two regimes. To do

so, we interact Dit with the values of the binary indicator function I(•) for whether a ﬁrm has

a debt-to-asset ratio above or below the estimated threshold in a certain year.

We add a vector of covariates zit that includes lagged values of the debt servicing capac-

ity (EBITDAit/debtit), the interest burden (interest expensesit/EBITDAit) to control for debt

service obligations, proﬁtability (EBITDAit/assetsit), sales growth, the change in leverage, ﬁrm

size (deﬁned as the natural logarithm of total assets), and the share of tangible assets in total

assets (Table 1).8 We furthermore include controls for country-, sector-, and year-ﬁxed eﬀects to

account for potential correlation between country- or sector-speciﬁc characteristics and regres-

sors, and to take macroeconomic factors into account. In addition, we follow Bick (2010) and

Kremer et al. (2013) in including a regime-speciﬁc intercept term (δ1).9 We assume the errors
εit to be independent and identically distributed10 and lag all right-hand-side variables by one

8See Kalemli- ¨Ozcan et al. (2015a), Goretti and Souto (2013), or Coricelli et al. (2012) for a discussion of

ﬁrm-level control variables usually used in investment and productivity regression models.

9As Bick (2010) and Kremer et al. (2013) show, not controlling for diﬀerences in the intercept terms estimated

for both regimes can cause estimation bias in both the slope coeﬃcients and, as a consequence, the threshold.

10However, we allow for potential heteroscedasticity in the errors εit in the estimation of the slope coeﬃcients
conditional on the identiﬁed threshold by using clustered-robust standard errors for the estimation of slope

ECB Working Paper 2101, September 201714period to avoid endogeneity issues. We will discuss potential endogeneity concerns further in

Section 6.

As regards the identiﬁcation of the debt threshold, we follow Hansen (1999) and ﬁrst estimate

equation 1 over an array of threshold candidates. The selected threshold is then deﬁned by the

minimum of the resulting concentrated sum of squared errors function Sn(γ). As the sum of

squared errors depends on the threshold estimate only via the indicator function I(•), Sn(γ)

is not a linear but a step function, with steps occurring at distinct values of γ. For a given

threshold candidate, however, Sn(γ) becomes linear in the regression coeﬃcients β1, β2, δ1 and

φ, whose estimated values in turn ultimately depend on the threshold estimate. Theoretically,

the sample provides the full range of

N
(cid:88)

n=1

Ti

threshold candidates. However, to reduce the computational burden, we follow Hansen (1999)

and others in solving the minimization problem by a grid search over 393 leverage ratio quantiles

{1%, 1.25%, 1.50%,. . . , 98.75%, 99%}.11

After having derived the threshold estimate, a conﬁdence interval has to be determined to

evaluate the statistical signiﬁcance of the derived estimate. Following Hansen (1999, 2000), we

use the derived conﬁdence interval to test the null hypothesis:

H0 : γ = γ0

where γ0 depicts the true underlying threshold value. However, standard likelihood ratio

test procedures cannot directly be applied, since, as Hansen (2000) has shown, the asymptotic

distribution of the likelihood ratio statistic LRn(γ) is non-standard. We derive ‘no-rejection

regions’ by computing the empirical likelihood ratio function

LRi =

Sn(γi) − Sn(ˆγ)
ˆσ2(γi)

, i ∈ [1, 393]

(2)

where Sn(ˆγ) depicts the estimated threshold derived from the minimization procedure. One

can then identify the respective conﬁdence intervals by comparing the discrete values of the

likelihood ratio distribution to the critical values which are, in accordance with Hansen (1999),

coeﬃcients in a second step, and report these standard errors in the regression tables.

11We deﬁne the respective quantiles after ensuring that neither of the two regime samples covers less than 5

percent of total observations (Hansen, 1999).

ECB Working Paper 2101, September 201715computed using the inverse of the distribution function for a determined conﬁdence level α:

c(α) = −2log(1 −

√

1 − α)

(3)

5 Estimation Results

5.1 Baseline Speciﬁcation

Table 3 reports our baseline results for estimations with either net or gross investment as the

dependent variable. The leverage thresholds γ are broadly in the same range for diﬀerent

versions of our baseline regression, with a debt-to-asset ratio of 80-85 percent. In 11-14 percent

of the observations over our sample period, ﬁrms were operating above the leverage threshold.

These ﬁrms are therefore classiﬁed as excessively leveraged for the period they operate above

the threshold.12

As postulated by the trade-oﬀ theory, leverage above the threshold is associated with a

negative and signiﬁcant eﬀect on investment in the following year as indicated by the signiﬁcantly

negative slope coeﬃcient β2. Conversely, there is a positive and statistically insigniﬁcant eﬀect

of debt on investment if the leverage ratio is below the threshold (β1). These results support

the view that a ﬁrm’s leverage ratio aﬀects its investment in a non-linear way. While ﬁrms with

moderate debt levels potentially make use of unused borrowing capacities to engage in further

investment, debt holdings beyond the threshold depress investment activity. This non-linearity

in the debt-investment link holds for both the net and gross investment speciﬁcation.

These results are economically non-negligible. A ﬁrm with a debt-to-asset ratio of 90 percent

(i.e. an overleveraged ﬁrm) has, ceteris paribus, 0.7 percentage point of forgone investment per

year compared with a ﬁrm with a debt-to-asset ratio of 80 percent. There are also signiﬁcant

diﬀerences across countries. Assuming an 80 percent threshold, Spain and Portugal have the

highest share of excessively leveraged ﬁrms (Figure 4). One-ﬁfth of Spanish ﬁrms operated

above the leverage threshold at the end of our sample period in 2014. Our analysis also suggests

signiﬁcant deleveraging needs for Portugal, with 13 percent of ﬁrms recording leverage above

the threshold. Excessive leverage seems to be lower in Italy, Slovenia and Greece.

12Note that if a ﬁrm is above the leverage threshold, this is a suﬃcient but not a necessary condition for a ﬁrm

to be excessively leveraged since excessive leverage may also be reﬂected in high debt servicing obligations.

ECB Working Paper 2101, September 201716t
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ECB Working Paper 2101, September 201717Figure 4: Share of ﬁrms above debt-to-asset ratio of 80 percent

Source: Own estimations.

Our results also provide evidence for an independent debt-servicing channel. Low earnings

relative to debt are negatively associated with investment activity, in line with the ﬁndings by
Kalemli- ¨Ozcan et al. (2015a). To evaluate the degree of interdependence between the channels,

we provide results for our baseline regression excluding the EBITDA-to-debt ratio (columns 2

and 5), and excluding the interest burden-to-EBITDA ratio (columns 3 and 6). Removing these

variables hardly aﬀects the debt-to-asset threshold, which remains slightly above 80 percent.

The coeﬃcient for the high-debt regime also remains strongly negative and signiﬁcant, thereby

conﬁrming the negative sensitivity of ﬁrms’ investment to high debt. The coeﬃcient for the

low-debt regime, however, decreases and even turns negative for the net investment equation.

We take this ﬁnding as evidence that not controlling for the debt-servicing channel would imply

underestimating the leverage channel of debt on investment.

Results for our additional explanatory variables are in line with expectations and previous

empirical ﬁndings. An increase in sales revenues positively aﬀects investment activity. The

investment sensitivity with respect to proﬁtability is signiﬁcant and positive, suggesting that

ﬁnancial constraints play a role beyond the debt overhang eﬀect. The negative coeﬃcient on

interest payments relative to earnings further reﬂects the importance of ﬁnancial constraints for

investment activity. Firm size is negatively associated with investment activity, potentially due

to decreasing returns to scale (Gala and Brandon, 2016). When controlling for the stock of debt,

an increase in leverage is positively related to investment, though not statistically signiﬁcant in

ECB Working Paper 2101, September 201718Figure 5: Beta coeﬃcients and likelihood ratio function of baseline model

The ﬁgure shows the estimated beta coeﬃcients of the net investment baseline speciﬁcation reported in column

1 of Table 3, as estimated for each of the potential 393 threshold candidates (primary axis). Furthermore, the

respective likelihood-ratio statistic computed according to equation 2 are reported for each threshold candidate

estimation. The critical value calculated according to equation 3 is also reported (red dashed line). Source: Own

estimations.

all speciﬁcations.13 Finally, tangible ﬁxed assets enter negatively, indicating that ﬁrms’ ability

to pledge collateral did not lead to more investment. Again, these ﬁndings are robust to the use

of net or gross investment as dependent variable.

For illustrative purposes, we evaluate for our baseline speciﬁcation on net investment (column

1 in Table 3) the sensitivity of the high- and low-debt coeﬃcients with respect to diﬀerent

thresholds. To do so, we plot the estimated coeﬃcients β1 and β2, together with the respective

conﬁdence intervals, for the 393 quantiles of our debt-to-asset ratio in Figure 5.14 We furthermore

show the empirical likelihood ratio function of equation 2 and the critical value for the 95 percent

conﬁdence interval of the threshold variable (equation 3). The likelihood ratio has its minimum

at our estimated threshold of a debt-to-asset ratio of 85 percent.

As can be seen, the correct identiﬁcation of the threshold is crucial for the proper interpreta-

13The exclusion of the variable change in leverage does not aﬀect our results in a qualitative way.
14We show this for the baseline result of column 1 exemplary. The implications are similar for results derived

using gross investment.

-1090190290390490590-0.5-0.4-0.3-0.2-0.100.10.20.070.150.230.310.380.450.530.610.710.83Beta 1Beta 2LR Statistic (RHS)ECB Working Paper 2101, September 201719tion of the debt-investment link. The relation between debt and investment diﬀers substantially

depending on the threshold. If, for instance, a leverage threshold had been set exogenously at

30 percent, high debt holdings would have been positively associated with investment activity,

whereas the opposite would be true for only weakly indebted ﬁrms. Thus, relying on an ad-hoc

threshold speciﬁcation without econometric testing for its signiﬁcance would have resulted in

fundamentally diﬀerent conclusions about debt-investment links. We take this ﬁnding as sup-

port for our claim that debt thresholds should be determined endogenously by the data, without

taking an a priori stance on expected threshold levels.

In Table 4, we compare results for the periods before and after the global ﬁnancial crisis of

2008 to discuss whether the crisis aﬀected ﬁrm investment via balance sheet deteriorations.15

Interestingly, while the leverage thresholds for both periods remain in the range of a debt-to-

asset ratio of 80-85 percent, the negative debt-investment link is much stronger for the crisis

years. First, the relationship between debt and investment is negative for both periods, but

only signiﬁcant in the post-crisis period. Second, there is even a negative eﬀect of low debt on

investment in the post-crisis years, compared to the signiﬁcantly positive link in the pre-crisis

years. This suggests a much larger need for deleveraging in crisis times. As indicated above,

ﬁrms were beneﬁtting from favourable ﬁnancing conditions in the boom years preceding the

global ﬁnancial crisis, whereas the debt overhang accumulated over the boom phase depicts an

investment-dampening factor in post-crisis years.

We also report subsample estimations for ﬁrms being assigned to diﬀerent proﬁtability

groups. We therefore split the sample by mean proﬁtability (which corresponds to an EBITDA-

to-asset ratio of six percent). A single ﬁrm might be included in both subsamples if it has been

operating above and below mean proﬁtability over the sample period. As discussed in the pre-

vious section, proﬁtable ﬁrms are expected to be less ﬁnancially constrained, implying a lower

investment sensitivity of debt. The empirical results point to a noticeable impact of proﬁtability

on the debt-investment nexus. Whereas the strongly negative eﬀect of high leverage on invest-

ment occurs for both types of ﬁrms, even low debt holdings depress investment when a ﬁrm is

unproﬁtable. These results suggest that high proﬁtability in combination with low debt is most

likely to spur investment.

15In line with the treatment of the period deﬁnitions in Section 3, we evaluated diﬀerent cut-oﬀ points for the

pre- and post-crisis periods. However, the basic results described here were not aﬀected.

ECB Working Paper 2101, September 201720Table 4: Sample split across time and ﬁrm proﬁtability

Pre-Crisis Post-Crisis Proﬁtable Unproﬁtable

Threshold Estimate
γ
95% Conﬁdence Interval
Impact of Debt/Assets
β1 (Low-Debt Regime)

β2 (High-Debt Regime)

Covariates
EBITDA/Debt

Interest Rate Burden

Sales Growth

log(Assets)

EBITDA/Assets

∆Debt/Assets

Tangibility

0.857
(0.852 0.864)

0.808
(0.782 0.855)

0.809
(0.793 0.809)

0.853
(0.850 0.856)

0.099***
(0.020)
-0.064
(0.041)

-0.047***
(0.010)
-0.099***
(0.007)

0.026**
(0.012)
-0.181***
(0.015)

-0.015
(0.011)
-0.086***
(0.008)

0.017
(0.012)
-0.002
(0.005)
0.037**
(0.014)
-0.005
(0.008)
0.374***
(0.065)
-0.004
(0.014)
-0.930***
(0.013)
Yes
Yes
Yes
Yes
646.944
85,14
14,86
323.472

0.010***
(0.003)
-0.002
(0.002)
0.059***
(0.009)
-0.007***
(0.002)
0.208***
(0.034)
0.029***
(0.006)
-0.319***
(0.027)
Yes
Yes
Yes
Yes
2.551.982
87,72
12,28
624.004

0.015***
(0.002)
-0.029***
(0.004)
0.074***
(0.006)
-0.008***
(0.003)
0.156***
(0.040)
0.005
(0.006)
-0.599***
(0.059)
Yes
Yes
Yes
Yes
1.702.366
89,54
10,46
336.374

0.018***
(0.003)
-0.002
(0.003)
0.043***
(0.011)
-0.007
(0.005)
0.291***
(0.026)
0.015
(0.010)
-0.316***
(0.034)
Yes
Yes
Yes
Yes
1.304.008
85,60
14,40
281.800

Sector FE
Year FE
Country FE
Regime-Dependent Intercept δ1
Observations
Percent of Obs. In Low-Debt Regime
Percent of Obs. In High-Debt Regime
No. Of Firms
Note: The threshold variable γ is the reported debt-to-asset ratio. Debt is deﬁned as ﬁnancial debt (short-and long-term
loans plus other current ﬁnancial liabilities). The dependent variable net investment is deﬁned as the annual percentage
change in tangible ﬁxed. Pre-crisis period covers years 2006-2008, the prost-crisis period the years 2009-2014. Firms
are deﬁned as (un-)proﬁtable, if their EBITDA-to-asset ratio has been below sample mean (six percent) in the previous
year. All independent variables are lagged by one period. Heteroskedasticity-robust standard errors of the covariates are
reported in brackets. ***p-value < 0.01, **p-value < 0.05, *p-value < 0.1.

ECB Working Paper 2101, September 2017215.2 The Role of Firm Size and Sectors

In this section, we conduct further subgroup regressions along ﬁrm size and operating sector.

We begin by evaluating whether we ﬁnd structural diﬀerences across ﬁrm size and follow the Eu-

ropean Commission’s ﬁrm size classiﬁcation. According to this deﬁnition, ﬁrms are categorized

based on employee headcount, turnover and balance sheet size.16 Results are reported in Table

5 and can be summarized as follows: First, while leverage thresholds decrease with ﬁrm size, the

non-linearity in the debt-investment link is less pronounced for large ﬁrms, as the signiﬁcance

of the regime-dependent coeﬃcients decreases with ﬁrm size. Second, for all size categories, we

ﬁnd a negative impact of high leverage on investment, though not statistically signiﬁcant for

large ﬁrms. Third, for micro and small ﬁrms, which account for 96 percent of all ﬁrms in our

sample, we ﬁnd evidence of a negative sensitivity of investment to debt service as measured in

terms of the EBITDA-to-debt ratio. Overall, these results are in line with empirical ﬁndings

by Vermeulen (2002) and the literature on the ﬁnancial propagation mechanism, which states

that large ﬁrms tend to be better diversiﬁed and are likely to face lower ﬁnancial constraints

(Gertler and Gilchrist, 1994). The results, therefore, provide some preliminary evidence for the

importance of bank credit supply restrictions, thereby supporting earlier studies which ﬁnd that

investment by borrowers that are more dependent on banks drops signiﬁcantly relative to that

of borrowers less dependent on banks (Buca and Vermeulen, 2017).

Next, we discuss whether the link between leverage and investment diﬀers across sectors.

We show results for the largest four NACE sectors, namely manufacturing, wholesale and retail

trade, food and accommodation services, and construction and real estate activities. These

sectors account for some 80 percent of ﬁrm-year observations in our sample.

Interestingly,

and in line with the ﬁndings by ECB (2013), heterogeneity across sectors is relatively limited

(Table 6). Leverage threshold estimates for all sectors remain within the interval identiﬁed in

our baseline estimation in Table 3, ranging from 80 percent for construction and real estate to

86 percent for food and accommodation services. For all sectors, we ﬁnd evidence of a non-

16The classiﬁcation is based on the following ceilings: Micro ﬁrms have less than ten employees and a turnover
or assets of less than e 2 million. Small ﬁrms are those with fewer than 50 employees and a turnover or assets of
less than e 10 million, whereas medium-sized ﬁrms have fewer than 250 employees, a turnover of less than e 50
million and assets of less than e 43 million. Above these cut-oﬀ points, ﬁrms are classiﬁed as large. See European
Commission (2015) for details. Whereas ﬁrm size is almost time-invariant in the sample, some changes in size
status within single ﬁrms can occur over time. Similar to the approach with respect to proﬁtability, we separate
the ﬁrm-year observations accordingly if a ﬁrm changes status. For instance, if we have data for a speciﬁc ﬁrm
for six consecutive years, and the size classiﬁcation changes after three years, the ﬁrm will appear in both class
size regressions with the data for the respective three-year period.

ECB Working Paper 2101, September 201722Table 5: Sample split across ﬁrm size
Small
Micro

Medium

Large

0.854
(0.854 0.854)

0.848
(0.812 0.853)

0.666
(0.658 0.727)

0.540
(0.137 0.837)

-0.030***
(0.011)
-0.122***
(0.011)

0.070***
(0.016)
-0.201***
(0.021)

0.006
(0.024)
-0.106***
(0.049)

-0.080
(0.126)
-0.014
(0.089)

Threshold Estimate
γ
95% Conﬁdence Interval
Impact of Debt/Assets
β1 (Low-Debt Regime)

β2 (High-Debt Regime)

Covariates
EBITDA/Debt

Interest Rate Burden

Sales Growth

log(Assets)

EBITDA/Assets

∆Debt/Assets

Tangibility

0.016***
(0.003)
-0.005
(0.003)
0.053***
(0.010)
-0.010***
(0.004)
0.235***
(0.043)
0.020***
(0.004)
-0.406***
(0.051)
Yes
Yes
Yes
Yes
1.915.596
86,75
13,25
450.259

0.009**
(0.005)
-0.004
(0.003)
0.072***
(0.007)
-0.031***
(0.009)
0.305***
(0.054)
-0.014***
(0.006)
-0.557***
(0.046)
Yes
Yes
Yes
Yes
672.958
93,63
6,37
146.674

-0.011***
(0.002)
0.007
(0.004)
0.083***
(0.008)
-0.046***
(0.008)
0.276***
(0.040)
0.021
(0.033)
-0.496***
(0.032)
Yes
Yes
Yes
Yes
130.110
88,81
11,19
26.541

-0.019
(0.030)
-0.029
(0.026)
0.144*
(0.076)
-0.107***
(0.027)
0.370*
(0.196)
-0.233**
(0.110)
-0.515***
(0.084)
Yes
Yes
Yes
Yes
6.496
64,66
35,34
1.550

Sector FE
Year FE
Country FE
Regime-Dependent Intercept δ1
Observations
Percent of Obs. In Low-Debt Regime
Percent of Obs. In High-Debt Regime
No. Of Firms
Note: The threshold variable γ is the reported debt-to-asset ratio. Debt is deﬁned as ﬁnancial debt (short-and long-term
loans plus other current ﬁnancial liabilities). The dependent variable net investment is deﬁned as the annual percentage
change in tangible ﬁxed. Micro ﬁrms have less than ten employees and a turnover or assets of less than e 2 million. Small
ﬁrms are those with fewer than 50 employees and a turnover or assets of less than e 10 million, whereas medium-sized
ﬁrms have fewer than 250 employees, a turnover of less than e 50 million and assets of less than e 43 million. Above these
cut-oﬀ points, ﬁrms are classiﬁed as large. See European Commission (2015) for details. All independent variables are
lagged by one period. Heteroskedasticity-robust standard errors of the covariates are reported in brackets. ***p-value
< 0.01, **p-value < 0.05, *p-value < 0.1.

ECB Working Paper 2101, September 201723linear relationship between debt and investment, including a strongly negative and statistically

signiﬁcant relation between highly-leveraged ﬁrms’ debt-to-asset ratios and investment. For

ﬁrms in food and accommodation services as well as ﬁrms in construction and real estate, we

even ﬁnd a negative (though not signiﬁcant) debt-investment relationship for low-debt ﬁrms,

possibly due to the smaller size of these ﬁrms and therefore a higher likelihood of ﬁnancial

constraints.

6 Robustness Checks

6.1

Instrumental Variable Approaches for the Leverage Ratio

A well-known problem of panel threshold models as well as panel estimation in general arises

from potential endogeneity concerns. In particular, the assumption of the error terms εit being

i.i.d.

is crucial when applying the framework of Hansen (1999), and thus exogeneity of the

threshold variable and the regressors is required.17 However, the strict exogeneity assumption

can be challenged by the fact that a ﬁrm’s decisions on investment and liabilities are taken

contemporaneously, and the direction of causality in the debt-investment link might not be

clear.

In our baseline estimation, we use lags to deal with these concerns.

In line with the

literature on panel estimation, in this section, we further address possible endogeneity concerns

by proposing two alternative instrumental variable candidates for our leverage ratio. Suitable

instruments need to be closely correlated with the endogenous regressors but uncorrelated with

the error term εit.

Following Coricelli et al. (2012), we ﬁrst use two-period lags of the regressors. This turns out

to be a suitable instrument candidate given the high correlation of consecutive leverage ratios

(the correlation between the one- and two-year lag of the leverage ratio is 0.87 in our sample),

and the assumption that debt reported two years before the investment decision does not aﬀect

current investment decisions. (The correlation between the two-year lag of the leverage ratio

and the residuals from the baseline regression of Table 4 is -0.001).

We furthermore undertake a more structural two-step approach and instrumentalise the

debt-to-asset ratio by building on dynamic panel estimation techniques. In doing so, we ﬁrst es-

17Recent studies have developed estimation procedures that directly take endogeneity in regressors and threshold
variables in threshold models into account. See Caner and Hansen (2004) or Kourtellos et al. (2008) for static as
well as Seo and Shin (2016) for dynamic panel threshold models. However, these approaches are not suitable for
the use of large unbalanced panels as the one in this paper.

ECB Working Paper 2101, September 201724Table 6: Sample split across sector

Manufact.

Trade

Accommod. Real Estate

Food/

Constr./

0.816
(0.812 0.816)

0.850
(0.834 0.854)

0.858
(0.615 1.097)

0.796
(0.796 0.802)

0.011
(0.069)
-0.140***
(0.032)

0.014
(0.060)
-0.139***
(0.006)

-0.052
(0.045)
-0.095***
(0.019)

-0.071
(0.051)
-0.089***
(0.032)

Threshold Estimate
γ
95% Conﬁdence Interval
Impact of Debt/Assets
β1 (Low-Debt Regime)

β2 (High-Debt Regime)

Covariates
EBITDA/Debt

Interest Rate Burden

Sales Growth

log(Assets)

EBITDA/Assets

∆Debt/Assets

Tangibility

0.005***
(0.001)
0.000
(0.002)
0.084***
(0.007)
-0.007***
(0.001)
0.273**
(0.109)
0.010
(0.009)
-0.554***
(0.023)
-
Yes
Yes
Yes
549.638
92,49
7,51
97.058

0.016***
(0.002)
-0.010***
(0.002)
0.081***
(0.005)
-0.004***
(0.001)
0.395***
(0.047)
0.003
(0.012)
-0.532***
(0.023)
-
Yes
Yes
Yes
914.038
90,65
9,35
183.613

-0.005
(0.005)
0.004
(0.003)
0.034***
(0.001)
0.018***
(0.001)
0.104***
(0.016)
0.024
(0.025)
-0.347***
(0.045)
-
Yes
Yes
Yes
193.452
82,16
17,84
43.260

0.009***
(0.002)
-0.008***
(0.002)
0.038***
(0.004)
-0.015***
(0.001)
0.254**
(0.115)
0.014
(0.021)
-0.297***
(0.062)
-
Yes
Yes
Yes
736.269
82,61
17,39
162.502

Sector FE
Year FE
Country FE
Regime-Dependent Intercept δ1
Observations
Percent of Obs. In Low-Debt Regime
Percent of Obs. In High-Debt Regime
No. Of Firms
Note: The threshold variable γ is the reported debt-to-asset ratio. Debt is deﬁned as ﬁnancial debt (short-and long-term
loans plus other current ﬁnancial liabilities). The dependent variable net investment is deﬁned as the annual percentage
change in tangible ﬁxed. Sectors are deﬁned according to the NACE-2 broad sector classiﬁcation. All independent
variables are lagged by one period. Heteroskedasticity-robust standard errors of the covariates are reported in brackets.
***p-value < 0.01, **p-value < 0.05, *p-value < 0.1.

ECB Working Paper 2101, September 201725timate a leverage equation including ﬁrm-speciﬁc characteristics as well as instruments related to

lagged observations. We then replace the actual debt-to-asset values in our baseline speciﬁcation

(equation 1) with the ﬁtted values of the estimated leverage model. Due to the persistence in

leverage ratios, we estimate the leverage model by applying a dynamic panel system generalized

method-of-moments (GMM) estimation following Arellano and Bover (1995) and Blundell and

Bond (1998).18 Such speciﬁcations allow for lagged values as well as diﬀerences of the dependent

variable as instruments.

As regards our leverage equation, we follow the existing literature and use a variety of ﬁrm

characteristics identiﬁed to aﬀect ﬁrm leverage (Rajan and Zingales, 1995; Coricelli et al., 2012;

ECB, 2013; Ferrando et al., 2017) as controls.

In stage one, we estimate the following model using the system GMM approach:

Leverageit = ϑ0Leverageit−1 + ϑ1Xit + µi + (cid:37)t + νit

(4)

Vector Xit contains a set of control variables previously identiﬁed as leverage determinants

by the literature19: sales growth, size (deﬁned as log of assets), tangible ﬁxed assets as a share of

total assets, proﬁts (EBITDA/assets), and cash holdings to total assets. We furthermore allow

for ﬁrm and time ﬁxed eﬀects µi and (cid:37)t in the speciﬁcation to control for macroeconomic and

institutional factors aﬀecting leverage decisions.

In our system GMM, we use both lagged levels and ﬁrst diﬀerences of the regressors as

instruments. This assumes that ﬁrst diﬀerences of our instruments are uncorrelated to ﬁrm-

speciﬁc ﬁxed eﬀects. Even though ﬁrm-speciﬁc ﬁxed eﬀects are a source of heterogeneity in our

leverage estimation, we expect leverage changes to be independent of the initial size of leverage

attributed to ﬁrm ﬁxed eﬀects. Whether factors captured by the ﬁxed eﬀects such as location

or operating sector may account for a large share of debt holdings or not, we assume that these

factors do not have a structural impact on the magnitude and direction of leverage changes.

Indeed, we use a system GMM instead of the Arellano and Bond (1991) diﬀerence GMM as

we expect previous changes in debt-to-asset ratios to be good predictors for current leverage

positions, but not vice versa. We estimate model 4 using the Blundell and Bond (1998) two-step

18We decided to use the more demanding system-GMM estimator instead of the standard Arellano and Bond
(1991) diﬀerence-GMM model as results drawn from this approach indicated a severe weak-instrument problem
(Blundell and Bond, 1998). The coeﬃcient on the lagged dependent variable turned out to be substantially lower
in respective speciﬁcations.

19See for instance ECB (2013) or Ferrando et al. (2017).

ECB Working Paper 2101, September 201726system GMM speciﬁcation where we use both lagged values of the endogenous variables for the

ﬁrst-diﬀerence equation and lagged values of the ﬁrst diﬀerences of the instruments in the level

equation. Our endogenous variables consist of the lagged leverage variable and the controls

contained in vector Xit and we use, based on results from sequential Arellano-Bond tests for

autocorrelation, the set of available lags starting at lag order two for respective instruments.

Results for the leverage equation are reported in Table 7.

Persistence in the leverage ratio is substantial, as indicated by the large coeﬃcient on lagged

leverage. However, the coeﬃcient is still below one, indicating stationarity in the data generating

process, thus not violating the stationarity assumption necessary for system-GMM (Arellano and

Bover, 1995).

In line with our expectations, sales growth negatively aﬀects leverage. Similarly, both in-

creasing proﬁts and cash holdings negatively aﬀect leverage, in line ﬁndings on pecking-order

behaviour in investment-ﬁnance Myers (1984) according to which ﬁrms rely on internally gen-

erated sources of funding before turning to external ﬁnance. Leverage decreases with ﬁrm size,

as in ECB (2013). Tangible ﬁxed assets have a weak negative impact on leverage.

Finally, results for the Sargan-test of instrument validity indicate an issue already discussed

in Ferrando et al. (2017), who also report test results indicating strong rejection of the instrument

set. Several studies have discussed the reliance on Sargan-test results in panels with dimensions

similar to ours, and highlighted a tendency of strong over-rejection by the Sargan-test.20 We thus

treat the results on the Sargan-test with caution and still use the described set of instruments.

The ﬁtted values from the system-GMM estimation are highly correlated with the actual

leverage ratio (0.86), but show basically zero correlation with the baseline model residuals (-

0.001). In total, we derive about 2.3 million ﬁtted ﬁrm-year observations.

6.2 Results of Robustness Checks

We present all our robustness test results in Table 8. Due to space limitations, we report only

estimations using net investment as the dependent variable.21 For convenience, results for the

baseline regression from Table 3 are reported in the ﬁrst column. In addition to the instrumental

variable approaches discussed above (using two-year lags and ﬁtted values), we report results for

further subsample speciﬁcations on which we estimated the standard model, using our baseline

20See Ding et al. (2013), Benito (2003), or Blundell et al. (1998).
21The main results did not change when gross investment was used. Respective results are available upon

request.

ECB Working Paper 2101, September 201727Table 7: Results leverage regression

System-GMM

Covariates
Leverage (t-1)

Sales Growth

log(Assets)

EBITDA/Assets

Tangibility

Cash Holdings

Constant

0.944***
(0.010)
-0.052***
(0.010)
-0.005***
(0.001)
-0.084**
(0.033)
-0.007*
(0.004)
-0.048**
(0.021)
0.119***
(0.019)
Yes
Yes
2.325.353
609.409
-105.56
0.000
-0.092
0.927
364.781
0.000
55

Firm FE
Year FE
Observations
No. Of Firms
AR(1) Test
P-Value
AR(2) Test
P-Value
Sargan-Test
P-Value
No. Of Instruments
Fitted Values
Correlation Leverage
Correlation Baseline Residuals
No. Of Fitted Values
Note: Results for a leverage equation estimation using
a two-step system-GMM dynamic panel model are re-
ported. We include both lagged values of the endogenous
variables for the ﬁrst-diﬀerence equation and lagged val-
ues of the frst diﬀerences of the instruments in the level
equation. Windmeijer (2005) WC-robust standard errors
are reported in parentheses. ***p-value < 0.01, **p-value
< 0.05, *p-value < 0.1.

0.861
-0.001
2.325.353

ECB Working Paper 2101, September 201728leverage ratio as the regime-dependent regressor.

Both of our instrumental variable regressions conﬁrm the non-linear relationship between

debt and investment. When two-year lags of the right-hand side variables are used, the estimated

threshold is almost identical to the baseline estimate, and results for the covariates are similar

to our baseline model.

In the estimation using the ﬁtted values from the dynamic panel instrument estimation,

the non-linearity in investment behaviour above and below a certain threshold level of debt

is conﬁrmed. Even though the estimated threshold is lower than in the baseline regression,

results for the regime-dependent debt coeﬃcients and the regime-independent covariates are

comparable with the baseline results. We take these results as conﬁrmation for our model setup

and as evidence for an adequate treatment of endogeneity concerns in our framework.

As a signiﬁcant proportion of ﬁrms report zero ﬁnancial debt, we estimate our baseline model

for the subset of observations with positive ﬁnancial debt only. Threshold estimates are not

substantially aﬀected, even though roughly one quarter of observations are excluded. High-debt

ﬁrms still tend to be associated with negative debt-investment linkages, while the corresponding

coeﬃcient for low debt ﬁrms is positive, but insigniﬁcant. We then exclude observations, one

at a time, for ﬁrms reporting less than three employees and observations with investment above

1000 percent. Furthermore, we report results for an estimation of the baseline model where we

deﬁne a ﬁner grid than the 393 bins we use in the baseline estimations. Results show that we

did not distort threshold and conﬁdence interval estimates simply because of excessively wide

bins. Finally, we re-estimate the baseline model by excluding Greek ﬁrms from our sample

as further investigation of the sample for Greece in Amadeus revealed that the distribution of

ﬁrms with respect to our key variables is signiﬁcantly inﬂuenced by outliers, whereas we did not

ﬁnd evidence for outlier dominance in the other country subsamples. The estimation results,

however, are only marginally aﬀected, conﬁrming our main results with respect to the leverage

threshold and the diﬀerences in terms of investment sensitivity for low- and high-debt ﬁrms.

ECB Working Paper 2101, September 201729.
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ECB Working Paper 2101, September 2017307 Conclusion

This paper aims to provide new evidence on the relationship between corporate debt and in-

vestment. We use ﬁrm-level data for ﬁve peripheral euro area countries and explicitly account

for non-linearities in the debt-investment link. Our empirical analysis allows us to endogenously

estimate debt thresholds, instead of relying on a priori assumptions about potential threshold

levels.

The estimations suggest that there is a threshold in the debt-investment link: when debt

is too high, debt overhang distorts investment due to higher default risks and higher costs of

ﬁnancing. We identify this threshold in the region of a debt-to-asset ratio of 80-85 percent. For

ﬁrms with a leverage ratio above this threshold, we ﬁnd evidence consistent with debt holding

back investment. Both the leverage threshold and the negative relation between debt and

investment for high-debt ﬁrms is highly robust across various speciﬁcations. In particular, for

highly leveraged ﬁrms we ﬁnd a negative debt-investment relationship in all major sectors and

for both proﬁtable and unproﬁtable ﬁrms. There is, however, evidence for some heterogeneity

across ﬁrm size. The negative investment impact of high debt is observed for micro, small and

medium-sized ﬁrms (which amount to 99 percent of ﬁrms in our sample), but not for large

ﬁrms, and the role of earnings relative to debt becomes less relevant as ﬁrm size increases. This

suggests that ﬁnancial constraints related to debt overhang play a less important role for large

ﬁrms. The latter result also provides some tentative evidence for the importance of loan supply

restrictions, though further work, e.g. by matching ﬁrm and bank balance sheet data, would be

needed to disentangle the relative contribution of supply and demand eﬀects.

For ﬁrms with debt levels below the threshold, the relationship between debt and invest-

ment is less robust and depends on a number of ﬁrm characteristics and the macroeconomic

environment. While our baseline regression suggests a slight (and insigniﬁcant) positive eﬀect of

debt on investment, during the ﬁnancial crisis period even low debt seems to constitute a drag

on investment, though the negative investment eﬀect is still smaller than for high debt ﬁrms.

Similarly, for smaller ﬁrms or when proﬁtability is low, even low levels of debt have a negative

impact on investment. Our analysis does not take a stance on whether the negative impact of

debt on investment is due to demand or supply eﬀects. The literature identifying the role of

credit conditions on ﬁrm investment suggests that banking shocks to the supply of credit can

have large eﬀects on corporate investment.

ECB Working Paper 2101, September 201731Overall, these results suggest still substantial deleveraging needs in peripheral countries to

support a stronger investment recovery. The economic environment in peripheral countries is

characterised by a large number of small ﬁrms, low productivity and high ﬁnancial uncertainty –

factors which reduce the capacity to tolerate high levels of debt and lead to a more negative debt-

investment relationship. Moreover, our results show that debt overhang is not only reﬂected in

a high stock of debt but also in a low capacity to service the debt (as measured by the EBITDA-

to-debt ratio and the interest expenses-to-EBITDA ratio), which implies that even ﬁrms with

a debt-to-asset ratio below the threshold may be in need of deleveraging if they face high debt

service obligations.

ECB Working Paper 2101, September 201732References

Amador, J. and Nagengast, A. (2016). The eﬀect of bank shocks on ﬁrm-level and aggregate

investment. ECB Working Paper Series, (1914).

Antonecchia, G. and Ferrari, M. (2016). The eﬀect of debt overhang on the investment decisions

of italian and spanish ﬁrms. Paper presented at the Euroframe conference on “Balance sheet

misalignments: Eﬀects and policy implications for the EU economies”, June.

Arellano, M. and Bond, S. (1991). Some tests for speciﬁcation of panel data: Monte carlo evi-

dence and an application to employment equations. Review of Economic Studies, 42(58):277–

297.

Arellano, M. and Bover, O. (1995). Another look at the instrumental variable estimation of

error-components models. Journal of Econometrics, (68):29–51.

Bancel, F. and Mittoo, U. (2004). The determinants of capital structure choice: A survey of

European ﬁrms. Financial Management, (33):103–132.

Barbiero, F., Brutscher, P.-B., Kolev, A., Popov, M., and Wolski, M. (2016). Misallocation of

investment in Europe: Debt overhang, credit market distress, or weak demand? European

Investment Bank, mimeo.

Barbosa, L., Lacerda, A., and Ribeiro, N. (2007). Investment decisions and ﬁnancial standing

of Portuguese ﬁrms. Banco de Portugal Economic Bulletin, Winter, pages 57–74.

Benito, A. (2003). The capital structure decisions of ﬁrms: Is there a pecking order? Bank of

Spain Working Paper Series, (031).

Benito, A. and Hernando, I. (2007). Firm behaviour and ﬁnancial pressure: Evidence from

Spanish Panel Data. Bulletin of Economic Research, 59(4):283–311.

Bernanke, B. S., Gertler, M., and Gilchrist, S. (1999). The ﬁnancial accelerator in a quanti-

tative business cycle framework. In Taylor, J. B. and Woodford, M., editors, Handbook of

Macroeconomics. Elsevier.

Bick, A. (2010). Threshold eﬀects of inﬂation on economic growth in developing countries.

Economic Letters, 108(2):126–129.

ECB Working Paper 2101, September 201733Blundell, R. and Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel

data models. Journal of Econometrics, 87(1):115–143.

Blundell, R., Bond, S., and Windmeijer (1998). Initial conditions and moment restrictions in

dynamic panel data models. Journal of Econometrics, 87(1):115–143.

Bornhorst, F. and Ruiz-Arranz, M. (2015). Indebtedness and deleveraging in the euro area. In

Brooks, P. K. and Pradhan, M., editors, The mechanics of a strong euro area - IMF policy

analysis. International Monetary Fund.

Brounen, D., De Jong, A., and Koedijk, K. (2004). Corporate ﬁnance in Europe: Confronting

theory with practice. Financial Management, 33:71–101.

Buca, A. and Vermeulen, P. (2017). Corporate investment and bank-dependent borrowers during

the recent ﬁnancial crisis. Journal of Banking and Finance, 78:164–180.

Caner, M. and Hansen, B. E. (2004). Instrumental variable estimation of a threshold model.

Econometric Theory, 20:813–843.

Cecchetti, S., Mohanty, F., and Zampolli, F. (2011). The real eﬀects of debt. BIS Working

Paper, 352.

Cingano, F., Manares, F., and Sette, E. (2016). Does credit crunch investment down? New

evidence on the real eﬀects of the bank-lending channel. Review of Financial Studies,

29(10):2737–2773.

Coluzzi, C., Ferrando, A., and Martinez-Carrascal, C. (2015). Financing obstacles and growth:

An analysis for euro area companies. The European Journal of Finance, 21(10-11):773–790.

Consolo, A., Pierluigi, B., and Staraci, G. (2017). Measuring Equilibrium Debt for the NFC

sector across euro area countries. mimeo.

Coricelli, F., Driﬃeld, N., Pal, S., and Roland, I. (2012). When does leverage hurt productivity

growth? A ﬁrm-level analysis. Journal of International Money and Finance, 31(6):1674–1694.

Cuerpo, C., Drumond, I., Lendvai, J., Pontuch, P., and Raciborsk, R. (2013). Indebtedness,

deleveraging dynamics and macroeconomic adjustment. Economic Papers, 477.

ECB Working Paper 2101, September 201734Ding, S., Guariglia, A., and knight, J. (2013). Investment and ﬁnancing constraints in China:

Does working capital make a diﬀerence? Journal of Banking and Finance, 37(5):1490–1507.

ECB (2013). Corporate ﬁnance and economic activity in the euro area. Structural Issues Report

2013. ECB Occasional Paper, 151.

European Commission (2015). User guide to the SME deﬁnition.

Ferrando, A., Marchica, M. T., and Mura, R. (2017). Financial Flexibility and Investment

Ability Across the Euro Area and the UK. European Financial Management, 23(1):87–126.

Gala, V. and Brandon, J. (2016). Firm size and corporate investment.

Gertler, M. and Gilchrist, S. (1994). Monetary policy, business cycles, and the behavior of small

manufacturing ﬁrms. The Quarterly Journal of Economics, 109(2):309–340.

Goretti, M. and Souto, M. (2013). Macro-ﬁnancial implications of corporate (de-)leveraging in

the euro area periphery. IMF Working Paper, (13/154).

Graham, J. R. and Harvey, C. R. (2001). The theory and practice of corporate ﬁnance: Evidence

from the ﬁeld. Journal of Financial Economics, 60(2):187–243.

Grossman, S. and Hart, O. (1982). Corporate ﬁnancial structure and managerial incentives. In

McCall, J. J., editor, The Economics of Information and Uncertainty. University of Chicago

Press.

Hansen, B. E. (1999). Threshold eﬀects in non-dynamic panels: Estimation, testing, and infer-

ence. Journal of Econometrics, 93(2):345–368.

Hansen, B. E. (2000). Non-parametric data dependent bootstrap for conditional moment model.

Econometrica, 68(3):575–603.

Hernando, I. and Martinez-Carrascal, C. (2008). The impact of ﬁnancial variables on ﬁrms real

decisions: evidence from Spanish ﬁrm-level data. Journal of Macroeconomics, 30(1):543–561.

J¨ager, A. (2003). Corporate balance sheet restructuring and investment in the euro area. IMF

Working Paper, (03/117).

Jensen, M. C. and Meckling, W. H. (1976). Theory of the ﬁrm: Managerial behaviour, agency

costs and ownership structure. Journal of Financial Economics, 3(4):305–360.

ECB Working Paper 2101, September 201735Kalemli- ¨Ozcan, S., Laeven, L., and Moreno, D. (2015a). Debt Overhang, Rollover Risk and

Investment in Europe. Mimeo, ECB and University of Maryland.

Kalemli- ¨Ozcan, S., S, B., Villegas-Sanchez, C., Volosovych, V., and Yesiltas, S. (2015b). How to

construct nationally representative ﬁrm level data from the ORBIS Global Database. NBER

Working Paper, (21558).

Kourtellos, A., Stengos, T., and Tan, C. M. (2008). THRET: Threshold regression with en-

dogenous threshold variables. The Rimini Centre for Economic Analysis Working Paper,

(05/08).

Kremer, S., Bick, A., and Nautz, D. (2013). Inﬂation and growth: new evidence from a dynamic

panel threshold analysis. Empirical Economics, 44(2):861–878.

Leland, H. E. and Pyle, D. H. (1977).

Informational asymmetries, ﬁnancial structure, and

ﬁnancial intermediation. The Journal of Finance, 32(2):371–387.

Martinez-Carrascal, C. and Ferrando, A. (2008). The impact of ﬁnancial position on investment:

An analysis for non-ﬁnancial corporations in the Euro Area. ECB Working Paper Series, 943.

Modigliani, F. and Miller, M. H. (1958). The cost of capital, corporation ﬁnance, and the theory

of investment. American Economic Review, 48(3):261–297.

Modigliani, F. and Miller, M. H. (1963). Corporate income taxes and the cost of capital: A

correction. American Economic Review, 53(3):437–434.

Myers, S. C. (1977). Determinants of corporate borrowing. Journal of Financial Economics,

5(2):147–175.

Myers, S. C. (1984). The capital structure puzzle. The Journal of Finance, 39(3):575–592.

Pal, R. and Ferrando, A. (2010). Financing constraints and ﬁrms cash policy in the euro area.

The European Journal of Finance, 16(2):153–171.

Rajan, R. G. and Zingales, L. (1995). What do we know about capital structure? Some Evidence

from International data. The Journal of Finance, 50(5):1421–1460.

Reinhart, C. and Rogoﬀ, K. (2010). Debt and growth revisited.

ECB Working Paper 2101, September 201736Ross, S. (1977). The Determination of ﬁnancial structure: the incentive-signalling approach.

The Bell Journal of Economics, 8(1):23–40.

Seo, M. H. and Shin, Y. (2016). Dynamic panels with threshold eﬀect and endogeneity. Journal

of Econometrics, 195(2):169–186.

Stiglitz, J. E. and Weiss, A. (1981). Credit rationing in markets with imperfect information.

American Economic Review, 71(3):393–410.

Storz, M., Koetter, M., Setzer, R., and Westphal, A. (2017). Do we want these two to tango?

On zombie ﬁrms and stressed banks in Europe. IWH Discussion Papers, 13.

Vermeulen, P. (2002). Business ﬁxed investment: Evidence of a ﬁnancial accelerator in Europe.

Oxford Bulletin of Economics and Statistics, 64(3):217–235.

Windmeijer, F. (2005). A ﬁnite sample correction for the variance of linear eﬃcient two-step

GMM estimators. Journal of Econometrics, 126(1):25–51.

ECB Working Paper 2101, September 201737Acknowledgements 

We thank Julien Brault, Franziska Bremus, Annalisa Ferrando, Anton Jevcak, Beatrice Pierluigi, Manuela Storz, Isabel Vansteenkiste, an 
anonymous referee and participants at the EIB ECON seminar, the Empirical Macroeconomics Research Seminar at FU Berlin, and the 
Eurosystem WGF meeting for useful comments. Views and conclusions expressed in this paper are those of the authors alone and 
cannot be attributed to the DIW Berlin or the European Central Bank. 

Stefan Gebauer 
German Institute for Economic Research, DIW Berlin, Germany; email: sgebauer@diw.de 

Ralph Setzer 
European Central Bank, Frankfurt am Main, Germany; email: ralph.setzer@ecb.europa.eu 

Andreas Westphal 
European Central Bank, Frankfurt am Main, Germany; email: andreas.westphal@ecb.europa.eu 

© European Central Bank, 2017 

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ISSN 
ISBN 

1725-2806 (pdf) 
978-92-899-2823-6 (pdf)

DOI 
EU catalogue No 

10.2866/987849 (pdf) 
QB-AR-17-113-EN-N (pdf)