Study on the effectiveness of COVID-aid on firms.
Date: 2024-06-04
Institutions: Joint Research Centre
Subjects: Italy, Poland, Spain, State aid, aid to undertakings, company in difficulties, comparative analysis, control of State aid, coronavirus disease, economic activity, economic consequence, financial instrument, performance measurement, research report, turnover, type of business

ISSN 1831-9424 

Study on the effectiveness of COVID-Aid 
on firms 

Canzian, G., Crivellaro, E., Duso, T., Ferrara, A., Sasso, A., 
Verzillo, S. 

2024 

1 

 EUR 31945 EN 

 
  
 
This document is a publication by the Joint Research Centre (JRC), the European Commission’s science and knowledge 
service. It aims to provide evidence-based scientific support to the European policymaking process. The contents of this 
publication do not necessarily reflect the position or opinion of the European Commission. Neither the European 
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concerning the delimitation of its frontiers or boundaries. 

Contact information  
Name: Alessandro Sasso 
Email: alessandro.sasso@ec.europa.eu 

EU Science Hub 
https://joint-research-centre.ec.europa.eu 

JRC135571 

EUR 31945 EN 

PDF  

ISBN 978-92-68-16460-0   ISSN 1831-9424   doi:10.2760/618465  

KJ-NA-31-945-EN-N 

Luxembourg: Publications Office of the European Union, 2024 

© European Union, 2024  

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How to cite this report: European Commission, Joint Research Centre, Canzian, G., Crivellaro, E., Duso, T., Ferrara, A., Sasso, A. and Verzillo, S., 
Study on the effectiveness of COVID-Aid on firms, Publications Office of the European Union, Luxembourg, 2024, 
https://data.europa.eu/doi/10.2760/618465, JRC135571. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Contents 

Abstract ................................................................................................................. 3 

Acknowledgements ..................................................................................................... 4 

Executive summary ..................................................................................................... 5 

1 

2 

Introduction ....................................................................................................... 10 

Institutional background ......................................................................................... 13 

2.1  The Temporary framework for State aid in Europe ...................................................... 13 

2.2  Evidence from the State aid Scoreboard .................................................................. 14 

2.2.1 

State aid evolution from 2017 to 2021 .......................................................... 14 

3  Literature Review ................................................................................................. 19 

4  Data ............................................................................................................... 21 

4.1  Sample selection ............................................................................................ 22 

4.1.1  Missing values ...................................................................................... 22 

4.1.2 

Firms not appearing in Orbis after 2019 ......................................................... 23 

4.2  Descriptive evidence ........................................................................................ 25 

4.2.1 

Firms’ characteristics by country .................................................................. 25 

4.2.2 

Financial characteristics of COVID aid beneficiaries with respect to non-beneficiaries ....... 26 

4.2.3 

Characteristics of COVID aid beneficiaries by firm size and sector ............................. 27 

4.2.4 
Relationship between the probability of receiving COVID aid in 2020 and pre-pandemic 
financial hardship ............................................................................................. 29 

4.2.5 

Distribution of COVID-19 State aid by the type of financial instrument ....................... 30 

5  Methods ........................................................................................................... 32 

5.1  Matching ..................................................................................................... 32 

5.2  Quality of Matching ......................................................................................... 33 

5.3  The difference-in-differences approach .................................................................. 34 

6  Results ............................................................................................................. 36 

6.1 

Impact of Covid Aid on firms’ performance .............................................................. 36 

6.2  Heterogeneity analysis ..................................................................................... 37 

6.3 

Impact of Covid Aid on the probability of default ........................................................ 40 

6.4  DiD validity .................................................................................................. 42 

6.4.1 

Event study for turnover ........................................................................... 43 

6.4.2 

Event study for probability of default ............................................................. 44 

Conclusions ........................................................................................................... 45 

References ............................................................................................................ 47 

List of tables .......................................................................................................... 50 

1 

 
 
 
 
List of Figures ......................................................................................................... 52 

Annexes ............................................................................................................... 53 

Annex 1. Additional tables and figures .......................................................................... 54 

Annex 2. Transparency reporting thresholds ................................................................... 61 

Annex 3. Comparison between TAM and national registry (NR) for Italy ..................................... 62 

Annex 4. Matching Balance Tests ............................................................................... 69 

Annex 5. Heterogeneity analysis for Poland .................................................................... 71 

Annex 6. Data and scripts ........................................................................................ 73 

2 

 
 
Abstract 

The study examines the impact of COVID State aid measures (COVID aid) on firm performance for three Member 
States, namely Italy, Poland and Spain, for which national state aid registers are available. These data on aid 
recipients are merged with balance sheet data obtained from Bureau Van Dijk to create a panel of firms covering 
the period 2017-2021. The performance indicators analysed are turnover and probability of default. To assess 
the causal effect of the aid, a difference-in-differences approach is used, comparing firms that received only 
COVID  aid  with  firms  that  did  not  receive  any  State  aid  during  the  period  under  analysis,  thus  excluding 
beneficiaries of other types of aid. Propensity score matching weighting (PSM) is used to account for pre-existing 
differences in financial characteristics between beneficiaries and non-beneficiaries that are likely to determine 
the probability of receiving aid. The basic model is estimated across different categories of (pre-crisis) firm size, 
economic sector and type of financial instrument received to assess the heterogeneity of impacts. The results 
show that, on average, COVID  state aid led to a statistically significant increase in firms' turnover in 2021, 
suggesting that the aid helped firms to remain active despite the slack caused by the COVID crisis. However, 
aid recipients also experienced a slight increase in their probability of default in the same year. The impact of 
COVID  aid  also  appears  to  be  heterogeneous  across  countries,  sectors,  and  firm  size  and  according  to  the 
instrument adopted. 

3 

 
 
 
Acknowledgements  

The  authors  would  like  to  thank  DG  COMP  colleagues  Giuseppina  Cannas,  Daniele  Vidoni,  Sara  Ferraro  and 
Lorenzo Isella for the fruitful collaboration established. Moreover, the authors would like to thank Louis Ronchail 
for the valuable research assistance. All errors are our own. 

Authors 

Giulia Canzian 

Elena Crivellaro 

Tomaso Duso 

Antonella Rita Ferrara 

Alessandro Sasso 

Stefano Verzillo 

4 

 
Executive summary  

Policy context 

The outbreak of the COVID-19 pandemic in March 2020 triggered an unprecedented crisis that had a severe 
negative impact on the EU economy. Lockdown policies created liquidity problems for all companies, especially 
small and medium enterprises (SMEs), as their access to financial markets became even more difficult. Sudden 
liquidity  shortages  affected  short-term  stability  and  threatened  the  long-term  survival  of  firms.  If  left 
unaddressed, such difficulties could have led to a large number of bankruptcies, causing serious disturbances 
throughout the entire EU economy. Since the outbreak of the pandemic, the Commission has reacted swiftly to 
this  emergency  by  introducing  the  so-called  Temporary  Framework  (TF)  for  Member  States,  a  temporary 
financial  measure  to  support  European  firms  during  this  crisis.  The  TF  allowed  Member  States  to  provide 
substantial  budgetary  support  to  keep  otherwise  viable  companies  afloat  through  the  various  waves  of  the 
pandemic (see for instance, European Commission, Directorate-General for Competition, 2022a, 2022b, 2022c). 
In 2020, the average total State aid expenditure (in terms of aid element) in EU countries peaked at over 320 
billion EUR, equivalent to 2.29% of the GDP, up from 0.96% in 2019. 

Although sizable aid was necessary in such exceptional circumstances, the TF included safeguards to minimise 
the  potential  risk  that  the  aid  would  distort  competition  in  the  single  market.  First,  it  limited  the  maximum 
amount  of  aid  that  could  be  granted  to  a  firm.  This  was  designed  to  ensure  the  survival  of  firms  without 
increasing their competitiveness. Second, medium and large companies that were already in serious financial 
difficulties before the crisis could not benefit from COVID-related aid.  

The TF has evolved to meet changing needs. While it was initially set to expire in June 2022, two tools remain 
in  place  until  31  December  2023  to  support  the  ongoing  economic  recovery.  Overall,  Member  States  have 
implemented a wide range of instruments, from credit-based instruments such as guarantees and subsidised 
loans to direct grants, recapitalisations, and other non-repayable instruments.  

The  State  aid  Scoreboard  (SARI2  data)  (European  Commission,  Directorate-General  for  Competition  2023), 
which tracks State aid expenditure and its impact on businesses, highlights that State aid expenses increased 
in all Member States after the COVID-19 outbreak, with significant differences across countries, possibly due 
to the different economic impact of the pandemic. The Scoreboard shows that a significant portion of State aid 
was directed toward COVID-related measures (COVID aid) in 2020 and 2021, but the relative importance of 
these  measures  differed  across  countries.  Spain,  Italy,  and  Hungary  stood  out  for  allocating  a  significant 
percentage of their GDP for such measures. Overall, Member States provided COVID aid support mostly using 
repayable instruments (i.e. guarantee and loans). Guarantees were used extensively, particularly in Romania, 
Italy, France (only in 2020), and Spain, while Poland and Greece made a significant use of loans on favourable 
terms. However, some countries, such as Austria, Denmark, France (in 2021), Hungary, Slovenia and Slovakia, 
made also an extensive use of direct grants. Repayable forms of support are generally considered less distorting 
to competition, but they come with credit risk, which may necessitate loan restructuring in the future. 

Background literature 

The  literature  on  the  effectiveness  of  State  aid  is  extensive  but  lacks  consensus  due  to  differences  in 
methodology, variables, time periods and units of analysis. A systematic review of aid measures to SMEs in the 
EU by Dvouletý et al. (2021) found generally positive effects on firm survival, employment, assets and sales, 
but mixed results on productivity.  

However, the framework for the implementation of the COVID aid was very specific, as some of the restrictions 
on the use of State aid were lifted in order to mitigate the increase in insolvencies and bankruptcies (OECD, 
2021). This variety of measures undoubtedly helped firms cope with the crisis, although it may have affected 
the allocation of resources in the market. Identifying the unintended effects of aid to companies, especially in 
terms of aggregate productivity and the zombification of the economy, is beyond the aim of this study, although 
this is a relevant aspect that has been explored in the literature (Andrews et al. 2016, 2017). 

Some  recent  studies  consider  the  impact  of  the  COVID-19  pandemic  on  productivity  and  debt,  bankruptcy 
triggers and predicted firm exits, focusing on specific countries (Cros et al., 2021 for France and Tielens and 
Piette, 2022 for Spain). Other studies (Tielens et al., 2020; Chundkkadan et al., 2022) assess the impact on 
capital constraints, while Dhyne & Duprez (2021) look at the impact of COVID-19 on various firm outcomes in 
Belgium and find that the crisis led to greater differences in firm performance than in normal times.  Other 
papers examine the impact of COVID-19 on labour market outcomes such as employment rates, job separations 

5 

 
and firm survival (Adams-Prassl et al., 2020; Altomonte et al., 2021; Bennedsen et al., 2020; Bertschek et al., 
2023), finding a broadly positive effect of COVID support measures and outlining some heterogeneity. 

This  report  makes  a  specific  contribution  to  the  literature  documenting  the  impact  of  COVID-related  policy 
support on firm outcomes. Some papers (Harasztosi et al., 2022; Bighelli et al., 2021; and Zegel et al., 2021) 
have  focused  on  the  characteristics  of  beneficiaries  to  assess  whether  pre-crisis  transfers  were  primarily 
targeted at weaker and less productive firms. In addition to academic contributions, the literature also includes 
some country-specific studies by national research institutes (the Netherlands in Davies et al., 2023; Belgium 
in Tielens et al., 2020, 2022; and Flanders in Zegel et al., 2021) across the EU.  

More specifically, the present study focuses on the impact of COVID aid on firms' turnover and probability of 
default. The contribution to existing literature is twofold. First, the analyses are based on detailed information 
on the universe of COVID State aid granted in three EU Member States, namely Italy, Spain and Poland, obtained 
from their national State aid registers. Second, unlike previous studies that focused on the probability of default 
as an outcome, the current analysis uses the estimated probability of default, a novel indicator published by 
Bureau Van Dijk, which also allows to overcome the limitations of the Orbis data in studying the exit of firms. 
The results presented in this report are  generally consistent with those  papers that examine the short-term 
effects of public support to firms on their liquidity measures. The positive effect on sales is a recurring theme 
in the literature on State aid, as found by Dvouletý et al. (2021). 

To some extent, the present study is comparable to Konings et al. (2023) in that it examines the short-term 
effects of aid to firms during the COVID-19 pandemic and finds largely positive effects on firm performance, 
although creative destruction and growth are not the main outcomes of interest. 

Data  

The  analysis  draws  from  several  data  sources  that  are  the  Italian,  Spanish  and  Polish  national  State  aid 
registers, and the Orbis dataset. 

The national registers (NR) provide information on all State aid awards in the countries analysed. Specifically, 
they report the date on which each aid award was granted; the nominal amount of the aid; the aid element, the 
name, location, and the national identifier of the firm receiving the aid; the industry  sector of the firm; the 
objective of the aid, and the reference to the aid measure. More importantly, NR include information about the 
type  of  financial  instrument  of  the  aid,  distinguishing  among  guarantees;  loans;  direct  grants;  interest  rate 
subsidies; tax advantages and other types of instruments. 

Orbis  is  a  comprehensive  database  widely  used  for  economic  and  financial  research,  providing  firm-level 
information  on  private  and  public  companies  in  different  countries.  The  coverage  of  this  database  varies 
according to the country, the company size, the industry sector, and the time period (Bajgar et al., 2020). As 
discussed in this report, this variability may affect the reliability of the information for specific groups of firms 
that  are  not  adequately  represented.  This  report  focuses  on  four  industry  sectors  that  include  enough 
observations for robust statistical analysis: manufacturing; construction; wholesale; and retail. 

Among the three countries analysed, Italy and Spain have better data coverage in Orbis as a large share of 
their total output is covered in the data (Bajgar et al., 2020). Poland also has a good coverage in Orbis, however 
large firms seem particularly over-represented and there is a lack of precision for some variables (e.g. value 
added  and  number  of  employees)  due  to  rounded  values.  Despite  its  limitations,  Orbis  remains  the  most 
comprehensive source of firm-level information available for cross-countries comparisons.  

The final dataset is a balanced panel, meaning that only firms with non-missing variables in the period 2017-
-2021 are used for the analysis. The firm-level information in Orbis is merged with national registers to identify 
which firms are beneficiaries of State aid. For the purpose of this study, beneficiaries are defined as those firms 
that received COVID aid in 2020. This excludes from the analysis beneficiaries of other types of State aid, as 
well as firms that received a combination of COVID and non-COVID State aid in the period 2017-2021. This 
simplifies the analysis and allows a clear comparison between COVID aid beneficiaries and firms that never 
received any aid over the period 2017-2021.  

Outcome variables  

The analysis focuses on two indicators of economic performance of the firms. The first outcome is turnover, 
which describes the total operating income (including net sales, other operating revenues, and stock variations) 
reported at the end of the fiscal year. The second outcome is the estimated ‘probability of default’ provided in 
the Financial Strength module distributed by Moodys/BvD. This variable is predicted using a statistical model 

6 

 
which  considers  both  balance  sheet  information  from  the  previous  financial  year  and  the  current  economic 
situation (accounting for specific sector and size economic trends).1. 

The samples 

The samples analysed differ in the number of observations observed in each country: 76,738 firms in Italy, 
142,395 firms in Spain, and only 7,213 firms in Poland (referring to the number of firms observed each year). 

The main difference in the composition among these three samples is the average size of firms. Italy and Spain 
have a similar sample composition, with 86.7% and 84.7% of micro firms, respectively, whereas Poland has a 
lower share of micro firms at 59.9%.  

The representativeness of the data also varies depending on whether firms received COVID aid. In Italy, the 
majority of beneficiaries were micro firms (90%) and small firms (9%). In Spain, 82% of micro firms and 15% 
of small firms received aid. In Poland, the firm size distribution was more diverse, with 64% of beneficiaries 
being micro, 27% small, and 8% were medium-sized firms. There are also differences in terms of economic 
activity. In Italy and Spain, aid is spread across sectors, whereas in Poland, it is more concentrated, particularly 
in sectors such as construction.  

Across all the  countries analysed, firms receiving COVID  aid exhibit lower turnover, capital, total assets and 
loans  compared  to  non-beneficiaries.  Additionally,  they  have  a  lower  probability  of  default,  possibly  due  to 
Temporary Framework rules prohibiting aid to companies already in financial distress before the pandemic. 

In terms of financial instruments used, Italy and Spain predominantly utilised guarantees during the first year 
of the pandemic, with over 91% of treated firms received this type of aid award, while direct grants were less 
commonly employed (fewer than 13% of treated firms). In contrast, Poland made a greater use of both direct 
grants and loans, with 83% and 75% of treated firms, respectively, receiving such aid. 

Main findings 

The empirical analysis primarily focuses on turnover as an outcome, as it is directly influenced by the type of 
aid designed to sustain firm survival. Other outcomes, such as profitability, may take longer to be affected by 
the aid. Furthermore, the analysis investigates the effect of COVID aid on the probability of default. Due to data 
constraints,  which  only  provide  information  for  one  year  after  treatment,  the  analysis  is  limited  to  these 
outcomes. 

Turnover  

COVID aid have a generally positive and significant effect on turnover for beneficiary firms in both Italy and 
Spain. In Italy, the average increase in turnover for aid recipients in 2021 is nearly 9%, compared to control 
firms, while in Spain, the effect is smaller, at around 3%. 

In both countries, the impact of COVID aid varies by firm size and sector. In Italy, the positive effect is primarily 
driven by micro-firms, with an increase in turnover of 16.2%, while a smaller yet significant effect is observed 
for medium-sized firms (5.1%). Conversely, in Spain, the impact is significant across micro, medium, and large-
sized  firms,  with  a  greater  effect  observed  for  large  firms  (10.4%).  In  terms  of  sectoral  composition,  Italy 
exhibits a positive effect across all sectors, particularly in construction with a 14.6% increase in turnover. In 
Spain, a positive effect is observed across all sectors except for retail trade, where the impact is not statistically 
significant. In Poland, the overall impact of the aid is insignificant. However, there's a marginally significant 
effect for  micro firms (5.6%) and those in construction (12.9%). Yet,  due to limited representation in Orbis, 
comparing these results with Italy and Spain is challenging.  

The analysis also explored heterogeneity to disentangle the interaction of the aid with firm size and sector. 
Difference-in-differences (DiD) estimation was conducted using separate subsamples based on firm size. Within 
each subsample, the role of size is examined by interacting the treatment variable with size indicators (micro, 
small, medium, and large). The results align with those discussed above. 

An additional heterogeneity analysis is carried out taking into account the type of financial instrument used to 
provide the aid. The sample of beneficiaries is split according to the type of financial instrument used to provide 

1 However, the details on the statistical model to predict such variable are publicly unavailable. 

7 

 
 
                                           
the aid: repayable (e.g., guarantees or loans) or non-repayable (e.g. direct grants, interest rate subsidies) or a 
combination of both. These models also explore the mediating role of firm size and sector. 

The results regarding the impact of repayable instruments on turnover closely mirror the main findings, with 
similar significance levels and effect magnitudes. This is not surprising, as repayable instruments were used to 
finance  the  vast  majority  of  State  aid  in  Italy  and  Spain.  Instead,  the  effect  of  "non-repayable  or  both" 
instruments varies more widely across different sizes and sectors. In Italy, these instruments have benefited 
mainly micro and small enterprises, although the effect on the latter group is less precisely estimated. In Spain, 
small  firms  that  received  non-repayable  aid  (or  a  mix  of  repayable  and  non-repayable  aid)  performed 
significantly worse than their controls.  

To validate these results, it is crucial to confirm the validity of a key assumption of the DiD models, known as 
the parallel trend assumption. This assumption implies that the average change in outcomes for the control 
group may be used as a credible counterfactual for the unobserved change in outcomes for the beneficiary 
group, as both outcomes would have evolved similarly in the absence of the pandemic. To formally test the 
parallel  trend  assumption,  an  event  study  analysis  (Autor,  2003)  is  employed,  as  commonly  done  in  the 
literature. 

Reassuringly, the results of this test suggest that the lead terms are not statistically different from zero in all 
three countries considered, thus confirming that there was no anticipatory effect of the aid and validating the 
common trend hypothesis. This is confirmed for the full sample and for most of the subsamples considered in 
the three countries, including separate estimates by sector and size. 

The validity of the DID estimation rests on the so-called parallel trend assumption. This assumption implies that 
the evolution of the pre-treatment performance of treated and control firms moves in parallel. It also implies 
that the average change in outcomes for the control group can be used as a credible counterfactual for the 
unobserved change in outcomes for the treated group if the COVID outbreak hadn't occurred. A formal test of 
the parallel trend assumption is to interact the treatment variable with time dummies, as suggested by Autor 
(2003).  This  allows  to  verify  whether  there  was  any  anticipation  of  the  aid  prior  to  its  adoption,  while  also 
showing the dynamics of the effect. Reassuringly, the results of these tests confirm the no anticipatory effect 
of the aid and validate the common trend hypothesis. This holds for both the full sample and most subsamples 
in the three countries, including separate estimates by sector and size. 

Probability of default  

The econometric method used to estimate the impact of the aid on the probability of default is the same as 
described above (DiD with propensity score matching). The probability of default is a continuous variable that 
assigns a risk of default to each firm. The variable is commonly utilised in financial markets to classify private 
firms into different categories of default risk, ranging from AAA (lowest risk of default) to D (highest risk of 
default).  Consequently,  the  DiD  models  are  also  estimated  for  different  subsampled  of  risk  categories. 
Additionally, a heterogeneity analysis is conducted to capture the effect of the different types of instruments 
on the probability of default. 

The results suggest that in Italy and Spain, there was a slight deterioration in the risk of default for aid recipients 
across most pre-existing risk categories. However, this effect is statistically insignificant for firms with both 
high  and  low  pre-pandemic  default  risks.  In  Poland,  there  are  marginally  significant  effects  in  the  opposite 
direction. However, these results should be interpreted with caution. The receipt of aid, particularly repayable 
instruments  affecting  debt  and  interest,  could  influence  the  algorithm  estimating  the  probability  of  default, 
potentially leading to a perception of riskier status. 

In terms of heterogeneity of the impact, in Italy and Spain, the negative impact of COVID aid on the probability 
of default of beneficiaries seems to be driven by repayable instruments. Conversely, in Poland, the improving 
effect of repayable instruments is statistically significant only for firms with pre-pandemic risk categories A, 
CC,  and  D.  Considering  the  magnitude  of  the  results,  the  effects  are  slightly  more  pronounced  for  low  to 
medium-risk firms (categories AA to B) in both Italy and Spain. This is expected, as these firms likely had easier 
access to guarantees and loans, resulting in higher recorded debt.  

8 

 
Conclusions and limitations 

In all the samples analysed (Italy, Spain, and Poland), COVID aid beneficiaries had lower pre-pandemic values 
for turnover, capital and total assets compared to non-beneficiaries. They also exhibited lower pre-pandemic 
probability of default, in line with provisions of not supporting financially unstable firms. 

The study reveals a positive overall effect of COVID aid on average turnover for beneficiaries in Italy (9%) and 
Spain (3.2%), while in Poland the effect was not statistically significant. However, this impact varied significantly 
by  country,  sector,  firm  size,  and  type  of  instrument  used.  In  Italy,  micro-firms,  especially  those  in  the 
construction sector, were most affected, while in Spain, larger firms initially benefited more, though this effect 
disappeared when considering sector-specific impacts. 

The study also finds a small but significant increase in the probability of default in 2021 for some categories 
of COVID aid beneficiaries compared to non-beneficiaries in Italy and Spain. Given the size of the effect, such 
increase does not imply an exit of the firm from the market.  

The distinctiveness of COVID-19 support schemes complicates direct comparisons with other forms of State 
aid. The study faces data limitations that prevented an examination of aid intensity and necessitates cautious 
interpretation of results for Poland due to the small sample size. Furthermore, it is important to note that the 
analysis  relies  on  a  selected  sample  from  the  entire  population  of  enterprises,  which  should  be  taken  into 
consideration when interpreting the results.  

Overall, the results suggest that COVID aid played a significant role in helping firms throughout the crisis. 

9 

 
 
  
1 

Introduction  

The outbreak of the COVID-19 pandemic in March 2020 triggered an unprecedented crisis which had a severe 
negative impact on the entire EU economy. Lockdown policies created liquidity challenges for all companies, 
especially small and medium enterprises (SMEs) whose access to financial markets is typically more difficult 
than for larger firms. In addition, the impact of this crisis was uneven across sectors, being harder on sectors 
which required physical contact with customers or between workers (European Commission, Directorate-General 
for  Economic  and  Financial  Affairs  2021).  Sudden  liquidity  shortages  affected  short-term  stability  and 
threatened the long-term survival of firms. If left unaddressed, the difficulties faced by companies during the 
pandemic could lead to a large number of bankruptcies, causing serious disruption to the EU economy as a 
whole. 

Since the onset of the pandemic, the Commission has promptly responded to this emergency by putting in place 
the so-called Temporary Framework (TF) for Member States, a temporary financial measure to help European 
firms  during  this  crisis.  The  TF  has  enabled  Member  States  to  employ  sizable  budgetary  support  to  keep 
otherwise  viable  companies  afloat  through  the  various  waves  of  the  pandemic  (see  for  instance,  European 
Commission,  Directorate-General  for  Competition  (2022a,  2022b)).  In  2020,  the  average  total  State  aid 
expenditure (in terms of aid element)2 in EU countries peaked at over 320 billion EUR, equivalent to 2.29% of 
the GDP, compared to 0.96% in 2019. 

Although sizable aid was necessary in such exceptional circumstances, the TF included safeguards to minimise 
the  potential  risk  that  the  aid  would  distort  competition  in  the  single  market.  First,  it  limited  the  maximum 
amount of aid that could be granted to a firm. This was to guarantee the survival of the undertakings without 
giving them a too strong advantage at the expenses of their competitors. Second, it prevented large companies 
that  were  already  in  serious  financial  distress  before  the  crisis  from  benefitting  from  COVID  aid.  While 
essentially all Member States made use of the TF and despite its setting common rules for Member States to 
grant COVID aid, the choice of the financial instruments used for aiding firms differed by Member State due to 
their different financial situation in terms of indebtedness and budget constraints. This resulted in a different 
distribution of refundable (e.g. loans) and non-refundable (e.g. direct grants) aid measures across countries (see 
Section 2.2). 

The main objective of this study is to present new causal evidence on whether the Temporary Framework was 
useful in helping viable firms to continue operating during the pandemic. Specifically, this study uses balance-
sheet data from Bureau Van Dijk and National State aid Registers coupled with a sound causal assessment 
framework,  with  a  three-fold  aim.  First,  it  descriptively  analyses  the  financial  characteristics  of  COVID  aid 
beneficiaries with respect to non-beneficiaries. Second, it investigates the short-term causal impact of COVID 
aid on two measures of firms’ performance: turnover (that is, the revenue generated by the operating activities); 
and a variable capturing the estimated probability of default of a firm. Third, the study examines whether these 
estimated effects vary across different dimensions of firm characteristics (economic sector of activity and size) 
and the type of financial instrument used.  As the impact of the pandemic varied across sectors and sizes, and 
because  Member  States  used  different  instruments  to  address  these  outcomes,  this  analysis  of  the 
heterogeneity of the impact of COVID aid is important to understand what worked best. 

The study focuses on three EU Members namely Italy, Spain, and Poland, for which National Registers were 
made available by their national authorities. Although data were not available for all the EU countries, the three 
countries analysed are of particular interest as they represent more than 30% of the EU population and their 
nominal  expenditure  on  COVID  State  aid  in  2020  accounted  for  approximately  39%  of  the  total  State  aid 
expenditure (European Commission, 2023; Eurostat, 2019). 

Different data sources were collected, merged, and used for the different steps of the empirical analysis. First, 
the study used the Orbis database distributed by Bureau Van Dijk, a longitudinal data set to collect detailed 
information on firms’ balance sheets. Orbis covers a large but selected sample of firms all over the world and 
it  is  commonly  used  for  research  purposes  (e.g.  Bajgar  et  al.,  2020).  Second,  this  data  were  matched  to 
information from National State aid Registers, that include the records of all State aid awards granted in the 

2 The aid element measures the financial advantage obtained by an undertaking when receiving an aid award (for 

details on the definition of aid element, see Section 2.2). 

10 

 
 
                                           
period between 2017 and 2021.3 The study focuses on a balanced sample of firms i.e., firms with non-missing 
information over the period between 2017 and 2021.  

The study uses a variety of methodologies to address the objectives stated above. Summary statistics and basic 
regression models are used to compare the financial situation of COVID aid beneficiaries with respect to non-
beneficiaries. A difference-in-differences (DiD) model combined with propensity score matching is employed to 
estimate the causal effect of receiving COVID State aid  on the  firms’ turnover and  probability of default in 
2021. The identification strategy consists of comparing firms that received COVID State aid in 2020 with similar 
firms that did not receive any aid during the period of analysis. 

The main findings of the study are as follows: COVID State aid had a generally positive and significant effect 
on  turnover  for  both  Italian  and  Spanish  beneficiary  firms.  In  Italy,  the  increase  in  turnover  of  COVID  aid 
beneficiaries in 2021 was on average almost 9% higher than that of control firms, while in Spain the average 
effect  of  aid  is  smaller,  at  around  3.2%.  While  in  Italy  the  result  is  mostly  driven  by  micro  enterprises, 
irrespective of their activity, the average result in Spain is largely driven by firms in the construction sector, 
irrespective of their size. The results for Poland do not show a significant effect, neither on average nor when 
considering the size and sector decomposition with an exception for medium firm-sized firms in construction 
sector. Regarding the effect on the probability of default, in both Italy and Spain, COVID aid seems to have had 
a small negative impact on beneficiary firms, as the latter appear to have a higher default category score on 
average than the controls. Nevertheless, the magnitude of the effect indicates that it does not pose a significant 
threat to the economic stability of the firm. 

These results on the effectiveness of COVID aid are compared with related literature examining the impact of 
State aid on financial performance and firm survival (e.g. Dvouletý et al., 2021; Bertschek et al., 2023) and 
largely match them. This analysis represents a contribution to this literature as there are only a few studies 
that have examined the impact of COVID-19 State aid on firm performance.  

Although this study does not directly assess whether COVID aid has had a negative impact on competition in 
the Single Market, which is a key element of State aid control, several elements may help to understand whether 
such  an  impact  was  to  be  expected.  First,  COVID  aid  was  used  more  or  less  simultaneously  by  all  Member 
States. This implies that the support offered to firms within the Common Market was not too asymmetric, even 
though  some  countries  spent  more  in  terms  of  aid  per  capita.  This  limits  the  potential  for  distortions  of 
competition  and  trade.  Second,  the  scope  of  aid  measures  in  each  Member  State  was  quite  broad,  as  such 
COVID aid was less selective than other aid measures given, in most cases, to several companies within a given 
product market. This feature of COVID aid also limits the potential for distortion of competition within each 
product market. Third, a large part of the COVID aid was targeted at micro and small enterprises, which have 
more difficulties to access finance, especially in such a crisis. Given their small size and limited involvement in 
cross-border  transactions,  aid  to  small  companies  is  less  likely  to  distort  competition  in  the  Single  Market. 
Finally, COVID aid was granted to several sectors within each Member State, with the aid targeted at the most 
affected sectors. Therefore, COVID aid is less likely to have had a strong potential to distort competition as it 
supported a large part of the economy rather than a narrow sector.  

While  all  these  elements  taken  together  reassures  that  the  bulk  of  COVID  aid  may  not  have  distorted 
competition, they do not necessarily mean that COVID aid could not have been problematic in certain sectors. 
This could be particularly the case in sectors where aid was concentrated on a small number of companies, 
where  the  initial  market  structure  was  particularly  concentrated  and  where  the  amount  of  aid  was 
disproportionately high. An example of this could be the airline sector, where major players such as Lufthansa 
or Air France have received substantial aid while some competitors such as Ryanair and EasyJet have not. A 
targeted assessment of the impact of State aid on competition in such sectors therefore seems important and 
justified but is beyond the scope of this study. 

The  remainder  of  the  study  is  structured  as  follows:  Section  2  discusses  the  institutional  background  and 
provides information on the Temporary Framework for State aid as well as first aggregate evidence from the 
European  Commission’s  Scoreboard  database.  Section  3  presents  a  relevant  literature  review.  Section  4 

3 As National Registers were not available for all EU Member States (MS), this study explored the possibility of 
using an additional source of data (the Transparency Award Module dataset) to expand the analysis to all the 
MS. However, the use of this dataset was found to be unfeasible for this analysis due to the poor data quality 
and the sample selection (representativeness) issues explained below in this study. 

11 

 
                                           
describes  the  data  used  and  provides  descriptive  analyses.  Section  5  discusses  the  methodology.  Section  6 
presents the results of the analysis, and Section 7 concludes with a few final considerations. 

12 

 
2 

Institutional background 

When a company receives government support through State aid, it gains a competitive advantage over the 
other market players.4 Favouring some firms and not others might create competition distortions, potentially 
allowing less efficient companies to survive or expand at the expense of the more efficient ones. Due to these 
reasons, the Treaty on the Functioning of the EU (TFEU,  or ‘the Treaty’) prohibits using State aid except for 
particular circumstances where the positive effect of the aid is expected to outweigh the negative impact on 
competition. In this  context, the EU  has adopted a stringent State aid regime that involves both an ex-ante 
screening  procedure  and  an  ex-post  assessment  mechanism  and  targets  subsidies  that  distort  the  internal 
market. Any state aid should be proportionate and appropriate, and the Commission undertakes a ‘balancing 
test’,  where  the  beneficial  effects  of  state  aid  are  weighted  against  their  potential  adverse  effects  on 
competition and trade. In particular, State aid is allowed in the EU when (i) it is well designed to appropriately 
solve the market failure, and (ii) distortions to competition are limited so that the overall balance is positive. 

To ensure compliance with these principles, State aid control rules provide a harmonized framework enforced 
by  the  European  Commission,  focusing  on  factors  such  as  necessity,  appropriateness,  incentive  effect, 
proportionality,  and  avoidance  of  undue  distortions  to  competition  (Piechhuka  et  al  2023).  These  principles, 
incorporated  into  regulations,  guidelines,  and  frameworks,  guide  the  Commission's  assessment  of  State  aid 
measures,  aiming  to  strike  a  balance  between  addressing  market  failures  and  minimizing  distortions  to 
competition within the single market. In some circumstances, it is recognised that government interventions are 
necessary  for  the  functioning  and  fairness  of  an  economy.  Therefore,  Member  States  can  use  State  aid  to 
achieve a wide range of policy objectives, including, among others, reducing regional disparities within a country; 
promoting  research,  development  and  innovation  activities;  and  promoting  a  high  level  of  environmental 
protection  that  might  not  be  realised  otherwise.  The  Treaty  also  explicitly  permits  aid  “to  remedy  a  serious 
disturbance in the economy of a Member State” as compatible with the internal market.5 

2.1  The Temporary framework for State aid in Europe 

The outbreak of the COVID-19 pandemic in March  2020 triggered an unprecedented crisis which  has  had a 
severe negative impact on the EU economy. Already during the first wave of the pandemic, the Commission, 
after close consultation with the Member States, promptly implemented the so-called Temporary Framework 
(TF) for State aid6 based on  Article 107(3)(b) TFEU. In line with the principle that exceptional measures are 
needed in exceptional times, these temporary measures enabled Member States to use the full flexibility under 
State  aid  rules  to  sustain  their  economy  during  the  COVID-19  pandemic  and  accelerate  its  recovery,  while 
preserving the level playing field in the EU Single Market. Governments were able to provide sizable budgetary 
support  to  keep  otherwise  viable  companies  afloat  throughout  the  various  waves  of  the  pandemic  (see  for 
instance, European Commission, Directorate-General for Competition (2022a, 2022c).  

The TF contains several provisions to ensure that beneficiaries receive aid in an amount proportionate to the 
minimum needed to ensure their viability (specific ceilings per beneficiary are set out for different categories 
of aid). Moreover, the TF establishes eligibility criteria to prevent companies that were in difficulty before the 
crisis from receiving COVID state aid. However, for micro or small enterprises that were already in difficulty 
before December 31, 2019, there is a derogation that considers the COVID-19 crisis as an additional threat to 
the ability of these small businesses to access finance compared to larger companies. If left unaddressed, these 
difficulties  could  lead  to  a  significant  number  of  bankruptcies  of  micro  and  small  companies,  causing 
instabilities  for  the  entire  EU  economy.  Given  their  limited  size  and  limited  involvement  in  cross-border 
transactions,  temporary  State  aid  to  micro  and  small  companies  is  less  likely  to  distort  competition  in  the 
Internal Market than State aid provided to larger companies. This is especially important for innovative start-

4 The notion of State aid is an objective and legal concept defined directly by the Treaty on the Functioning of the 
European Union, which is narrower than the possibilities available to Member States to provide support to 
their economies. There are four constitutive elements for there to be State aid: (1) it must be granted by a 
Member  State  or  through  State resources;  (2)  there must be  a  selective  advantage;  (3)  there  must  be  a 
(threat  of)  distortion  of  competition;  and  (4)  there  must  be  an  effect  on  trade  between  Member  States. 
Therefore,  subsidies  granted  to  individuals  or  general  measures  open  to  all  enterprises  (e.g.  temporary 
unemployment schemes, VAT reductions) are not State aid. 

5 Article 107(3)(b) TFEU. 
6 https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=OJ%3AJOC_2020_091_I_0001 

13 

 
 
                                           
ups that may incur in losses during their initial high-growth phase. The survival of these firms is considered to 
be crucial for the economic recovery of the Union.  

Member States have implemented a wide range of State aid measures, making use of different aid instruments, 
from credit-based instruments such as guarantees and subsidised loans to direct grants, recapitalisations and 
other non-repayable instruments. The TF has been amended seven times to adapt to the evolving needs during 
different waves of the pandemic. The TF expired at the end of June 2022, with the exception of the two new 
tools designed to support the ongoing recovery of the European economy: investment support (section 3.13) 
and  solvency  support  (section  3.14),  which  remained  in  place  until  December  31,  2023.  Additionally,  the  TF 
already includes provisions for a flexible transition, with clear safeguards, including options for converting and 
restructuring debt instruments, such as loans and guarantees, into other forms of aid, like direct grants, until 
June 30, 2023. 

2.2  Evidence from the State aid Scoreboard 

This section provides an overview of the actual expenditures incurred by the Member States on existing State 
aid  measures,  based  on  the  evidence  from  the  State  aid  Scoreboard  (SARI2  data)  (European  Commission, 
Directorate-General for Competition 2023). 7  

The most recent version of the Scoreboard covers until 2021 and provides updates of State aid expenditure in 
the previous years (European Commission, Directorate-General for Competition 2023). Therefore, it includes the 
two years when the COVID-19 crisis was most significant (2020 and 2021), providing important insights on the 
implementation of COVID-19 State aid measures across Member States, as well as on all the other measures 
regularly  implemented  to  address  the  other  policy  objectives  foreseen  by  State  aid  control.  Data  include 
information on the instruments used.  

The State Aid Scoreboard presents State aid expenditures in terms of aid element granted by the Member State 
to the recipient of the aid. The aid element measures the financial advantage obtained by an undertaking when 
receiving an aid award. For example, in the case of a loan, the aid element is the financial value saved by the 
firm  when  receiving  a  government  loan  (due  to  lower  interest  rate)  as  compared  to  receiving  a  loan  in  the 
private market. This is opposed to the nominal amount that measures the total value of the State aid that is 
the amount borrowed by the firm. In general, the way the aid element is computed depends on the type of 
instrument  used: for grants,  the advantage passed on to  the beneficiary normally corresponds to the entire 
budgetary  expenditure.  For  other  repayable  instruments  such  as  loans  or  guarantees,  the  advantage  to  the 
beneficiary and the cost to government is respectively the lower interest rate and the reduced guarantee fee 
actually paid by the undertaking with respect to the one that should have been paid at market values (European 
Commission 2023). 

2.2.1  State aid evolution from 2017 to 2021 

Table 1 shows the evolution of State aid expenditure over the period 2017-2021. It illustrates that State aid 
expenditure, expressed as aid elements, have increased over time for all countries. In line with the expectations, 
State  aid  expenditure  increased  more  after  the  Covid-19  outbreak,  although  with  some  sizable  differences 
across countries. For instance, for Greece, Italy, Slovenia, Romania, and Portugal the normalized expenditure in 
2020 was twice as much than in 2019. In contrast, the expenditure of Sweden, Lithuania, Estonia, Belgium, and 
Finland increased by less than 40%. Note that the State aid expenditure is normalized with respect to the 2019 
GDP to net out the effect of the sharp drop in GDP in 2020 caused by the pandemic 8.  

7  SARI2  is  an  administrative  tool  used  by  Member  States  to  fulfil  the  annual  reporting  obligations  as  per 
Commission Regulation (EC) 794/2004. The reporting obligations provide that Member States annually report 
to the Commission information on all existing State aid measures, including the actual expenditure (or actual 
revenue  foregone  in  the  case  of  tax  expenditure).  As  such,  SARI2  contains  aggregate  data  on  the  total 
amounts disbursed to companies for all State aid measures. 

8 Figures normalized with respect to the current GDP are reported in the State aid Scoreboard of the European 

Commission (see European Commission (2022b, 2023)). 

14 

 
 
 
 
                                           
 
Table 1. Total and Normalized State aid expenditure (based on aid element) 1 

Austria 
Belgium 
Bulgaria 
Croatia 
Cyprus 
Czech Republic 
Denmark 
Estonia 
Finland 
France 
Germany 
Greece 
Hungary 
Ireland 
Italy 
Latvia 
Lithuania 
Luxembourg 
Malta 
Netherlands 
Poland 
Portugal 
Romania 
Slovakia 
Slovenia 
Spain 
Sweden 
Total EU 

2017 

1948.1 
2400.8 
772.5 
730.7 
116.4 
2952.5 
4494.5 
247.2 
2241.4 
22317.4 
45458.4 
585.8 
3414.8 
878.4 
5142.9 
981.2 
531.4 
157.8 
178.4 
2923.3 
7243.9 
1082.1 
968.5 
274.0 
381.4 
3845.6 
4416.3 
116685.7 

Aid Elements (EUR Million) 
– at current prices 
2019 

2020 

2018 

1901.8 
3761.9 
639.0 
820.6 
104.9 
3715.7 
4677.0 
278.7 
2347.3 
21715.2 
46480.1 
611.7 
2940.7 
922.4 
8008.0 
319.0 
740.0 
164.5 
352.1 
3274.6 
6093.4 
1175.0 
1152.3 
463.0 
432.7 
4921.0 
4301.2 
122314.0 

2008.1 
4605.3 
388.1 
751.8 
125.1 
3442.0 
4450.3 
344.9 
2267.9 
24056.3 
52545.2 
1047.9 
2620.0 
1236.3 
8255.5 
327.1 
899.7 
193.7 
305.3 
3349.6 
5966.1 
1084.5 
1470.0 
612.6 
417.1 
8188.9 
3928.1 
134887.6 

7605.5 
6516.7 
804.5 
1350.5 
232.6 
6032.2 
7716.3 
461.8 
3151.3 
52929.7 
118694.1 
7127.5 
4310.4 
1551.1 
32328.4 
813.9 
1141.0 
446.8 
646.8 
7470.6 
27192.7 
3576.1 
5357.7 
1653.5 
2142.3 
14297.7 
4947.3 
320498.9 

2017 

2021 
10477.6  0.49 
6337.6  0.50 
1210.8  1.25 
1289.2  1.31 
517.5  0.50 
6423.3  1.31 
5289.2  1.45 
451.0  0.89 
3960.0  0.93 
63299.3  0.92 
121211.5  1.31 
5369.4  0.32 
5337.7  2.33 
3143.7  0.25 
31526.1  0.29 
934.7  3.20 
994.7  1.09 
651.5  0.25 
673.1  1.26 
16210.5  0.36 
10498.9  1.36 
2879.3  0.50 
4984.6  0.43 
2260.6  0.29 
1524.1  0.79 
20447.4  0.31 
6635.3  0.93 
334538.7  0.83 

Normalized expenditure 
(% of 2019 GDP) 
2019 
0.51 
0.96 
0.63 
1.35 
0.54 
1.53 
1.44 
1.24 
0.95 
0.99 
1.51 
0.57 
1.79 
0.35 
0.46 
1.07 
1.84 
0.31 
2.15 
0.41 
1.12 
0.51 
0.66 
0.65 
0.86 
0.66 
0.82 
0.96 

2018 
0.48 
0.79 
1.04 
1.47 
0.45 
1.65 
1.51 
1.00 
0.98 
0.89 
1.34 
0.33 
2.01 
0.26 
0.45 
1.04 
1.51 
0.26 
2.48 
0.40 
1.14 
0.55 
0.51 
0.49 
0.89 
0.40 
0.90 
0.87 

2020  2021 
1.91 
1.36 
1.31 
2.43 
1.00 
2.67 
2.49 
1.66 
1.31 
2.17 
3.42 
3.89 
2.94 
0.43 
1.80 
2.65 
2.33 
0.72 
4.56 
0.92 
5.11 
1.67 
2.39 
1.75 
4.41 
1.15 
1.04 
2.29 

2.64 
1.32 
1.97 
2.32 
2.23 
2.85 
1.71 
1.62 
1.65 
2.60 
3.49 
2.93 
3.64 
0.88 
1.75 
3.05 
2.03 
1.04 
4.75 
1.99 
1.97 
1.34 
2.22 
2.39 
3.14 
1.64 
1.39 
2.39 

1         The  table  shows  the  evolution  of  total  aid  expenditure  –  in  terms  of  aid  element  –  by  Member  States  for  the  years  2017-2021- 
Normalized expenditure is computed as a percentage of the 2019 GDP (Eurostat). Totals are in Millions. 

Source: Authors’ calculations based on State Aid Reporting Interactive (SARI2) data and EUROSAT (GDP), May 2023 version.   

As documented by (European Commission, Directorate-General for Competition 2022b), the total amount spent 
by Member States in 2020 exceeded 320 billion EUR, more than 2.5 higher than the expenditure in 2019. The 
increase is stable between 2020 and 2021 in all countries except Poland for which there is a relatively large 
drop in its 2021 normalized expenditure. Moreover, not only the amount, but also the distribution of instrument 
types has changed substantially between 2019 and the 2020-2021 period (see figure A1b in Appendix 1).  

15 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 1. Total State aid expenditure (% of 2019 GDP), by policy objective (COVID vs. Non-COVID) 1 

)

P
D
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non-Covid

Covid

1     The figure shows the total aid expenditure - in terms of aid element as share of the 2019 national GDP, by related objectives (COVID 
and non-COVID) and Member State. Totals are in EUR million.  

Source: Authors’ calculations based on State aid Reporting Interactive (SARI2) data and EUROSAT (GDP), May 2023 version.   

Figure  1 provides an insight  on the  difference in State aid expenditure (aid element)  for COVID-related and 
other State aid measures in 2020 and 2021. In both years, the expenditure is larger for COVID-related measures 
than for other aid types. The picture, however, is much nuanced across countries, with large differences in both 
the normalised and absolute (see figure A1 in Appendix 1) values of the expenditure, showing that the relative 
importance of COVID aid expenditure differs across countries (e.g. higher for Poland and Italy, lower for Belgium 
and Spain). This may be due to both the different mix of policy measures used by Member States to tackle the 
economic effects of the pandemic and the different types of instruments used in the context of COVID aid.  

Table 2 focuses only on the COVID State aid, in particular, looking at the nominal values of the aid measures.9 
Differently from aid element which measures the economic advantage passed on to the beneficiaries, nominal 
amounts represent the nominal amount granted by the public authority. These figures therefore represent the 
nominal  amounts  of  aid  disbursed  via  different  instruments,  which  should  be  interpreted  with  caution  as 
different  instruments  have  different  economic  impact  on  the  distortion  of  competition  (repayable  forms  of 
support are generally less distortive of competition).  

9 For COVID-19 related measures, State aid expenditures in terms of aid element are available in SARI. 

16 

 
 
 
 
 
 
 
 
 
                                           
 
 
 
Table 2. Total COVID aid expenditure (based on nominal amounts) 1 

Nominal Amount   
 (EUR Million) 

Normalized expenditure  
 (% of 2019 GDP) 

2020 

2021 

2020 

2021 

Austria 

Belgium 

Bulgaria 

Croatia 

Cyprus 

Czech Republic 

Denmark 

Estonia 

Finland 

France 

Germany 

Greece 

Hungary 

Ireland 

Italy 

Latvia 

Lithuania 

Luxembourg 

Malta 

5999.2 

4795.8 

530.5 

220.3 

149.1 

3202.3 

7352.4 

536.3 

8609.9 

1603.6 

1068.3 

485.0 

413.9 

5183.6 

3280.9 

184.1 

1707.9 

1805.0 

142006.8 

50432.9 

123700.2 

95159.2 

7607.4 

4497.7 

303.0 

5944.7 

6135.3 

2167.5 

147635.5 

91392.3 

438.3 

519.7 

530.7 

555.6 

661.0 

392.5 

467.9 

408.5 

Netherlands 

15533.3 

30663.6 

Poland 

Portugal 

Romania 

Slovakia 

Slovenia 

Spain 

Sweden 

Total EU  

25435.6 

9045.6 

3369.3 

1139.5 

1917.4 

8075.1 

2017.7 

3528.8 

1828.9 

1093.3 

41380.0 

81314.4 

1492.3 

1309.7 

551601.6  405627.5 

1.51 

1.00 

0.86 

0.40 

0.64 

1.42 

2.38 

1.93 

0.71 

5.83 

3.56 

4.15 

3.07 

0.08 

8.22 

1.43 

1.06 

0.85 

3.92 

1.91 

4.78 

4.22 

1.50 

1.21 

3.95 

3.32 

0.31 

2.17 

0.34 

1.74 

0.87 

1.79 

2.30 

1.06 

0.66 

0.75 

2.07 

2.74 

3.24 

4.19 

0.61 

5.09 

2.15 

0.80 

0.75 

2.88 

3.77 

1.52 

0.94 

1.57 

1.94 

2.25 

6.53 

0.27 

1     The figure shows the total aid expenditure for COVID-related State aid, in terms of nominal amounts, by Member State. Totals are in 
Millions. Normalized expenditure is computed as a percentage of the 2019 GDP (Eurostat). Totals are in Millions 

Source: Authors’ calculations based on State Aid Reporting Interactive (SARI2) data.   

In absolute terms, Germany has the largest amount of aid (123.7 EUR billion in 2020 and 95.16 EUR billion in 
2021), corresponding to around less than a fourth (in 22.4% 2020 and 23.3%  in 2021) of the total amount 
spent in the EU (551,601 EUR billion in 2020 and 405,627 EUR billion in 2021), followed by Italy with 147.6 
EUR billion in 2020 (26.5% of the EU total) and 91.3 EUR billion in 2021 (22.5% of EU total), France with 160 
in 2020 and 50 EUR billion in 2021  (19% and 25% of EU total),  and Spain with 81.3 EUR billion in 2021 (20%, 
of EU total). Poland granted only 25.4 EUR billion, 4.6% of the total State aid. In relative terms, in 2021, Spain 
has the largest share of expenditure (6.5%) followed by Italy (5.9%), and Hungary (4.2%). 

17 

 
  
  
  
  
 
 
 
 
 
Figure 2. Normalized COVID aid expenditure (% of GDP), by type of instrument 2020-20211 

%

5

4

3

2

1

0

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1
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0
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1
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1
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2

T
A

E
B

G
B

Y
C

Z
C

E
D

K
D

E
E

L
E

I

S F
E

R
F

R
H

U
H

E

I

T

I

T
L

U
L

V
L

T
M

L
N

L
P

T
P

O
R

I

E S
S

K
S

Direct Grant (DG)

DG/Int.Rate subsidy

Guarantee

Other

Loan

Tax advantage

1     The figure shows the COVID-related total aid expenditure (in nominal amounts) as share of 2019 national GDP, by type of financial 
instrument used and Member State. 

Source: Authors’ calculations based on State aid Reporting Interactive (SARI2) data and EUROSAT (GDP), May 2023 version.   

Figure  2  illustrates  that  the  majority  of  Member  States  provided  most  of  their  COVID-related  support  to 
undertakings via repayable instruments (i.e. guarantee and loans). Guarantees have been widely used, especially 
in Romania, Italy, France (only in 2020), and Spain, while Poland and Greece made a significant use of loans at 
favourable terms. Nonetheless, some countries, such as Austria, Denmark, France (in 2021), Hungary, Slovenia 
and Slovakia, made also an extensive use of direct grants. As the European Commission (2022b) indicates, the 
use  of  repayable  forms  of  support  is  generally  less  distortive  of  competition  and  allows  mobilising  large 
volumes of aid with a relatively smaller direct and immediate budgetary cost for the State (subsidised loans), 
if any at all (guarantees only have a budgetary impact if they are called), as compared to direct grants. This 
explains  the  widespread  use  of  repayable  instruments  to  channel  COVID-19  aid.  Nonetheless,  this  type  of 
measures expose the State to a credit risk, which may trigger a need to restructure the loans in the future.  

18 

 
  
 
 
 
3  Literature Review  

The literature on the effectiveness and impact of State aid is extensive and long-standing. Previous studies 
differ  in  terms  of  the  methodology  used,  the  outcome  variable  of  interest,  the  time  period  and  the  unit  of 
analysis. There is no clear consensus on the State aid effectiveness. A systematic review of the existing studies 
focusing on the impact of grants on SMEs in the EU is provided by Dvouletý et al. (2021). Their results mostly 
indicate a positive effect of grants on firm survival, employment, tangible/fixed assets and sales/turnover, while 
the evidence on the effects on labour productivity and total factor productivity (TFP) is mixed. However, the 
study highlights that the effects are very heterogeneous depending on firm characteristics (such as age, size, 
and sector of activity) and the time elapsed since the intervention (short-term vs. long-term effects). The review 
focuses  on  longitudinal  studies  using  firm-level  data.  In  terms  of  methodology,  the  most  commonly  used 
methods  are  matching  techniques  combined  with  DiD,  with  propensity  score  being  the  preferred  matching 
method. Notably, fewer studies use RDD or two-stage Heckman selection models. 

The framework for the implementation of the COVID-19 aid differed compared to other forms of State aid as 
it included milder restrictions to access the aid, with the aim of mitigating the huge risk of insolvencies and 
bankruptcies (OECD, 2021). This variety of measures undoubtedly helped companies to cope with the crisis, 
although it may have affected the allocation of resources in the market. Identifying the unintended effects of 
aid to companies, especially in terms of aggregate productivity and zombification of the economy, is beyond 
the aim of this paper, albeit this is a relevant aspect that has been explored in the literature (Andrews et al. 
2016, 2017). 

Some recent studies consider the effects of the pandemic on productivity and debt, bankruptcy triggers and 
predicted firm exit, focusing on specific countries (Cros et al., 2021 for France; and Tielens and Piette, 2022 for 
Spain). Other studies (Tielens et al., 2020; Chundkkadan et al., 2022) assess the impact on capital constraints, 
while Dhyne & Duprez (2021) look at the impact of COVID-19 on several firm outcomes in Belgium and find 
that  the  crisis  led  to  greater  differences  in  firm  performance  than  in  normal  times.  Other  related  studies 
examine the impact of COVID-19 on labour market outcomes such as employment rates, job separation, and 
firms’ survival. In particular, Adams-Prassl et al. (2020) present real-time survey evidence from the UK, the US 
and Germany showing that COVID-19 had significantly different effects on labour market outcomes (job loss 
and  hours  worked)  across  countries,  and  in  some  cases  exacerbated  existing  inequalities  within  countries.  
Altomonte  et  al.  (2021)  assess  the  impact  on  employment  and  find  that  the  measures  were  effective  in 
containing unemployment, with some heterogeneity across the three countries considered (Italy, Germany and 
France).  

Bennedsen  et  al.  (2020)  examine  whether  government  support  during  the  COVID-19  crisis  reduced  job 
separations (number of workers employed, laid off and on leave). Their results show that the support measures 
sorted effects in the expected direction, thus reducing the social impact of the pandemic. Bertschek et al. (2023) 
found that emergency assistance had a significant effect in (slightly) increasing the survival probability of the 
self-employed, using a sample of about 20000 observations in Germany with some heterogeneity in terms of 
education, risk tolerance and sectors.  

Furthermore,  a  few  recent  studies  document  the  effect  of  COVID-related  policy  support  on  different  firm 
outcomes. The analysis presented in this report makes a specific contribution to this area of research. Some 
papers (Harasztosi et al., 2022, Bighelli et al., 2021 and Zegel et al., 2021) focus on the characteristics of the 
beneficiaries to assess whether the pre-crisis transfers  were primarily targeting  weaker and less  productive 
firms. Additionally, there are some non-academic policy evaluations carried out by national research institutes 
in the Netherlands (Davies et al., 2023) and in Belgium (Tielens et al., 2020, 2022; Zegel et al., 2021). A broader 
geographical coverage is provided by Harasztosi et al. (2022), using EIBIS survey data on the entire EU area, 
and Bighelli et al. (2021) focusing on Croatia, Finland, Slovakia and Slovenia. 

The results presented in this report are generally consistent with those papers focusing on short-term effects 
of public support to firms on their liquidity measures. The effect of State aid on turnover is usually positive, as 
found by Dvouletý et al. (2021), that identified this relationship in about ten of the examined studies (Bernini 
and Pellegrini, 2011; Hartšenko and Sauga, 2013; Cerqua and Pellegrini, 2014, 2017; Söderblom et al., 2015; 
Decramer and Vanormelingen, 2016), while a negative or non-significant effect is found in only four studies 
(Roper and Hewitt-Dundas, 2001; Mole et al., 2009; Brachert et al., 2018; Srhoj et al., 2019). 

19 

 
 
 
Triggered by the 2014 State aid Modernisation (SAM) initiative of the Directorate General of Competition that 
introduced the obligation for retrospective evaluations of aid schemes, the European Commission is increasingly 
recognising the role of economic analysis in the control of State aid to companies. Nonetheless, there are still 
no  comprehensive  retrospective  studies  performed  on  State  aid  control  to  date  assessing  the  so-called 
balancing test. The only exception is the study by Duso et al 2021, which analysed whether a Broadband subsidy 
in  Germany  was  effective  in  solving  a  market  failure  –  low  broadband  coverage  in  rural  areas  –  but  also 
examined its impact on competition by assessing the aid effect on both rival firms and consumers (i.e. entry 
and market prices),.  

Assessing the impact of COVID-aid on competition is beyond the scope of this study. The present study focuses 
on the impact of COVID Aid on firms’ turnover and probability of default. The contribution to existing literature 
is twofold. First, the analyses rely on  detailed information on the  universe of State aid  granted in three EU 
Member States namely Italy, Spain and Poland. This allows a much more cleaner definition of treated entities 
(e.g. firms receiving COVID aid) but, even more importantly, adequate controls (e.g. firms that did not receive 
any sort of aid in the years between 2017-2021) Second, the current analysis uses the estimated probability 
of default, a novel indicator published by Bureau Van Dijk that also allows to overcome the limitations of the 
Orbis data in studying the exit of firms. 10 To some extent, the present study is comparable with Konings et al. 
(2023)  in  that  it  examines  the  short-term  effects  of  aid  to  firms  during  the  COVID-19  pandemic  and  finds 
largely  positive  effects  on  firm  performance,  although  creative  destruction  and  growth  are  not  the  main 
outcomes of interest.  

The COVID-19 support schemes were an emergency tool to avoid massive economic fallout. They were designed 
to be temporary and could therefore be loosely targeted (Gourinchas et al. 2021). They were not thought to 
boost  growth  or  foster  financial  stability.  This  poses  some  challenges  in  terms  of  the  comparability  of  the 
present results with existing literature. 

10 See Bajar et al (2020), pag. 9 "[Orbis] ability to capture evolution of the entire firm distribution remains limited, and it continues to 
perform poorly at measuring entry and exit" 

20 

 
 
 
 
 
 
                                           
 
4  Data 

To gather accurate information on State aid beneficiaries the analysis combines data from National State aid 
Registers of Italy, Poland, and Spain, including all firm-level aid awards with firms’ balance sheet data extracted 
from  Orbis.  Additionally,  SARI2  has  been  used  to  complement  information  on  State  aid  expenditures  at  an 
aggregate level as this does not include any information on single beneficiaries. The selected National State 
aid Registers are compiled by the Ministry of Economy and Finance of the respective Member States to keep 
track of all State aid awarded in these countries.11 

The National Registers (NR) provide information on the date on which each individual aid award was granted; 
the  nominal  amount  of  the  aid;  the  aid  element,  the  name,  location,  and  the  national  identifier  of  the  firm 
receiving the aid; the industry sector of the firm; the objective of the aid, and the reference to the aid measure. 
Most importantly, the NR include information about the type of financial instrument of the aid, distinguishing 
among guarantees; loans; direct grants; interest rate subsidies; tax advantages and “other types” of instruments 
(a residual category grouping less commonly used instruments). 

Firms can in principle receive more than one aid award per year, and each of them can be awarded under a 
different type of instrument. In other words, the NR datasets have a multi-level structure, with firms potentially 
reporting more than one row (aid) per year. To build a firm-year panel dataset, the information is collapsed to 
compute the total amount granted to a firm in a given year. In addition, the amount spent by type of instrument 
is also calculated.  

Data from the NR cover the period between 2017 and 2022. COVID State aid schemes have been identified 
based on the case reference or, when this was not available, using the aid objective description. The aid was 
classified as a COVID measure whenever the words “COVID State aid” or the relevant legal basis (i.e. Remedy 
for a serious disturbance in the economy) for COVID aid were mentioned. 

Overall, the number of aid interventions reported in the NR is: 7,871,867 for Italy (reported by 4,283,337 firms); 
4,969,028 for Spain (1,200,782 firms) and 14,053,636 for Poland (3,830,540 firms). 

Firm-specific financial data are obtained from the Orbis database distributed by Bureau Van Dijk (BvD)/Moody's. 
Orbis is a comprehensive database widely used for economic and financial research, providing information on 
private and public companies in different countries. As discussed later in this report, this variability may affect 
the reliability of the information for specific group of firms that are not adequately represented (Bajgar et al., 
2020). It is important to note that Orbis focuses on the non-farm business economy, which includes all sectors 
except agriculture and public services. The data available covers approximately 10 years, which allows for the 
construction of a balanced firm-level panel.  

Despite its limitations, Orbis remains the most comprehensive source of firm-level information available for 
cross-countries comparisons. The selection of countries for the analysis is determined by the availability of data 
from  State  aid  NRs.  The  final  dataset  is  a  balanced  panel  meaning  that  only  firms  including  non-missing 
variables in each year are used for analysis. The process of data selection is described in detail in Section 4.2.4. 
Financial information is measured at the firm level, by considering the “unconsolidated accounts”. The firm-
level information in Orbis is then merged with National registers using national identifiers of the beneficiaries 
of State aid.  

Matching Orbis with the State aid NRs undoubtedly represents a unique advantage of the current analysis in 
terms  of  data  granularity  and  accuracy.  However,  relying  on  a  subset  of  countries  could  be  considered  a 
limitation of the study, given the heterogeneous coverage of Orbis across countries. Yet, 2 out of the 3 countries 
considered, namely Italy and Spain, are among those with better coverage (around 60% of total output and 
40% of total value added) according to the comparison at the aggregated country-industry cell carried out in 
Bajgar et al. (2020), which refers to OECD-STAN. The third country considered, Poland, also shows a generally 
good  coverage,  but  lacks  precision  for  some  variables  (e.g.  value  added  and  number  of  employees)  due  to 
rounded values.  

11 The creation and maintenance of National State Aid Registers is not mandated by the European Commission. 

The initiative of single 
Member States in this direction should be taken as a best practice. Please note that other National Registers, 
for example Romania and Slovenia, follow the transparency rules but only publish aid awarded above the 
reporting thresholds presented in Annex 2. 

21 

 
 
                                           
Examining the coverage and representativeness of Orbis is beyond the scope of the proposed analysis, which 
focuses on a specific and well-identified set of firms, as will be discussed in the following sections. Furthermore, 
the results of the matching procedure were reassuring, as Orbis covers a large number of firms present in the 
National Registers.12 Specifically, Orbis covers 82.1% of firms recorded in the Italian National Register, 83.1% 
of firms of the Spanish Register, and 66.6% of firms in the Polish register. 

In this study, the treatment group is defined as those firms that received COVID aid in 2020. This excludes from 
the analysis beneficiaries of other types of State aid, as well as firms that received a combination of COVID 
and non-COVID State aid over the period 2017-2022. This simplifies the analysis and allows a clear comparison 
between COVID aid beneficiaries and firms that never received any aid over the period 2017-2022. 

4.1  Sample selection 

The following section discusses the representativeness of the sample used in the analysis with respect to the 
Orbis population, focusing on two issues: 1) the sample selection due to excluding observations with missing 
values; 2) the selection due to attrition i.e. observations not being observed in some specific years. 

4.1.1  Missing values 

Table 3 compares the characteristics of the unbalanced sample (i.e. the sample excluding missing values in the 
variables of interest) with the raw data.  

In all the countries, micro firms are more likely to be excluded from the analysis due to missing values. In Italy, 
firms in the retail sector are under-represented with respect to the wider Orbis population, whilst in Spain and 
Poland firms are under-represented in the construction sector. 

Table 3. Firms characteristics of analysed samples compared to excluded observations 

Analysed  
sample 
% 

Panel A - Italy 

Excluded 
observations 
% 

Diff. 

p-value 

N analysed  
sample 

N excluded  
observations 

90.2% 
7.7% 
1.6% 
0.5% 

22.0% 
33.6% 
27.8% 
16.6% 
Mean 
2.60 
0.17 
0.15 
2.57 
5.10 
9.67 

SD 
116.01 
7.76 
12.18 
72.85 
22.92 
17.90 

99.6% 
0.3% 
0.0% 
0.0% 

13.9% 
30.0% 
23.4% 
32.7% 
Mean 
0.31 
0.15 
0.10 
1.24 
6.15 
24.46 

-0.090 
0.070 
0.020 
0.000 

0.000 
0.000 
0.000 
0.000 

0.080 
0.040 
0.040 
-0.160 
Diff. 
2.290 
0.020 
0.050 
1.330 
-1.050 
-14.790 

0.000 
0.000 
0.000 
0.000 
p-value 
0.000 
0.308 
0.165 
0.000 
0.000 
0.000 

SD 
9.45 
4.80 
2.18 
26.44 
27.95 
29.27 

131,878 
131,878 
131,878 
131,878 

131,878 
131,878 
131,878 
131,878 

131,878 
131,878 
131,878 
131,878 
131,878 
131,799 

834,131 
834,131 
834,131 
834,131 

834,131 
834,131 
834,131 
834,131 

834,131 
335,355 
117,624 
117,624 
65,415 
117,518 

 Firm type 
Size1 
Micro 
Small 
Medium 
Large 
Sector2 
Manufacturing 
Construction 
Wholesale 
Retail 
Financial variables3 
Turnover (€M) 
Capital (€M) 
Loans (€M) 
Total assets (€M) 
EBITD margin (M) 
Probability of default 
(continue in the next page) 

Table 3 (continued). Firms characteristics of analysed samples compared to excluded observations 

12 Firms excluded from the merging are mainly beneficiaries that are not obliged to publish their balance sheet. 

This may happen for partnerships, for example. 

22 

 
 
 
 
 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
 
 
 
 
 
 
 
                                           
Panel B - Spain 

Analysed  
sample 
% 

Excluded  
observations 
% 

Diff. 

p-value 

N analysed  
sample 

N excluded  
observations 

84.8% 
12.0% 
2.6% 
0.6% 

21.4% 
28.5% 
31.4% 
18.7% 
Mean 
2.71 
0.29 
0.13 
2.35 
3.93 
10.53 

SD 
48.17 
11.19 
1.42 
33.07 
17.79 
20.66 

94.3% 
4.5% 
0.9% 
0.2% 

15.4% 
38.0% 
27.8% 
18.7% 
Mean 
1.23 
0.28 
0.10 
1.93 
3.55 
21.06 

-0.090 
0.070 
0.020 
0.000 

0.060 
-0.100 
0.040 
0.000 
Diff. 
1.480 
0.010 
0.020 
0.420 
0.390 
-10.530 

0.000 
0.000 
0.000 
0.000 

0.000 
0.000 
0.000 
0.747 
p-value 
0.000 
0.810 
0.017 
0.073 
0.000 
0.000 

194,144 
194,144 
194,144 
194,144 

194,144 
194,144 
194,144 
194,144 

194,144 
194,144 
194,144 
194,144 
194,144 
194,077 

SD 
57.25 
9.83 
3.45 
91.30 
26.64 
29.97 

103,393 
103,393 
103,393 
103,393 

103,393 
103,393 
103,393 
103,393 

93,218 
103,385 
97,483 
103,393 
81,384 
103,301 

Panel C - Poland 

Analysed  
sample 
% 

Excluded  
observations 
% 

Diff. 

p-value 

N analysed  
sample 

N excluded  
observations 

68.4% 
22.1% 
7.5% 
2.0% 

24.3% 
23.5% 
37.2% 
15.1% 

91.7% 
6.5% 
1.5% 
0.3% 

21.0% 
31.8% 
32.2% 
14.9% 

Mean 

SD 

Mean 

SD 

-0.230 
0.160 
0.060 
0.020 

0.000 
0.000 
0.000 
0.000 

    19,708  
    19,708  
    19,708  
    19,708  

           44,559  
           44,559  
           44,559  
           44,559  

    19,708  
    19,708  
    19,708  
    19,708  

           44,559  
           44,559  
           44,559  
           44,559  

0.030 
-0.080 
0.050 
0.000 
Diff. 
5.320 
0.340 
0.000 
2.590 
-2.470 
-11.570 

0.000 
0.000 
0.000 
0.614 
p-value 
0.000 
0.000 
0.961 
0.000 
0.000 
0.000 

 Firm type 
Size1 
Micro 
Small 
Medium 
Large 
Sector2 
Manufacturing 
Construction 
Wholesale 
Retail 
Financial variables3 
Turnover (€M) 
Capital (€M) 
Loans (€M) 
Total assets (€M) 
EBITD margin (M) 
Probability of default 

Firm type 
Size1 
Micro 
Small 
Medium 
Large 
Sector2 
Manufacturing 
Construction 
Wholesale 
Retail 
Financial variables3 
Turnover (€M) 
Capital (€M) 
Loans (€M) 
Total assets (€M) 
EBITD margin (M) 
Probability of default 

6.82  64.66 
8.34 
0.50 
0.21 
2.28 
3.79  25.96 
5.55  21.50 
10.33  19.71 

           38,418  
          44,099  
           16,251  
           44,267  
           32,400  
           43,791  
Firm size is based on the average turnover for 2017-2019. Size categories are defined as: Micro: average turnover < 2 EUR Millions; 
Small: average turnover between 2 and 10 Millions of EUR; Medium: average turnover between 10 and 50 Millions of EUR; Large: 
average turnover over 50 Millions of EUR. 
The sectoral classification is based on the NACE Rev. 2 system 
Turnover, Capital, Loans and Total assets are expressed in millions of EUR.  

1.50  15.97 
5.14 
0.16 
0.21 
2.26 
1.20  12.11 
8.02  32.33 
21.90  29.77 

    19,708  
    19,708  
    19,708  
    19,708  
    19,708  
    19,568  

1 

2 
3 

Firms that are excluded from the analysis have, on average, lower turnover, lower profitability (EBIT margin) 
and higher probability of default as compared to the analysed samples. In particular, the probability of default 
of excluded firms is more than double with respect to those included in the analysis. This is in line with the fact 
that excluded firms are relatively smaller and have worse financial parameters as compared to those included 
in the analysis.  

4.1.2  Firms not appearing in Orbis after 2019 

Since the focus of the analysis is on a balanced panel of firms, this section investigates the representativeness 
of the latter as compared to firms that disappeared in the post-treatment period (2020 and 2021). This is to 
facilitate the interpretation of results, given that the goal of COVID aid was to support firms at risk of going 
bankrupt. It should be noted, however, that Orbis data are unsuitable for survival analysis - i.e. estimating the 
probability of a firm surviving over time (Bajgar et al., 2020) - due well-known reporting problems. Specifically, 
firms may not report information in a particular year or report with a lag of up to 5 years. Therefore, it is not 

23 

 
 
 
  
  
  
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
 
 
  
  
  
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
possible to distinguish whether firms disappear from the sample because of bankruptcy or due to reporting 
issues. 

Table 4 presents two separate probit estimations, modelling the probability of disappearing in a given year (e.g. 
2020) as compared to being observed in the entire period of analysis (e.g. 2017-2020). 

In all the countries analysed, firms that disappear in the post-treatment period are more likely to have lower 
turnover, capital, and EBIT, and be smaller in size. There are also some significant differences in their sectoral 
and geographical distributions and these differences are larger in 2021. This year is more likely to be affected 
by reporting lags, as this is the most recent one. 

Finally, the sample used for the analysis is a selected sample of the total population of firms. This needs to be 
taken into account when reading the results. For example, the exclusion of small firms, which may have worse 
financial  performance  and  are  more  likely  to  be  misreported  in  the  2021  Orbis  data,  could  lead  to  an 
overestimation of the (positive) effect of the aid on firm performance.  

Table 4. Probit models. Dependent variable = 1 if firm is not reported in a given year (see table); marginal 
effects1. Year: 2019 

Outcome2 
Country 
Turnover3 

Capital 
2nd quartile 

3rd quartile 

4th quartile 

EBIT margin 
2nd quartile 

3rd quartile 

4th quartile 

Sector4 
Construction 

Wholesales 

Retail 

Firm size5 
Small 

Medium 

Large 

NUTS 2 Regions 
Region 2 

Region 3 

Region 4 

Region 5 

Region 6 

Region 7 

Firms disappearing  
from 2021 

Firms disappearing 
 from 2020 

IT 
-0.004*** 
(0.001) 

ES 
-0.022*** 
(0.001) 

PL 
-0.013*** 
(0.002) 

IT 
-0.018*** 
(0.000) 

ES 
-0.009*** 
(0.000) 

PL 
-0.026*** 
(0.002) 

-0.055*** 
(0.005) 
-0.099*** 
(0.005) 
-0.107*** 
(0.005) 

-0.029*** 
(0.004) 
-0.018*** 
(0.004) 
0.031*** 
(0.004) 

0.031*** 
(0.004) 
0.007* 
(0.004) 
0.016*** 
(0.004) 

-0.082*** 
(0.005) 
-0.114*** 
(0.010) 
-0.124*** 
(0.019) 

0.010 
(0.033) 
0.007 
(0.010) 
-0.073*** 
(0.006) 
0.113*** 
(0.012) 
0.042* 
(0.024) 
0.107*** 
(0.007) 

-0.010*** 
(0.003) 
-0.024*** 
(0.003) 
-0.014*** 
(0.003) 

-0.054*** 
(0.003) 
-0.058*** 
(0.003) 
-0.047*** 
(0.003) 

-0.021* 
(0.012) 
-0.067*** 
(0.009) 
-0.064*** 
(0.009) 

-0.013 
(0.010) 
-0.024** 
(0.009) 
0.004 
(0.010) 

0.004 
(0.003) 
0.002 
(0.003) 
0.011*** 
(0.003) 

0.077*** 
(0.004) 
0.228*** 
(0.009) 
0.321*** 
(0.016) 

0.021*** 
(0.007) 
0.015* 
(0.009) 
0.050*** 
(0.005) 
0.004 
(0.007) 
0.193*** 
(0.014) 
0.040*** 
(0.005) 

0.005 
(0.009) 
-0.002 
(0.008) 
0.016 
(0.010) 

0.005 
(0.010) 
0.021 
(0.016) 
0.050* 
(0.029) 

-0.017 
(0.014) 
0.008 
(0.014) 
-0.000 
(0.019) 
0.056** 
(0.027) 
-0.004 
(0.016) 
0.000 
(0.026) 

0.011*** 
(0.002) 
0.013*** 
(0.002) 
0.025*** 
(0.002) 

-0.057*** 
(0.002) 
-0.056*** 
(0.002) 
-0.030*** 
(0.002) 

-0.003* 
(0.002) 
-0.008*** 
(0.002) 
-0.008*** 
(0.002) 

0.018*** 
(0.004) 
0.034*** 
(0.011) 
0.059* 
(0.031) 

0.005 
(0.014) 
0.003 
(0.005) 
0.000 
(0.003) 
0.009* 
(0.005) 
-0.007 
(0.008) 
-0.001 
(0.003) 

-0.001* 
(0.001) 
-0.000 
(0.001) 
0.009*** 
(0.001) 

-0.000 
(0.008) 
-0.005 
(0.006) 
-0.006 
(0.006) 

-0.027*** 
(0.001) 
-0.026*** 
(0.001) 
-0.020*** 
(0.001) 

-0.035*** 
(0.007) 
-0.032*** 
(0.007) 
-0.012* 
(0.007) 

0.004*** 
(0.001) 
0.002*** 
(0.001) 
0.001 
(0.001) 

0.015*** 
(0.002) 
0.024*** 
(0.006) 
0.065*** 
(0.021) 

0.005** 
(0.002) 
0.010*** 
(0.003) 
0.004** 
(0.002) 
0.002 
(0.002) 
0.001 
(0.003) 
0.004** 
(0.002) 

0.044*** 
(0.006) 
0.024*** 
(0.006) 
0.015** 
(0.007) 

0.021*** 
(0.008) 
0.068*** 
(0.015) 
0.068** 
(0.031) 

-0.020** 
(0.010) 
-0.014 
(0.010) 
0.011 
(0.014) 
-0.004 
(0.018) 
-0.002 
(0.011) 
0.019 
(0.020) 

(continue in the next page) 

24 

 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 4 (continued) 
Country 
Region 8 

Region 9 

Region 10 

Region 11 

Region 12 

Region 13 

Region 14 

Region 15 

Region 16 

Region 17 

Region 18 

Region 19 

Region 20 

Observations 

IT 
0.100*** 
(0.008) 
0.098*** 
(0.020) 
0.104*** 
(0.012) 
0.125*** 
(0.009) 
0.271*** 
(0.012) 
-0.071*** 
(0.007) 
-0.051*** 
(0.012) 
-0.046*** 
(0.007) 
-0.006 
(0.007) 
0.066*** 
(0.012) 
-0.006 
(0.009) 
0.072*** 
(0.006) 
-0.056*** 
(0.012) 
131,878 

ES 
0.127*** 
(0.004) 
0.111*** 
(0.005) 
0.067*** 
(0.005) 
0.051*** 
(0.009) 
0.092*** 
(0.003) 
0.064*** 
(0.004) 
0.249*** 
(0.007) 
0.133*** 
(0.004) 
0.022*** 
(0.005) 
0.035 
(0.031) 
0.330*** 
(0.039) 
0.104*** 
(0.006) 

PL 

IT 

-0.024 
(0.019) 
-0.006 
(0.026) 
-0.011 
(0.016) 
-0.001 
(0.017) 
0.051 
(0.031) 
-0.016 
(0.020) 
0.010 
(0.022) 
0.021 
(0.026) 
0.030** 
(0.013) 
0.001 
(0.022) 

0.005 
(0.003) 
0.002 
(0.007) 
-0.005 
(0.004) 
0.002 
(0.004) 
0.011** 
(0.005) 
-0.003 
(0.003) 
-0.002 
(0.005) 
0.003 
(0.003) 
0.004 
(0.003) 
0.008 
(0.005) 
0.004 
(0.004) 
0.008*** 
(0.003) 
-0.012*** 
(0.005) 
131,878 

PL 

-0.004 
(0.014) 
0.003 
(0.019) 
0.016 
(0.012) 
0.001 
(0.013) 
-0.022 
(0.020) 
-0.013 
(0.014) 
-0.027* 
(0.014) 
0.009 
(0.019) 
0.011 
(0.009) 
-0.026* 
(0.015) 

ES 
0.011*** 
(0.001) 
0.001 
(0.001) 
0.001 
(0.001) 
0.001 
(0.002) 
0.006*** 
(0.001) 
0.007*** 
(0.001) 
0.022*** 
(0.003) 
0.006*** 
(0.001) 
0.004** 
(0.002) 
0.020 
(0.015) 
0.055*** 
(0.020) 
0.010*** 
(0.002) 

194,144 

19,708 

194,144 

19,708 

1 
2 
3 

4 
5 

Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 
Base category of the outcome variable: whether the firm has being observed up to the 2021 (or 2020). 
Reference categories of the independent variables: - Size: Micro; Sector: Manufacturing; Region: Region 1 of the NUTS2 classification 
in each country. Capital and EBIT margin: 1st quartile of their distribution. 
The sectoral classification is based on the NACE Rev. 2 system 
Firm size is based on the average turnover for 2017-2019. Size categories are defined as: Micro: average turnover < 2 EUR Millions; 
Small: average turnover between 2 and 10 Millions of EUR; Medium: average turnover between 10 and 50 Millions of EUR; Large: 
average turnover over 50 Millions of EUR. 

4.2  Descriptive evidence  

The analysis presented in this study focuses on the four economic sectors that are better represented in Orbis, 
namely  manufacturing,  construction,  wholesale  and  retail.  As  discussed  in  Section  4.1.1  above,  the  study 
focuses on the balanced sample of firms that are observable throughout the period 2017-2021, and therefore 
have observations for all five years. 

4.2.1  Firms’ characteristics by country 

Table 5 shows the average sample characteristics of firms in the countries analysed. 

The distributions of firm size and sector differ by country. However, Italy and Spain are more similar in their 
sample compositions with respect to Poland. For example, in Poland, there is lower share of micro firms (59.9%) 
than  Italy  and  Spain,  where  these  shares  are  86.7%  and  84.7%.  There  is  also  a  lower  share  of  firms  in 
construction  (20%)  as  compared  to  Italy  and  Spain  (>27%),  and  a  higher  proportion  of  firms  in  wholesales 
(39.4%) than in Italy and Spain (<32%). Finally, the samples of Italy and Spain also have, on average, firms 
with lower levels of turnover, capital, and total assets.  

In principle, these figures  may partly reflect differences in sectoral specialisation and business demography 
across countries. However, Eurostat (2019) shows that the proportion of small firms (defined as those with less 
than 10 employees) is 95% in all the countries analysed. Hence, these figures – and in particular the higher 
proportion of medium and large firms for Poland - are more likely to reflect Orbis’ well-known differences in 
data representativeness (Bajgar, et al., 2020).   

25 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 5. Firm’s 2019 characteristics, by country 
Italy 

Spain 

% 

% 

% 

Poland 

84.7 
12.5 
2.4 
0.5 

86.7 
10.3 
2.3 
0.7 

24.2 
29.2 
29.9 
16.6 

59.9 
26.6 
10.6 
2.9 

Country 
Firm type 
Size1 
Micro 
Small 
Medium 
Large 
Sector2 
manufacturing 
construction 
wholesale 
retail 
Financial  
Variables3 
Turnover  
Capital  
Loans  
Total assets  
EBIT margin 
Probability of default 
Observations 
Firm size is based on the average turnover for 2017-2019. Size categories are defined as: Micro: average turnover < 2 € Millions; 
Small: average turnover between 2 and 10 Millions of €. 
The sectoral classification is based on the NACE Rev. 2 system 
Turnover, Capital, Loans and Total assets are expressed in millions of EUR.  

Min 
0.0 
0.0 
0.0 
0.0 
-99.9 
0.1 
142,395 

Max 
   16,494  
     1,517  
        154  
    6,005  
       100  
       100  

Max 
  30,670  
     2,347  
     4,367  
   18,504  
       100  
       100  

Max 
   6,214  
     389  
     110  
  1,697  
     100  
     100  

Mean 
10.2 
0.6 
0.3 
5.3 
5.6 
8.6 

Mean 
2.5 
0.2 
0.1 
2.1 
4.6 
9.0 

Mean 
3.8 
0.2 
0.2 
3.3 
5.6 
7.1 

SD 
151.3 
9.9 
15.9 
89.3 
16.9 
14.5 

0.0 
-261.7 
0.0 
0.0 
-100.0 
0.1 

Min 
0.0 
0.0 
0.0 
0.0 
-99.9 
0.1 

SD 
51.5 
6.4 
1.3 
29.9 
14.8 
18.6 

SD 
97.6 
8.6 
2.7 
35.0 
16.3 
17.5 

25.7 
20.0 
39.4 
14.9 

22.2 
27.3 
31.9 
18.7 

76,738 

7,213 

Min 

1 

2 
3 

Source: Authors’ computations based on ORBIS data. 

4.2.2  Financial  characteristics  of  COVID  aid  beneficiaries  with  respect  to  non-

beneficiaries  

To minimize distortions to competition in the EU, the Temporary Framework COVID aid set rules to prevent firms 
in financial difficulty before the pandemic to receive State aid. Hence, financial characteristics of beneficiaries 
are  expected  to  be  different  from  those  of  non-beneficiaries.  Table  6  presents  the  descriptive  statistics  for 
beneficiaries  and  non-beneficiaries  separately.  As  explained  above,  beneficiaries  are  defined  as  firms  that 
received COVID aid in 2020.  

The table reveals systematic differences between treated (i.e. firms that received COVID aid) and non-treated 
firms across all countries. Treated firms tend to be  smaller in size and in other financial indicators such as 
turnover,  capital,  loans,  EBIT  margin,  and  total  assets.  On  the  other  hand,  non-beneficiaries  had  higher 
probability of default. Differences in firm characteristics between beneficiaries and non-beneficiaries also vary 
by country, with more pronounced differences observed in Poland and relatively milder differences in Spain. For 
instance, in Poland, the average total assets of beneficiaries are 10,670 EUR million lower than those of non-
beneficiaries.  In  Italy  and  Spain,  these  differences  amount  to  4,460  and  1,240  EUR  million,  respectively. 
Regarding the sector of activity, COVID aid beneficiaries in Italy are over-represented in retail compared to non-
beneficiaries (+6pp), while in Poland, beneficiaries are over-represented in both the wholesale and retail sectors 
(-7pp). This is in line with the evidence that the sectors mostly hit by the pandemic were the wholesale and 
retail ones, as they suffered more prolonged and strict containment measures. To summarize, these figures 
shows that COVID beneficiaries were distinct from non-beneficiaries, and one needs to account for this selection 
into treatment when assessing the impact of the aid (see section 5). 

Table 6 also shows that the characteristics of treated firms vary by country analysed. In Italy, mainly micro 
firms received COVID aid (90% of the treated firms) and only 9% of the treated firms were small. In Spain, 
there were 82% of micro firms and 15% of small firms among those receiving COVID aid. In Poland, the sample 
size of firms receiving COVID aid was more mixed (64% of treated firms are micro, 27% are small, and 8% are 
medium-sized).  13 

Some differences arise also in terms of sector of activity. In Italy and Spain, aid is more spread across sectors 
while in Poland it is more concentrated in some specific sectors such as construction. For instance, in Poland 
the proportion of beneficiaries in wholesale is 40% whist in Spain and Italy this proportion is lower than 33%. 

13 Please note that these figures are based on the analysed samples and therefore the results cannot 
be  generalized  to  the  entire  firms’  population.  The  representativeness  of  the  analysed  samples  is 
discussed in this section (4). 

26 

 
 
 
 
 
 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
 
  
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
  
 
 
  
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
  
 
 
 
  
  
  
  
  
  
  
  
  
 
                                           
In all the countries analysed, firms that received COVID aid exhibit lower turnover, and have lower levels of 
capital, total assets and loans than their non-beneficiary counterparts. They also have a lower probability of 
default, and this might reflect the fact that the Temporary Framework rules prohibited to grant aid to companies 
that where in difficulty before the pandemic. 

4.2.3  Characteristics of COVID aid beneficiaries by firm size and sector 

This section investigates how the figures presented in Table 6 differ by firm size and industry sector. Differences 
by  size  are  of  interest  as  the  requirements  to  access  COVID  aid  were  looser  for  micro  and  small  firms  (as 
explained above). Moreover, these firms may experience more difficulties in accessing financial markets during 
periods of crisis. On the other hand, differences by sector are useful as the pandemic has had a different impact 
depending on the sectors of activity. These statistics are summarized here and reported in Appendix (Tables A2 
and A3).  

Differences by firm size 

In  Spain,  beneficiaries  had  a  higher  level  of  loans  than  non-beneficiaries,  and  this  difference  was  more 
pronounced for medium and large firms. In contrast, beneficiaries had lower total assets than non-beneficiaries, 
for all firm dimensions. In Italy, small and medium-sized beneficiaries had higher loans than non-beneficiaries 
(positive difference), while this difference was not significant among large firms. Italian beneficiaries also had 
lower levels of total assets as compared to non-beneficiaries, across all analysed firm dimensions. In contrast, 
the difference in EBIT margin was negative only for micro and small firms. In Poland, beneficiaries had lower 
loans than non-beneficiaries, which contrasts with the cases of Italy and Spain. Additionally, beneficiaries in 
Poland had lower total assets and EBIT margin. 

Across  all  analysed  countries  and  firm  dimensions,  beneficiaries  had  lower  levels  of  capital  than  non-
beneficiaries.  As  noted  before,  the  probability  of  default  was  lower  for  firms  that  received  COVID  aid  as 
compared  to  non-beneficiaries.  Furthermore,  this  difference  varied  across  countries,  ranging  from  0.27 
percentage points (pp) of Spain to 4.8 pp of Poland. Table A2 also indicates that in Italy and Spain, the difference 
in the probability of default between beneficiaries and non-beneficiaries did not significantly vary with firm 
size. In contrast, in Poland, this gap was higher among micro and small firms. 

Differences by sector 

Table A3 also reveals some interesting differences based on the sector of economic activity. For example, in all 
analysed  countries,  differences  in  turnover  between  beneficiaries  and  non-beneficiaries  were  larger  in  the 
wholesale sector. In Italy, the difference in probability of default between these two groups was larger in the 
wholesale sector, while in Poland, it was larger in the retail sector. In Italy and Spain, beneficiaries were less 
capitalised than non-beneficiaries in the manufacturing sector. This pattern was also observed in other sectors 
but with smaller gaps. 

In  summary,  there  were  substantial  differences  in  firm  characteristics  between  sector,  firm  size,  and  the 
interaction between the two. This heterogeneity will be considered in the econometric analysis. 

27 

 
Table 6. Descriptive statistics of firm pre-treatment characteristics (2019), by treatment status and country 

Beneficiaries 
% 

Italy 
Non- 
beneficiaries 
% 

Diff.3  p-value  % 

Beneficiaries 

Spain 
Non- 
beneficiaries 
% 

Diff. 

p-value  % 

Beneficiaries 

Poland 
Non- 
beneficiaries 
% 

89.6% 
8.6% 
1.6% 
0.3% 

24.5% 
26.9% 
29.4% 
19.2% 

83.1% 
12.5% 
3.2% 
1.2% 

23.9% 
32.1% 
30.6% 
13.4% 

SD 
Mean 
9.71 
1.47 
1.02 
0.07 
0.13 
1.25 
1.29  10.02 
5.39  12.92 
6.37  13.22 
           42,352  

Mean 

SD 

6.70  225.68 
14.67 
0.41 
0.27 
23.74 
5.75  132.97 
20.76 
5.79 
7.90 
15.93 
            34,386  

0.07 
-0.04 
-0.02 
-0.01 

0.01 
-0.05 
-0.01 
0.06 

Diff. 
-5.23 
-0.34 
-0.14 
-4.46 
-0.40 
-1.54 

0.000  82.2% 
0.000  15.1% 
2.4% 
0.000 
0.3% 
0.000 

0.067  23.4% 
0.000  24.4% 
0.001  33.0% 
0.000  19.2% 

86.7% 
10.2% 
2.4% 
0.7% 

21.1% 
29.7% 
30.9% 
18.2% 

p-value  Mean 

SD 

Mean 

SD 

0.000 
0.000 
0.218 
0.000 
0.001 
0.000 

1.98  10.72 
0.67 
0.10 
1.30 
0.17 
1.38 
6.59 
9.72 
3.09 
8.81  17.49 
          64,827  

2.97  69.04 
8.65 
0.33 
0.06 
1.22 
2.62  40.04 
5.94  17.90 
9.07  19.53 
           77,568  

-0.05 
0.05 
0.00 
0.00 

0.02 
-0.05 
0.02 
0.01 

Diff. 
-0.99 
-0.23 
0.11 
-1.24 
-2.85 
-0.27 

0.000 
0.000 
0.984 
0.000 

0.000 
0.000 
0.000 
0.000 

64% 
27% 
8% 
1% 

25% 
19% 
40% 
16% 

50% 
24% 
18% 
8% 

28% 
22% 
37% 
13% 

Diff. 

p-value 

0.14 
0.03 
-0.10 
-0.07 

-0.04 
-0.03 
0.04 
0.03 

0.000 
0.007 
0.000 
0.000 

0.002 
0.002 
0.004 
0.001 

p-value  Mean 

SD 

Mean 

SD 

0.000 
0.000 
0.000 
0.000 
0.000 
0.007 

5.70  87.42  22.09  119.98 
16.25 
1.55 
1.57 
0.26 
4.34 
0.22 
0.68 
1.78 
62.83 
2.35  13.15  13.02 
20.84 
6.28 
5.37  14.24 
20.81 
7.27  15.97  12.02 
             1,967  
          5,246  

Diff. 
-16.39 
-1.28 
-0.46 
-10.67 
-0.91 
-4.75 

p-value 
0.000 
0.000 
0.000 
0.000 
0.034 
0.000 

Country 

Variable 
Size1 
Micro 
Small 
Medium 
Large 
Sector 
Manufacturing 
Construction 
Wholesale 
Retail 
Financial 
variables2 
Turnover (€M) 
Capital (€M) 
Loans (€M) 
Total assets (€M) 
EBITD margin (M) 
Probability of default 
Observations 

1 

2 
3 

Firm size is based on the average turnover for 2017-2019. Size categories: micro: average turnover < 2 EUR Millions; small: average turnover between 2 and 10 Millions of EUR Medium: average turnover 
between 10 and 50 Millions of EUR; Large: average turnover over 50 Millions of EUR. 
Turnover, Capital, Loans and Total assets are expressed in Millions of EUR. 
the table reports average differences between beneficiaries and non-beneficiaries and the associated p-value. 

Source: Authors’ computations based on ORBIS and State Aid National registry data. 

28 

 
 
 
 
 
  
 
 
  
  
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
 
 
4.2.4  Relationship  between  the  probability  of  receiving  COVID  aid  in  2020  and  pre-

pandemic financial hardship 

The analysis also seeks to investigate whether COVID aid was granted to firms that were less likely to be in 
financial difficulties before the pandemic, as outlined in the objectives of the EC Temporary Framework. In this 
section,  firms  in  financial  difficulty  are  defined  as  those  undertakings  that  experienced  a  drop  in  turnover 
greater than 20% between 2019 and 2018.14 To descriptively analyse the relationship between receiving COVID 
aid and having experienced financial difficulties, a probit model is estimated. The outcome is a variable equal 
to one if the firm received COVID aid in  2020 and the independent variable is a  binary indicator  capturing 
whether  the  firm  was  in  financial  difficulty  in  2019.  Other  control  variables  capturing  pre-treatment 
characteristics such as capital, EBIT margin, loans, total assets, firm size, sector, and region in 2019 are also 
included. The same regression model is also estimated on different subsamples of firm sizes (micro, small, 
medium, and large size) and industry sectors (manufacturing, construction, wholesales, and retail).  

Table 7. Probit model: relationship between having had a large decrease in turnover in 2019 and the probability 
of receiving Covid Aid in 2020; marginal effects. 

 Country 

All firms 

Micro 

Small 

Medium 

Large 

By  
size 

Manufacturing 

Construction 

By  
sector 

Wholesales 

Retail 

IT 
-0.110*** 
(0.004) 
-0.113*** 
(0.005) 
-0.092*** 
(0.015) 
-0.056* 
(0.032) 
-0.167*** 
(0.037) 
-0.124*** 
(0.009) 
-0.105*** 
(0.007) 
-0.117*** 
(0.008) 
-0.079*** 
(0.013) 

ES 
-0.051*** 
-0.004 
-0.053*** 
(0.004) 
-0.050*** 
(0.012) 
0.020 
(0.031) 
0.058 
(0.069) 
-0.045*** 
(0.008) 
-0.047*** 
(0.006) 
-0.057*** 
(0.007) 
-0.057*** 
(0.011) 

PL 
-0.020 
(0.015) 
-0.026 
(0.017) 
-0.008 
(0.031) 
-0.015 
(0.061) 
0.017 
(0.110) 
-0.005 
(0.031) 
-0.019 
(0.029) 
-0.025 
(0.024) 
-0.011 
(0.042) 

Notes: Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Dependent variable = 1 if the firm was a COVID aid beneficiary in 
2020-2021 (and 0 otherwise). The main independent variable for which the coefficient estimates are reported is a binary variable equal 
to 1 if the firm had a drop in turnover of at least 20% between 2019 and 2018 (and 0 otherwise). Other control variables included: capital, 
EBIT margin, loans, total assets, firm size, sector, and region. The estimation is based on data from 2019. 

Table  7  reports  the  coefficients  of  interest  for  different  specifications  estimated  on  different  sub-samples. 
Overall,  they  show  that  there  is  a  negative  association  between  having  experienced  financial  difficulties  in 
2019 and receiving COVID aid in 2020-2021. While the analysis only identifies a correlation, this descriptive 
evidence  aligns  with  the  fact  that  COVID  aid  should  have  been  granted  to  firms  that  were  not  in  financial 
distress before the pandemic. Additionally, there are differences by country. For example, these associations 
are larger in magnitude for Italy and statistically insignificant for Poland, probably due to the lower sample 
size. In Italy, the negative association between receiving COVID aid and having experienced financial distress 
is also stronger for large firms. This is not surprisingly as the requirements to access COVID aid (i.e. not being 
in  financial  difficulty  in  2019)  were  stricter  for  larger  firms.  In  contrast,  the  association  between  receiving 
COVID aid and having experienced financial distress in 2019 is non-significant for large firms in Spain and 
Poland. 

14 Due to the complexity of the TF and data limitations, we are not able to identify the exact eligibility criteria to 
access  the  funding.  The  20%  threshold  used  here  is  arbitrary but  the  results  are  robust  to  changing  the 
threshold to 30% or above. 

29 

 
 
  
 
 
 
 
  
 
 
 
  
 
                                           
4.2.5  Distribution of COVID-19 State aid by the type of financial instrument  

Although the Temporary Framework set the rules to access State aid awards in all the EU countries, Member 
States could decide to provide support using different financial instruments (direct grant, loans, etc.). This choice 
might have reflected different liquidity constraints and levels of indebtedness by country (as discussed above). 
Table 8 presents the distribution of single State aid awards by type of instrument (i.e. whether direct grant, 
guarantee, loan, or other) and the average nominal aid amount–- as reported in the National Registers–- for 
each  type  of  instrument.  To  investigate  whether  a  firm  received  more  than  one  type  of  instrument,  aid 
instruments were then grouped into two broader categories: non-repayable instruments (including direct grants, 
interest rate subsidies, and other non-repayable grants), and repayable instruments (including guarantees and 
loans). Table 9 reports whether a firm received only repayable aid awards, only non-repayable aid awards, or 
a combination of both repayable and non-repayable aid. 

Table 9 shows that Italy and Spain made considerable use of guarantees (as more than 91% of treated firms 
received this type of aid award) and resorted much less to direct grants (less than 13% of treated firms). In 
contrast,  Poland  made  a  greater  use  of  both  direct  grants  and  loans  (83%  and  75%  of  treated  firms, 
respectively). The average nominal amounts awarded in Poland is larger than in Spain and Italy (Table 8), for 
each type of instrument. This probably reflects differences in sample characteristics given that Polish firms are 
larger. In addition, Polish firms were more likely to receive more than one type of aid award. In particular, 60% 
of them received a combination of repayable and non-repayable instruments while in Italy and Spain these 
percentages were 6% and 3%, respectively, (see Table 9).  

Table 8. Proportions of aids received by beneficiaries and nominal aid amount by type of instrument (2020) 

Country 
IT 
Type of instrument  %1 
Direct Grants3 
Guarantee 
Loan (aid) 
Other aid 
Only de-minimis 
Observations 

12.6 
91.8 
0.7 
0.1 
2.6 
  42,352  

Instrument 
type 
ES 
% 

4.8 
95.1 
0.1 
0.0 
50.5 
  64,827  

Average Aid Amount  
by instrument type2 

IT 

ES 

PL 

Mean  SD  Mean  SD  Mean  SD 
0.04  0.05 
0.01  0.02 
3.30  4.71 
1.04  7.30 
0.13  0.20 
0.16  0.44 
0.00  0.00 
- 

0.01  0.04 
0.42  2.55 
0.07  0.27 
0.00  0.00 

- 

       42,352  

       64,827  

         5,246  

PL 
% 
83.3 
1.2 
74.7 
0.9 
0.0 
  5,246  

1 
2 
3 

The proportions results do not sum up to 100 as a firm may receive more than one type of aid in the same year.  
The nominal amounts are expressed in millions of €. 
Includes Interest Rate subsidies. 

Source: Authors’ computations based on ORBIS and State Aid National registry data. Year: 2020. 

Table 9. Aid awards combinations.  

Country 
Instruments combination 
Repayable only 
Non-repayable only  
Both repayable and non-repayable 
Observations 

IT 
% 
87.2 
6.5 
6.3 
  42,352  

ES 
% 
93.4 
3.3 
3.3 
  64,827  

PL 
% 
16.2 
24.4 
59.4 
  5,246  

1 
2 

Repayable instruments include guarantees and loans. 
Non-repayable instruments include direct grants, interest rate subsidies and other non-repayable grants. 

Source: Authors’ computations based on ORBIS and State Aid National registry data. Year: 2020. 

Finally, Table 10 investigates differences across sectors and firm dimensions. It shows that the distribution of 
instruments across countries does not vary substantially by sector and size. Notably, in Poland a very small 
number of guarantees were used to assist large firms (see Table 9). In Italy and Spain, large firms were less 
likely to receive aid in the form of de-minimis as compared to smaller firms. 

30 

 
 
  
  
  
 
 
 
 
 
 
 
 
 
 
Table 10. Distribution of instrument type by country (Panel A) and firm dimension (Panel B)  

PANEL A 
Firm size1 

Direct Grants2 
Guarantee 
Loan (aid) 
Other aid 
Only  
de-minimis 
Observations 

PANEL B 
Sector3 

Direct Grants 
Guarantee 
Loan (aid) 
Other aid 
Only  
de-minimis 
Observations 

Micro 
% 
13.1 
91.6 
0.7 
0.1 

Italy 
Small  Medium 

% 

8.3 
93.2 
0.3 
0.2 

% 

6.3 
93.8 
0.2 
0.3 

Large 
% 

0.0 
100.0 
0.0 
0.0 

Micro 
% 

5.6 
94.4 
0.1 
0.0 

Spain 

Small  Medium 

Large  Micro 

Poland 
Small  Medium 

% 

1.4 
98.6 
0.0 
0.0 

% 

0.8 
99.2 
0.1 
0.0 

% 
1.0 
98.5 
0.5 
0.0 

% 
84.0 
0.0 
78.9 
0.8 

% 
86.4 
0.8 
70.0 
0.9 

% 
70.0 
9.2 
62.1 
2.0 

Large 
% 
50.9 
27.3 
36.4 
1.8 

2.8 
  37,945  

1.1 
  3,623  

1.2 
     666  

0.0 
      118  

52.8 
  53,280  

42.8 
  9,810  

28.1 
   1,533  

5.4 
   204  

0.0 
  3,348  

0.0 
  1,439  

0.0 
      404  

0.0 
      55  

C 
% 

9.2 
93.6 
0.5 
0.2 

Italy 

M 
% 
14.4 
89.6 
0.7 
0.1 

W 
% 
11.0 
92.9 
0.6 
0.1 

Spain 

Poland 

R 
% 
16.8 
90.9 
0.9 
0.3 

C 
% 

6.0 
94.0 
0.1 
0.0 

M 
% 

4.3 
95.7 
0.0 
0.0 

W 
% 

3.4 
96.5 
0.0 
0.0 

R 
% 

6.5 
93.4 
0.1 
0.0 

C 
% 
82.4 
1.7 
75.0 
1.0 

M 
% 
83.2 
0.6 
79.8 
1.4 

W 
% 
83.5 
1.6 
72.3 
0.8 

R 
% 
84.1 
0.1 
74.4 
0.5 

1.7 
  10,378  

4.1 
  11,378  

2.3 
  12,459  

2.3 
  8,137  

47.6 
  15,140  

54.1 
  15,832  

48.5 
  21,405  

52.9 
  12,450  

0.0 
  1,297  

0.0 
  1,005  

0.0 
  2,120  

0.0 
   824  

1 

2 
3  

Firm size is based on the average turnover for 2017-2019. Size categories: micro: average turnover < 2 EUR Millions; small: average 
turnover between 2 and 10 Millions of EUR; Medium: average turnover between 10 and 50 Millions of EUR; Large: average turnover 
over 50 Millions of EUR. 
Includes Interest Rate subsidies. 
M = manufacturing; C = construction; W = wholesales; R = retail. 

Source: Authors’ computations based on ORBIS and State Aid National registry data. Year: 2020. 

31 

 
 
 
  
5  Methods  

To identify the causal effects of receiving a COVID State aid on different indicators of firms ‘performance, a 
difference-in-differences (DiD) estimation strategy is adopted. Firms that applied for and received COVID aid 
– the treated group –are compared with firms that did not receive any State aid in the considered period from 
2017 to 2021 (the control group).  

A standard DiD  model would allow to identify the Average Treatment Effect (ATE) of  COVID aid only if the 
treatment was assigned randomly. However, the assignment of COVID aid was clearly not a random process, 
given the large heterogeneity in COVID-19 exposure across sectors, geographical areas, and types of firms. 
Furthermore,  according  to  the  TF  rules,  large  firms  with  worse  pre-pandemic  financial  conditions  were  less 
likely  to  receive  aid.  Thus,  simply  comparing  treated  and  non-treated  firms  without  considering  firm 
characteristics would bear a risk of introducing systematic bias in the estimated impact. This was evident from 
the descriptive analysis (Section 4) showing significant differences in financial characteristics between the two 
groups. To address this key identification issue, the study adopts a matching procedure which is discussed in 
the next section. 

5.1  Matching  

A matching approach tries to reduce the selection bias into the treatment due to observable characteristics. 
For each aided firm, the method selects one (or more) non-aided firm(s) with similar observable pre-treatment 
characteristics. In this way, the firms that did not received the aid provide the counterfactual outcome for how 
the beneficiaries would have performed without the subsidy. Assuming that there are no additional differences 
across these firms in terms of non-observable characteristics, this approach would therefore mimic a situation 
in which the treatment, i.e., the aid, was randomly assigned. 

To implement the matching procedure, the likelihood that a firm received COVID aid based on its observable 
characteristics, i.e., the so-called “propensity score” it is first estimated. Second, each treated firm is matched 
to a non-treated one 15 with similar observable characteristics, thus having a similar propensity score. It is worth 
noting that this regression does not aim at explaining why a specific firm was awarded the aid, rather it seeks 
to develop a metric that synthetise information from several characteristics into a one-dimensional indicator: 
the propensity score. 

The matching is performed based on 2019 variables to avoid any spurious correlation with the effect of the 
aid (received in 2020). The estimated logit regression takes the following form:  

Pr(AIDi | Zi) = F(ai + βZi)   

 (1) 

Where AIDi is an indicator for firm i having received COVID aid and F(.) is the logit function; Zi is a vector of 
pre-treatment  firm  characteristics.  The  observable  characteristics  considered  are  as  follows:  firm  sector  of 
activity, based on the NACE Rev. 2 two-digit classification reported in Orbis; four macro-categories of industry 
sectors are considered, namely manufacturing, construction;16.firm’s size, which is important as it relates to 
firm financial structure, indebtedness, productivity, and the ability to respond to external shocks. To account 
for  geographical  effects,  a  dummy  variable  identifying  the  region  is  included  in  the  analysis  using  the 
Nomenclature of Territorial Units for Statistics (NUTS) at level-2 classification. The remaining financial variables 
used in the logit model are capital, loans, total assets, and EBIT margin. These variables are defined in Table 
A1 in Annex 1. In addition, following the existing literature, the regressions conditioned on the value of the pre-
treatment outcome (Heckman, Ichimura, and Todd, 1997) and hence it is done separately for the two dependent 
variables of interest. 

Propensity scores measuring the probability of receiving COVID aid for both treated and non-treated firms are 
computed based on model (1).  

The frequency distributions of the propensity scores for the treated and untreated firms will be shown in Figure 
3 (Section 5.2).  

15 Importantly, as the State Register provides a complete mapping of all State aid measures implemented in the 
selected countries, we can be sure that the pool of firms in the control groups consists of firms that did not 
receive any form of aid in the period 2017-2021. This is a major advantage as it allows for cleaner inference. 

16 Only sectors with sample size that was large enough for statistical analysis were included. 

32 

 
 
 
 
 
 
 
                                           
The  propensity  score  can  be  used  in  different  ways  in  a  difference-in-difference  setting.  It  can  be  used  to 
constrain the control group to only those firms that are similar enough to the beneficiaries. It can however also 
be used as a weight in a DiD regression using all observations.17 In this way, the observations are reweighted 
so that the treated and control firms have identical reweighted distributions of receiving COVID aid.  

The weights are calculated for each firm i as follows:  

1
𝑝𝑝𝑝𝑝𝑖𝑖                         𝑓𝑓𝑓𝑓𝑓𝑓 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑓𝑓𝑎𝑎𝑓𝑓𝑓𝑓𝑝𝑝
1

⎧

⎨
⎩

1 − 𝑝𝑝𝑝𝑝𝑖𝑖         𝑓𝑓𝑓𝑓𝑓𝑓 𝑛𝑛𝑓𝑓𝑛𝑛 − 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑓𝑓𝑎𝑎𝑓𝑓𝑓𝑓𝑝𝑝
To  ensure  the  internal  validity  of  the  DiD  estimand,  the  common  support  condition  is  enforced  restricting 
attention to the set of firms that belong to the intersection of the supports of the propensity score distributions 
for both treated and controls.  

5.2  Quality of Matching 

This section checks the performance of the matching procedure in terms of covariate balancing and overlap of 
the propensity score distribution between treated and untreated firms. This is to verify whether differences in 
observable characteristics between the two groups are removed after applying the matching procedure. For 
the first outcome, the exact set of matching covariates includes turnover in the three years before the pandemic 
(2017-2018-2019),  firm’s  size  (split  in  three  categories:  Small,  Medium,  Large),  firm’s  sector  (divided  in 
Constructions,  Wholesales,  Retails),  firm’s  region,  capital  (in  quartiles),  loans  (in  quartiles),  EBIT  margin  (in 
quartiles) plus interactions between firms’ sector with size and loans and interactions of region with size and 
sector.   

The  covariate  balancing  is  assessed  in  Table  A11  (Panels  (a)  to  (c))  of  the  Annex  4  for  the  three  countries 
analysed. The composition of firms by size (Small, Medium and Large) is non-statistically different between 
treated and untreated with a percentage of remaining bias after matching always lower than 2% 18 and with 
the p-values of the corresponding two samples t-tests (Rosenbaum and Rubin, 1985) in each class and country 
which are always non-significant.   

The composition of firms by sector (Construction, Wholesale and Retail) is perfectly balanced for Poland, while 
there are small differences in the proportion of firms in construction in Spain  and wholesale in Italy,  but with 
level of bias close to 1.6%. Italy also exhibits small significant differences in turnover during the three years 
before the pandemic (2017, 2018, 2019), with a level of bias close to 3%, while this is not the case for Spain 
and Poland that are perfectly balanced. 

Finally, there are some minor differences in the regional distributions of firms after matching, with one or two 
regions in each country (out of about 20 regions) not being perfectly matched. However, the standardized bias 
is always below 5% and in most of the cases is even below 3% which is usually considered sufficient in the 
relevant literature (see Caliendo and Kopeinig, 2008). 

The Pseudo-R2, a measure of how well the covariates explain the probability of receiving treatment, is also low 
after matching (Pseudo-R2= 0.001 for Italy and Spain, 0.003 for Poland) as opposed to a Pseudo-R2> 0.100 for 
the three countries before matching.19  

All this evidence suggests that the proposed matching strategy almost completely and significantly reduces 
the differences between treated and untreated firms in terms of observable characteristics. 

To analyse the issue of overlap (or common support), Figure 3 shows that the distribution of the propensity 
scores by treatment status, for the three countries. The figures illustrate areas in the distributions where there 
are either only treated or only untreated firms. To avoid issues related to lack of common support, we exclude 

17 Kernel weights were computed following Heckman, Ichimura, and Todd (1997, 1998) using psmatch2 Stata 

package. 

18 In most empirical studies a standardized bias below 3% or 5% after matching is considered as sufficient (see 

Caliendo and Kopeinig, 2008) 

19 Specifically, the Pseudo-R2 was 0.1050 for Italy, 0.1707 for Spain, 0.1088 for Poland) 

33 

 
 
                                           
firms in the 1st and 99th percentiles of the distribution, following Imbens (2015)20. The results presented in this 
study are robust across different matching techniques. 

Figure 3. Propensity score distribution and common support for Italy, Spain, and Poland 

 Panel (a): Italy     

Panel (b): Spain 

 Panel (c): Poland 

5.3  The difference-in-differences approach  

In the difference-in-differences framework, the baseline empirical model to estimate the effect of COVID aid 
on outcome Y takes the form of the following equation: 

Yit = α + β yeart + δ Aidi × postt + λ Xit + μi + εit   

 (2) 

where the outcome Y of firm i in year t is a function of firms’ characteristics (Xit), firm fixed-effects (μi) to 
account for time-invariant unobserved heterogeneity across firms, year fixed effects (yeart) and an error term 
εit which is assumed to be heteroskedastic. The dummy Aidi x postt is equal to one for the firms that received 
the COVID aid in the post-intervention years, and zero otherwise. In our study, we consider 2021 as the post-
treatment period, excluding 2020, as we cannot identify whether the treatment has already been implemented 

20 In the case of Poland, which exhibits a greater imbalance in the left side of the propensity score distribution, 
we  conduct  additional  analyses  by  excluding  a  wider  range  of  propensity  score  scores.  The  results 
consistently align with the main specification. 

34 

 
 
 
 
 
 
 
 
 
 
       
 
 
 
 
    
 
 
                                           
by the end of 2020. This is due to the fact that the payment of the aid (treatment) may, in some cases, be 
delayed. 

The  coefficient  of  interest  (δ)  measures  the  average  change  in  the  outcome  of  aided  firms  relative  to  the 
average change in the outcome of non-aided firms after treatment. Therefore, this coefficient quantifies the 
variation in the outcome of beneficiaries that is due to the aid.  

This basic DiD model can then be expanded to estimate heterogeneous treatment effects by partitioning the 
coefficient δ between different sub-groups. Specifically, separate regressions to identify the treatment effect 
for  different  firm  size  and  different  sectors  are  estimated.  Moreover,  the  analysis  also  investigates  the 
heterogeneity of the aid’s effect depending on the instrument through which the aid is provided. 

The DiD relies on the validity of the common or parallel trend assumption, ensuring that the change in the 
outcome for beneficiary firms after treatment is solely attributable to the aid. In other words, this assumption 
implies that in the absence of the treatment, treated firms would have experienced the same trends in their 
potential outcome as firms in the control group. While this is not testable, an event-study analysis (Autor, 2003) 
can  shed  some  light  on  the  validity  of  the  research  design.    In  this  framework,  the  treatment  variable  is 
interacted with year time dummies (excluding t-1 which would serve as baseline) and it is verified that the 
respective coefficients in the pre-treatment period are not statistically different from zero. Finally, it should be 
noted that the proposed approach –which is standard in the policy evaluation literature– controls for selection 
issues based on firms’ observable characteristics and it is valid as long as the implemented propensity score 
matching  procedure  allows  to  account  for  the  main  drivers  of  the  selection  into  the  treatment.  It  however 
cannot  control  for  unobservable  differences  between  treated  and  control  firms  which  are  assumed  to  be 
uncorrelated with the assignment mechanism and the outcomes of interests. 

35 

 
 
 
6  Results 

The  COVID-19  outbreak  posed  unprecedented  challenges  for  firms  worldwide.  The  generous  aid  measures 
approved  by  national  governments  and  the  European  Commission  might  have  played  a  significant  role  in 
mitigating firms’ default risk and market exit.  

Given the nature of COVID aid scheme and the limited time elapsed from the intervention, the study seeks to 
uncover the impact of receiving COVID aid by looking first at the ability of firms to continue their usual operating 
activities,  proxied  by  the  revenues  generated  by  such  activity  (turnover).  Secondly,  the  focus  will  be  on  a 
measure capturing the estimated ‘probability of the default’ provided in Orbis.  

6.1 

Impact of Covid Aid on firms’ performance 

To investigate whether the aid helped firms navigate through the crisis, the analysis considers turnover as the 
most appropriate outcome indicator in the short run, notwithstanding its limitations in terms of comparability 
across  different  size  classes.21  Turnover  is  a  continuous  variable  describing  the  total  operating  income 
(including net sales, other operating revenues, and stock variations) reported in the current year. The variable 
is  gathered  from  firms’  balance-sheet  information  reported  in  Orbis  and  shows  a  good  coverage  across 
countries. Table 11 reports the results of the estimation of the Average Treatment Effect on the Treated (ATT) 
for  turnover  as  obtained  from  a  DID  model  reweighted  by  propensity  scores,  as  explained  in  the  previous 
section. Turnover is transformed using the Inverse Hyperbolic Sine (HIS) function to deal with the non-normality 
of the distribution. 

Table 11. The effects of COVID State aid on firms’ turnover1 

Country 
Average treatment 
effect 
Micro 

Effect 
by  
Size 

Small 

Medium 

Large 

Effect 
by  
Sector 

Manufacturing 

Construction 

Wholesale 

Retail 

Constant 

Observations 
R-squared 

PL 

ES 

IT 
0.091***  0.032*** 
(0.006) 
(0.004) 
0.102***  0.037*** 
(0.004) 
(0.006) 
-0.002 
0.015 
(0.006) 
(0.011) 
0.044*** 
0.051** 
(0.017) 
(0.023) 
0.104*** 
0.034 
(0.055) 
(0.037) 
0.096***  0.057*** 
(0.008) 
(0.011) 
0.146***  0.031*** 
(0.014) 
(0.006) 
0.065***  0.029*** 
(0.006) 
(0.010) 
0.002 
0.032** 
(0.008) 
(0.013) 
13.592***  13.782***  14.636*** 
(0.000) 
(0.001) 
298,248  551,276 

0.029 
(0.027) 
0.056* 
(0.029) 
-0.021 
(0.029) 
0.006 
(0.046) 
0.035 
(0.106) 
0.046 
(0.045) 
0.129* 
(0.073) 
-0.037 
(0.040) 
0.048 
(0.068) 

(0.003) 
27,956 
0.944 

0.926 

0.957 

1 

2 

Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. The average treatment effects is obtained as in equation 
(2). The effects by size and sector are estimated in separate equations. The outcome variable, turnover is in log form 
(Inverse Hyperbolic Sine Transformation). Standard errors clustered at the firm level.  
The effects by size and sector are estimated interacting the treatment variable with an indicator of either the NACE code 
of activity of the firm or an indicator of its size, in two separate equations.  

21 To mitigate these issues, the impact is estimated within size-sectoral groups.  

36 

 
 
 
 
 
 
  
 
 
 
 
 
 
  
  
 
 
                                           
COVID aid has a general positive and significant effect on Italian and Spanish beneficiary firms. In Italy, the 
change in turnover of aid recipients in 2021 results to be, on average, more than 9% higher as compared to 
the  control  group,  whilst  in  Spain  the  effect  of  the  aid  is  smaller  (3.2%).  The  two  countries  differ  for  the 
composition of the impact in terms of size and sector. Indeed, in Italy the positive effect is mainly driven by 
the impact on micro-firms (16.2%) and a more contained significant effect is found for medium-sized firms 
(5.1%).  In  Spain,  micro,  medium  and  large-sized  firms  positively  contribute  to  the  average  result,  with  the 
impact being greater for large firms (10.4%). The change in turnover for small Spanish beneficiaries is about 
4% smaller compared to non-beneficiaries.  

In terms of sectoral composition, in Italy the positive effect is observed across all sectors, with construction 
firms showing an increase in turnover of up to 14.6% compared to non-treated firms. In Spain, the effect is 
positive across all sectors except for retail trade where the effect is not statistically significant. 

In Poland, the average impact of the aid is not statistically different from zero. However, a marginally significant 
effect is observed for micro firms (5.6%) and those in the construction sector (12.9%). The results for Poland 
have the limitation that this sample is not well represented in Orbis (as explained in the previous section) and 
are therefore difficult to compare with the results for Italy and Spain. 

6.2  Heterogeneity analysis 

To analyse the effect of the aid within specific intersections of firm size and sector of activity, the DiD model 
is  estimated  on  separate  sectors  and  it  includes  an  interaction  term  between  the  treatment  variable  and 
different  categories  of  size  (i.e.  micro,  small,  medium  and  large).  Table  12  presents  the  results  of  these 
estimations.  

In Italy, for firms operating in manufacturing, the positive effect of COVID aid is widespread across micro, small 
and medium- sized firms. For the remaining sectors, the impact is concentrated on micro firms with a larger 
impact on construction. Specifically, for micro firms in the construction sector the effect of receiving COVID aid 
is an increase in turnover in 2021 compared to the pre-COVID period which is 7% higher with respect to their 
respective controls. In Spain, in the manufacturing sector, the effect is positive and significant only for micro 
and medium-sized firms. The effect on micro firms is also positive in construction and wholesales. A  small 
negative effect, but only significant at 10% confidence level, is found for small firms operating in construction 
and  retail  trade.  In  Poland,  the  impact  is  statistically  significant  only  for  micro  and  medium  firms  in  the 
construction sector.   

Overall, the results confirm a prevailing positive but small impact of COVID State aid on turnover. This aligns 
with the objective of the guiding principles of the Temporary Framework, which aimed to sustain firms during 
the pandemic while minimizing interference with market competition.  

37 

 
 
PL 

Construction 

Manufacturing 

Table 12. The effects of COVID State aid on firms’ turnover, by different combinations of sector/size groups1 
Sector 
Country 
Treat x 
Micro 
Treat x 
Small 
Treat x 
Medium 
Treat x 
Large 
Constant 

ES 
0.077*** 
(0.009) 
-0.041** 
(0.019) 
0.055 
(0.087) 
0.167 
(0.121) 

IT 
0.074*** 
(0.011) 
0.066*** 
(0.016) 
0.102*** 
(0.030) 
0.029 
(0.057) 

ES 
0.029*** 
(0.007) 
-0.001 
(0.011) 
0.068*** 
(0.023) 
-0.120 
(0.116) 

IT 
0.191*** 
(0.014) 
-0.050 
(0.037) 
-0.041 
(0.163) 
-0.136 
(0.638) 

IT 
0.068*** 
(0.011) 
-0.008 
(0.017) 
0.024 
(0.028) 
0.099* 
(0.055) 

PL 
0.206*** 
(0.078) 
0.015 
(0.097) 
0.220* 
(0.116) 

0.059 
(0.050) 
-0.010 
(0.049) 
0.094 
(0.087) 
0.133 
(0.169) 

Wholesales 
ES 
0.031*** 
(0.007) 
0.019** 
(0.009) 
0.037** 
(0.018) 
0.141** 
(0.057) 

PL 

-0.030 
(0.047) 
-0.060 
(0.049) 
-0.070 
(0.067) 
-0.052 
(0.130) 

IT 
0.041*** 
(0.013) 
-0.017 
(0.025) 
-0.057 
(0.096) 
0.012 
(0.030) 

Retail 

ES 

PL 

0.011 
(0.008) 
-0.032* 
(0.017) 
0.048 
(0.044) 
0.151* 
(0.083) 

0.078 
(0.075) 
0.015 
(0.066) 
0.030 
(0.237) 
0.106 
(0.293) 

13.906***  13.856***  14.655***  13.069***  13.348***  13.984***  13.858***  14.129***  14.984***  13.405***  13.620***  14.230*** 

Observations 

(0.001) 
72,528 

(0.001) 
122,816 

(0.006) 
7,020 

(0.002) 
85,932 

(0.001) 
149,136 

(0.009) 
5,464 

(0.001) 
89,684 

(0.001) 
176,208 

(0.006) 
10,924 

(0.002) 
49,620 

(0.001) 
102,992 

(0.009) 
4,144 

1  

The average treatment effects is obtained as in equation (2). The effects by size and sector are estimated in separate equations. The outcome variable, turnover is in log form (Inverse Hyperbolic Sine 
Transformation). Standard errors clustered at the firm level. Standard errors clustered at the firm level. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 

38 

 
  
  
 
 
An additional heterogeneity analysis was conducted with respect to the type of financial instrument used to 
deliver the aid. The sample of beneficiaries was split according to whether the financial instrument adopted 
was repayable (i.e. taking the form of guarantees or loans) or whether it was non-repayable (i.e. being a direct 
grant, an interest rate subsidy or other types on non-repayable grants) or a combination of the two types.22 
Again, the DiD estimation was run within each subsample, and the treatment variable was interacted with size 
and sector indicators to assess the role of the two characteristics in mediating the effect. Results are reported 
in Table 13.  

Table 13. The effects of COVID State aid on firms’ turnover, by type of aid instrument1 

Instrument type 
Country 
Average  
effect 
Treat x Micro 

Effect 
by  
Size 

Treat x Small 

Treat x Medium 

Treat x Large 

Treat x Manufacturing 

Treat x Construction 

Treat x Wholesale 

Treat x Retail 

Effect 
by  
Sector 

Repayable 
ES 

IT 
0.094***  0.035*** 
(0.006) 
(0.004) 
0.105***  0.040*** 
(0.004) 
(0.007) 
-0.001 
0.020* 
(0.006) 
(0.011) 
0.042** 
0.040 
(0.017) 
(0.026) 
0.105*** 
0.062 
(0.054) 
(0.038) 
0.102***  0.034*** 
(0.011) 
(0.006) 
0.142***  0.059*** 
(0.014) 
(0.008) 
0.070***  0.032*** 
(0.007) 
(0.011) 
0.004 
0.036*** 
(0.008) 
(0.013) 

PL 

0.049 
(0.040) 
0.089* 
(0.051) 
-0.055 
(0.060) 
0.064 
(0.062) 
0.033 
(0.121) 
0.071 
(0.060) 
0.210* 
(0.110) 
-0.044 
(0.058) 
-0.017 
(0.081) 

Non-Repayable or both 

IT 
0.068*** 
(0.026) 
0.067** 
(0.029) 
0.057 
(0.035) 
0.125 
(0.113) 
0.136** 
(0.068) 
0.111*** 
(0.019) 
-0.010 
(0.069) 
0.083** 
(0.035) 
0.144*** 
(0.035) 

ES 

PL 

0.029 
(0.021) 
0.030 
(0.022) 
-0.091* 
(0.048) 
0.575*** 
(0.116) 
0.212** 
(0.090) 
0.032* 
(0.017) 
0.043 
(0.054) 
-0.006 
(0.029) 
0.057 
(0.039) 

0.021 
(0.027) 
0.051* 
(0.030) 
-0.018 
(0.030) 
-0.030 
(0.057) 
-0.053 
(0.165) 
0.035 
(0.048) 
0.120* 
(0.073) 
-0.044 
(0.041) 
0.042 
(0.071) 

Constant 

13.647***  13.791***  14.769***  13.560***  13.791***  14.674*** 

(0.000) 
(0.000) 
277,064  534,576 

(0.005) 
10,584 

(0.003) 
(0.000) 
152,008  534,576 

(0.003) 
24,584 

Observations 
Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. The average treatment effects is obtained as in equation (2). 
The effects by size and sector are estimated in separate equations. The outcome variable, turnover is in log form (Inverse 
Hyperbolic Sine Transformation). Standard errors clustered at the firm level.  
Repayable instruments includes guarantees and loans. Non-repayable instruments includes direct grants, interest rate 
subsidies, and other non-repayable grants.  

1  

2  

For the three countries, the results only considering repayable instruments are very similar to the main findings 
presented in Table 7, both in terms of the accuracy of the estimates and the magnitude of the effects. This is 
not surprising as the vast majority of COVID state aid were provided under form of repayable instruments. 
Looking at the  subsample of "non-repayable or  both" instruments, the effect in Italy is positive and is only 
observed for micro firms, which experienced an increase of around 10%. Moreover, the effect is statistically 
different from zero in all sectors, with the largest effect (14%) in construction.   

When looking at the sub-sample of non-repayable or combined instruments, the results are slightly different 
from  those  presented  above.  While  the  effect  in  Italy  is  broadly  comparable  between  the  two  types  of 
instruments, except for the existence of a significant effect (13%) on large enterprises, the results in Spain 
show  a  large  effect  on  medium  and  large  enterprises.  This  may  be  due  to  the  nature  of  non-repayable 

22 This might have happened since firms could have received multiple aids during 2020. It could have been the 
case that some aids were granted in the form of repayable instruments and others as non-repayable. In the 
latter  case,  we  identify  the  firm  as  having  received  a  combination  of  the  two  (what  in  the  estimation  is 
referred to as “both”).  

39 

 
 
 
 
 
 
  
 
 
 
 
  
  
  
  
 
                                           
instruments, which are specifically targeted at larger enterprises. There are no relevant effects for Poland in 
this sub-sample. 

6.3 

Impact of Covid Aid on the probability of default 

To dig deeper into the effect of Covid aid scheme in supporting firms during the pandemic, this section takes 
a measure of the ‘probability of default’ as outcome.  

The considered probability of default is an estimated measure of the likelihood a firm has to go bankrupt and 
exit the market. The variable is extracted from the Financial Strength module of Orbis, computed by the private 
company modeFinance® using Transition Matrix theory. More specifically, the probability of default is predicted 
using a statistical model which takes into account both balance sheet information from the previous financial 
year and the current macroeconomic situation (accounting for specific sector and size economic trends).  As 
such, the variable ranges from 0 to 100. The estimated probability of default is also commonly used to classify 
private firms in different categories of risk of default, varying from AAA (lowest risk of default) to D (highest 
risk of default). An increase in the probability of default has a negative impact on firms if such increase is large 
enough to produce a shift in the default risk category, making access to credit more difficult. The relationship 
between the probability of default and the index describing the risk of default is reported in the following table: 

Table 14. Categories of Risk of Default 

Numerical 
value 

1 
2 
3 
4 
5 
6 
7 
8 
9 
10 

Risk of  
default 
AAA 
AA 
A 
BBB 
BB 
B 
CCC 
CC 
C 
D 

Probability  
of default  
0.00 - 0.05% 
0.05 - 0.12% 
0.12 - 0.21% 
0.21 - 0.45% 
0.45 - 1.27% 
1.27 - 4.47% 
4.47 - 17.76% 
17.76 - 43.66% 
43.66 - 68.30% 
68.30 – 100.00% 

The width of the intervals of the probability of default associated with each category  of default risk is not 
homogeneous across the distribution and tends to be larger as the probability of default increases. This might 
pose  some  issues  when  pooling  together  data  across  different  categories  of  default  and  implies  that  the 
relationship between these two variables is not linear (i.e. it is easier to switch from AAA to AA than from CCC 
to CC). 

Figure  4  presents  the  distribution  of  firms  according  to  categories  of  default  for  the  three  countries 
investigated. 

While this variable is certainly useful as it allows a short-term assessment of the probability of default without 
having a long post-aid period that would be needed to observe an actual default, it should be recognised that 
it also has important limitations. The outcome under consideration is not a true measure of firm survival, nor 
is it a precise and objective measure of default risk. Being based upon balance sheet data, it is influenced by  
debt and interest rate dynamics, which may in turn raise some problems in the way the variable accounts for 
firms' investments. Therefore further research and a more nuanced approach are needed to properly isolate 
the effect of Covid aid on firm survival. 

40 

 
 
 
 
 
 
 
 
 
Figure 4. Distributions of category of default by country 

     Italy                                                                    Spain 

Categories of default

Categories of default

2

5
.
1

y
t
i
s
n
e
d

1

5
.

0

AAA

AA

A

BBB

BB

B

CCC

CC

C

D

AAA

AA

A

BBB

BB

B

CCC

CC

C

D

categories of default

categories of default

           Poland 

Categories of default

y
t
i
s
n
e
d

5
1

.

1

5

.

0

2

5
.
1

y
t
i
s
n
e
d

1

5
.

0

AAA

AA

A

BBB

BB

B

CCC

CC

C

D

categories of default

Source: Authors’ calculations based on 2019 Orbis data. 

Bearing  this  in  mind,  the  impact  of  Covid  aid  on  the  probability  of  default  is  identified  applying  the  DiD 
specification  presented  in  equation  (2)  over  homogeneous  groups  of  firms  identified  by  their  category  of 
default in 2019. 

The analysis aims at measuring the average change in  percentage points of the probability of default that 
treated  firms  have  experienced  after  receiving  COVID  aid,  with  respect  to  their  respective  controls,  both 
belonging to the same default category. A positive (negative) coefficient for the Treat x Post dummy in the DiD 
regression would signal that, after receiving the aid, the average probability of default of treated firms is higher 
(lower) than the average for controls.  

The results of the DiD estimation are reported in Table 15. They suggest that in all countries, except Poland, 
there was a little deterioration in the risk of default for aid recipients for most of the pre-existing risk categories. 
The  effect  is  not  statistically  different  from  zero  for  firms  that  had  a  high  pre-pandemic  risk  of  default 
(category D in Spain, D, C, CC in Italy) and for those with the lowest category (AA). In contrast, Poland shows 
marginally significant effects in the opposite direction.23 However, these results should be treated with caution, 
as the receipt of aid as an instrument affecting debt and interest (repayable instruments) could in turn feed 
into the algorithm estimating the probability of default and be considered as a riskier status. 

23 Results are omitted for brevity but available upon request. 

41 

 
 
 
                      
 
 
 
 
 
 
                                           
To tentatively tackling this concern, Table 15 shows the heterogeneity of the impact according to the type of 
instrument through which the aid is  provided.  Hence, the table presents the  coefficients of the interactions 
between the treatment variable with the instrument type (repayable only and non-repayable or both) and the 
constant that informs about the mean value of the probability of default in each category for the baseline 
group. On average, in Italy and Spain, a negative impact of COVID State aid on the probability of default of 
beneficiary firms supported by a repayable instrument is observed, especially for those belonging to categories 
ranging from A to C, except for CC and C in Italy. Conversely, in Poland, the effect is not statistically different 
from zero for all categories, except for firms in categories A, CC and D, for which the effect is negative and 
significant. 

In terms of non-repayable instruments (or both), the coefficients are largely negative  and indistinguishable 
from zero for all countries considered. 

Considering the magnitude of the results, the effect is more pronounced, in relative terms, for low-medium risk 
firms (categories AA to B) in both Italy and Spain. This is not surprising as firms in these categories may have 
had easy access to guarantees and loans, thus recording higher debt in their balance sheets. 

Table 15. The relationship between COVID State aid and category of default1 

Panel (a) - Italy 
Category of default 
Repayable only 

Non-repayable  
(or both) 
Constant 

Observations 

Panel (b) - Spain 
Category of default 
Repayable only 

Non-repayable 
(or both) 
Constant 

Observations 

Panel (c) - Poland 
Category of default 
Repayable only 

Non-repayable 
(or both) 
Constant 

Observations 

A 

BBB 

0.526***  0.504*** 
(0.148) 
(0.117) 
0.150 
-0.106 
(0.328) 
(0.184) 

AA 
0.374 
(0.396) 
-0.008 
(0.121) 
0.303***  0.849***  1.446***  2.710***  5.139***  10.026***  19.919***  34.134***  50.957*** 
(0.027) 
2,166 

C 
2.085* 
(1.125) 
-4.259** 
(2.075) 

CCC 
1.145*** 
(0.402) 
-0.882 
(0.735) 

B 
0.887*** 
(0.259) 
0.284 
(0.480) 

D 
4.337 
(3.263) 
-8.599* 
(4.899) 

BB 
0.480** 
(0.200) 
-0.402 
(0.376) 

CC 
0.729 
(0.656) 
-1.878 
(1.275) 

(0.027) 
55,724 

(0.037) 
57,858 

(0.054) 
39,446 

(0.080) 
22,648 

(0.127) 
12,418 

(0.340) 
2,724 

(0.011) 
57,972 

(0.016) 
47,585 

B 

A 

BB 

BBB 

1.900***  1.658***  1.693***  2.809*** 
(0.278) 
(0.355) 
0.983 
0.203 
(1.031) 
(0.771) 

AA 
0.338 
(0.685) 
-1.818 
(1.354) 
0.478***  1.408***  1.830***  3.083***  5.349***  10.868***  25.073***  40.301***  62.926*** 
(0.043) 
3,724 

D 
1.973 
(1.628) 
-14.340** 
(5.766) 

C 
3.498*** 
(1.331) 
5.000 
(3.594) 

CCC 
3.021*** 
(0.477) 
1.975 
(1.412) 

CC 
3.427*** 
(0.746) 
4.607* 
(2.463) 

(0.284) 
1.782 
(1.400) 

(0.267) 
0.492 
(1.303) 

(0.032) 
74,285 

(0.037) 
64,017 

(0.041) 
87,541 

(0.070) 
51,215 

(0.097) 
36,525 

(0.155) 
19,205 

(0.182) 
13,392 

(0.028) 
78,553 

A 
-1.818* 
(1.099) 
-1.108** 
(0.466) 

B 
BBB 
AA 
-1.467 
0.539 
0.694 
(1.370) 
(1.042) 
(0.796) 
-0.609 
-0.361 
0.185 
(0.340) 
(1.165) 
(0.727) 
0.828***  1.547***  1.912***  2.695***  4.780*** 
(0.140) 
(0.094) 
(0.060) 
5,090 
7,134 
460 

BB 
-1.729 
(1.133) 
-0.561 
(0.689) 

(0.080) 
3,954 

(0.064) 
2,771 

CCC 
-3.718 
(2.513) 
-0.494 
(1.899) 
9.974*** 
(0.220) 
2,165 

D 
-17.750* 
(10.651) 
-15.832 
(9.883) 

CC 
-7.654*** 
(2.786) 
-0.901 
(2.308) 

C 
0.971 
(4.399) 
4.932 
(3.991) 
24.799***  38.986***  64.429*** 
(0.463) 
1,308 

(1.402) 
400 

(0.237) 
2,448 

1  

Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. The outcome variable is the probability of default. Standard 
errors clustered at the firm level. Repayable instruments includes guarantees and loans. Non-repayable instruments includes 
direct grants, interest rate subsidies, and other non-repayable grants. 

6.4  DiD validity 

The validity of the DID estimation relies on the so-called parallel trend assumption. This assumption implies 
that the evolution of the pre-treatment performance of treated and control firms moves in parallel. This also 
implies that the average change in outcomes for the control group can be used as a credible counterfactual 
for the unobserved change in outcomes for the treated group if the COVID outbreak had not happened. Figure 
A2 in the Appendix plots the evolution of turnover as a dependent variable for the treated and control groups 
across  pre-treatment  and  post-treatment  periods.  Outcome  variables  show  very  similar  trends  before  the 

42 

 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
treatment starts, but also after it for both Spain and Poland, while this is not the case for Italy where trends 
are substantially divergent.  

6.4.1  Event study for turnover  

The credibility of the parallel assumption is tested by implementing an event study analysis following Autor 
(2003), as explained in Section 5. The estimated coefficients for the models including turnover as an outcome 
variable and the respective confidence intervals are shown in Figure 5.24 

Figure 5. Event study analysis–turnover, full sample 

Panel A: Italy 

Panel B: Spain  

Panel C: Poland  

1  

The figures show the effect of receiving aid on firm performance. The blue dots are the point estimates of the effect of the 
aid in each year; vertical lines represent the respective 90%, 95% and 99% confidence intervals. Regressions include fixed 
effects and controls as from Equation (1).  

Source: Authors’ elaboration based on ORBIS and State Aid National registry data 

Reassuringly,  results  suggest  that  the  lead  terms  are  not  statistically  different  from  zero  in  all  the  three 
countries considered, thus confirming there was no anticipatory effect of the aid and validating the common 
trend hypothesis. This is the case for the full sample and for the majority of the subsamples considered in both 
Italy and Spain. To ensure that the results discussed above are not driven by a single group of observations, all 
estimates  are  run  separately  by  sector  and  size.  These  are  presented  in  Figures  A3  and  A4  in  Appendix, 

24 2020 is excluded from the analysis and 2019 is taken as reference period.  

43 

 
 
 
 
 
 
 
 
 
 
                                           
respectively. The effect in the post treatment period is always positive and statistically significant, except for 
the  retail  sector  in  Spain  and  for  large  firms  in  Italy  for  which  the  effect  is  not  precisely  estimated. 
Unfortunately, the data available cannot  provide insights  on the dynamics of the effects, showing how the 
impact of the aid changes over time. 

6.4.2  Event study for probability of default  

The Autor’s test is also performed for the models using the probability of default as an outcome (see Figure 
5). These results further validate the common trend assumption. Moreover, the results separated by sector and 
size are consistent with this hypothesis and can be provided upon request. 

Figure 6. Event study analysis – probability of default, full sample 

Panel A: Italy 

Panel B: Spain  

t
l

u
a
f
e
d

f
o

y
t
i
l
i

b
a
b
o
r
p

n

i

e
g
n
a
h
C

1

.5

0

-.5

t
l

u
a
f
e
d

f
o

y
t
i
l
i

b
a
b
o
r
p

n

i

e
g
n
a
h
C

.5

0

-.5

-1

-1.5

-2

1  

2

1.5

1

.5

0

t
l

u
a
f
e
d

f
o

y
t
i
l
i

b
a
b
o
r
p

n

i

e
g
n
a
h
C

2017

2018

Time

2021

2017

2018

Time

2021

Panel A: Poland 

2017

2018

Time

2021

The figures show the effect of receiving aid on firm performance. The blue dots are the point estimates of the effect of the 
aid in each year; vertical lines represent the respective 90%, 95% and 99% confidence intervals. Regressions include fixed 
effects and controls as from Equation (1).  

Source: Authors’ elaboration based on ORBIS and State Aid National registry data 

44 

 
 
 
 
 
 
 
 
    
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Conclusions  

At the onset of the COVID-19 pandemic, the European Commission approved the Temporary Framework (TF), 
enabling Member States to provide immediate support to companies using different financial instruments (e.g. 
direct grants, guarantees, etc.). This study has investigated whether the Temporary framework was useful in 
helping  viable  firms  to  continue  operating  during  the  pandemic,  using  data  available  for  three  EU  Member 
States  (Italy,  Poland,  and  Spain).  Specifically,  it  has  analysed  the  pre-pandemic  financial  characteristics  of 
COVID aid beneficiaries and it has examined the causal short-term impact of the policy measure on two firms’ 
outcomes,  namely  turnover  and  firms’  risk  of  default.  Additionally,  it  has  explored  how  the  measure 
heterogeneously  impacted  firms  depending  on  their  sector  (manufacturing,  construction,  wholesales,  and 
retail), size (micro, small, medium and large), and the type of aid instrument used by the national authorities 
to support them.  

The descriptive evidence shows that in the first year of the pandemic, Italy and Spain made an extensive use 
of guarantees and lighter use of direct grants, whereas Poland resorted to both type of instruments. In fact, 
more than 91% of beneficiaries received aid in the form of a guarantee in the two southern countries, while 
less than 13% received direct grants. Poland made greater use of both direct grants and loans (83% and 75% 
of treated firms, respectively). 

Firms that received COVID aid had lower pre-pandemic values of turnover, capital and total assets than non-
beneficiaries, in all the countries analysed. COVID Aid beneficiaries also had lower probability of default before 
the pandemic and lower risk of being under financial stress (as defined in Section 4.2.3), in line with the TF 
provisions that envisaged support only for companies that had no financial difficulties before the pandemic. 
The results also show specific-country differences in the (pre-pandemic) characteristics of beneficiaries and 
non-beneficiaries.  In  Italy  and  Spain,  where  State  aid  was  primarily  provided  in  the  form  of  guarantees, 
beneficiaries had higher level of loans and often lower total assets compared to non-beneficiaries. In Poland, 
where COVID State aid was provided in the form of loans and direct grants, beneficiaries had (pre-pandemic) 
lower loans, total assets and EBIT margin.  

The proposed study compares beneficiaries of COVID aid with firms that did not receive any aid in the reference 
period (2017-2021), therefore excluding firms that received other forms of aid. The impact of COVID aid is 
estimated in 2021 with respect to the pre-treatment period, comparing treated and untreated firms. To address 
the potential selection-based endogeneity, Propensity Score Matching is used to account for differences in pre-
treatment observed characteristics between beneficiaries and non-beneficiaries. 

The study finds an overall positive effect of COVID Aid on the turnover of beneficiaries. In Italy, the average 
increase in turnover for aid recipients in 2021 was nearly 9%, compared to control firms, while in Spain the 
effect was smaller, at around 3%. In Poland, there is no evidence indicating a positive effect of the aid on the 
turnover of beneficiaries, as the estimated coefficients are statistically insignificant.  

The impact of COVID aid did not only vary by country but also depended on the firm size and sector. In Italy, 
the positive effect was primarily driven by micro-firms (especially in the construction sector), with an increase 
in  turnover  of  16.2%,  while  a  smaller  yet  significant  effect  was  observed  for  medium-sized  firms  (5.1%). 
Conversely, in Spain, the impact was significant across micro, medium, and large-sized firms, with a greater 
effect  observed  for  large  firms  (10.4%).  However,  this  effect  was  not  significant  when  considering  sector-
specific  impacts.  There  were  also  some  interesting  differences  in  terms  of  financial  instrument  used.  For 
instance, non-repayable instruments primarily benefited micro and small enterprises in Italy, whereas in Spain, 
small firms receiving non-repayable instruments performed worse compared to controls. 

The results on turnover are in line with previous studies that found positive short-term effects of State aid on 
firms’ turnover, with some heterogeneity by firm size and sector (Bernini and Pellegrini, 2011; Hartšenko and 
Sauga, 2013; Cerqua and Pellegrini, 2014, 2017; Söderblom et al., 2015; Decramer and Vanormelingen, 2016). 

The  present  study  also  assessed  the  effect  of  COVID  aid  on  a  measure  of  firms’  exposure  to  default. 
Unfortunately, due to data limitations, it was not possible to examine firms’ market exit, as conducted in other 
comparable  studies.  Instead,  the  analysis  focused  on  the  estimated  probability  of  default  provided  by 
Moodys/Bureau  Van  Dijk,  computed  using  firms’  balance  sheet  data  from  previous  year  adjusted  for  the 
economic cycle.  

The results outline a small and significant increase in the probability of default for COVID aid beneficiaries in 
2021,  as  compared  to  non-beneficiaries,  both  in  Italy  and  Spain,  in  all  the  sectors  and  firms’  dimensions 
considered. On the contrary, there does not appear to be any association between receiving COVID aid and the 
risk of default in Poland. However, the receipt of aid as an instrument affecting debt and interest (repayable 

45 

 
instruments) could in turn feed into the algorithm estimating the probability of default and lead to a riskier 
status. To confirm this hypothesis, the analysis examines the heterogeneity of the impact according to the type 
of instrument (only repayable and non-repayable or both) through which the aid is provided. In this setting, an 
increase  in  the  probability  of  default  due  to  COVID  aid  is  confirmed  only  for  beneficiaries  of  repayable 
instruments in Italy and Spain. 

It is important to highlight that the study has few weaknesses. In particular, data limitations did not allow for 
an investigation of the impact according to different aid intensities, i.e. how the effect varied with the amount 
of aid spent, which is scope for future research. Furthermore, the analysis for Poland should be take cautiously 
and due to the limited sample size and the restricted coverage of the economy by the Orbis database Similarly, 
it is important to be cautious when interpreting results that focus on specific subsamples, such as combinations 
of size, sectors, and instrument types, as these often have low counts. These findings should be approached 
with care. For the sample of large firms, which exhibits a greater deal of heterogeneity in terms of firm size 
and turnover, an analysis on aid intensity could be particularly beneficial. Finally, it has to be acknowledged 
that the sample used for the analysis, although extremely rich and broad, represents only a selected sample 
of  the  overall  population  of  firms.  Specifically,  it  excludes  small  firms  which  might  have  a  worse  financial 
performance  and  were  more  likely  to  be  misreported  in  2021  Orbis  data.  This  could  potentially  lead  to  an 
overestimation of the (positive) effect of the aid on firms’ performances. Despite these limitations, the results 
suggest that COVID aid substantially helped companies cope with the crisis.  

Two important points need to be considered when interpreting the results and have potential policy implications. 
Firstly, this study analyses the causal impact of the aid by estimating the average treatment effect on the 
beneficiaries. The counterfactual (i.e. comparison scenario) is what would have happened to the beneficiaries 
had they not received the treatment. Therefore, these results cannot be extended to non-beneficiaries. In other 
words, the study does not provide evidence of  what  would  have  happened to non-beneficiaries if they had 
received  the  benefit.  Similarly,  the  conclusions  of  the  study  cannot  be  extended  to  the  wider  population  of 
treated firms in Italy, Spain and Poland, as the sample used for analysis is a restricted, selected, subsample of 
the overall population of treated firms.  

Secondly, it is important to note that this study does not provide an analysis of State aid Control as it is not 
possible to assess the impact on competition. While there may be a potential for distortions of competition in 
some specific sectors where the COVID aid was concentrated on a small number of firms, the initial market 
structure was highly concentrated, and the amount of aid was disproportionately high, this does not appear to 
be  the  case  for  the  bulk  of  COVID  aid  for  several  reasons.  COVID  aid  was  employed  simultaneously  by  all 
Member States, the scope of aid measures in each Member State was quite broad, affecting many firms in 
many sectors, and a large part of it was targeted at micro and small enterprises. All these aspects make it less 
likely  that  COVID  aid  had  a  distortive  effect  on  competition  in  the  Single  Market.  However,  a  targeted 
assessment of its impact on competition in potentially problematic sectors would be important. In addition, the 
data used for the analysis does not allow verification of whether firms that received COVID aid were eligible 
to apply for such benefits (i.e., whether large firms were not in difficulty before the pandemic) and whether the 
eligibility  criteria  were  respected.  Despite  these  caveats,  there  is  suggestive  evidence  that,  in  the  sample 
analysed, non-beneficiaries had, on average, a lower probability of default than beneficiaries. 

46 

 
References  

Adams-Prassl, A., Boneva, T., Golin, M., & Rauh, C. (2020). Inequality in the impact of the coronavirus shock: 
104245. 
Journal 
surveys. 
real 
Evidence 
https://doi.org/10.1016/j.jpubeco.2020.104245 

Economics, 

Public 

from 

189, 

time 

of 

Altomonte,  C.,  Demertzis,  M.,  Fontagné,  L.,  &  Müller,  S.  (2021). COVID-19  financial  aid  and  productivity:  has 
support been well spent?. Bruegel. 

Andrews,  A.,  Criscuolo,  C.,  Gal,  P.N.  (2016).  The  Best  versus  the  Rest:  The  Global  Productivity  Slowdown, 
Divergence across Firms and the Role of Public Policy. OECD Productivity Working Papers, (5), OECD Publishing, 
Paris. 

Andrews, D., Adalet McGowan, M., Millot, V. (2017). Confronting the zombies: Policies for productivity revival. 
OECD Economic Policy Papers, (21), OECD Publishing, Paris.  

Autor,  D  (2003),  Outsourcing  at  Will:  The  Contribution  of  Unjust  Dismissal  Doctrine  to  the  Growth  of 
Employment Outsourcing (2003) Journal of Labor Economics, 21 (1), pp. 1 – 42. DOI: 10.1086/344122 

Bajgar,  M.,  et  al.  (2020),  "Coverage  and  representativeness  of  Orbis  data", OECD  Science,  Technology  and 
Industry Working Papers, No. 2020/06, OECD Publishing, Paris, https://doi.org/10.1787/c7bdaa03-en. 

Bennedsen,  M.,  Larsen,  B.,  Schmutte,  I.,  &  Scur,  D.  (2020). Preserving  job  matches  during  the  COVID-19 
pandemic: firm-level evidence on the role of government aid (No. 588). GLO discussion paper. 

Bernini, C., & Pellegrini, G. (2011). How are growth and productivity in private firms affected by public subsidy? 
Evidence 
regional  policy.  Regional  Science  and  Urban  Economics,  41(3),  253–265. 
https://doi.org/10.1016/j. regsciurbeco.2011.01.005. 

from  a 

Bertschek, I., Block, J., Kritikos, A. S., & Stiel, C. (2023). German financial state aid during Covid-19 pandemic: 
Higher 
impact  among  digitalized  self-employed.  Entrepreneurship  and  Regional  Development. 
https://doi.org/10.1080/08985626.2023.2196267 

Bighelli, T., Lalinsky, T., Compnet Data Providers (2021). COVID-19 government support and productivity: Micro-
based cross-country evidence. Compnet Policy Brief (14). 

Brachert, M., Dettmann, E., &Titze, M. (2018). Public investment subsidies and firm performance–evidence from 
Germany.  Jahrbücher  für  Nationalökonomie  und  Statistik,  238(2),  103–124.  https://doi.org/10.1515/jbnst-
2017-0131. 

Cerqua, A., & Pellegrini, G. (2014). Do subsidies to private capital boost firms’ growth? A multiple regression 
discontinuity 
126. 
https://doi.org/10.1016/j.jpubeco.2013.11.005. 

Economics, 

109,114– 

approach. 

Journal 

design 

Public 

of 

Cerqua,  A.,  &  Pellegrini,  G.  (2017).  Industrial  policy  evaluation  in  the  presence  of  spillovers.  Small  Business 
Economics, 49(3), 671–686. https://doi.org/10.1007/s11187-017-9855-9. 

Chundakkadan, R., Natarajan, R.R., Sasidharan, S. (2022).  Small firms amidst Covid-19: financial constraints 
and role of government support. Economic Notes. 

Cros, M., Epaulard, A., Martin, P. (2021). Will Schumpeter Catch COVID-19. CEPR Discussion Paper, (15834), p. 
25. 

Davies, L., Kattenberg, M., Vogt, B. (2023). Predicting Firm Exits with Machine Learning: Implications for Selection 
into COVID-19 Support and Productivity Growth. CPB Discussion Paper, Centraal Plan Bureau. 

Decramer,  S.,  &  Vanormelingen,  S.  (2016).  The  effectiveness  of  investment  subsidies:  evidence  from  a 
regression discontinuity design. Small Business Economics, 47(4), 1007–1032. https://doi.org/10.1007/s11187-
016-9749-2. 

Dhyne, E., & Duprez, C. (2021). Belgian firms and the COVID-19 crisis. NBB Economic Review, 2021(September), 
68–89. 

Duso, T, M Nardotto and J Seldeslachts (2021), ‘DP15779 A Retrospective Study of State Aid Control in the 
German  Broadband  Market‘,  CEPR  Discussion  Paper  No.  15779.  CEPR  Press,  Paris  &  London. 
https://cepr.org/publications/dp15779 

47 

 
 
Dvouletý,  O.,  Srhoj,  S.,  &  Pantea,  S.  (2021).  Public  SME  grants  and  firm  performance  in  European  Union:  A 
systematic 
243–263. 
https://doi.org/10.1007/s11187-019-00306-x 

Economics, 

evidence. 

empirical 

Business 

review 

57(1), 

Small 

of 

European Commission, Directorate-General for Economic and Financial Affairs (2021), Canton, E., Colasanti, F., 
Durán, J. et al., The sectoral impact of the COVID-19 crisis – An unprecedented & atypical crisis, Publications 
Office of the European Union, 2021, https://data.europa.eu/doi/10.2765/982245 

European Commission, Directorate-General for Competition (2022a), Cannas, G., Ferraro, S., Mathieu Collin, A. 
et  al.,  Competition  state  aid  brief.  Issue  3/2022  –  October  2022,  European  Commission,  2022, 
https://data.europa.eu/doi/10.2763/631679 

European  Commission,  Directorate-General  for  Competition  (2022b),  State  aid  Scoreboard  2021,  DG  COMP 
https://competition-policy.ec.europa.eu/system/files/2023-04/state_aid_scoreboard_note_2021.pdf 

European Commission, Directorate-General for Competition, (2022c) Mathieu Collin, A., Ferraro S., Cannas, G., 
Van de Casteele, K., “The use of COVID-19 related State aid measures by EU Member States – First semester 
2021 
2022, 
brief. 
update”, Competition 
https://data.europa.eu/doi/10.2763/41582 

February 

1/2022 

Issue 

state 

aid 

– 

European  Commission,  Directorate-General  for  Competition  (2023),  State  aid  Scoreboard  2022,  DG  COMP 
https://competition-policy.ec.europa.eu/system/files/2023-06/state_aid_scoreboard_note_2022.pdf 

Gourinchas,  P.  O.,  Kalemli-Özcan,  Ş.,  Penciakova,  V.,  &  Sander,  N.  (2021,  May).  COVID-19  and  small-and 
medium-sized enterprises: A 2021 “time bomb”?. In AEA Papers and Proceedings. 111: 282-286. 

Harasztosi, P., Maurin, L., Pál, R., Revoltella, D., & van der Wielen, W. (2022). Firm-level policy support during the 
crisis: So far, so good? International Economics, 171(April), 30–48. https://doi.org/10.1016/j.inteco.2022.04.003 

Hartšenko, J., & Sauga, A. (2013). The role of financial support in SME and economic development in Estonia. 
Business & Economic Horizons, 9(2), 10–22. https://doi.org/10.15208 /beh.2013.6. 

Imbens, G.W (2015). Matching Methods in Practice: Three Examples. Journal of Human Resources, 50 (2),  

Kalemli-Ozcan,  S.,  Sorensen,  B.,  Villegas-Sanchez,  C.,  Volosovych,  V.  and  Yesiltas,  S.,  (forthcoming)  How  to 
Construct Nationally Representative Firm Level Data from the Orbis Global Database: New Facts on SMEs and 
Aggregate Implications for Industry Concentration , American Economic Journal: Macroeconomics. 

Konings,  J.,  Magerman,  G.,  &  Van  Esbroeck,  D.  (2023).  The  impact  of  firm-level  Covid  rescue  policies  on 
reallocation.  European  Economic  Review,  157(October  2022),  104508. 
productivity  growth  and 
https://doi.org/10.1016/j.euroecorev.2023.104508. 

Mole, K. F., Hart, M., Roper, S., & Saal, D. S. (2009). Assessing the effectiveness of business support services in 
England:  evidence  from  a  theory-based  evaluation.  International  Small  Business  Journal,  27(5),  557–582. 
https://doi. org/10.1177/0266242609338755. 

OECD (2021), COVID-19 Government Financing Support Programmes for Businesses: 2021 Update, OECD Paris, 
https://www.oecd.org/finance/COVID-19-Government-Financing-Support-Programmes-for-Businesses-2021-
Update.pdf. 

Pelosi, M., Rodano, G., & Sette, E. (2021). Zombie firms and the take-up of support measures during Covid-
19. Bank of Italy Occasional Paper, (650). 

Piechucka, J.,Saurí-Romero, L., & Smulders, B. (2023). Industrial Policies, Competition, and Efficiency: The Need 
for State Aid Control, Journal of Competition Law & Economics, Volume 19, Issue 4, December 2023, Pages 
503–526, https://doi.org/10.1093/joclec/nhad014 

Roper, S., & Hewitt-Dundas, N. (2001). Grant assistance and small firm development in Northern Ireland and 
the  Republic  of  Ireland.  Scottish  Journal  of  Political  Economy,  48(1),  99–117.  https://doi.org/10.1111/1467-
9485.00187. 

Söderblom, A., Samuelsson, M., Wiklund, J., & Sandberg, R. (2015). Inside the black box of outcome additionality: 
effects of early-stage government subsidies on resource accumulation and new venture performance. Research 
Policy, 44(8), 1501–1512. https://doi.org/10.1016/j.respol.2015.05.009. 

48 

 
Srhoj, S., Škrinjaric, B., & Radas, S. (2019). Bidding against the odds? The Impact Evaluation of Grants for Young 
Micro and Small Firms During the Recession. Small Business Economics. https://doi.org/10.1007/s11187-019-
00200-6 

Tielens, J., Piette, C. (2022). How Belgian firms fared in the COVID-19 pandemic? Natl. Bank Belg. Econ. Rev. 

Tielens, J., Piette, C.,  De Jonghe, O. (2020).  Belgian corporate sector liquidity and solvency in the COVID-19 
crisis: a post-first-wave assessment. Natl. Bank Belg. Econ. Rev. 

Zegel,  S.,  van  der  Graaf,  A.,  Karsten,  E.,  Konings,  J.,  Magerman,  G.,  Van  Esbroeck,  D.    (2021).  Evaluatie 
Coronamaatregelen VLAIO: Impact van de Steunmaatregelen. Vlaio Report. 

49 

 
List of tables  

Table 1. Total and Normalized State aid expenditure (based on aid element) 1 .................................................................. 15 

Table 2. Total COVID aid expenditure (based on nominal amounts) 1 ......................................................................................... 17 

Table 3. Firms characteristics of analysed samples compared to excluded observations ............................................ 22 

Table 4. Probit models. Dependent variable = 1 if firm is not reported in a given year (see table); marginal 
effects1. Year: 2019 ...................................................................................................................................................................................................... 24 

Table 5. Firm’s 2019 characteristics, by country ..................................................................................................................................... 26 

Table 6. Descriptive statistics of firm pre-treatment characteristics (2019), by treatment status and country
.................................................................................................................................................................................................................................................... 28 

Table 7. Probit model: relationship between having had a large decrease in turnover in 2019 and the 
probability of receiving Covid Aid in 2020; marginal effects. ............................................................................................................ 29 

Table 8. Proportions of aids received by beneficiaries and nominal aid amount by type of instrument (2020)
.................................................................................................................................................................................................................................................... 30 

Table 9. Aid awards combinations. .................................................................................................................................................................... 30 

Table 10. Distribution of instrument type by country (Panel A) and firm dimension (Panel B) ................................. 31 

Table 11. The effects of COVID State aid on firms’ turnover1 ........................................................................................................ 36 

Table 12. The effects of COVID State aid on firms’ turnover, by different combinations of sector/size groups1
.................................................................................................................................................................................................................................................... 38 

Table 13. The effects of COVID State aid on firms’ turnover, by type of aid instrument1 ............................................ 39 

Table 14. Categories of Risk of Default ......................................................................................................................................................... 40 

Table 15. The relationship between COVID State aid and category of default1 .................................................................. 42 

Table A 1. Variables definition ............................................................................................................................................................................. 54 

Table A 2.  Firms characteristics by size and country........................................................................................................................... 59 

Table A 3. Firms characteristics by sector and country. ...................................................................................................................... 60 

Table A 4. Overlap of observations in NR and TAM data: 2017-2021 data ........................................................................... 62 

Table A 5. Distribution of State aid awards by firm size .................................................................................................................... 62 

Table A 6. Number of State aid awards by firm size ............................................................................................................................ 63 

Table A 7. Distribution of State aid awards by sector and firm size ........................................................................................... 65 

Table A 8. Quality of match between Orbis and the registry data ............................................................................................... 66 

Table A 9. Comparing beneficiaries in TAM and in the NR; Sample: all firms in 20191 .................................................. 67 

Table A 10. Comparing beneficiaries in TAM and in the NR; Sample: firms with turnover >2 Millions € 1 ......... 67 

Table A 11. Comparing beneficiaries in TAM and in the NR; Sample: firms with turnover >50 Millions € ......... 68 

Table A 12. Proportion of beneficiaries in the subsamples of interest...................................................................................... 68 

Table A 13. Matching Balance Tests for Italy, Poland, and Spain ................................................................................................. 69 

Table A 14. The effects of COVID State aid on firms’ turnover, by different combinations of sector/size 
groups1 .................................................................................................................................................................................................................................. 71 

Table A 15. The effects of COVID State aid on firms’ turnover, by type of aid instrument1 ....................................... 71 

50 

 
 
 
Table A 16. The relationship between COVID State aid and category of default, by different combinations of 
sector/size groups 1 ........................................................................................................................................................................................................ 72 

Table A 17. The relationship between COVID State aid and category of default, by type of aid instrument1 . 72 

51 

 
 
List of Figures  

Figure 1. Total State aid expenditure (% of 2019 GDP), by policy objective (COVID vs. Non-COVID) 1 ................. 16 

Figure 2. Normalized COVID aid expenditure (% of GDP), by type of instrument 2020-20211 ................................. 18 

Figure 3. Propensity score distribution and common support for Italy, Spain, and Poland .......................................... 34 

Figure 4. Distributions of category of default by country .................................................................................................................. 41 

Figure 5. Event study analysis–turnover, full sample ........................................................................................................................... 43 

Figure 6. Event study analysis – probability of default, full sample ........................................................................................... 44 

Figure A 1. Total State aid expenditure (aid elements), by policy objective (COVID vs. Non-COVID) 1 .................. 55 

Figure A 2. Firms’ Turnover in time, across treated and control firms1 .................................................................................... 56 

Figure A 3. Event study analysis, Turnover by sector ........................................................................................................................... 57 

Figure A 4. Event study analysis, Turnover by size ................................................................................................................................ 58 

52 

 
 
 
 
Annexes  

53 

 
 
Annex 1. Additional tables and figures  

Table A 1. Variables definition 

Outcome variables 

Description 

Turnover 

Continuous  Variable.  Total  operating  revenues  (including  net  sales,  other 
operating revenues, and stock variations) expressed in millions of EUR. 

Probability of default  

Continuous Variable (range 0-100). Firm's probability of default 

Explanatory variables 

Size (Micro) 

Size (Small) 

Size (Medium) 

Size (Large) 

Binary variable (0-1). 1 if the firm had an average turnover lower than 2 € Millions 
in the pre-treatment period; 0 otherwise. 

Binary variable (0-1). 1 if the firm had an average turnover between 2 and 10 € 
Millions in the pre-treatment period; 0 otherwise. 

Binary variable (0-1). 1 if the firm had an average turnover between 10 and 50 
€ Millions in the pre-treatment period; 0 otherwise. 

Binary variable (0-1). 1 if the firm had an average turnover over 50 € Millions in 
the pre-treatment period; 0 otherwise. 

Sector (Manufacturing) 

Binary  variable  (0-1).  1  if  the  firm  operates  in  the  manufacturing  sector;  0 
otherwise. 

Sector (Construction) 

Binary variable (0-1). 1 if the firm operates in the construction; 0 otherwise. 

Sector (Wholesale) 

Binary variable (0-1). 1 if the firm operates in the wholesale sector; 0 otherwise. 

Sector (Retail) 

Binary variable (0-1). 1 if the firm operates in the retail sector; 0 otherwise. 

Capital 

Loans 

Continuous  Variable.  Issued  Share  capital  (Authorized  capital).  Expressed  in 
millions of EUR 

Continuous Variable. Short term financial debts such as bonds, fractions of long 
term  financial  debts  payable  within  the  year,  debts  to  credit  institutions,  etc. 
Expressed in millions of EUR 

Total assets 

Continuous  Variable.  Total  assets  (including  fixed  assets  and  current  assets. 
Expressed in millions of EUR 

Continuous Variable.  Firm's profitability computed as: (earnings before interest 
and tax + depreciation + amortization) / total revenue 

EBIT margin 

Expressed in units (-100;100) 

54 

 
 
 
 
 
 
 
 
 
 
 
 
 
Figure A 1. Total State aid expenditure (aid elements), by policy objective (COVID vs. Non-COVID) 1 

s
n
o

i
l
l
i

M
€

200000

150000

100000

50,000

0

0
2
0
2

1
2
0
2

0
2
0
2

1
2
0
2

0
2
0
2

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2
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2

0
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1
2
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2

0
2
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2

1
2
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0
2
0
2

1
2
0
2

0
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2

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0
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0
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2

1
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2

1
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1
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2

0
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1
2
0
2

0
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0
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1
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2

0
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0
2

1
2
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0
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1
2
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0
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0
2

1
2
0
2

T

I

E
D

R
F

S
E

L
N

L
P

T
A

K
D

E
B

U
H

L
E

Z
C

T
P

E
S

I

OF
R

E

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KS
S

R
H

T
L

V
L

G
B

T
M

U
L

E
E

Y
C

non-Covid

Covid

1     The figure shows the total aid expenditure - in terms of aid element by related objectives (COVID and non-COVID) and Member State. 
Totals are in EUR million.  

Source: Authors’ calculations based on State aid Reporting Interactive (SARI2) data. 

Figure A 1b. Normalized COVID aid expenditure (% of GDP), by type of instrument 2020-20211 

1     The figure shows the COVID-related total aid expenditure (in nominal amounts) as share of 2019 national GDP, by type of financial 
instrument used and Member State. 

Source: Authors’ calculations based on State aid Reporting Interactive (SARI2) data. 

55 

 
 
 
 
 
Figure A 2. Firms’ Turnover in time, across treated and control firms1 

Panel A: Italy 

Panel B: Spain  

Pan 

Panel C: Poland  

56 

 
 
 
 
 
 
 
 
 
 
Figure A 3. Event study analysis, Turnover by sector  

           1 – Manufacturing 

          2 - Construction 

Panel A: Italy 

               3 – Wholesale  

                   4- Retail  

           1 – Manufacturing 

                 2 - Construction 

Panel B: Spain 

                    3 – Wholesale  

      4- Retail  

Source: Authors’ elaboration based on ORBIS and State aid National registry data  

57 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure A 4. Event study analysis, Turnover by size 

1 – Micro 

2 - Small 

Panel A: Italy 

3 – Medium 

4- Large 

Panel B: Spain 

1 – Micro 

2 - Small 

3 – Medium 

4- Large 

Source: Authors’ elaboration based on ORBIS and State aid National registry data 

58 

 
 
 
 
 
 
 
 
 
 
 
 
Table A 2.  Firms characteristics by size and country 

Country 

Variable2 
Manufacturing 
Construction 
Wholesale 
Retail 
Turnover (€M) 
Capital (€M) 
Loans (€M) 
Total assets (€M) 
EBITD margin (M) 
Z-score 

Country 

Variable 
Manufacturing 
Construction 
Wholesale 
Retail 
Turnover (€M) 
Capital (€M) 
Loans (€M) 
Total assets (€M) 
EBITD margin (M) 
Z-score 

Country 

Variable 
Manufacturing 
Construction 
Wholesale 
Retail 
Turnover (€M) 
Capital (€M) 
Loans (€M) 
Total assets (€M) 
EBITD margin (M) 
Z-score 

Benef. 
Mean / % 
28.6% 
23.6% 
27.7% 
20.1% 
0.51 
0.03 
0.03 
0.56 
5.62 
6.53 
      37,945  

Benef. 
Mean / % 
13.1% 
31.3% 
42.7% 
12.9% 
4.01 
0.13 
0.29 
3.39 
3.21 
5.24 
         3,623  

Italy 
Non-Benef. 
Mean / % 
36.1% 
22.3% 
27.8% 
13.7% 
0.43 
0.05 
0.03 
0.86 
6.11 
8.27 
      28,569  

Italy 
Non-Benef. 
Mean / % 
13.6% 
30.4% 
43.4% 
12.6% 
4.17 
0.25 
0.17 
4.91 
4.33 
6.24 
         4,298  

Benef. 
Mean / % 
5.3% 
38.0% 
49.5% 
7.2% 
20.66 
1.05 
2.39 
17.16 
4.13 
3.85 
            666  

Italy 

Non-Benef. 
Mean / % 

9.0% 
34.1% 
46.9% 
9.9% 
20.18 
3.81 
0.94 
40.33 
3.57 
6.33 
            1,110  

Size = Micro1 

Benef. 
Mean / % 
23.5% 
26.3% 
29.6% 
0.20 
0.61 
0.06 
0.04 
0.54 
3.01 
9.90 
      53,277  

Size = Small 

Benef. 
Mean / % 
22.3% 
16.7% 
46.9% 
0.14 
4.20 
0.19 
0.34 
2.88 
3.51 
3.66 
         9,805  

Spain 
Non-Benef. 
Mean / % 
20.7% 
32.5% 
28.0% 
0.19 
0.43 
0.11 
0.01 
0.68 
5.95 
9.90 
      67,268  

Spain 
Non-Benef. 
Mean / % 
22.8% 
12.1% 
48.6% 
0.17 
4.25 
0.41 
0.11 
4.19 
6.02 
3.49 
         7,922  

Size = Medium 

Benef. 
Mean / % 
23.4% 
11.0% 
58.3% 
0.07 
20.47 
0.88 
2.44 
13.24 
3.25 
4.45 
          1,530  

Spain 
Non-Benef. 
Mean / % 
26.6% 
10.1% 
53.5% 
0.10 
21.56 
3.25 
0.76 
21.26 
5.52 
4.11 
          1,832  

Diff. 3 
-0.08* 
0.01* 
0.00 
0.06* 
0.08* 
-0.03* 
0.00* 
-0.31* 
-0.49* 
-1.74* 

Diff. 
-0.01 
0.01 
-0.01 
0.00 
-0.16* 
-0.12* 
0.12* 
-1.51* 
-1.13* 
-1.01* 

Diff. 
-0.04* 
0.04 
0.03 
-0.03 
0.48 
-2.75 
1.45* 
-23.17 
0.55 
-2.48* 

Diff. 
0.03* 
-0.06* 
0.02* 
0.02* 
0.18* 
-0.05* 
0.03* 
-0.14* 
-2.94* 
-0.01 

Diff. 
-0.01 
0.05* 
-0.02* 
-0.02* 
-0.06 
-0.22* 
0.23* 
-1.31* 
-2.51* 
0.17 

Benef. 
Mean / % 

25.4% 
23.2% 
34.1% 
17.2% 
0.68 
0.08 
0.04 
0.55 
5.44 
8.90 
         3,326  

Benef. 
Mean / % 

24.1% 
13.6% 
47.1% 
15.2% 
4.63 
0.34 
0.18 
2.71 
5.62 
4.58 
          1,428  

Diff. 
-0.03* 
0.01 
0.05* 
-0.02* 
-1.09* 
-2.37* 
1.68* 
-8.02* 
-2.26* 
0.34 

Benef. 
Mean / % 
22.8% 
7.7% 
63.6% 
5.9% 
20.46 
0.93 
0.84 
9.71 
4.40 
3.79 
             401  

Poland 
Non-Benef. 
Mean / % 

24.7% 
29.0% 
31.8% 
14.5% 
0.55 
-0.11 
0.10 
0.99 
6.96 
16.75 
            969  

Poland 
Non-Benef. 
Mean / % 
29.3% 
23.4% 
32.4% 
14.9% 
5.21 
0.81 
0.37 
5.52 
6.25 
10.48 
            475  

Poland 
Non- Benef. 
Mean / % 

36.6% 
9.5% 
44.7% 
9.2% 
24.70 
1.50 
1.41 
17.35 
5.51 
4.81 

            357  

Diff. 
0.01* 
-0.06* 
0.02* 
0.03* 
0.12* 
0.19* 
-0.07* 
-0.44* 
-1.52* 
-7.85* 

Diff. 
-0.05* 
-0.1* 
0.15* 
0.00* 
-0.58* 
-0.46* 
-0.19* 
-2.81* 
-0.63* 
-5.9* 

Diff. 
-0.14* 
-0.02* 
0.19* 
-0.03* 
-4.25* 
-0.58* 
-0.56* 
-7.64* 
-1.11* 
-1.02* 

Country 

Variable 
Manufacturing 
Construction 
Wholesale 
Retail 
Turnover (€M) 
Capital (€M) 
Loans (€M) 
Total assets (€M) 
EBITD margin (M) 
Z-score 

Benef. 
Mean / % 

5.1% 
42.4% 
50.0% 
2.5% 
125.96 
5.77 
13.94 
82.17 
3.62 
2.81 
              118  

Italy 

Non-Benef. 
Mean / % 
5.4% 
40.1% 
45.5% 
9.0% 
435.33 
17.31 
16.58 
262.38 
4.44 
4.28 
            409  

Diff. 

0.42 
0.00 
0.05 
-0.07* 
-309.37 
-11.53 
-2.64 
-180.21* 
-0.82 
-1.47 

Size = Large 

Benef. 
Mean / % 
21.1% 
8.8% 
62.7% 
0.07 
114.13 
2.38 
10.95 
59.03 
3.07 
4.37 
            204  

Spain 

Non-Benef. 
Mean / % 
30.3% 
5.0% 
56.6% 
0.08 
239.04 
17.12 
3.76 
159.43 
5.12 
4.81 
            533  

Diff. 
-0.09* 
0.04 
0.06 
-0.01 
-124.9* 
-14.74* 
7.19* 
-100.41* 
-2.06* 
-0.44 

Benef. 
Mean / % 
1.8% 
10.9% 
80.0% 
7.3% 
231.15 
4.39 
7.34 
49.13 
1.77 
3.93 
               54  

Poland 
Non-Benef. 
Mean / % 
7.7% 
28.8% 
61.5% 
1.9% 
202.36 
14.27 
3.54 
101.27 
3.95 
3.79 
             155  

Diff. 
-0.06* 
-0.18* 
0.18* 
0.05* 
28.79 
-9.88* 
3.80 
-52.14* 
-2.18* 
0.14* 

1 

2 
3 

Firm size is based on the average turnover for 2017-2019. Size categories: micro: average turnover < 2 EUR Millions; small: average 
turnover between 2 and 10 Millions of EUR Medium: average turnover between 10 and 50 Millions of EUR; Large: average turnover 
over 50 Millions of EUR. 
Turnover, Capital, Loans and Total assets are expressed in Millions of EUR. 
The table reports average differences between beneficiaries and non-beneficiaries and the associated p-value. 

Source: Authors’ computations based on ORBIS and State Aid National registry data. 

59 

 
  
  
  
  
  
  
  
  
 
  
  
  
  
  
  
  
  
 
  
  
  
  
  
  
  
  
 
 
  
  
  
  
  
  
  
  
 
 
 
 
 
Table A 3. Firms characteristics by sector and country. 

Sector = Manufacturing 

Country 

Variable2 
Micro1 
Small 
Medium 
Large 
Turnover (€M) 
Capital (€M) 
Loans (€M) 
Total assets (€M) 
EBITD margin (M) 
Z-score 

Benef. 
Mean / % 
86.2% 
10.9% 
2.4% 
0.5% 
2.03 
0.12 
0.20 
1.90 
4.38 
6.80 

Italy 
Non-Benef. 
Mean / % 

77.5% 
15.9% 
4.6% 
2.0% 
7.99 
0.87 
0.21 
10.06 
5.78 
6.70 

 10,378  

   8,230  

Diff. 3 
0.09* 
-0.05* 
-0.02* 
-0.02* 
-5.96* 
-0.75* 
-0.01* 
-8.15* 
-1.40* 
0.10* 

Benef. 
Mean / % 

88.5% 
10.4% 
1.1% 
0.1% 
1.28 
0.10 
0.09 
1.26 
4.29 
8.28 
          15,832  

Spain 
Non-Benef. 
Mean / % 

94.9% 
4.2% 
0.8% 
0.1% 
0.82 
0.53 
0.05 
2.67 
8.45 
9.21 
           23,055  

Diff. 
-0.06* 
0.06* 
0.00* 
0.00 
0.46* 
-0.43* 
0.03* 
-1.41* 
-4.17* 
-0.93* 

Benef. 
Mean / % 

65.7% 
26.8% 
7.1% 
0.5% 
3.48 
0.36 
0.16 
2.23 
6.06 
6.41 
           1,297  

Poland 
Non-Benef. 
Mean / % 

43.3% 
25.1% 
23.5% 
8.1% 
17.10 
0.74 
0.84 
12.61 
5.47 
10.07 
               557  

Diff. 
0.22* 
0.02* 
-0.16* 
-0.08* 
-13.62* 
-0.38* 
-0.68* 
-10.38* 
0.59 
-3.66* 

Country 

Variable 
Micro 
Small 
Medium 
Large 
Turnover (€M) 
Capital (€M) 
Loans (€M) 
Total assets (€M) 
EBITD margin (M) 
Z-score 

Country 

Variable 
Micro 
Small 
Medium 
Large 
Turnover (€M) 
Capital (€M) 
Loans (€M) 
Total assets (€M) 
EBITD margin (M) 
Z-score 

Country 

Variable 
Micro 
Small 
Medium 
Large 
Turnover (€M) 
Capital (€M) 
Loans (€M) 
Total assets (€M) 
EBITD margin (M) 
Z-score 

Benef. 
Mean / % 
95.5% 
4.2% 
0.3% 
0.1% 
0.70 
0.05 
0.06 
1.18 
8.50 
5.00 
       11,378  

Italy 
Non-Benef. 
Mean / % 

93.6% 
5.3% 
0.9% 
0.2% 
0.88 
0.25 
0.12 
3.44 
7.99 
9.61 
       11,033  

Sector = Construction 

Diff. 
0.02* 
-0.01* 
-0.01* 
0.00* 
-0.18* 
-0.20* 
-0.06* 
-2.26* 
0.51 
-4.61* 

Benef. 
Mean / % 
82.9% 
14.5% 
2.4% 
0.3% 
1.92 
0.14 
0.19 
1.59 
3.15 
7.98 
       15,140  

Spain 
Non-Benef. 
Mean / % 

85.0% 
11.0% 
3.0% 
1.0% 
2.85 
0.34 
0.05 
2.92 
5.05 
8.01 

      16,405  

Benef. 
Mean / % 
84.5% 
12.4% 
2.6% 
0.5% 
2.16 
0.07 
0.19 
1.32 
5.03 
5.63 
      12,459  

Benef. 
Mean / % 
93.6% 
5.8% 
0.6% 
0.0% 
0.79 
0.03 
0.03 
0.60 
2.87 
8.86 
        8,137  

Italy 

Non-Benef. 
Mean / % 

75.5% 
17.7% 
5.0% 
1.8% 
12.71 
0.27 
0.56 
5.99 
5.09 
6.30 
       10,515  

Italy 

Non-Benef. 
Mean / % 

85.0% 
11.8% 
2.4% 
0.8% 
4.67 
0.26 
0.05 
3.07 
2.08 
9.64 

       4,608  

Sector = Wholesale 

Diff. 

0.09* 
-0.05* 
-0.02* 
-0.01* 
-10.55* 
-0.20* 
-0.38* 
-4.67* 
-0.07* 
-0.67* 

Benef. 
Mean / % 
73.7% 
21.5% 
4.2% 
0.6% 
2.98 
0.11 
0.30 
1.67 
2.85 
7.80 
      21,405  

Spain 
Non-Benef. 
Mean / % 

78.6% 
16.1% 
4.1% 
1.3% 
5.70 
0.28 
0.10 
3.30 
5.61 
7.87 

     23,996  

Sector = Retail 

Diff. 
0.09* 
-0.06* 
-0.02* 
-0.01* 
-3.88* 
-0.23* 
-0.02* 
-2.47* 
0.78 
-0.78* 

Benef. 
Mean / % 
87.9% 
11.0% 
0.9% 
0.1% 
1.23 
0.07 
0.06 
0.79 
1.91 
12.21 
      12,450  

Spain 
Non-Benef. 
Mean / % 

89.2% 
9.2% 
1.3% 
0.3% 
1.96 
0.10 
0.02 
1.04 
3.42 
12.14 
        14,112  

Diff. 
-0.02* 
0.03* 
-0.01* 
-0.01* 
-0.93* 
-0.20* 
0.14* 
-1.33* 
-1.91* 
-0.03 

Diff. 
-0.05* 
0.05* 
0.00 
-0.01* 
-2.72* 
-0.16* 
0.19* 
-1.63* 
-2.77* 
-0.06 

Benef. 
Mean / % 
77.4% 
19.4% 
3.1% 
0.1% 
1.99 
0.23 
0.12 
2.12 
8.58 
6.03 
        1,005  

Benef. 
Mean / % 
53.8% 
32.0% 
12.1% 
2.1% 
7.13 
0.24 
0.35 
2.68 
4.20 
7.12 
        2,120  

Diff. 
-0.01* 
0.02* 
0* 
0* 
-0.74* 
-0.04* 
0.03* 
-0.26* 
-1.51* 
0.07 

Benef. 
Mean / % 
70.0% 
26.6% 
2.9% 
0.5% 
10.03 
0.19 
0.08 
1.99 
3.38 
10.51 
           824  

Poland 
Non-Benef. 
Mean / % 

64.2% 
25.4% 
7.7% 
2.7% 
6.16 
1.09 
0.70 
15.12 
13.32 
11.24 
            441  

Poland 
Non-Benef. 
Mean / % 

43.0% 
21.5% 
22.2% 
13.3% 
41.39 
2.82 
0.71 
15.76 
3.77 
11.60 
            721  

Poland 
Non-Benef. 
Mean / % 

56.9% 
28.6% 
13.3% 
1.2% 
5.49 
0.45 
0.18 
2.25 
2.90 
18.95 

           248  

Diff. 

0.13* 
-0.06* 
-0.05* 
-0.03* 
-4.17* 
-0.87* 
-0.59* 
-12.99* 
-4.74* 
-5.21* 

Diff. 

0.11* 
0.10* 
-0.10* 
-0.11* 
-34.25* 
-2.58* 
-0.36* 
-13.08* 
0.43* 
-4.48* 

Diff. 
0.13* 
-0.02* 
-0.1* 
-0.01* 
4.55 
-0.26* 
-0.1* 
-0.26* 
0.48* 
-8.44* 

1 

2 
3 

Firm size is based on the average turnover for 2017-2019. Size categories: micro: average turnover < 2 EUR Millions; small: average 
turnover between 2 and 10 Millions of EUR Medium: average turnover between 10 and 50 Millions of EUR; Large: average turnover 
over 50 Millions of EUR. 
Turnover, Capital, Loans and Total assets are expressed in Millions of EUR. 
The table reports average differences between beneficiaries and non-beneficiaries and the associated p-value. 

Source: Authors’ computations based on ORBIS and State Aid National registry data. 

60 

 
  
  
  
  
  
  
  
  
 
  
  
  
  
  
  
  
  
 
  
  
  
  
  
  
  
  
 
  
  
  
  
  
  
  
  
 
 
 
Annex 2. Transparency reporting thresholds 

The relevant threshold varies according to the legal basis of the State aid measure and the economic sector of 
the beneficiary. More specifically, the thresholds are defined as follows.  

For cases under the Block Exemption Regulations, the thresholds are: 

i. 
ii. 
iii. 

EUR 60,000 for aid granted under the ABER; 
EUR 30,000 for aid granted under the FIBER; 
EUR 500,000 for aid granted under the GBER (before the enter into force of the Green Deal GBER 
amendment), except as follows: 

o  EUR 60,000 for aid granted under the GBER to companies active in primary agricultural 

production 

o  EUR 30,000 for aid granted under the GBER to companies active in the fishery and 

aquaculture sector.  

For notified COVID-19 aid, the thresholds are: 

iv. 
v. 

EUR 10,000 for aid granted to companies active in primary agriculture and in the fisheries sectors; 
EUR 100,000 for aid granted to companies active in all other economic sectors. 

For aid granted under all the other legal bases, the thresholds are: 

vi. 
vii. 
viii. 

EUR 60,000 for aid to companies active in the agriculture sector; 
EUR 30,000 for aid to companies active in the fisheries sector; 
EUR 500,000 for aid to companies active in all other economic sectors.  

TAM data 

TAM (Transparency Award Module) is a tool developed by the EC allowing Member States fulfil their obligations 
to publish information on the largest individual aid awards (known as ‘the transparency obligations’). In this IT 
tool,  Member  States  report  all  firm-level  aid  awards  that  exceed  the  above  reporting  thresholds.  The 
information  reported  in  TAM  includes  the  reference  to  the  aid  measure,  the  name  of  the  company  and  its 
identifier, the nominal amount of the awards, the aid element, the type of aid instrument, the location and size 
of the company, the objective of the aid, and the award date. Currently, 23 Member States plus Iceland use the 
TAM  to  fulfil  their  transparency  obligations.  Poland,  Romania,  Spain  and  Slovenia  use  their  own  national 
transparency websites.  

Although the initial plan ofthis study was to use TAM25 to assess the impact of COVID aid on firms’ performance 
in all EU Member States, this was not possible due to the representativeness issues caused by the reporting 
thresholds. To get a sense of the magnitude of the missing data, it is necessary to compare TAM data with the 
national registers hat include information on all aid awards. Italy represents a good case study to test TAM 
coverage as it voluntarily opted for full transparency and publishes a well-structured national register. This 
exercise is shown in detail in Annex 3 below. The partial non-random coverage of aid awards in TAM, makes 
this dataset not suitable to perform a robust impact evaluation of State aid. 

25 As complemented by the information from the national Registers of Poland, Romania, Spain and Slovenia.  

61 

 
 
 
                                           
Annex 3. Comparison between TAM and national registry (NR) for Italy 

Table A4 analyses the overlap between the information on TAM relating to Italian firms and the Italian NR, for 
the period of analysis between 2017 and 2021. This can be done by verifying that the firms’ national identifiers 
appear in both datasets. We do not compare the amounts or the number of aids per firms as the focus on this 
analysis is on the intensive margin (the impact of receiving State aid) rather than on the extensive margin (the 
impact of the amount of the State Aid) of using State aid. Most of the observations (aid awards) in TAM have 
a corresponding identifier in the NR while only 2,129 awards in TAM do not have an identifier matching with 
the National Registry. This mismatch is possibly due to coding errors. After some quick investigation, we exclude 
this  small  number  of  unmatched  observations  from  the  analysis,  as  it  is  not  possible  to  identify  the 
corresponding entry in the NR 26. 

Table A 4. Overlap of observations in NR and TAM data: 2017-2021 data 
% 

Obs. 

NR only 
TAM only 
NR and TAM  
Total 

4,223,684  98.56 
0.05 
1.39 
100 
Source: Authors’ computations based on TAM and State Aid National registry data.  

2,129 
59,653 
4,285,466 

The  next  section  investigate  how  the  distribution  of  firm  size  and  sector  differ  between  TAM  and  the  NR. 
Specifically, Table A5 present the distribution of State aid awards by firm size. As expected, there is a higher 
proportion of awards (8%) reported by large firms in TAM than in the National Registry (0.4%). Large firms are 
overrepresented  in  TAM  due  to  the  legal  reporting  thresholds,  as  they  are  more  likely  to  receive  larger  aid 
awards. 

Table A 5. Distribution of State aid awards by firm size 

NR  
only 
15,651 

TAM & 
NR 
4,832 

Size1 
Large firms 
SMEs2 
Missing info 
Total 

% 
0.4 

% 
0.5 
4,182,415  99.0  54,405  91.2  4,236,820  98.9 
0.6 
4,223,684  100  59,653  100  4,283,337  100 

Total 
20,483 

% 
8.1 

26,034 

25,618 

416 

0.7 

0.6 

Source: Authors’ computations based on TAM and State aid National registry data. Year: 2017-2021. 
1 

The  definition  of  firm  size  used  here  is  the  one  adopted  by  the  European  commission,  available  at:  https://single-market-
economy.ec.europa.eu/smes/sme-definition_en  
SME= small and medium enterprise. 

2 

26  We  also  test  whether  the  aid  amounts  received  by  the  excluded  observations  are  statistically  significantly 
different from those matching with the NR. Specifically, we run an OLS regression of the aid amounts on a 
dummy  variable  capturing  whether  the  observation  is  present  in  TAM  only  (as  compared  to  whether  the 
observation is in both samples) and a set of year binary variables. The results show that the difference is 
not statistically significant. 

62 

 
 
 
 
 
 
 
 
 
                                           
To investigate the role of COVID State aid, table A6 also presents the distribution of aid awards by size and over time. After 2019, there is a higher proportion of small 
firms and this may be due to lower reporting thresholds for Covid State aid, and a wider use of State aid during the COVID pandemic.  

Table A 6. Number of State aid awards by firm size 

Year 

2017 
TAM & 
NR  
 NR 
only 
1,637 
340 
1.2%  23.4% 

2018 
TAM & 
NR  
 NR 
only 
3,306 
688 
0.9%  21.5% 

SME2 

138,599 

Size1 
Large 

Missing info 

Total 
1,977 
1.4% 
917  139,516  371,953 

Total 
3,994 
1.1% 
2,442  374,395  258,557 
98.8% 
99.0%  76.3% 
324 
414 
71 
0.1% 
0.1% 
2.2% 
3,201  378,803  261,415 
100% 
100% 
100% 
Source: Authors’ computations based on TAM and State Aid National registry data. Year: 2017-2021. 
1 
2 

97.2% 
343 
2,074 
0.1% 
1.4% 
1,453  143,567  375,602 
100% 
100% 
100% 

97.5%  63.1% 
1,878 
196 
1.3%  13.5% 

142,114 
100% 

Total 

NR  
only 

Total 

NR  
only 

Total 

2019 
TAM & 
NR  
 NR 
only 
2,534 
586 
1.0%  22.7% 

Total 
3,120 
1.2% 

2020 
TAM & 
 NR 
1,129 
3.9% 

2,733 
0.2% 

7,530 
0.4% 
1,940  260,497  1,655,107  27,796  1,682,903  1,758,199  21,310  1,779,509 
98.5% 
98.9%  75.2% 
20,576 
55 
2.1% 
1.1% 
2,581  263,996  1,660,377  28,979  1,689,356  1,784,176  23,439  1,807,615 
100% 
100% 

98.5% 
20,536 
1.2% 

98.7% 
379 
0.1% 

95.9% 
54 
0.2% 

99.6% 
2,591 
0.2% 

90.9% 
40 
0.2% 

99.7% 
2,537 
0.2% 

5,441 
0.3% 

3,862 
0.2% 

100% 

100% 

100% 

100% 

100% 

100% 

2021 
TAM & 
 NR 
2,089 
8.9% 

The definition of firm size used here is the one adopted by the European commission, available at: https://single-market-economy.ec.europa.eu/smes/sme-definition_en  
SME= small and medium enterprise. 

63 

 
 
 
 
  
 
 
 
Table A 7. Distribution of State aid awards by sector and firm size 

TAM & NR 

Size1 

Large 

SME3 

Missing 
info 

Total 

Large 

NR only 

Size 

SME 

Missing 
info 

Total 

Sector2 

Agriculture, forestry 
   & fishing 
Mining & quarrying 

Manufacturing 

Electricity & gas 

Water supply, sewerage 
& waste management 

Construction 

Wholesale & retail trade 

Transporting and storage 

Accommodation  
   & food services 
Information 
   & communication 
Financial & insurance 

Real estate  

Professional, scientific 
   & technical activities 
Administrative  
   & support services 
Public administration 

Education 

Human health and  
   & social work activities 
Arts, entertainment  
   & recreation 
Other 

Total 

709 
32 
1.3% 
0.7% 
169 
4 
0.1% 
0.3% 
2,632  21,312 
39.2% 
54.5% 
1,178 
67 
2.2% 
1.4% 
768 
81 
1.4% 
1.7% 
4,709 
96 
2.0% 
8.7% 
377  11,763 
21.6% 
7.8% 
2,338 
287 
4.3% 
     5.9% 
2,488 
65 
4.6% 
1.4% 
1,964 
182 
3.6% 
3.8% 
55 
236 
0.1% 
4.9% 
1,500 
53 
2.8% 
1.1% 
2,145 
289 
3.9% 
6.0% 
1,495 
181 
2.8% 
3.8% 
3 
7 
0.0% 
0.1% 
168 
64 
0.3% 
1.3% 
832 
134 
1.5% 
2.8% 
556 
31 
1.0% 
0.6% 
253 
14 
0.3% 
0.5% 
4,832  54,405 
100% 
100% 

4 
1.0% 
0 
0.0% 

0.7% 
155 
37.3% 
0 
0.0% 
7 
1.7% 

745 
1.3% 
173 
0.3% 
3  23,947 
40.1% 
1,400 
2.4% 
849 
1.4% 
4,812 
8.1% 
0  12,140 
20.4% 
2,626 
4.4% 
2,555 
4.3% 
2,160 
3.6% 
291 
0.5% 
1,553 
2.6% 
2,579 
4.3% 
1,677 
2.8% 
17 
0.0% 
296 
0.5% 
968 
1.6% 
597 
1.0% 
268 
0.5% 

66,910 
549 
1.6% 
2.1% 
3,246 
2 
0.1% 
0.0% 
544,539 
757 
12.9% 
3.0% 
9,469 
445 
0.2% 
1.7% 
13,835 
10 
0.3% 
0.0% 
496,161 
1,054 
4.1% 
11.8% 
1,523  1,042,785 
24.7% 
6.0% 
0.0% 
164,373 
166 
1 
3.9% 
0.7% 
0.2% 
525,083 
402 
2 
12.4% 
1.6% 
0.5% 
107,993 
604 
14 
2.6% 
2.4% 
3.4% 
41,615 
266 
0 
1.0% 
1.0% 
0.0% 
151,988 
532 
0 
3.6% 
2.1% 
0.0% 
396,393 
9,472 
145 
9.4% 
37.0% 
34.9% 
160,806 
361 
1 
3.8% 
1.4% 
0.2% 
835 
325 
7 
0.0% 
1.3% 
1.7% 
55,402 
520 
64 
1.3% 
2.0% 
15.4% 
127,535 
3,770 
2 
3.0% 
14.7% 
0.5% 
88,356 
1,076 
10 
2.1% 
4.2% 
2.4% 
226,360 
3,784 
1 
0.2% 
5.4% 
14.8% 
416  59,653  15,651  4,182,415  25,618  4,223,684 
100% 

66,217 
144 
1.6% 
0.9% 
3,220 
24 
0.1% 
0.2% 
537,773 
6,009 
12.9% 
38.4% 
8,739 
285 
0.2% 
1.8% 
13,517 
308 
0.3% 
2.0% 
494,742 
365 
2.3% 
11.8% 
2,376  1,038,886 
24.8% 
15.2% 
163,331 
876 
5.6% 
3.9% 
524,386 
295 
12.5% 
1.9% 
106,717 
672 
4.3% 
2.6% 
40,615 
734 
4.7% 
1.0% 
151,190 
266 
1.7% 
3.6% 
386,092 
829 
5.3% 
9.2% 
159,483 
962 
3.8% 
6.2% 
468 
42 
0.3% 
0.0% 
54,515 
367 
1.3% 
2.3% 
123,070 
695 
4.4% 
2.9% 
87,061 
219 
2.1% 
1.4% 
222,393 
183 
5.3% 
1.2% 

100% 

100% 

100% 

100% 

100% 

Source: Authors’ computations based on ORBIS, TAM, and State aid National registry data. Year: 2017-2021. 
1 

The  definition  of  firm  size  used  here  is  the  one  adopted  by  the  European  commission,  available  at:  https://single-market-
economy.ec.europa.eu/smes/sme-definition_en  
SME= small and medium enterprise. 
The sector definition is based on the NACE 2 classification 

2 
3 

Table A7 compares the distribution of aid awards by sector and firm size. 

Construction, a sector with a high prevalence of large firms, is overrepresented in TAM (40.1%) as compared 
to the NR (12.9%). In contrast, the sector “accommodation and food” that has received large support during 
the pandemic is under-represented in TAM (4.6%) as compared to the NR (12.4%). Focusing on large firms only, 
there is still a higher proportion of aids awarded in the construction sector in TAM (54.5%) as compared to the 
NR (38.%) while the proportion of aids awarded in the “wholesale and retail” sector is smaller in TAM (7.8%) as 
compared to the NR (15.2.%). 

65 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Match Orbis with NR and TAM 

For the analysis of the causal impact of COVID-19 State aid, we use Orbis data along with information from 
the National Registry and TAM. Orbis is a large longitudinal dataset containing detailed firm-level information 
on balance sheet data for millions of firms across the world, including those in the European Union. The national 
representativeness of Orbis with respect to the universe of firms in each country is discussed elsewhere (Bajgar 
et al., 2020). In this section, we show the number of firms in TAM and in the NR matching with the information 
in Orbis.  

Table A 8. Quality of match between Orbis and the registry data 

Match Orbis with TAM 
Not matching observations 
     (in Orbis only) 
     (in TAM only) 
Matching observations both (in Orbis and TAM) 

N 
25,972,004 
25,954,915 
17,089 
55,884 

%  of  TAM  
un/matched 

24.4% 
76.6% 

%  of  NR 
un/matched 

Match Orbis with NR 
Not matching observations 
     (in Orbis only) 
     (in NR only) 
Matching observations both (in Orbis and TAM) 

15% 
85.0% 
Source: Authors’ computations based on ORBIS, TAM, and State Aid National registry data. Year: 2017-2021. 

N 
24,102,579 
23,692,395 
410,184 
2,318,404 

In the NR, 15% of firms do not match with Orbis while 24.4% of the firms in TAM do not match with Orbis. In 
addition, the number of firms matching with the Orbis dataset is much higher in the NR than in TAM (2,318,404 
vs. 55,884) showing that using TAM to analyse the impact of COVID State aid leave out over 2 millions of State 
aid. This means that in the period between 2017 and 2021 almost 98% of the aid awards would be excluded 
from the analysis. 

Hence, this section aims at quantifying what would be the potential selection bias of using TAM along with 
Orbis data (as compared to using Orbis along with the NR) to analyse the causal impact of the COVID State aid 
on firms. Given that TAM has information only on a subsample of State aid beneficiaries, some firms may be 
identifies  non-beneficiaries  in  TAM  despite  having  received  some  State  aid  in  the  real  world  (and  cannot 
therefore be considered as a real comparison group). We can identify these firms by comparing TAM with the 
NR within the Orbis sample. 

To do this, we identify four categories of beneficiaries: 

-  COVID and non-COVID beneficiaries are those firms that received any type of State aid in the 

period 2017-2021 

-  Only COVID beneficiaries are firms that received COVID State aid in the period 2019-2021 but 

did not receive other type of aids over the period 2017-2021 

-  Only non-COVID beneficiaries received any type of State aid in the period 2017-2021 different 

from COVID 

-  Non-beneficiaries are firms who did not received any State aid in the period 2017-2021 

Table A9 compares the number of beneficiaries identified using TAM and the real beneficiary status identified 
using the NR. There is a large number of firms (46%) which are seen in TAM as non-beneficiaries but received 
some kind of State aid during 2017-2021 (the orange cells). The blue cells represent beneficiaries for which 
there is some under-reporting (e.g. firms identified as COVID only beneficiaries but they received both COVID 
and Non-COVID State aid in the real world). Note that grey cells represent mismatches probably due to coding 
errors but represent 0.2% of the sample. 

66 

 
 
  
 
 
  
 
 
 
 
Table A 9. Comparing beneficiaries in TAM and in the NR; Sample: all firms in 20191 

TAM data 

COVID and 
non-COVID 

COVID  
only 

Non-COVID 
only 

Non- 

beneficiaries  Total 

National 
Registry 
data 

COVID and non-COVID 
COVID only 
Non-COVID only 
Non-beneficiaries 
Total 

1461  21,580 
5,982 
4,496 
46 
1,913  32,104 

8 
444 
0 

859 
30 
1,611 
9 
2,509 

231,185 
679,249 
179,415 

255,085 
685,269 
185,966 
1,235,959  1,236,014 
2,325,808  2,362,334 

Source: Authors’ computations based on ORBIS, TAM, and State Aid National registry data. 
1 

Orange  cells  represent  “false  non-beneficiaries”  (firms  receiving  aids  but  are  recorded  in  TAM  as  non-beneficiaries).  Blue  cells 
represent under-reported beneficiaries (e.g. received both COVID and non-COVID aid but only reported COVID aid in TAM). Green cells 
represent TAM beneficiaries with matching information in the NR. Grey cells are mismatches probably due to coding errors. 

It is clear that an analysis of the effect of COVID Aid using TAM data only would be problematic as half of 
firms are “false controls” , that is they appear as non-beneficiaries but are instead treated i.e. have some State 
aid reported in the NR. The number of false beneficiaries (1,089,849, i.e. the sum of the orange cells) is also 
much (about 30 times) bigger than the number of beneficiaries (36,526) recorded in TAM. 

A possible solution to use TAM data for a plausible causal-impact analysis is to focus on large firms to reduce 
the mismatch between TAM and the NR as large firms are better represented in TAM. To explore the feasibility 
of this solution, we analysed the two following subsamples: 1) firms with turnover > 2 millions of €; and 2) 
firms with turnover > 50 millions of € (see Tables A10 and A11). Table A12 also compare the proportions of 
the different beneficiary types in these subsamples. 

Table A10 shows that restricting the analysis to firms with turnover above 2 Millions € reduces the number of 
non-beneficiaries (the control group) from 1,236,014 to 16,074 observations (or, as shown in Table A12, from 
52% to 12% of the analysed samples). In addition, the proportion of false zeros increases from 46 to 62% 
(see Table A12). The results are not considerably better when focusing on firms with Turnover > 50 of € Millions. 

Table A 10. Comparing beneficiaries in TAM and in the NR; Sample: firms with turnover >2 Millions € 1 

COVID and 
non-COVID 

COVID  
only 

TAM data 
Non-COVID 
only 

National 
Registry 

COVID and non-COVID 
COVID only 
Non-COVID only 
Non-beneficiaries 
Total 

1,347  20,455 
5,124 
4,225 
34 
1,763  29,838 

7 
409 
0 

604 
16 
1,192 
8 
1,820 

Non- 

beneficiaries  Total 
56,275 
33,869 
23,109 
17,962 
34,254 
28,428 
16,074 
16,116 
96,333  129,754 

Source: Authors’ computations based on ORBIS, TAM, and State Aid National registry data. 
1 

Orange  cells  represent  “false  non-beneficiaries”  (firms  receiving  aids  but  are  recorded  in  TAM  as  non-beneficiaries).  Blue  cells 
represent under-reported beneficiaries (e.g. received both COVID and non-COVID aid but only reported COVID aid in TAM). Green cells 
represent TAM beneficiaries with matching information in the NR. Grey cells are mismatches probably due to coding errors. 

67 

 
 
 
 
 
  
 
 
 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
Table A 11. Comparing beneficiaries in TAM and in the NR; Sample: firms with turnover >50 Millions €  

TAM data 

COVID and 
non-COVID 

Non-COVID 
only 

National 
Registry 

COVID and non-COVID 
COVID only 
Non-COVID only 
Non-beneficiaries 
Total 

89 
1 
482 
1 
573 
Source: Authors’ computations based on ORBIS, TAM, and State Aid National registry data. 
1 

COVID  
only 
167  1,100 
341 
405 
4 
252  1,850 

1 
84 
0 

Non- 

beneficiaries  Total 
422  1,778 
696 
353 
1,576  2,547 
1,062  1,067 
3,413  6,088 

Orange  cells  represent  “false  non-beneficiaries”  (firms  receiving  aids  but  are  recorded  in  TAM  as  non-beneficiaries).  Blue  cells 
represent under-reported beneficiaries (e.g. received both COVID and non-COVID aid but only reported COVID aid in TAM). Green cells 
represent TAM beneficiaries with matching information in the NR. Grey cells are mismatches probably due to coding errors. 

Table A 12. Proportion of beneficiaries in the subsamples of interest 

Sample 
Matched  
False zeros 
Mismatched 
Coding errors 
Non-beneficiaries 

All Firms 
in Orbis 
52.7% 
46.1% 
1.0% 
0.2% 
52.3% 

Firms with 
Turnover > 
2 € Millions  
18.3% 
61.9% 
16.2% 
3.6% 
12.4% 

Firms with 
Turnover > 
50 € Millions € 
33.7% 
38.6% 
19.5% 
8.1% 
17.5% 

Source: Authors’ computations based on ORBIS, TAM, and State Aid National registry data. 

1 

Orange cells represent “false non-beneficiaries” (firms receiving aids but are recorded in TAM as non-beneficiaries). Blue cells 
represent under-reported beneficiaries (e.g. received both COVID and non-COVID aid but only reported COVID aid in TAM). Green 
cells represent TAM beneficiaries with matching information in the NR. Grey cells are mismatches probably due to coding errors. 

68 

 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 4. Matching Balance Tests 

Table A 13. Matching Balance Tests for Italy, Poland, and Spain 

Panel (a) - Italy 

Mean 

t-test 

Variable 
turnover (HIS) 2017 
turnover (HIS) 2018 
turnover (HIS) 2019 
Size 
Small 
Medium 
Large 
Capital 
2nd quartile 
3rd quartile 
4th quartile 
Loans 
Above median 
EBIT margin 
2nd quartile 
3rd quartile 
4th quartile 
Regions 
Region 2 
Region 3 
Region 4 
Region 5 
Region 6 
Region 7 
Region 8 
Region 9 
Region 10 
Region 11 
Region 12 
Region 13 
Region 14 
Region 15 
Region 16 
Region 17 
Region 18 
Region 19 
Region 20 
Sector 
Construction 
Wholesales 
Retail 

Treated  Control  %bias  t 
13.567 
13.689 
13.703 

13.513 
13.637 
13.652 

-3.2 
-3.1 
-3.1 

-5.300  0.000 
-5.320  0.000 
-5.350  0.000 

p>t 

0.086 
0.016 
0.003 

0.402 
0.154 
0.349 

0.088 
0.014 
0.003 

0.401 
0.155 
0.352 

-0.8 
0.9 
0.3 

-1.230  0.218 
1.650  0.099 
0.670  0.505 

0.3 
-0.3 
-0.6 

0.370  0.713 
-0.480  0.631 
-0.910  0.364 

0.360 

0.352 

1.8 

2.480  0.013 

0.3 
0.2 
0.7 

0.370  0.714 
0.250  0.799 
1.080  0.281 

0.9 
0.2 
0.1 
1.3 
-1.2 
-2.8 
0.2 
0.3 
0.6 
0.9 
-0.5 
-1.2 
0.5 
-0.5 
1.7 
0.5 
1.0 
-0.1 
-0.3 

1.400  0.161 
0.330  0.743 
0.170  0.862 
1.850  0.064 
-1.620  0.105 
-3.580  0.000 
0.250  0.801 
0.400  0.687 
0.840  0.398 
1.210  0.226 
-0.740  0.459 
-1.770  0.076 
0.720  0.470 
-0.740  0.457 
2.340  0.019 
0.630  0.527 
1.430  0.152 
-0.130  0.894 
-0.520  0.604 

1.2 
-1.6 
1.2 

1.870  0.062 
-2.390  0.017 
1.600  0.109 

0.389 
0.303 
0.141 

0.001 
0.024 
0.254 
0.012 
0.003 
0.078 
0.030 
0.003 
0.016 
0.032 
0.006 
0.099 
0.015 
0.101 
0.102 
0.014 
0.032 
0.106 
0.013 

0.268 
0.294 
0.192 

0.387 
0.302 
0.138 

0.001 
0.024 
0.254 
0.011 
0.003 
0.085 
0.030 
0.003 
0.015 
0.030 
0.006 
0.103 
0.014 
0.103 
0.097 
0.014 
0.030 
0.107 
0.013 

0.262 
0.302 
0.188 

69 

 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Variable 
turnover (HIS) 2017 
turnover (HIS) 2018 
turnover (HIS) 2019 
Size 
Small 
Medium 
Large 
Capital 
2nd quartile 
3rd quartile 
4th quartile 
Loans 
2nd tercile 
3rd tercile 
EBIT margin 
2nd quartile 
3rd quartile 
4th quartile 
Regions 
Region 2 
Region 3 
Region 4 
Region 5 
Region 6 
Region 7 
Region 8 
Region 9 
Region 10 
Region 11 
Region 12 
Region 13 
Region 14 
Region 15 
Region 16 
Region 17 
Region 18 
Region 19 
Sector 
Construction 
Wholesales 
Retail 

Panel (b) - Spain 

Mean 

Treated  Control  %bias  t 
13.955 
14.036 
14.077 

13.948 
14.028 
14.069 

-0.5 
-0.5 
-0.6 

-0.920  0.355 
-1.040  0.297 
-1.070  0.283 

t-test 

p>t 

0.152 
0.024 
0.003 

0.207 
0.287 
0.243 

0.152 
0.022 
0.003 

0.212 
0.282 
0.239 

0 
1 
-0.5 

-0.060  0.951 
1.830  0.067 
-1.040  0.298 

-1.3 
0.9 
1.1 

-2.390  0.017 
1.640  0.101 
1.920  0.055 

0.152 
0.493 

0.152 
0.491 

-0.1 
0.5 

-0.100  0.917 
0.700  0.482 

0.364 
0.332 
0.150 

0.018 
0.012 
0.041 
0.013 
0.005 
0.039 
0.117 
0.040 
0.066 
0.012 
0.224 
0.130 
0.019 
0.117 
0.035 
0.001 
0.001 
0.032 

0.244 
0.330 
0.192 

0.355 
0.330 
0.157 

0.018 
0.011 
0.041 
0.014 
0.006 
0.039 
0.115 
0.040 
0.068 
0.013 
0.221 
0.131 
0.019 
0.118 
0.038 
0.001 
0.001 
0.032 

0.237 
0.329 
0.197 

2.1 
0.4 
-1.7 

3.640  0.000 
0.690  0.489 
-3.440  0.001 

-0.4 
0.9 
0.1 
-0.2 
-0.6 
-0.1 
0.9 
-0.1 
-0.7 
-0.7 
0.6 
-0.2 
-0.4 
-0.5 
-1.4 
0.8 
-0.5 
-0.2 

-0.710  0.478 
1.540  0.124 
0.180  0.856 
-0.460  0.646 
-1.050  0.292 
-0.120  0.908 
1.650  0.100 
-0.180  0.854 
-1.200  0.229 
-1.210  0.227 
1.110  0.267 
-0.360  0.719 
-0.680  0.496 
-0.840  0.403 
-2.410  0.016 
1.440  0.150 
-0.830  0.404 
-0.280  0.776 

1.6 
0.3 
-1.3 

3.060  0.002 
0.540  0.586 
-2.320  0.020 

70 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Variable 
turnover (HIS) 2017 
turnover (HIS) 2018 
turnover (HIS) 2019 
Size 
Small 
Medium 
Large 
Capital 
2nd quartile 
3rd quartile 
4th quartile 
Loans 
2nd tercile 
3rd tercile 
EBIT margin 
2nd quartile 
3rd quartile 
4th quartile 
Regions 
Region 2 
Region 3 
Region 4 
Region 5 
Region 6 
Region 7 
Region 8 
Region 9 
Region 10 
Region 11 
Region 12 
Region 13 
Region 14 
Region 15 
Region 16 
Region 17 

Sector 
Construction 
Wholesales 
Retail 

Panel (c) - Poland 

Mean 

Treated  Control  %bias  t 
14.402 
14.528 
14.568 

14.443 
14.546 
14.590 

1.9 
0.8 
1.0 

1.150  0.249 
0.510  0.613 
0.610  0.541 

t-test 

p>t 

0.273 
0.077 
0.010 

0.104 
0.342 
0.390 

0.265 
0.077 
0.012 

0.113 
0.350 
0.369 

1.8 
0.2 
-0.6 

0.920  0.360 
0.130  0.898 
-0.660  0.512 

-3.1 
-1.8 
4.4 

-1.500  0.135 
-0.870  0.382 
2.290  0.022 

0.127 
0.369 

0.137 
0.352 

-3.2 
3.6 

-1.460  0.144 
1.880  0.061 

0.369 
0.320 
0.156 

0.150 
0.113 
0.038 
0.014 
0.080 
0.017 
0.043 
0.016 
0.062 
0.052 
0.013 
0.034 
0.031 
0.019 
0.209 
0.035 

0.190 
0.403 
0.158 

0.378 
0.315 
0.151 

0.148 
0.110 
0.039 
0.018 
0.076 
0.017 
0.052 
0.010 
0.065 
0.046 
0.016 
0.042 
0.026 
0.020 
0.216 
0.030 

0.199 
0.394 
0.159 

-1.8 
1.2 
1.3 

-0.920  0.357 
0.600  0.549 
0.680  0.497 

0.8 
0.7 
-1.0 
-3.1 
1.6 
0.3 
-4.8 
4.9 
-1.5 
2.5 
-2.8 
-4.8 
2.7 
-0.7 
-1.5 
2.4 

0.380  0.700 
0.370  0.709 
-0.510  0.611 
-1.550  0.122 
0.820  0.411 
0.150  0.880 
-2.260  0.024 
2.710  0.007 
-0.740  0.458 
1.290  0.197 
-1.250  0.210 
-2.180  0.029 
1.290  0.196 
-0.360  0.722 
-0.830  0.409 
1.160  0.246 

-2.0 
1.7 
-0.2 

-1.060  0.288 
0.880  0.379 
-0.120  0.904 

71 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Annex 5. Heterogeneity analysis for Poland  

Table A 14. The effects of COVID State aid on firms’ turnover, by different combinations of sector/size groups1 

Size category 
Treat x Manufacturing 

Treat x Construction 

Treat x Wholesales 

Treat x Retail 

  Micro 
-0.144 
(0.103) 
-0.169 
(0.141) 
-0.155 
(0.107) 
0.041 
(0.176) 

Small 
-0.154 
(0.124) 
0.121 
(0.209) 
-0.099 
(0.111) 
0.033 
(0.134) 

Medium 
0.306 
(0.265) 
0.564** 
(0.266) 
0.196 
(0.128) 
-0.257 
(0.412) 

Large 
-0.365*** 
(0.017) 

-0.021 
(0.362) 

Constant 

  13.484***  15.888***  17.407***  19.273*** 

Observations 

(0.001) 
16,616 

(0.000) 
7,208 

(0.001) 
2,736 

(0.004) 
624 

1  

Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. The average treatment effects is obtained as in equation (2). The 
effects by size and sector are estimated in separate equations. The outcome variable, turnover is in log form (Inverse Hyperbolic 
Sine Transformation). Standard errors clustered at the firm level. Standard errors clustered at the firm level. 

Table A 15. The effects of COVID State aid on firms’ turnover, by type of aid instrument1 

Instrument  
type 
Average  
effect 
Treat x Micro 

Treat x Small 

Treat x Medium 

Treat x Large 

Effect  
by  
Size 

Treat x Manufacturing 

Effect 
by  
Sector 

Treat x Construction 

Treat x Wholesale 

Treat x Retail 

Constant 

Observations 

Repayable2 
-0.028 
(0.081) 
-0.278** 
(0.122) 
0.159 
(0.144) 
0.259** 
(0.107) 
-0.217 
(0.217) 
0.067 
(0.147) 
0.263** 
(0.130) 
-0.161 
(0.135) 
-0.156 
(0.190) 
14.776*** 
(0.001) 
10,584 

Non-Repayable 
or both 
-0.100* 
(0.055) 
-0.071 
(0.069) 
-0.102 
(0.071) 
-0.405 
(0.258) 
0.006 
(0.081) 
-0.080 
(0.081) 
-0.200* 
(0.118) 
-0.074 
(0.086) 
-0.101 
(0.143) 
14.678*** 
(0.000) 
24,584 

1  

2  

Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. The average treatment effects is obtained as in equation (2). The 
effects by size and sector are estimated in separate equations. The outcome variable, turnover is in log form (Inverse Hyperbolic 
Sine Transformation). Standard errors clustered at the firm level. Standard errors clustered at the firm level. 
Repayable instruments includes guarantees and loans. Non-repayable instruments includes direct grants, interest rate subsidies, 
and other non-repayable grants.  

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
  
  
  
  
 
 
 
Table A 16. The relationship between COVID State aid and category of default, by different combinations of sector/size 
groups 1 

Size category 
Treat x Manufacturing 

Treat x Construction 

Treat x Wholesales 

Treat x Retail 

Constant 

Observations 

Large 
-1.569* 
(0.913) 

Small  Medium 
-0.873* 
-0.449 
(0.469) 
(0.374) 
0.037 
1.010 
(0.733) 
(0.785) 
-0.112 
0.029 
(0.187) 
(0.276) 
0.239 
-0.149 
(0.221) 
(0.409) 

Micro 
0.361 
(0.298) 
-0.254 
(0.383) 
0.036 
(0.267) 
0.124 
(0.281) 
5.561***  4.794***  4.676***  4.998*** 
(0.005) 
(0.001) 
622 
16,628 

(0.001) 
7,161 

(0.001) 
2,675 

0.291 
(0.404) 

1  

Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. The associations are obtained as in equation (2). The effects by 
size and sector are estimated in separate equations. The outcome variable (category of default) is measured with a numerical 
variable ranging from 0 to 10. Standard errors clustered at the firm level. Standard errors clustered at the firm level. 

Table A 17. The relationship between COVID State aid and category of default, by type of aid instrument1 

Instrument 
 type 
Average  
effect 
Treat x Micro 

Treat x Small 

Treat x Medium 

Treat x Large 

Effect  
by  
Size 

Treat x Manufacturing 

Effect 
by  
Sector 

Treat x Construction 

Treat x Wholesale 

Treat x Retail 

Constant 

Observations 

Repayable2 
-0.180 
(0.212) 
-0.147 
(0.464) 
0.334 
(0.516) 
-0.558** 
(0.250) 
-0.044 
(0.349) 
-0.650 
(0.396) 
0.525 
(0.441) 
-0.276 
(0.362) 
0.075 
(0.460) 
5.677*** 
(0.002) 
10,672 

Non-Repayable  
or both 
0.135 
(0.118) 
0.205 
(0.149) 
-0.049 
(0.206) 
0.034 
(0.272) 
0.254* 
(0.136) 
0.205 
(0.235) 
0.014 
(0.388) 
0.156 
(0.172) 
0.101 
(0.233) 
5.193*** 
(0.001) 
24,584 

1  

Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. The associations are obtained as in equation (2). The effects by 
size and sector are estimated in separate equations. The outcome variable (category of default) is measured with a numerical 
variable ranging from 0 to 10. Standard errors clustered at the firm level. Standard errors clustered at the firm level. 
2  Repayable instruments includes guarantees and loans. Non-repayable instruments includes direct 

grants, interest rate subsidies, and other non-repayable grants.  

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
  
  
  
  
 
 
Annex 6. Data and scripts 

The following files contain the scripts for the injection of Orbis data in the ElasticSearch infrastructure and to 
perform specific companies searches within the infrastructure: 

-

-

-

-

-

-

-

-

Check errors

Companies matching

Configuration

To split files

Infer

Infer mapping

Insert

Insert in ElasticSearch

- Match

-

-

Search

Search in ElasticSearch

Two  additional  reports  produced  by  the  contractor  who  took  care  of  implementing  the  data  handling  in 
ElasticSearch  give  an  overview  of  how  inserting  information  in  ElastiSearch  (first  file)  and  how  to  perform 
companies searches (second file). 

For what the main econometric analysis is concerned, the database are available for all countries following the 
country specific links: Italy, Spain and Poland. 

Moreover, the Stata do-files for turnover as an outcome are available here: for Italy, for Spain and for Poland. 
Regarding the probability of default as outcome, the Stata do-files are the following: Italy, Spain and Poland  

The extensive literature review can be found at this link. 

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