Medium-term investment responses to activity shocks : the role of corporate debt.
Date: 2022-11-22
Institutions: European Central Bank
Subjects: business policy, corporate finance, debt, international finance, investment

Rodrigo Barrela, Paloma Lopez-Garcia, 
Ralph Setzer 

Working Paper Series 

Medium-term investment responses 
to activity shocks: the role of corporate 
debt 

No 2751 / November 2022 

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 analyses the implications of corporate indebtedness for investment following large 

economic shocks. The empirical analysis is based on a large Orbis-iBACH firm-level data set 

for euro area countries from 2005 to 2018. Our results suggest that investment of high-debt 

firms is significantly depressed for an extended period in the aftermath of economic crises. In 

the four years after a negative economic shock, the cumulative loss of capital of high-debt firms 

is around 15% higher than that of firms with lower debt burdens. The negative impact of high 

debt on investment is most evident for firms in Southern and Eastern Europe and for micro 

firms. These findings suggest a potentially significant negative impact of increased corporate 

indebtedness on investment in the post-COVID-19 recovery. 

JEL codes: E22, F34, G31, G32 

Keywords: Corporate debt, leverage, investment, local projections, COVID shock 

ECB Working Paper Series No 2751 / November 20221Non-technical summary  

The corporate sector has been hit hard by the COVID-19 pandemic and the associated 

containment measures and supply disruptions. The rapid pace of the decline in revenue in 

conjunction with the difficulty of adjusting costs sufficiently led to a sudden increase in 

liquidity needs. Notably firms operating in sectors that were sensitive to lockdown measures 

had to resort to additional borrowing via loans and securities. While the additional lending 

helped covering corporates’ increased liquidity needs, it also increased corporate debt levels 

which had already been high in several euro area countries before the pandemic. 

We investigate whether the high corporate debt burden in many euro area countries holds 

back investment of the non-financial corporate sector in the aftermath of economic crises. 

Our analysis is based on a comprehensive firm-level data set including small private firms 

from the ORBIS and iBACH databases. The dataset consists of more than 15 million 

observations, covering firms in 14 euro area countries over the period from 2005 to 2018. We 

construct resampling weights to improve representativeness. We assess the differential 

impact on investment of different levels of leverage. We condition on country-sector-specific 

cyclical conditions using two-digit industry value added growth and control for time-invariant 

sector and country characteristics as well as for cross-country common shocks. We also 

control for firm characteristics such as size, age, profits, sales growth, interest burden and 

cost of capital. 

Our results highlight the important role of firm balance sheet factors for investment. We find a 

strong interaction between firm indebtedness and investment amid activity shocks. Firms with 

higher leverage reduce investment significantly more than their peers with lower debt. Over 

the four years after a large economic contraction, the growth rate of tangible fixed capital of 

high-debt firms is some 15 percentage points below that of their counterparts with lower debt 

burdens. This result is most pronounced for micro firms, which experience a sustained fall in 

investment post-crisis. Highly-indebted micro firms reduce their capital by close to one fifth in 

the four years after a shock, which compares to some eight percent in the case of small and 

medium-sized firms.  Investment by larger firms, which typically face lower financial frictions, 

is not significantly influenced by higher leverage.  

There are also significant cross-country differences. The effect of high leverage on 

investment is strongest for firms in Southern and Eastern European countries, possibly due 

ECB Working Paper Series No 2751 / November 20222to weaker banks and less efficient insolvency frameworks. Our main findings are robust to 

alternative definitions of investment and different computations of activity shocks.  

Applying our findings to the COVID-19 crisis, a simple back-of-the-envelope calculation 

suggests that the COVID-19 shock may lead to a 5% decline in the aggregate stock of 

tangible fixed assets by 2024. The decline stems mostly from the large fall in investment of 

high-debt firms, which are estimated to reduce their capital by one fifth over this period. By 

contrast, low-debt firms show greater resilience to the shock and increase their capital.  

Our results question the capacity of the corporate sector to promote the recovery from the 

COVID-19 crisis via an increase in capital spending. The ramifications of the Russian war in 

Ukraine, the fight against climate change and the digital transition will also require large-scale 

corporate investments. Addressing these challenges warrants structural policies to address 

concerns about corporate debt overhang and strengthen the resilience of the corporate 

sector to future shocks. Failing to address the high leverage of SMEs could lead to an 

increase in the number of non-viable firms with subdued investment prospects. Moreover, 

strengthening the incentives for equity financing would be conducive to sustainable 

investment growth in euro area countries.   

ECB Working Paper Series No 2751 / November 20223 
1.  Introduction  

The  corporate  sector  has  been  hit  hard  by  the  COVID-19  pandemic.  In  most  euro  area 

countries non-financial corporations substantially increased their debt since the end of 2019. 

For  the  euro  area  as  a  whole,  the  outstanding  amount  of  corporate  debt  increased  by  5 

percentage points between the end of 2019 and end-2021, to 84% of GDP, thereby coming 

close to the historical peak before the COVID-19 crisis. Notably firms operating in sectors that 

were sensitive to lockdown measures had to resort to additional borrowing, often compounded 

by policy responses to the pandemic, which tended to focus on instruments that enabled firms 

to  increase  their  leverage  (for  instance  through  direct  lending  or  loan  guarantees).  This 

additional financing has helped covering corporates’ increased liquidity needs as cash-flows 

plummeted, limited the number of insolvencies and preserved employment. At the same time, 

however, it increased concerns about undercapitalisation. Small and medium-sized enterprises 

(SMEs) in particular were affected due to their higher prevalence in COVID-sensitive sectors 

(such as tourism), more difficult access to finance and lower capital and liquidity buffers (OECD 

2020, Nicoletti et al. 2022). 

The legacy of high debt could negatively impact on the economic recovery in the medium-term. 

The corporate finance literature suggest that high debt has a bearing on corporate investment 

(Jensen  and  Meckling  1976,  Myers  1977,  Bernanke  and  Gertler  1999).  High  corporate 

indebtedness implies higher interest expenses and thus fewer funds available for investment. 

Firms with high leverage also find it harder to obtain new funds from external financing sources 

due to their higher default risk. Moreover, the desire to repair weak balance sheets leads firms 

to  deleverage,  and  thereby  forgo  investment  opportunities,  potentially  also  with  negative 

implications for profits. 

A negative link between high debt and investment is well established empirically by the large 

body of literature that investigates the link between indebtedness and investment at the firm 

level. Several studies have shown that high debt can exacerbate constraints on firms' access 

to  credit,  which  restrains  firm  investment.  Many  economists  have  suggested  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,  Durante  et  al. 

2022). The empirical literature also finds non-linear effects of debt on investment (Martinez-

Carrascal  and  Ferrando  2008,  Goretti  and  Souto  2013,  Popov  et  al.  2018,  ,  Rodriguez 

Palenzuela and Dees 2016, Gebauer et al. 2018, Auer et al. 2021, Kalemli-Özcan et al. 2022).  

ECB Working Paper Series No 2751 / November 20224To the best of our knowledge, the empirical literature has however not analysed the medium- 

and  long-term  impact  of  activity  shocks  on  investment  for  firms  at  different  points  of  the 

leverage distribution. Previous recessions have indicated strong, persistent effects of negative 

activity shocks on the growth of capital, especially of intangibles. For example, in spite of easy 

financing conditions, investment was rather weak in the aftermath of the Great Recession, with 

investment only recovering to pre-crisis levels after seven years (Figure 1). It has also been 

shown  that  investment  was  hit  hardest  in  those  countries  that  had  accumulated  the  largest 

debt imbalances during the pre-crisis credit boom (Lane and Milesi-Ferretti 2011).  

Figure 1 – Net investment rates in the euro area  

Source: Eurostat.  
Note: Net investment is computed as the differences between gross fixed capital 
fornation and consumption of fixed capital over value added.  

This  paper  builds  on  these  considerations  and  investigates  the  link  between  firm-level 

indebtedness and investment following large economic shocks. In particular, we address the 

following questions: What are the implications of corporate indebtedness for investment in a 

crisis? Does high corporate indebtedness provide a drag on investment in the recovery phase? 

Which structural features determine the investment sensitivity to debt (e.g. firm size, sector, 

country-specific framework conditions)?  

Our  findings  suggest  that  investment  of  high-debt  firms  is  significantly  depressed  for  an 

extended  period  following  an  economic  crisis.  In  the  four  years  after  a  large  economic 

contraction, investment of high-debt firms is some 15% below that of their counterparts with 

lower debt burdens. This result is driven in particular by micro firms and by firms in Southern 

European  countries,  which  experience  a  more  protracted  fall  in  investment  post-crisis  than 

00.0050.010.0150.020.0250.030.0350.0420052006200720082009201020112012201320142015201620172018(GFCF -CFC)/VAECB Working Paper Series No 2751 / November 20225 
 
larger firms. Applying these findings to the COVID-19 crisis suggests that the COVID-19 shock 

may lead to a 5% drop in the aggregate stock of tangible fixed assets by 2024.  

The  remainder  of  the  paper  is  structured  as  follows:  Section  2  presents  the  data  set  and 

provides some illustration of the descriptive statistics on the debt and investment link. In section 

3, we report results of the baseline model estimations on the debt-investment nexus and then 

provide evidence on structural differences according to firm size and region. Section 4 presents 

robustness  tests,  including  an  instrumental  variable  approach  based  on  dynamic  panel 

estimations. We conclude in Section 5. 

2.  Data and descriptive statistics 

Our analysis is based on firm-level data from fourteen euro area countries covering the period 

from 2005 to 2018 using the Bureau Van Dijk’s Orbis and the European Committee of Central 

Balance  Sheet  Data  Offices’  BACH  (Bank  for  the  Accounts  of  Companies  Harmonised) 

datasets,  which  report  balance  sheet  and  profit-and-loss  data  for  both  listed  and  unlisted 

companies in the euro area (BACH working group 2015). For ORBIS, historical vintages are 

used to improve the representativeness of the data and reduce the sampling bias, as explained 

in  Kalemli-Özcan  et  al.  (2019).  For  the  six  countries  for  which  iBACH  data  is  available  – 

Belgium, France, Italy, Spain, Portugal, and Slovakia – the ORBIS and iBACH datasets are 

merged to improve coverage. Whenever a duplicate firm is observed, we keep the one from 

ORBIS. 

For the construction of our dataset, we closely follow Kalemli-Özcan et. al (2019) and Gopinath 

et.  al  (2017)  to  pursue  a  standard  cleaning  procedure.  In  particular,  we  keep  only 

unconsolidated accounts, corporate firms, and remove sole proprietorships.1 We restrict our 

analysis to non-financial and non-governmental sectors (NACE Rev. 2 codes B to N except K), 

and remove firms in the mining and quarrying (B) and real estate (L) sectors. In addition, we 

remove firm-year observations with inconsistent balance sheet or income statement relations, 

including those with negative asset holdings (see Appendix for details). We also exclude five 

euro  area  countries  for  which  limited  data  was  available  (Cyprus,  Latvia,  Lithuania, 

Luxembourg and Malta). 

Furthermore, due to the dynamic nature of our econometric analysis, we keep only firms with 

at least five consecutive years of reporting. Finally, all variables are winsorised at the 1st and 

1The inclusion of consolidated accounts would combine the financials of subsidiaries across different countries and industries and thus 

complicate comparisons across countries and sectors. 

ECB Working Paper Series No 2751 / November 2022699th percentiles, except for the investment ratios, which are winsorised at the 95th percentile 

to  account  for  the  variables’  fat  tail  distribution.  The  resulting  dataset  covers  approximately 

three  million  firms  and more  than  15 million  firm-year  observations from fourteen  euro area 

countries for the period from 2005 until 2018.  

A  well-known  concern  with  Orbis  data  is  the  poor  representativeness  of  the  data  for  some 

countries  and  sectors.  In  Table  A1,  we  benchmark  our  dataset  with  Eurostat’s  Structural 

Business Statistics data which shows the total population of firms. The coverage of our dataset 

is highly heterogeneous, varying from 8% of the firms’ population in Ireland to more than 90% 

in Portugal. In order to replicate the national account aggregates of fixed tangible assets (the 

relevant variable in our study) and improve the representativeness of the data we use inverse 

population weights according to the following formula:  

 𝑤𝑖 =

𝐸𝑢𝑟𝑜𝑠𝑡𝑎𝑡
𝑆𝑎𝑚𝑝𝑙𝑒  

𝑇𝐹𝐴𝑆
𝑇𝐹𝐴𝑆

where 𝑤𝑖 is the weight of firm 𝑖 in our dataset and 𝑆 is the country-sector (one-digit) in which 

the firm operates. Thus, the weight of a firm in a given sector is the ratio between the total fixed 

assets of the sector in Eurostat and in our sample (see Table A2 for more information on the 

sectoral distribution of assets). 

Our main dependent variable of interest is investment, defined as the annual change in tangible 

fixed  assets  over  previous  year’s  tangible  fixed  assets.  The  reasons  for  focusing  on  net 

investment  as  opposed  to  gross  investment  (net  investment  plus  depreciation)  are  twofold. 

First, net investment is more relevant from a policy point of view than gross investment given 

its close link to an economy’s level of productivity (Kalemli-Özcan, Laeven and Moreno 2022). 

Second, due to different reporting standards, the depreciation variable in our dataset is non-

harmonised  and  contains  many  missing  values.  The  available  variable,  “Depreciation  and 

Amortization”, refers, in some cases, to tangible fixed assets depreciation only, while in other 

cases it also includes amortization of intangibles. The use of gross investment could therefore 

introduce an additional degree of measurement error to our dataset. This notwithstanding, we 

conduct robustness  checks  using  both  gross  and net  investment  by resorting  to  EUKLEMS 

depreciation  rates,  which  are  available  at  the  country-sector  level  and  allow  us  to  discern 

between average depreciation rates for tangibles and intangibles assets. This comes at the 

cost of introducing some measurement error at the firm level, which explains some negative 

gross investment ratios. 

ECB Working Paper Series No 2751 / November 20227The other key variable of interest is the debt ratio, which is defined as the ratio of long-term 

debt and other current liabilities (mainly pension liabilities, accounts payable and trade credit) 

to total assets.  

In  the  baseline  regressions  we  also  control  for  firms’  size,  age,  profits,  sales  growth,  and 

interest  burden.  In  addition,  we  include  the  country-level  cost  of  borrowing  for  corporations 

from the ECB MFI interest rate statistics to control for the cost of capital.  

Table 1 – Firm variable definitions 

Variable 

Definition 

Net Tangible 
Investment  

Net Intangible 
Investment  

Annual change in tangible fixed assets over previous year’s tangible fixed 
assets (in percent) 

Annual  change  in  intangible  fixed  assets  over  previous  year’s  intangible 
fixed assets (in percent) 

Net Total Investment  

Annual change in total fixed assets over previous year’s total fixed assets 
(in percent) 

Gross Tangible 
Investment  

Gross Intangible 
Investment  

Annual  change  in  tangible  fixed  assets  plus  depreciation  over  previous 
year’s tangible fixed assets (in percent) 

Annual change in intangible fixed assets plus depreciation over previous 
year’s intangible fixed assets (in percent) 

Gross Total Investment   Annual change in total fixed assets plus depreciation over previous year’s 

total fixed assets (in percent) 

Debt Ratio 

Ratio of current liabilities plus long-term debt over total assets 

Size  

Age 

Logarithm of total assets 

Current year minus the year of incorporation 

Interest Burden 

Ratio of interest payments to EBITDA 

Profitability 

Ratio  of  earnings  before  interest,  taxes,  depreciation  and  amortisations 
(EBITDA) over total assets 

Sales Growth 

Annual growth rate of sales 

Tables  1  and  2  present  the  definitions  and  summary  statistics  of  our  regression  variables. 

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  5  percent,  the  median  firm’s  net 

investment rate is a negative 7 percent. Firms’ debt-to-asset ratio amounts to 70 percent on 

average.  

ECB Working Paper Series No 2751 / November 20228 
Table 2 – Summary statistics 

P10 

P50 

P90 

Mean 

Net Total Investment Ratio 
Net Tangible Investment Ratio 
Net Intangible Investment Ratio 
Gross Total Investment Ratio 
Gross Tangible Investment Ratio 
Gross Intangible Investment Ratio 
Gross Total Investment Ratio 
Debt-to-asset Ratio 
Size (log assets) 
Age (years) 
Interest payments (net) to EBITDA 
Profitability 
Sales Growth 

-0.40 
-0.42 
-0.92 
-0.28 
-0.33 
-0.65 
-0.28 
0.19 
10.99 
2.00 
-0.07 
-0.06 
-0.34 
Note: All variables are expressed in real terms using the OECD STAN’s price deflators, see Gal 
(2013). Number of observations: 15,618,397. Note that the number of observations in our empirical 
exercises below is lower as not all variables are available for all firm-years.  

0.04 
0.05 
-0.01 
0.17 
0.14 
0.27 
0.17 
0.69 
13.04 
14.65 
0.11 
0.09 
0.16 

0.67 
0.74 
0.48 
0.79 
0.83 
0.75 
0.79 
1.03 
15.22 
31.00 
0.46 
0.28 
0.50 

-0.06 
-0.07 
-0.09 
0.04 
0.01 
0.19 
0.04 
0.66 
12.95 
11.00 
0.05 
0.08 
0.01 

Std. 
Deviation 
0.55 
0.60 
1.23 
0.55 
0.60 
1.23 
0.55 
0.51 
1.66 
13.60 
0.47 
0.19 
1.05 

Figure 2 provides a first indication that leverage becomes a constraint for investment beyond 

certain thresholds. Firms’ debt ratios are classified into percentiles of the historical (2005-2018) 

debt ratio distribution. Percentiles are calculated at the two-digit sectoral level as some sectors 

(such as construction) have structurally higher debt ratios than other sectors (trade, services). 

For lower percentiles of debt, any extra degree of leverage is associated with highly positive 

increase  in  the  investment  ratio.  As  we  go  along  the  distribution,  this  relationship  becomes 

weaker, eventually turning negative at around the 80th percentile of the debt ratio distribution. 

Interestingly, this hump-shaped curve is consistent across sectors (Figure A1), regions (Figure 

A2), and size-classes (Figure A3). As such, it is possible to distinguish three types of firms: 

low-debt firms, which are below the 20th percentile of the debt ratio distribution; medium-debt 

firms, which are positioned between the 20th and the 80th percentile; and high-debt firms, which 

record debt ratios above the 80th percentile of the leverage distribution. Throughout the paper, 

we refer to these groups as low-, medium-, and high-debt firms, respectively. 

ECB Working Paper Series No 2751 / November 20229 
  
 
 
Figure 2 – Debt ratio and net tangible investment 

Note: The percentiles of the debt-to-assets ratio are 
defined at the two-digit sector level for a sample of 14 
euro area countries from 2005 to 2018. The investment 
ratio is defined as the annual change in tangible fixed 
assets over previous year’s tangible fixed assets. 

Figure 3 plots net tangible investment over time for the three different types of firms. While 

the global financial crisis led to large negative effects on investment for all firms, the 

subsequent recovery was heterogeneous. Even though investment remained below pre-crisis 

levels for all types of firms, low- and medium-debt firms recovered faster than their high-debt 

counterparts whose investment ratio turned positive again only in 2015.  

Figure 3 – Net tangible investment ratio by firm leverage 

Note: Low debt firms are below the 20th percentile of the debt ratio distribution; 
medium-debt firms are positioned between the 20th and the 80th percentile; and 
high-debt firms record debt ratios above the 80th percentile of the leverage 
distribution. The leverage distribution is defined at the sectoral level.  

-25%-20%-15%-10%-5%0%5%10%15%020406080100Debt Ratio percentilesNet tangible investmentratio-0.10-0.050.000.050.100.150.202006200720082009201020112012201320142015201620172018Low debtMedium debtHigh debtECB Working Paper Series No 2751 / November 202210 
 
 
In the following, we investigate whether Figure 2 simply reports a correlation that fades after 

controlling for country-, sector- and firm-specific characteristics, such as sales, profits, or 

size. To do so, we run the following OLS regression 

𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡 𝑅𝑎𝑡𝑖𝑜𝑖,𝑡 = 𝛼 + 𝛽𝐷𝑒𝑏𝑡 𝑅𝑎𝑡𝑖𝑜𝑖,𝑡−1 +  𝜸 ∑

𝑙∈𝐷𝑒𝑏𝑡 𝐵𝑖𝑛𝑠

D𝑙𝑖,𝑡−1

+ 𝝍 ∑

𝑙∈𝐷𝑒𝑏𝑡 𝐵𝑖𝑛𝑠

D𝑙𝑖,𝑡−1 ×

𝐷𝑒𝑏𝑡 𝑅𝑎𝑡𝑖𝑜𝑖,𝑡−1 + 𝛀 𝑋𝑖,𝑡−1 +  𝜙𝑆𝑒𝑐𝑡𝑜𝑟 𝑉𝐴 𝑔𝑟𝑜𝑤𝑡ℎ𝑖,𝑡−1 +  𝐶𝑜𝑢𝑛𝑡𝑟𝑦𝑖 + 𝑆𝑒𝑐𝑡𝑜𝑟𝑖 + 𝑌𝑒𝑎𝑟𝑖 + 𝑢𝑖,𝑡   (1) 

where Investment Ratio refers to the net tangible investment ratio, and 𝐷𝑒𝑏𝑡 𝐵𝑖𝑛𝑠 ∈ {low-

debt, high-debt}, 𝐷𝑙 = 1 if the firm belongs to debt bin 𝑙 and 𝐷𝑙 = 0 otherwise. The reference 

group is medium-debt firms. We add a vector of covariates 𝑋𝑖 that includes firm profitability, 

size, age, interest burden, sales growth and the country-specific lending rate to corporations. 

We furthermore include controls for country-, two-digit-sector-, and year-fixed effects to 

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

regressors, and to take macroeconomic factors into account. We lag all time-varying right-

hand-side variables by one year to address endogeneity concerns.  

Note that, as in equation (1), we do not control for firm-specific fixed effects. The inclusion of 

firm fixed-effects would risk confounding our key coefficient of interest, 𝜷, as firm’ debt bins, 

𝐷𝑙, have little time variation. However, the rich set of firm-specific controls coupled with the 

country-, two-digit sector-, and year-fixed effects mitigate concerns about individual 

unobserved heterogeneity. 

The results in Table 3 confirm the unconditional correlations and highlight the importance of 

accounting for different debt bins. The first column estimates the average impact of leverage 

on investment (that is, without considering different debt bins), suggesting a negative 

relationship between debt and investment. Column 2 allows for the elasticity of investment to 

leverage to vary for highly leveraged firms relative to the rest of firms and column 3 

distinguishes between low-, medium- and high-debt firms. The results support the view that 

firm’s leverage ratio affects its investment in a non-linear way. The results are also 

economically significant. While firms with moderate debt levels increase their investment by 

1.76 to 2.59 pp in the year after a 1 pp increase in the debt ratio, high-debt firms decrease it 

by 2.96 to 3.29 percentage points (Table 3).  

ECB Working Paper Series No 2751 / November 202211 
 
 
Table 3 – OLS regression results 

Debt Ratio 

High-debt = 1 

High-debt x Debt Ratio 

Low-debt = 1 

Low-debt x Debt Ratio 

Sector Value Added 
Growth 

Profits 

Sales Growth 

Size 

Age 

Lending Rate 

Interest Burden 

Constant 

(1) 
-.0125*** 
(.0006) 

.0325** 
(.0103) 

.2690*** 
(.0016) 

.0004** 
(.0001) 

.0061*** 
(.0001) 

-.0013*** 
(.0002) 

-2.1261*** 
(.0533) 

4.95e-07 
(6.04e-07) 

.0431*** 
(.0028) 

(2) 
.0259*** 
(.0009) 

.0318*** 
(.0016) 

-.0585*** 
(.0015) 

.0325** 
(.0102) 

.2588*** 
(.0016) 

.0004** 
(.0001) 

.0053*** 
(.0001) 

-.0013*** 
(.0002) 

-2.1011*** 
(.0533) 

4.63e-07 
(6.15e-07) 

.0332*** 
(.0028) 

(3) 
.0176*** 
(.0016) 

.0263*** 
(.0018) 

-.0505*** 
(.0019) 

-.0140*** 
(.0015) 

.0399*** 
(.0047) 

.0325** 
(.0103) 

.2578*** 
(.0016) 

.0004** 
(.0001) 

.0054*** 
(.0001) 

-.0013*** 
(.0002) 

-2.1001*** 
(.0533) 

4.66e-07 
(6.14e-07) 

.0387*** 
(.0030) 

.0269 
13,210,039 
Yes 
Yes 
Yes 

R-squared 
Observations 
Country FE 
Sector FE 
Year FE 
Notes: Dependent variable: net tangible investment ratio. Standard errors clustered at 
the firm-level. The reference group is the medium-debt bin. Sector value added 
growth defined for the 2-digit level aggregation available in Eurostat National 
Accounts. Standard errors in brackets. * p < 0.1; ** p < 0.05; *** p < 0.01. 

.0271 
13,210,039 
Yes 
Yes 
Yes 

.0271 
13,210,039 
Yes 
Yes 
Yes 

3.  Empirical Methodology and Results 

3.1. Aggregate impact and heterogeneous effects of debt 

In the next step, we estimate the dynamic effects of firms’ debt ratio on corporate investment 

in the aftermath of large economic shocks. The objective is to analyse how investment evolves 

ECB Working Paper Series No 2751 / November 202212 
 
 
 
 
 
 
 
after boom and after bust periods. To do this, we resort to panel regressions using the local 

projections framework developed by Jordà (2005) and recently applied by Cloyne et al. (2018), 

Jeenas (2019) and Ottonello and Winberry (2020) in a similar context.  

We  start  by  assessing  whether  positive  and  negative  shocks  have  a  symmetric  impact  on 

investment. In this first exercise we pool all firms together. The estimated equation is as follows: 

Δℎ𝑦𝑖,𝑡+ℎ = 𝛼 + 𝜓1𝜀𝑃𝑡  + 𝜓2𝜀𝑁𝑡 + 𝛀𝑿𝑖, 𝑡−1 + 𝐶𝑜𝑢𝑛𝑡𝑟𝑦𝑖 + 𝑆𝑒𝑐𝑡𝑜𝑟𝑖 + 𝑌𝑒𝑎𝑟𝑖 + 𝑢𝑖,𝑡+ℎ                         (2) 

with ℎ = 0,   … ,  4 𝑎𝑛𝑑  Δℎ 𝑦𝑖,𝑡+ℎ   ≡  

𝐾𝑡+ℎ − 𝐾𝑡−1
𝐾𝑡−1

, with 𝐾𝑡 being tangible fixed assets. The two shock 

variables,𝜀𝑁𝑡  and  𝜀𝑃𝑡,  are  dummy  variables  that  capture  negative  or  positive  shocks  to 
economic activity, respectively, with 𝜀𝑁𝑡(𝜀𝑃𝑡) equalling one if the two-digit sector value added 
growth is inferior to (exceeds) the country-sector-specific average historical (2005-2018) value-

added  growth  by  1.5  standard  deviations  (see  Tables  A4  and  A5  for  summary  statistics  of 

value-added  growth).  The  choice  of  magnitude  of  the  shock  is  based  on  two  criteria:  i)  our 

shock variable should capture only the largest changes in value added; and ii) there should be 

sufficient variability across countries, years and sectors. In Table A6 we show three different 

options considered and their respective impact on the number of firms affected by a shock in 

each year. The choice of 1.5 standard deviations from the average reflects the optimisation of 

the above criteria.  

The  vector  of  control  variables,  𝑿𝑖,𝑡−1,  and  the  fixed  effects  structure  follow  equation  (1). 

Standard errors are clustered at the firm-level to account for time dependence of individual-

level standard errors, following Jordà (2005) and Jordà et al. (2015).  

Figure 4 shows the estimated impulse-response function. Investment peaks two years after a 

positive shock and troughs one year after a negative shock. The impact of a negative shock 

on investment is twice as strong in absolute terms than the corresponding effect for positive 

shocks. Four years after the shock, tangible capital has decreased by more than five percent 

in case of a negative shock while it has increased by 2.5 percent after a positive economic 

shock.2  

2 For the specification with firm fixed effects, see Figure A4 in the Appendix. 

ECB Working Paper Series No 2751 / November 202213 
 
Figure 4 – Baseline model of investment without debt bins  

(a)  Positive value-added shock 

(b)  Negative value-added shock 

Note: This figure shows the impulse response functions for the response of cumulative net tangible fixed asset 
growth  following  a  positive  (panel  a)  and  a  negative  (panel  b)  activity  shock.  A  positive  (negative)  shock  to 
economic activity is defined by a rise (fall) in the two-digit sector value added growth that exceeds its country-
sector-specific historical average by 1.5 standard deviations. The impulse response functions are estimated using 
the local projection approach by Jordà (2005). Standard errors are clustered at the firm level. 

In the following step, we extend equation (2) to investigate the impact of positive and negative 

activity shocks on investment depending on firm leverage. Specifically, the equation, estimated 

by OLS, is as follows: 

Δℎ𝑦𝑖,𝑡+ℎ = 𝛼 + 𝜓1𝜀𝑃𝑡  + 𝜓2𝜀𝑁𝑡 + 𝜷 ∑

𝑙∈𝐷𝑒𝑏𝑡 𝐵𝑖𝑛

D𝑙𝑖,𝑡−1 +

𝜸𝟏 ∑

𝑙∈𝐷𝑒𝑏𝑡 𝐵𝑖𝑛

D𝑙𝑖, 𝑡−1 × 𝜀𝑃𝑖,𝑡

+

𝜸𝟐 ∑

𝑙∈𝐷𝑒𝑏𝑡 𝐵𝑖𝑛

D𝑙𝑖, 𝑡−1 × 𝜀𝑁𝑖, 𝑡

+ 𝛀𝑿𝑖, 𝑡−1 + 𝐶𝑜𝑢𝑛𝑡𝑟𝑦𝑖 + 𝑆𝑒𝑐𝑡𝑜𝑟𝑖 + 𝑌𝑒𝑎𝑟𝑖 + 𝑢𝑖,𝑡+ℎ                               (3)    

with ℎ = 0,   … ,  4 and Δℎ 𝑦𝑖,𝑡+ℎ   ≡  

𝐾𝑡+ℎ − 𝐾𝑡−1
𝐾𝑡−1

, with 𝐾𝑡 being tangible fixed assets.  

Similar to equation (1), we include dummy variables for low-, medium-, and high-debt firms, 

which  we  interact  with  the  value-added  shocks  to  analyse  the  impact  of  leverage  on  firm’s 

investment response in the post-shock period.  

The coefficients of interest are 𝜓1, 𝜓2, 𝜸𝟏 and 𝜸𝟐. The first two inform about the direct effects of 

positive and negative value-added shocks on investment of medium-debt firms (the reference 

group).  The  coefficients  𝜸𝟏  and  𝜸𝟐  show  whether  the  investment  impact  of  shocks  is 

significantly different for high- and low-debt firms respectively, relative to medium-debt firms.  

The impulse responses in Figure 5 provide evidence that firms’ debt ratio strongly influences 

the investment response after both positive and negative activity shocks. In the four years after 

00.010.020.030.040.050.06t-1tt+1t+2t+3t+4Lower 95% CIPositive ShockUpper 95% CIh-0.1-0.09-0.08-0.07-0.06-0.05-0.04-0.03-0.02-0.010t-1tt+1t+2t+3t+4Lower 95% CINegative shockUpper 95% CIhECB Working Paper Series No 2751 / November 202214 
 
 
a positive activity shock, low-debt firms increase capital expenditure by 12 percent. By contrast, 

high-  and  medium-debt  firms  record  only  a  short-lived  increase  in  investment,  with  tangible 

assets reverting to their initial levels four years after the shock. This indicates that while firms 

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

further investment, high debt liabilities depress investment activity in a prolonged manner.  

Figure 5 – Baseline model of investment with debt bins 

(a)  Positive value-added shock 

(b)  Negative value-added shock 

Notes: This figure shows the impulse response functions for the response of cumulative net tangible fixed assets 
growth  following  a  positive  (panel  a)  and  negative  (panel  b)  activity  shock.  A  positive  (negative)  shock  to 
economic activity is defined by a rise (fall) in the two-digit sector value added growth that exceeds its country-
sector-specific historical average by 1.5 standard deviations. Low debt firms are defined as firms in the bottom 
20th percentile of the debt ratio distribution, medium-debt firms are firms between the 20th and the 80th percentile 
and  high  debt  firms  are  firms  standing  above  the  80th  percentile  of  this  distribution.  The  impulse  response 
functions are estimated using the local projection approach by Jordà (2005). Standard errors are clustered at the 
firm level. A cross represents a statistically significant coefficient different from zero at 90% confidence. Diamonds 
and circles represent statistically significant coefficients (90% confidence) vis-à-vis the reference group.  

After a negative shock, the capital stock of high debt firms shrinks by almost one fifth after four 

years.  By  contrast,  low-  and  medium-debt  firms  suffer  only  an  initial  drop  in  capital  that  is 

almost  fully  recovered  after  four  years.  In  principle,  one  explanation  for  the  more  resilient 

response of low- and medium-debt firms could be that low- and medium-debt firms have larger 

cash  holdings  to  withstand the  shock.  However,  the  above  findings  are  confirmed  when  we 

replace in equation (3) gross debt with net debt, defined as gross debt minus cash holdings 

(Figure A5).  

Note that these estimates suffer from a possible survivorship bias, and therefore represent a 

lower bound, given that they only capture the impact along the intensive margin, but excludes 

the possible impacts due to the exit of high-leveraged firms from the market.  

-0.2-0.18-0.16-0.14-0.12-0.1-0.08-0.06-0.04-0.020t-1tt+1t+2t+3t+4Low-debt firmsMedium-debt firms (Reference group)High-debt firmsh00.020.040.060.080.10.120.14t-1tt+1t+2t+3t+4Low-debt firmsMedium-debt firms (Reference group)High-debt firmshECB Working Paper Series No 2751 / November 202215 
 
 
3.2. Effect of firm size and country 

In the next step, we explore whether our above findings hold across firm sizes and country 

groups.  

We start with firm size differences, distinguishing between i) micro, ii) small and medium and 

iii) large firms. Large firms are those with more than EUR 43 million in total assets; small and 

medium firms are those whose total assets exceed EUR 2 million but do not exceed EUR 43 

million; and micro firms have total assets of no more than EUR 2 million.3  

We build on equation (2) and add a three-way interaction between activity shock, debt bin and 

size-class. The equation then becomes: 

Δℎ𝑦𝑖,𝑡+ℎ = 𝛼 + 𝜓1𝜀𝑃𝑡  + 𝜓2𝜀𝑁𝑡 + 𝜷 ∑ D𝑙𝑖,𝑡−1 +

𝜸𝟏 ∑ D𝑙𝑖, 𝑡−1 × 𝜀𝑃𝑖,𝑡

𝑙∈𝐷𝑒𝑏𝑡 𝐵𝑖𝑛
+ 𝜸𝟐 ∑ D𝑙𝑖, 𝑡−1 × 𝜀𝑁𝑖, 𝑡

𝑙∈𝐷𝑒𝑏𝑡 𝐵𝑖𝑛

𝑙∈𝐷𝑒𝑏𝑡 𝐵𝑖𝑛

+ 𝝓 ∑ S𝑘𝑖,𝑡−1 + 𝜽𝟏 ∑

∑ D𝑙𝑖, 𝑡−1 × 𝑆𝑘𝑖,𝑡−1 × 𝜀𝑃𝑖,𝑡

𝑘∈𝑆𝑖𝑧𝑒 𝐶𝑙𝑎𝑠𝑠

+ 𝜽𝟐 ∑

𝑘∈𝑆𝑖𝑧𝑒 𝐶𝑙𝑎𝑠𝑠
𝑙∈𝐷𝑒𝑏𝑡 𝐵𝑖𝑛
∑ D𝑙𝑖, 𝑡−1 × 𝑆𝑘𝑖,𝑡−1 × 𝜀𝑁𝑖,𝑡

𝑘∈𝑆𝑖𝑧𝑒 𝐶𝑙𝑎𝑠𝑠

𝑙∈𝐷𝑒𝑏𝑡 𝐵𝑖𝑛

+ 𝛀𝑿𝑖, 𝑡−1 + 𝐶𝑜𝑢𝑛𝑡𝑟𝑦𝑖 + 𝑆𝑒𝑐𝑡𝑜𝑟𝑖

+ 𝑌𝑒𝑎𝑟𝑖 + 𝑢𝑖,𝑡+ℎ                                                                                                                        (3) 

where 𝑆𝑖𝑧𝑒 𝐶𝑙𝑎𝑠𝑠 ∈ {Small and Medium, Large} and 𝐷𝑒𝑏𝑡 𝐵𝑖𝑛 ∈ {Low-debt, High-debt}.  

As we are mostly interested in the implications of the COVID-19 crisis, we focus in the following 

on negative shocks. The results for positive shock are in the appendix (Figures A6 to A8).  

We estimate equation (3) first without and then with taking into account the debt ratio of firms 

in different size classes. This allows us to disentangle the impact of size from that of the debt 

ratio in the firm’s investment response. The idea is to understand how firms across the size 

distribution react to activity shocks, and if these reactions depend on debt levels.  

From panel a) in Figure 6, it becomes evident that micro firms drive the aggregate results of 

Figures  4  and 5.  The  capital  stock  of  micro  firms  decreases  by 6  percent  in  the  four  years 

following a negative shock. The corresponding reactions of small and medium firms as well as 

large firms are non-negligible but more muted. The differences between micro firms and their 

peers  are  even  more  pronounced  when  restricting  the  sample  to  high  debt  firms.  Highly-

3 This definition is closely aligned with EU classification but uses only the asset criterion due to data availability reasons. See 

Official Journal of the European Union – “Commission Recommendation of 6 May 2003 concerning the definition of micro, 
small, and medium-sized enterprises”  

ECB Working Paper Series No 2751 / November 202216indebted micro firms reduce their capital by close to one fifth in the four years after a shock, 

which compares to some eight percent in the case of small and medium-sized firms and less 

than two percent for large firms. 

Figure 6 – Baseline model by firm size 

(a) All debt bins 

(b) Low-debt firms.  

(c) Medium-debt firms.  

(d) High-debt firms.  

Notes: This figure shows the impulse response functions for the response of cumulative net tangible fixed assets 
growth following a negative activity shock. A negative shock to economic activity is defined by a fall in the two-
digit  sector  value  added  growth  that  exceeds  its  country-sector-specific  historical  average  by  1.5  standard 
deviations. Low debt firms are defined as firms in the bottom 20th percentile of the debt ratio distribution, medium-
debt firms are firms between the 20th and the 80th percentile and high debt firms are firms standing above the 
80th  percentile  of  this  distribution.  The  impulse  response  functions  are  estimated  using  the  local  projection 
approach  by  Jordà  (2005).  Standard  errors  are  clustered  at  the  firm  level.  A  cross  represents  a  statistically 
significant  coefficient  different  from  zero  at  90%  confidence.  Diamonds  and  circles  represent  statistically 
significant coefficients (90% confidence) vis-à-vis the reference group. Micro firms are those with  total assets 
lower than EUR 2m; small and medium firms are those with total assets larger than EUR 2m and lower than EUR 
43m; large firms are those with total assets exceeding EUR 43m. 

-0.1-0.08-0.06-0.04-0.0200.02t-1tt+1t+2t+3t+4Large firmsMicro firms (Reference group)Small and medium firmsh-0.08-0.07-0.06-0.05-0.04-0.03-0.02-0.0100.01t-1tt+1t+2t+3t+4Large firmsMicro firmsSmall and medium firmsh-0.15-0.1-0.0500.050.10.15t-1tt+1t+2t+3t+4Large firmsMicro firmsSmall and medium firmsh-0.2-0.18-0.16-0.14-0.12-0.1-0.08-0.06-0.04-0.020t-1tt+1t+2t+3t+4Large firmsMicro firmsSmall and medium firmshECB Working Paper Series No 2751 / November 202217 
 
 
 
  
In the following, we analyse cross-country differences in the investment response to negative 

activity  shocks.  We  split  our  sample  in  three  country  groups  based  on  geographical 

considerations and re-estimate equation (2) for each region. The three groups are Northern 

and Central European economies (Austria, Belgium, Germany, France, Ireland, Netherlands 

and Finland), Southern European economies (Greece, Spain, Italy and Portugal) and Eastern 

European economies (Estonia, Slovenia and Slovakia). 

There are significant differences across countries in terms of the magnitude and persistence 

of  the  investment  effects  after  a  negative  shock.  High  debt  firms  in  Southern  and  Eastern 

European  countries  record  a  more  pronounced  and  protracted  fall  in  investment  than  their 

peers in Northern and Central Europe (Figure 7).  

This suggests an interesting link to the literature on zombie firms, defined as non-viable but 

still operating firms with high levels of debt and negative profitability. There is evidence that 

zombie  firms  are  more  relevant  in  Southern  European  than  in  Northern  Europe  (Adalet 

McGowan et al. 2017) which is, inter alia, associated with the role of distressed banks that roll 

over loans to non-viable firms rather than writing them off in a gamble for resurrection (Storz 

et al. 2017, Andrews and Petroulakis 2019). Such evergreening of loans is not associated with 

the financing of new capital. Instead, as shown by Banerjee and Hofmann (2022), zombie firms 

record lower investment than non-zombie firms.  

Figure 7 – Baseline model by country groups 

(a) Northern and Central European 

(b) Southern European countries.  

countries.  

-0.06-0.04-0.0200.020.040.060.080.1t-1tt+1t+2t+3t+4Low-debt firmsMedium-debt firms (Reference group)High-debt firmsh-0.2-0.15-0.1-0.0500.050.1t-1tt+1t+2t+3t+4Low-debt firmsMedium-debt firmsHigh-debt firmshECB Working Paper Series No 2751 / November 202218 
 
 
 
(c) Eastern European countries 

Notes: The figure shows the impulse response functions for the response of cumulative net tangible fixed assets 
growth following a negative activity shock. A negative shock to economic activity is defined by a fall in the two-
digit  sector  value  added  growth  that  exceeds  its  country-sector-specific  historical  average  by  1.5  standard 
deviations. Low debt firms are defined as firms in the bottom 20th percentile of the debt ratio distribution, medium-
debt firms are firms between the 20th and the 80th percentile and high debt firms are firms standing above the 
80th  percentile  of  this  distribution.  The  impulse  response  functions  are  estimated  using  the  local  projection 
approach  by  Jordà  (2005).  Standard  errors  are  clustered  at  the  firm  level.  A  cross  represents  a  statistically 
significant  coefficient  different  from  zero  at  90%  confidence.  Diamonds  and  circles  represent  statistically 
significant  coefficients  (90%  confidence)  vis-à-vis  the  reference  group.  A  cross  represents  a  statistically 
significant coefficient different from zero at 90% confidence. Northern and Central European countries: AT, BE, 
DE, FR, IE, NL and FI. Southern European countries: EL, ES, IT and PT. Eastern European countries: EE, SI 
and SK. 

Another  explanation  could  be  that  the  firm  composition  in  Southern  and  Eastern  European 

countries  is  biased towards smaller, more financially  distressed  companies  (Table  A7). The 

depressed  investment  activity  of  highly  indebted  firms  in  Southern  and  Eastern  European 

countries  could  stem  from  a  higher  share  of  micro  firms  in  these  countries.  To  better 

understand if the cross-country differences in investment are driven by differences in firm size, 

we  rerun  equation  (2)  without  micro  firms  for  all  country  groups.  The  results  suggest  that 

regional  differences  shrink  somewhat  if  micro  firms  are  excluded  (Figure  8).  This 

notwithstanding, Southern European countries still record a much more depressed investment 

response to an activity shock for high-debt firms than for firms with low or medium debt. This 

suggest that not only firm size but also other structural features are important to explain cross-

country differences.  

-0.5-0.4-0.3-0.2-0.100.10.2t-1tt+1t+2t+3t+4Low-debt firmsMedium-debt firmsHigh-debt firmshECB Working Paper Series No 2751 / November 202219 
 
 
 
 
Figure 8 – Baseline model by country group and without micro firms 

(a) Northern and Central European countries.   (b) Southern European countries 

(c) Eastern European countries 

Notes: This figure shows the impulse response functions for the response of cumulative net tangible fixed 
assets growth following a negative activity shock. A negative shock to economic activity is defined by a fall in 
the two-digit sector value added growth that exceeds its country-sector-specific historical average by 1.5 
standard deviations. Low debt firms are defined as firms in the bottom 20th percentile of the debt ratio 
distribution, medium-debt firms are firms between the 20th and the 80th percentile and high debt firms are firms 
standing above the 80th percentile of this distribution. The impulse response functions are estimated using the 
local projection approach by Jordà (2005). Standard errors are clustered at the firm level. A cross represents a 
statistically significant coefficient different from zero at 90% confidence. Diamonds and circles represent 
statistically significant coefficients (90% confidence) vis-à-vis the reference group. Northern and Central 
European countries: AT, BE, DE, FR, IE, NL and FI. Southern European countries: EL, ES, IT and PT. Eastern 
European countries: EE, SI and SK. 

3.3. Sensitivity analysis 

This  section  analyses  the  robustness  of  our  main  findings  to  alternative  definitions  of 

investment and a different computation of the activity shock.  

-0.08-0.07-0.06-0.05-0.04-0.03-0.02-0.0100.01t-1tt+1t+2t+3t+4Low-debt firmsMedium-debt firms (Referencegroup)High-debt firmsh-0.2-0.15-0.1-0.0500.050.1t-1tt+1t+2t+3t+4Low-debt firmsMedium-debt firms (Referencegroup)High-debt firmsh-0.5-0.4-0.3-0.2-0.100.1t-1tt+1t+2t+3t+4Low-debt firmsMedium-debt firms (Reference group)High-debt firmshECB Working Paper Series No 2751 / November 202220 
 
 
 
We  first  discuss the  robustness  of  our results to  other types  of  investment.  Specifically,  we 

replace net tangible fixed assets in equation (2) with a) net total fixed assets, and b) gross total 

fixed assets. Furthermore, we provide in the appendix results using intangible fixed assets (see 

Figure A9). 

Figure 9 – Alternative investment definition (negative shock) 

(a) net total investment  

(b) gross total investment 

Notes: This figure shows the impulse response functions for the response of cumulative net fixed assets growth 
(panel a) and gross total investment (panel b) following a negative activity shock. A negative shock to economic 
activity  is  defined by a  fall in the  two-digit  sector  value  added  growth  that  exceeds  its  country-sector-specific 
historical average by 1.5 standard deviations. Low debt firms are defined as firms in the bottom 20th percentile of 
the debt ratio distribution, medium-debt firms are firms between the 20th and the 80th percentile and high debt 
firms  are  firms  standing  above  the  80th  percentile  of  this  distribution.  The  impulse  response  functions  are 
estimated using the local projection approach by Jordà (2005). Standard errors are clustered at the firm level. A 
cross represents a statistically significant coefficient different from zero at 90% confidence. Diamonds and circles 
represent statistically significant coefficients (90% confidence) vis-à-vis the reference group.  

In  both  cases,  results  are  similar  to  the  baseline,  with  high-debt  firms  showing  the  most 

protracted negative investment reaction. The initial investment response of low-debt firms is 

more  adverse  than  in  the  baseline,  however  these  firms’  investment  recovers  the  fastest, 

returning almost to their previous capital level at t+4, similar to the baseline. (See Figure A10 

in the appendix for the investment response to a positive shock.)  

One concern with our baseline specification might be that the link between value added and 

investment  might  not  be  straightforward  as  investment  could  lead  to  periods  of  high  or  low 

value-added growth. While in equation (2) we lag all our control variables for this purpose, it 

cannot be taken for granted that our shocks, 𝜀𝑃 and 𝜀𝑁 are uncorrelated with the error term. 

To address these endogeneity concerns, we propose in this section an alternative definition of 

activity  shocks that captures  the  exogenous cyclical  variation  of  a  sector  using  input-output 

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tables (see Shea 1993 and applied in Bartelsman, Caballero, and Lyons 1994 and Bartelsman, 

Lopez-Garcia, and Presidente 2018). We identify exogenous changes in the cyclical position 

of a sector by calculating changes in downstream sectors’ demand for each ISIC Revision 4 

sector  using  the  OECD’s  Inter-Country  Input-Output  tables  (OECD  2021).  In  line  with  the 

literature, and to address potential endogeneity concerns, we remove any downstream sector 

that contributes more than one percent to a given sector’s inputs.4 

As such, the total demand for sector 𝑖 is given by 

𝑇𝑜𝑡𝑎𝑙 𝐷𝑒𝑚𝑎𝑛𝑑𝑖 =   ∑ 𝐼𝑛𝑡𝑒𝑟𝑚𝑒𝑑𝑖𝑎𝑡𝑒 𝐷𝑒𝑚𝑎𝑛𝑑𝑗,𝑖

𝑗∈𝐽

+ 𝐹𝑖𝑛𝑎𝑙 𝐸𝑥𝑝𝑒𝑛𝑑𝑖𝑡𝑢𝑟𝑒𝑖  

(4) 

where 𝐽 is the set of sectors that do not contribute with more than 1% to sector 𝑖’s inputs. The 

subscript 𝑗, 𝑖 indicates that sector 𝑗 demands from sector 𝑖. 

Figure 10 – Alternative activity shock 

(a) Positive shock 

(b) Negative shock 

Notes: This figure shows the impulse response functions for the response of cumulative net tangible fixed assets 
growth  following  a  positive  (panel  a)  and  negative  (panel  b)  activity  shock.  A  positive  (negative)  shock  to 
economic  activity  is  defined  by  a  rise  (fall)  in  the  sector’s  demand  that  exceeds  its  country-sector-specific 
historical average by 1.5 standard deviation. Demand is defined by the output of a given sector to its downstream 
sectors. Low debt firms are defined as firms in the bottom 20th percentile of the debt ratio distribution, medium-
debt firms are firms between the 20th and the 80th percentile and high debt firms are firms standing above the 
80th  percentile  of  this  distribution.  The  impulse  response  functions  are  estimated  using  the  local  projection 
approach  by  Jordà  (2005).  Standard  errors  are  clustered  at  the  firm  level.  A  cross  represents  a  statistically 
significant  coefficient  different  from  zero  at  90%  confidence.  Diamonds  and  circles  represent  statistically 
significant coefficients (90% confidence) vis-à-vis the reference group. 

4 Note that the sectoral disaggregation of this measure is less granular than our baseline measure. This implies that we need to pool 
several sectors together and lose information on the cyclical position of each sector. For this reason, we do not use the approach 
described in this section as our main measure.  

-0.100.10.20.30.40.50.60.7t-1tt+1t+2t+3t+4Low-debt firmsMedium-debt firms(Reference group)High-debt firmsh-0.2-0.18-0.16-0.14-0.12-0.1-0.08-0.06-0.04-0.020t-1tt+1t+2t+3t+4Low-debt firmsMedium-debt firms (Reference group)High-debt firmshECB Working Paper Series No 2751 / November 202222 
 
 
 
 
 
Equation (4) allows to define yearly changes in total demand for each country-sector and thus 

provides  an  indicator  of  exogenous  changes  in  sector  demand.  We  then define  our  activity 

shocks as above. A positive (negative) shock occurs whenever demand exceeds (falls below) 

1.5  standard  deviation  from  the  country-sector’s  historical  (2005-2018)  average.  Finally,  we 

run  equation  (2)  using  these  alternative  shocks.  The  results  in  Figure  10  highlight  the 

robustness  of  the  findings.  While  investment  decreases  across  all  types  of  firms,  high-debt 

firms record the strongest and most sustained decline.  

4.  Implications for the recovery from the COVID-19 pandemic 

The above findings can be applied to provide a tentative assessment on the medium-term 

impact of the COVID-19 shock on corporate investment. The COVID-19 shock led to a drop 

in euro area value added of around 6.5% from 2019 to 2020. This compares with a drop in 

value-added of 5.3% during the global financial crisis from 2008 to 2009. 

We base our calculation of the potential investment impact of the COVID-19 shock on 

equation (2), but redefine negative and positive activity shocks, now labelled 𝜑𝑁 and 𝜑𝑃, as 

the standardised annual change in sector value-added. 

Δℎ𝑦𝑖,𝑡+ℎ = 𝛼 + 𝜓1𝜑𝑃𝑡  + 𝜓2𝜑𝑁𝑡 + 𝜷 ∑ D𝑙𝑖,𝑡−1 +

𝜸𝟏 ∑ D𝑙𝑖, 𝑡−1 × 𝜑𝑃𝑖,𝑡

𝑙∈𝐷𝑒𝑏𝑡 𝐵𝑖𝑛
+ 𝜸𝟐 ∑ D𝑙𝑖, 𝑡−1 × 𝜑𝑁𝑖, 𝑡

𝑙∈𝐷𝑒𝑏𝑡 𝐵𝑖𝑛

+ 𝛀𝑿𝑖, 𝑡−1 + 𝐶𝑜𝑢𝑛𝑡𝑟𝑦𝑖 + 𝑆𝑒𝑐𝑡𝑜𝑟𝑖 + 𝑌𝑒𝑎𝑟𝑖

𝑙∈𝐷𝑒𝑏𝑡 𝐵𝑖𝑛

+ 𝑢𝑖,𝑡+ℎ                                                                                                               (5) 

This definition allows us to extract the elasticity of a one standard deviation change in value-

added on investment. Table 4 shows the resulting elasticities by firm type for period 𝑡 + 4. 

After running this regression and obtaining the elasticities, 𝜸𝟏 and 𝜸𝟐, we apply the 
normalised change in value-added in 20205 to these elasticities.  

Table 4 – Investment elasticities to COVID shock 

Dep. variable: 𝚫𝒚𝒕+𝟒 
𝝋𝑵𝒕 × DLow-debt𝒕−𝟏 
𝝋𝑵𝒕 × DMedium-debt𝒕−𝟏 
𝝋𝑵𝒕 × DHigh-debt𝒕−𝟏

Lower 95% CI 

-0.0048 

-0.0140 

-0.0828 

Note: The elasticities are calculated based on equation (5). 

Average 

0.0132 

-0.0069 

-0.0666 

Upper 95% CI 

0.0302 

0.0002 

-0.0503 

5 According to Eurostat national account data, and taking value-added data from 1995 to 2020, the standardised annual change in value-

added in 2020 was 3.11 standard deviations. 

ECB Working Paper Series No 2751 / November 202223 
Figure 11 shows the range of results which suggest a decrease in capital for all firms of 

around 5% by 2024 compared to 2019. The drop stems mostly from the large fall in 

investment of high-debt firms, which are estimated to reduce their capital by one fifth over 

this period. By contrast, low-debt firms show a stronger resilience to the shock and 

increase their capital.  

Figure 11 –COVID-sized value-added shock on capital accumulation in 2024 

Notes: Results based on equation (5) and investment elasticities of Table 4. 

Overall, these computations should be interpreted with caution due to their rather mechanical 

nature. In particular, this exercise does not explicitly consider general equilibrium issues, 

such as the impact of economic policies in laying the ground for the recovery, changes in the 

distribution of debt across firms, or other factors that might influence the post-COVID-19 

investment path, including those related to the Russian war in Ukraine. Note also that 

equation (5) does not account for the rising debt ratio as a result of the pandemic but 

conditions on the initial debt ratio. 

5.  Conclusions 

This paper provides new evidence on the relationship between corporate debt and investment. 

Based on firm-level data for fourteen euro area countries, our empirical analysis  shows that 

high  leverage impedes  investment  in the years  following  a large  economic  contraction.  The 

negative investment impact of high leverage is highly robust across different specifications. It 

is observed for micro and, to a lower extent, for small and medium firms (which amount to 99 

percent of firms in our weighted sample). Large firms are more resilient to economic shocks, 

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

large firms.  

-0.3-0.25-0.2-0.15-0.1-0.0500.050.10.15Low-debtMedium-debtHigh-debtAll firmsLower 95% CIAverageUpper 95% CIECB Working Paper Series No 2751 / November 202224 
 
We also find considerable cross-country heterogeneity. Highly indebted firms in Southern and 

Eastern  Europe  record  significantly  lower  investment  after  a  negative  economic shock  than 

their  peers  in  Northern  Europe.  Our  results  are  largely  unaffected  if  we  use  alternative 

definitions of investment and are also qualitatively similar if we apply different computation of 

economic shocks.  

Our findings suggest that high corporate leverage could deter investment in the years after 

the COVID-19 shock. A failure to address the high leverage of micro, small and medium 

sized firm could negatively affect the capacity of the corporate sector to pursue the large-

scale investment needs over coming years, related inter alia to promote the green and digital 

transitions, increase energy security, and support more diversified supply chains. The strains 

on firm balance sheets put a premium on policies that promote larger equity buffers in the 

corporate sector. Governments would need to ensure timely debt restructurings for viable 

firms, and the implementation of efficient liquidation procedures to address concerns about 

debt overhang and to ensure that resources are not misallocated to unviable firms (Nicoletti 

et al. 2022).  

Our analysis does not take a stance on whether the negative impact of high 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 (Amiti and Weinstein 2013). Our finding that investment 

decisions by large firms are less restrained by high leverage suggests that large firms have 

better access to alternative financing sources and are thus less vulnerable to adverse effects 

of bank shocks on investment. From a structural point of view, diversification across 

alternative sources of financing could thus make an important contribution for SMEs to the 

resilience of investment against real shocks.  

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ECB Working Paper Series No 2751 / November 202228 
 
 
Appendix 

Construction of the dataset 

We  performed  the  following  balance  sheet  tests  to  account  for  data  irregularities,  largely 

drawing on Gopinath et. al (2017). 

•  Drop observations for which the ratio between total liabilities and total assets is above 

1.02 or below 0.98. 

•  Drop observations for which tangible, intangible or total fixed assets are below zero. 

•  Drop observations for which tangible fixed assets exceed total fixed assets. 

•  Drop  observations  for  which  the  sum  of  tangible,  intangible  and  other  fixed  assets 

exceeds total fixed assets by more than 20%.  

•  Drop  observations for  which  the ratio  of  employees  per  million  assets  and  sales  per 

million assets falls above the 99.9th percentile or below the 0.1st percentile.  

•  Whenever a variable was missing for a given firm and year, but lagged and forward 

values were available, the missing value was imputed by the average of the forward 

and lagged value. The same procedure was applied if the gap was of two years. 

Table A1 – Coverage relative to Eurostat 

AT 
BE 
DE 
EE 
ES 
FI 
FR 
GR 
IE 
IT 
NL 
PT 
SI 
SK 

Number of firms 
56% 
54% 
35% 
20% 
30% 
22% 
36% 
9% 
8% 
43% 
12% 
92% 
27% 
43% 

Total fixed assets  
90% 
84% 
69% 
43% 
65% 
50% 
60% 
53% 
46% 
64% 
80% 
93% 
55% 
35% 

Source: ORBIS-iBACH and Eurostat 
Note: The total fixed asset series were extracted by one-digit NACE Rev.2 sectors from Eurostat’s National Accounts 
(Eurostat Annual National Accounts – Breakdowns of non-financial assets by type, industry and sector – Cross-
classification of fixed assets by industry and by assets (stocks)). Since Eurostat only provides current and previous 
year replacement costs, a volume series was constructed using chain-linking. To complete the dataset, EUKLEMS 
2019 data on real capital was used (https://euklems.eu/). Due to the lack of coverage for Portugal in both of these 
datasets, data for Portugal was taken from the OECD’s STAN 2020 edition 
(https://www.oecd.org/sti/ind/stanstructuralanalysisdatabase.htm). Remaining missing values were interpolated. 

ECB Working Paper Series No 2751 / November 202229 
 
 
 
Table A2 – Distribution of Total Fixed Assets in sample and Eurostat 
Utilities & Construction 

Trade & Transport 

Manufacturing 

Unweighted 
Sample 
18.3% 
16.0% 
27.3% 
17.5% 
21.3% 
24.2% 
30.9% 
24.4% 
22.3% 
30.6% 
41.4% 
25.0% 
29.6% 
28.5% 

Unweighted 
Sample 
4.1% 
3.4% 
0.9% 
3.3% 
6.8% 
1.8% 
4.3% 
14.2% 
3.1% 
5.5% 
1.2% 
9.3% 
4.5% 
2.9% 

AT 
BE 
DE 
EE 
ES 
FI 
FR 
GR 
IE 
IT 
NL 
PT 
SI 
SK 

AT 
BE 
DE 
EE 
ES 
FI 
FR 
GR 
IE 
IT 
NL 
PT 
SI 
SK 

Weighted 
Sample 
24.1% 
25.1% 
30.2% 
23.1% 
17.7% 
40.3% 
28.0% 
13.5% 
16.8% 
28.1% 
31.4% 
20.9% 
31.6% 
34.4% 

Eurostat 

23.9% 
25.7% 
30.4% 
22.9% 
18.2% 
40.4% 
28.3% 
13.6% 
25.5% 
28.1% 
31.7% 
21.1% 
31.8% 
33.8% 

Unweighted 
Sample 
30.0% 
21.7% 
24.8% 
38.7% 
31.0% 
40.8% 
18.2% 
10.6% 
12.0% 
23.8% 
15.8% 
24.7% 
17.6% 
24.5% 

Weighted 
Sample 
22.7% 
15.4% 
20.3% 
27.1% 
40.5% 
24.5% 
19.2% 
18.4% 
16.7% 
20.1% 
18.4% 
23.7% 
21.5% 
35.6% 

Eurostat 

22.8% 
15.3% 
20.5% 
26.9% 
39.1% 
24.5% 
19.3% 
18.3% 
16.8% 
20.1% 
18.7% 
24.3% 
21.4% 
35.8% 

Unweighted 
Sample 
28.4% 
35.4% 
24.1% 
31.7% 
26.1% 
19.9% 
26.7% 
40.2% 
16.4% 
28.7% 
18.8% 
30.9% 
37.4% 
32.7% 

Weighted 
Sample 
31.2% 
39.18% 
24.5% 
32.0% 
25.5% 
20.2% 
21.7% 
40.9% 
20.4% 
30.0% 
28.7% 
33.6% 
29.3% 
19.0% 

Eurostat 

31.4% 
38.8% 
24.2% 
31.9% 
25.8% 
20.2% 
21.6% 
40.6% 
20.5% 
30.1% 
28.9% 
32.6% 
29.0% 
19.5% 

Accommodation 

Information and communication 

Professional and administrative 
services 

Weighted 
Sample 
5.9% 
2.4% 
1.9% 
2.3% 
3.2% 
1.5% 
3.3% 
8.3% 
2.9% 
6.3% 
1.5% 
8.4% 
4.9% 
1.6% 

Eurostat 

6.1% 
2.4% 
1.9% 
2.3% 
3.3% 
1.5% 
3.3% 
8.5% 
3.1% 
6.3% 
1.5% 
8.5% 
5.0% 
1.6% 

Unweighted 
Sample 
3.9% 
7.6% 
4.5% 
1.6% 
6.4% 
5.5% 
7.3% 
5.4% 
5.7% 
5.3% 
11.9% 
4.7% 
5.6% 
2.7% 

Weighted 
Sample 
4.9% 
4.5% 
5.9% 
5.4% 
6.6% 
6.9% 
8.9% 
12.8% 
18.5% 
5.6% 
8.4% 
7.9% 
5.8% 
5.2% 

Eurostat 

4.9% 
4.4% 
5.7% 
5.5% 
6.7% 
6.9% 
8.8% 
13.0% 
18.0% 
5.6% 
8.2% 
7.9% 
5.7% 
5.3% 

Unweighted 
Sample 
14.3% 
15.9% 
18.4% 
7.3% 
8.3% 
7.9% 
12.7% 
5.3% 
41.1% 
6.1% 
11.5% 
5.5% 
5.3% 
8.8% 

Weighted 
Sample 
11.1% 
13.4% 
17.1% 
10.1% 
6.6% 
6.6% 
18.9% 
6.1% 
24.7% 
9.9% 
11.6% 
5.6% 
6.7% 
4.2% 

Eurostat 

10.9% 
13.4% 
17.2% 
10.3% 
6.9% 
6.5% 
18.8% 
6.0% 
33.4% 
9.8% 
11.0% 
5.6% 
7.1% 
4.1% 

Source: ORBIS-iBACH and Eurostat 
Note: To remove astronomical weights, we keep only country-year and two-digit sector cells with more than ten firms 
represented in our dataset. 

Table A3 – Distribution of firm weights 

Min 

Weights 

0.67 

P10 

1.05 

P50 

2.11 

P90 

4.19 

Max 

Average 

182.77 

2.50 

Source: ORBIS-iBACH and ECB calculations.  

Table A4 – Distribution of value-added growth 

Min 

P50 

Average 

Max 

SD 

2006 

2007 

2008 

2009 

2010 

2011 

2012 

-52.84% 

-64.09% 

-96.08% 

-78.98% 

-70.07% 

-86.03% 

-70.84% 

4.07% 

4.23% 

0.24% 

5.66% 

148.46% 

13.46 

6.22% 

143.29% 

12.8 

0.93% 

137.56% 

-7.89% 

-8.26% 

364.29% 

2.67% 

1.18% 

5.09% 

310.71% 

1.37% 

102.48% 

-1.61% 

-0.83% 

371.84% 

16.28 

23.47 

21.83 

14.68 

19.02 

ECB Working Paper Series No 2751 / November 202230 
 
 
 
 
 
 
 
 
 
 
2013 

-166.03% 

-0.22% 

-0.87% 

149.38% 

2014 

-113.39% 

2015 

-899.76% 

2016 

-92.31% 

2017 

-750.00% 

2018 

-42.93% 

Source: Eurostat. 

1.7% 

1.4% 

1.67% 

3.72% 

2.47% 

15.06 

17.80 

2.81% 

245.07% 

1.41% 

 431.34% 

40.07 

2.31% 

148.83% 

4.29% 

705.35% 

3.40% 

400.00% 

12.30 

41.28 

17.97 

Table A5 – Average value-added growth across 

country groups 

Northern and 

Southern 

Eastern 

2006 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 

2016 

2017 

2018 

Central 

2.81% 

7.66% 

0.29% 

-5.74% 

3.02% 

1.99% 

2.62% 

-0.57% 

1.68% 

1.94% 

0.71% 

5.11% 

5.02% 

4.23% 

4.76% 

-1.56% 

-6.75% 

0.56% 

-2.49% 

-3.93% 

1.58% 

1.65% 

0.65% 

2.01% 

4.40% 

3.21% 

7.57% 

7.40% 

3.80% 

-13.71% 

10.21% 

4.04% 

8.55% 

-1.44% 

-7.36% 

4.47% 

2.00% 

8.65% 

14.24% 

Source: Eurostat. 
Note: Unweighted average across countries and sectors. Northern 
and Central European countries – AT, BE, DE, FI, FR, IE, NL; Southern 
European countries – ES, GR, IT, PT; Eastern European countries – 
EE, SI, SK. 

ECB Working Paper Series No 2751 / November 202231 
 
 
 
 
 
Table A6 – Share of firms affected by shocks 

Mild shock 

Medium shock 

Severe shock 

Positive 

Negative 

Positive 

Negative 

Positive 

Negative 

2006 

2007 

2008 

2009 

2010 

2011 

2012 

2013 

2014 

2015 

2016 

2017 

2018 

24% 

24% 

10% 

4% 

26% 

15% 

7% 

7% 

11% 

12% 

10% 

21% 

14% 

6% 

4% 

15% 

62% 

13% 

12% 

22% 

18% 

8% 

10% 

6% 

4% 

6% 

9% 

10% 

5% 

2% 

13% 

7% 

3% 

2% 

4% 

5% 

4% 

8% 

5% 

3% 

1% 

6% 

45% 

5% 

4% 

8% 

5% 

3% 

4% 

2% 

2% 

2% 

3% 

4% 

1% 

1% 

6% 

2% 

2% 

1% 

2% 

2% 

2% 

3% 

3% 

1% 

1% 

2% 

27% 

2% 

1% 

2% 

1% 

1% 

1% 

1% 

0% 

1% 

Source: ORBIS-iBACH 
Note: The shock variables are defined as dummy  variables which equal one whenever a positive or negative 
shock occurs. The table shows the percentage of observations in each year  for which the dummy is one. The 
mild shock is one whenever the value added growth of the sector is more (less) than 1 standard deviations from 
the  historical  average;  the  medium  shock  is  defined  in  terms  of  1.5  standard  deviations  from  the  historical 
average; and the severe shock is one whenever the value added growth of the sector is 2 standard deviations 
from the historical average.  

Table A7 – Distribution of firms by size-class 

Micro firms 

Small firms 

Medium firms 

Large firms 

Northern and 

90.6% 

7.9% 

1.3% 

0.2% 

Central 

Southern 

Eastern  

94.8% 

95.5% 

4.5% 

3.6% 

0.6% 

0.7% 

0.1% 

0.2% 

Source: Eurostat Structural Business Statistics 
Note: Northern and Central European countries – AT, BE, DE, FI, FR, IE, NL; Southern European countries – 
ES, GR, IT, PT; Eastern European countries – EE, SI, SK.  

ECB Working Paper Series No 2751 / November 202232 
 
 
 
 
 
 
Figure A1 – Debt ratio and investment by sector 

Figure A2 – Debt ratio and investment by region 

Note: Northern and Central European countries: AT, BE, DE, FR, IE, NL and FI. Southern European countries: 
EL, ES, IT and PT. Eastern European countries: EE, SI and SK. 

-0.30-0.25-0.20-0.15-0.10-0.050.000.050.100.150.20020406080100ManufacturingConstruction and UtilitiesTrade and TransportAccommodationInformation and CommunicationProfessional and Administrative ServicesDebt ratio percentilesNet Tangible Investment Growth-0.30-0.20-0.100.000.100.200.30020406080100Northern and Central European CountriesSouthern European CountriesEastern European CountriesDebt ratio percentilesNet Tangible Investment GrowthECB Working Paper Series No 2751 / November 202233 
 
 
 
 
 
 
 
 
 
 
Figure A3 – Debt ratio and investment by firm size 

Note: Micro firms are those with Total Assets lower than EUR 2m; Small and Medium firms are those with Total 
Assets larger than EUR 2m and lower than EUR 43m; Large firms are those with Total Assets larger than EUR 
43m. 

Figure A4 – Baseline with firm fixed effects 

Positive value-added shock 

Negative value-added shock 

Notes: This figure shows the impulse response functions for the response of cumulative net tangible fixed asset 
growth following a positive (panel a) and a negative (panel b) activity shock. A positive (negative) shock to 
economic activity is defined by a rise (fall) in the two-digit sector value added growth that exceeds its country-
sector-specific historical average by 1.5 standard deviations. The impulse response functions are estimated 
using the local projection approach by Jordà (2005). Standard errors are clustered at the firm level. 

-0.30-0.25-0.20-0.15-0.10-0.050.000.050.100.150.20020406080100MicroSmallMediumLargeDebt ratio percentilesNet Tangible Investment Growth00.010.020.030.040.050.06t-1tt+1t+2t+3t+4Lower 95% CIPositive ShockUpper 95% CIh-0.07-0.06-0.05-0.04-0.03-0.02-0.010t-1tt+1t+2t+3t+4Lower 95% CINegative shockUpper 95% CIhECB Working Paper Series No 2751 / November 202234 
 
 
 
 
 
 
(a)  Positive value-added shock 

(b)  Negative value-added shock 

Figure A5 – Net debt 

Notes:  Net-debt  is  defined  as  debt  minus  cash  holdings  over  total  assets.  A  cross  represents  a  statistically 
significant  coefficient  different  from  zero  at  90%  confidence.  Diamonds  and  circles  represent  statistically 
significant coefficients (90% confidence) vis-à-vis the reference group. 

00.010.020.030.040.050.060.070.080.09t-1tt+1t+2t+3t+4Low-debt firmsMedium-debt firms (Reference group)High-debt firmsh-0.14-0.12-0.1-0.08-0.06-0.04-0.0200.02t-1tt+1t+2t+3t+4Low-debt firmsMedium-debt firms (Reference group)High-debt firmshECB Working Paper Series No 2751 / November 202235 
 
 
 
 
Figure A6 – Positive value-added shocks by firm size 

(a) All debt bins  

(b) Low debt  

(a) Medium debt 

(b) High debt 

Notes: This figure shows the impulse response functions for the response of cumulative net tangible fixed 
assets growth following a positive activity shock. A positive shock to economic activity is defined by a rise in the 
two-digit sector value added growth that exceeds its country-sector-specific historical average by 1.5 standard 
deviations. Low debt firms are defined as firms in the bottom 20th percentile of the debt ratio distribution, 
medium-debt firms are firms between the 20th and the 80th percentile and high debt firms are firms standing 
above the 80th percentile of this distribution. The impulse response functions are estimated using the local 
projection approach by Jordà (2005). Standard errors are clustered at the firm level. A cross represents a 
statistically significant coefficient different from zero at 90% confidence. Diamonds and circles represent 
statistically significant coefficients (90% confidence) vis-à-vis the reference group. Micro firms are those with 
Total Assets lower than EUR 2m; Small and Medium firms are those with Total Assets larger than EUR 2m and 
lower than EUR 43m; Large firms are those with Total Assets larger than EUR 43m. 

-0.04-0.0200.020.040.060.080.1t-1tt+1t+2t+3t+4Large firmsMicro firms(Reference group)Small and medium firms-0.04-0.0200.020.040.060.080.10.12t-1tt+1t+2t+3t+4Large firmsMicro firmsSmall and medium firms-0.0500.050.10.150.2t-1tt+1t+2t+3t+4Large firmsMicro firmsSmall and medium firms-0.1-0.08-0.06-0.04-0.0200.020.040.06t-1tt+1t+2t+3t+4Large firmsMicro firmsSmall and medium firmsECB Working Paper Series No 2751 / November 202236 
 
 
 
 
 
 
Figure A7 – Positive value-added shock by region 

(a)  Northern  and  Central  European 

(b) Southern European countries.  

countries.  

(c) Eastern European countries.  

Notes: This figure shows the impulse response functions for the response of cumulative net tangible fixed assets 
growth following a positive activity shock. A positive shock to economic activity is defined by a rise (fall) in the 
two-digit sector value added growth that exceeds its country-sector-specific historical average by 1.5 standard 
deviations. Low debt firms are defined as firms in the bottom 20th percentile of the debt ratio distribution, medium-
debt firms are firms between the 20th and the 80th percentile and high debt firms are firms standing above the 
80th  percentile  of  this  distribution.  The  impulse  response  functions  are  estimated  using  the  local  projection 
approach  by  Jordà  (2005).  Standard  errors  are  clustered  at  the  firm  level.  A  cross  represents  a  statistically 
significant  coefficient  different  from  zero  at  90%  confidence.  Diamonds  and  circles  represent  statistically 
significant coefficients (90% confidence) vis-à-vis the reference group. Northern and Central European countries: 
AT, BE, DE, FR, IE, NL and FI. Southern European countries: EL, ES, IT and PT. Eastern European countries: 
EE, SI and SK. 

-0.04-0.0200.020.040.060.080.10.120.14t-1tt+1t+2t+3t+4Low-debt firmsMedium-debt firms (Referencegroup)High-debt firms-0.0500.050.10.150.20.25t-1tt+1t+2t+3t+4Low-debt firmsMedium-debt firmsHigh-debt firms-0.0500.050.10.150.20.250.30.35t-1tt+1t+2t+3t+4Low-debt firmsMedium-debt firmsHigh-debt firmsECB Working Paper Series No 2751 / November 202237 
 
 
 
 
 
 
Figure A8 – Positive value-added shocks by region and without micro firms 

(a) Northern and Central European 

(b) Southern European countries 

countries 

(c) Eastern European countries 

Notes: This figure shows the impulse response functions for the response of cumulative net tangible fixed 
assets growth following a positive activity shock. A positive shock to economic activity is defined by a rise (fall) 
in the two-digit sector value added growth that exceeds its country-sector-specific historical average by 1.5 
standard deviations. Low debt firms are defined as firms in the bottom 20th percentile of the debt ratio 
distribution, medium-debt firms are firms between the 20th and the 80th percentile and high debt firms are firms 
standing above the 80th percentile of this distribution. The impulse response functions are estimated using the 
local projection approach by Jordà (2005). Standard errors are clustered at the firm level. A cross represents a 
statistically significant coefficient different from zero at 90% confidence. Diamonds and circles represent 
statistically significant coefficients (90% confidence) vis-à-vis the reference group. Northern and Central 
European countries: AT, BE, DE, FR, IE, NL and FI. Southern European countries: EL, ES, IT and PT. Eastern 
European countries: EE, SI and SK. Micro firms are excluded from the sample.  

00.010.020.030.040.050.060.070.08t-1tt+1t+2t+3t+4Low-debt firmsMedium-debt firmsHigh-debt firmsh-0.0500.050.10.150.20.250.30.350.4t-1tt+1t+2t+3t+4Low-debt firmsMedium-debt firmsHigh-debt firmsh-0.200.20.40.60.81t-1tt+1t+2t+3t+4Low-debt firmsMedium-debt firmsHigh-debt firmshECB Working Paper Series No 2751 / November 202238 
 
 
 
 
 
 
 
Figure A9 – Intangible investment 

(a) Positive shock 

(a) Negative shock 

Notes: This figure shows the impulse response functions for the response of cumulative net intangible fixed 
assets growth following a positive (panel a) and negative (panel b) activity shock. A positive (negative) shock 
to economic activity is defined by a rise (fall) in the two-digit sector value added growth that exceeds its 
country-sector-specific historical average by 1.5 standard deviations. The impulse response functions are 
estimated using the local projection approach by Jordà (2005). Standard errors are clustered at the firm 
level. A cross represents a statistically significant coefficient different from zero at 90% confidence. 
Diamonds and circles represent statistically significant coefficients (90% confidence) vis-à-vis the reference 
group.  

Figure A10 – Alternative investment definitions (positive shock) 

(a) Net total fixed assets 

(a) Gross total fixed assets 

Notes: This figure shows the impulse response functions for the response of cumulative net total fixed assets 
growth (panel 1) and gross total assets growth (panel b) following a positive activity shock. A positive shock to 
economic activity is defined by a rise in the two-digit sector value added growth that exceeds its countrysector-
specific historical average by 1.5 standard deviations. The impulse response functions are estimated using the 
local projection approach by Jordà (2005). Standard errors are clustered at the firm level. A cross represents a 
statistically significant coefficient different from zero at 90% confidence. Diamonds and circles represent 
statistically significant coefficients (90% confidence) vis-à-vis the reference group.  

-0.02-0.0100.010.020.030.040.050.060.070.08t-1tt+1t+2t+3t+4Low-debtMedium-debtHigh-debth-0.04-0.03-0.02-0.0100.010.020.030.040.050.06t-1tt+1t+2t+3t+4Low-debt firmsMedium-debt firmsHigh-debt firmsh-1.4-1.2-1-0.8-0.6-0.4-0.200.20.40.60.8t-1tt+1t+2t+3t+4Low-debt firmsMedium-debt firms (Reference group)High-debt firmsh-1.6-1.4-1.2-1-0.8-0.6-0.4-0.20t-1tt+1t+2t+3t+4Low-debt firmsMedium-debt firms (Reference group)High-debt firmshECB Working Paper Series No 2751 / November 202239 
 
 
 
 
 
 
 
 
Acknowledgements 

We thank Malin Andersson, Martin Bijsterbosch, Hélène Gaston, Philip Lane, Wolfgang Modery, David Sondermann, Roberto de Santis, 
participants of the ECB workshop on corporate economics in June 2022 and an anonymous referee for useful comments. This study  
was conducted while Rodrigo Barrela worked for the ECB. 
Views and conclusions expressed in this paper are those of the authors alone and cannot be attributed to the European Central Bank. 

Rodrigo Barrela 
Nova School of Business and Economics, Lisbon, Portugal; email: rodrigo.barrela@novasbe.pt 

Paloma Lopez-Garcia 
European Central Bank, Frankfurt am Main, Germany; email: paloma.lopez-garcia@ecb.europa.eu 

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

© European Central Bank, 2022 

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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 978-92-899-5399-3 

ISSN 1725-2806 

doi:10.2866/797403 

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