Productivity and responses to the pandemic : firm-level evidence.
Date: 2022-06-01
Institutions: European Investment Bank
Subjects: coronavirus disease, digitisation, sample survey, small and medium-sized enterprises, work productivity

ECONOMICS – WORKING PAPERS 2022/09

Productivity and responses  

to the pandemic

Firm-level evidence 

Productivity and responses  
to the pandemic

Firm-level evidence 

June 2022

 
Productivity and responses to the pandemic: Firm-level evidence 

© European Investment Bank, 2022. 
EIB Working Paper 2022/09 
June 2022

This is a publication of the EIB Economics Department.
economics@eib.org
www.eib.org/economics

About the Economics Department
The mission of the EIB Economics Department is to provide economic analyses and studies to support the Bank in its 
operations and in the definition of its positioning, strategy and policy. The department, a team of 40 economists, is headed by 
Director Debora Revoltella.

Authors
Péter Harasztosi (EIB)
Simon Savšek  (EIB)

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Investment Bank.

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pdf: QH-BK-22-009-EN-N ISBN 978-92-861-5332-7 ISSN 2599-736X doi: 10.2867/011145

Productivity and responses to the pandemic: 
Firm-level evidence 

Péter Harasztosi  

 Simon Savšek 

European Investment Bank 

Abstract 

In  this  paper,  we  empirically  assess  repercussions  of  the  pandemic  on  the  firm-level 
productivity worldwide. COVID-19 shock was very heterogeneous across sectors. Our findings 
show  that  firms’  responses  to  the  shock  also  varied  within  sectors:  more  productive  firms 
coped with the crisis better in terms of closures and employment adjustments. Besides, they 
were  more  likely  to  speed  up  some  digitalization  processes.  These  findings  imply  that  the 
recent crisis could amplify the difference between highly productive and less productive firms. 
As regards the governments’ policy measures, we find strong utilization at the firm level, but 
very little differentiation across productivity quantiles, suggesting room for a more targeted 
approach in the reminder of the pandemic.  

Keywords: Covid-19, Productivity, Enterprise Survey 

We would like to thank Koray Alper and prof. Dragan Tevdovski, as well as other participants of the 8th 
Research Conference co-organised by the National Bank of the Republic of North Macedonia and the 
EIB. The views expressed in this paper are those of the authors and do not necessarily reflect the views 
of the European Investment Bank. Parts of the analysis presented here is referenced in EIB-EBRD-IMF 
report entitled “Business resilience in the pandemic and beyond” 

1 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1  Introduction  

The pandemic has had a strong impact on firms. Many enterprises were forced to shut down 
their businesses temporarily, some even permanently. Many needed to lay off a large share of 
their employees, cut their profit margins or re-orient their business models to diversify their 
stakeholder base. The COVID-19 shock affected both, the supply-side and the demand-side of 
their  business,  forcing  firms  to  re-optimize  their  plans  and  take  immediate  actions  to  stay 
afloat. Some firms were able to use the crisis as an opportunity to digitalize their processes, 
stepped up the online delivery, introduced or extended teleworking, while others were not 
allowed to operate at all due to governments’ measures to contain the spread of the virus. 
Besides, the policy response was unprecedented. Governments and central banks stepped in, 
preventing an even deeper and more protracted economic crisis.  

In such a crisis environment, it is easy to assume that productivity was impacted. The pandemic 
led many observers to believe that a K-shaped recovery is underway in which firms that are 
highly productive, digital, and financially sound, will acquire market share and prosper, while 
smaller  firms,  operating  particularly  in  some  services  sectors,  might  lose  market  share  or 
possibly even close down. In circumstances, where many factors interplayed, it is exceedingly 
difficult  to  disentangle  which  of  them  affected  the  firm-level  productivity  the  most  and  to 
which  extent  and  how  firms  reacted  to  the  COVID-19  shock.  Once  unprecedented  policy 
support is added to this puzzle, the picture becomes even less clear.  

In  this  paper,  we  examine  the  COVID-19  module  of  the  EIB-EBRD-WB  Enterprise  Survey  to 
provide an empirical assessment of how firms of different productivity levels were impacted 
by  the  pandemic  and  how  they  fared.  We  confirm  that  COVID-19  shock  was  very 
heterogeneous across sectors, hitting particularly the consumer-driven services sectors, while 
manufacturing, ICT and similar industries are likely to feel more transitory impacts. In addition 
to the cross-sectoral differences, we also analyse within-sector changes. We show that more 
productive  firms  coped  with  the  crisis  better  in  terms  of  closures  and  adjustments  in 
production, such as introduction or extension of online sales or remote work. The sources of 
their  relative  success  may  lay  in  their  state-of-the-art  technology,  the  resilience  of  their 
portfolio or simply in the superiority of their management or workforce. 

This finding leads us to believe that the pandemic could amplify the difference between highly 
productive and less productive firms. Such a result is confirmed also once additional waves of 
the COVID-19 module are considered. As regards the governments’ policy measures, we find 
strong utilization at the firm level, but little differentiation across productivity quantiles. On 
the one hand, this could be a result of less targeted support in the initial phase of the crisis, 
which was meant mainly to prevent even worse economic outcomes. On the other hand, it 
could  be  that  factors  outside  of  our  investigation,  such  as  the  political  economy  and 
institutional considerations, might have played a stronger role in this respect.  

The paper is organized as follows. Section 2 motivates the paper via literature review. Next, we 
present some descriptive statistics in Section 3. Section 4 offers the empirical results. Finally, 
we conclude with some policy recommendations.  

2 

 
 
2  Motivation and literature  

The  pandemic  wiped  out  3.5%  of  global  GDP  in  2020  (IMF,  2021a),  together  with  8.8% 
reduction  of  global  working  hours  or  equivalent  to  255  million  full-time  jobs  (ILO,  2021). 
Without  the  extraordinary  fiscal  support,  which  the  IMF  estimates  of  about  USD  14  trillion 
(IMF, 2021b), and monetary policy stimuli implemented by major central banks, the pandemic 
would leave even more lasting scars on the world economy.  

From  a  theoretical  standpoint,  such  an  unprecedented  crisis  is  likely  to  generate  shifts  in 
productivity.  Fuentes  &  Moder  (2021)  argue  that  the  pandemic  could  affect  total  factor 
productivity  in  several  ways.  First,  the  crisis  could  initially  lock  resources  in  unproductive 
sectors,  slowing  down  the  reallocation  of  productive  resources  towards  fast-growing 
industries. Second, innovation could be impaired through lower spending on R&D, both in the 
public sector on the account of consolidation needs and in the private sector owing to elevated 
uncertainty.  Third,  reshoring  of  global  value chains  in  the  aftermath  of the  pandemic  could 
hamper  innovation  and  knowledge  spillovers  across  countries.  On  the  other  hand,  digital 
technologies  spurred  by  the  COVID-19  crisis  have  the  potential  to  accelerate  the  digital 
transformation  of  the  global  economy  and  therefore  contribute  positively  to  total  factor 
productivity. 

Evidence from previous crises also offer some useful insights. They suggest that firms were 
more likely to cut investment and innovation on such occasions (e.g. Granja and Moreira (2019) 
or Duval et al. (2020)). This led Baker et al. (2020) to believe that firms will significantly cut their 
expenditures on innovation and general management improvements, which is likely to lower 
future productivity as the COVID-19 uncertainty persists. Nevertheless, not all firms experience 
a similar magnitude of the shock. In their recent work, di Mauro & Syverson (2020) illustrate 
that “globalisation, labour mobility and small firms may all still fall victim to the crisis” but “the 
broad adoption of modern technologies – such as IT skills during the epidemic – and strong 
reallocation pressures may provide an independent boost on productivity as we come out of 
the crisis.”  

Overall, there are reasons to believe that the overall impact on TFP is ambiguous, which calls 
for  an  empirical  investigation.  Some  preliminary  empirical  evidence  on  the  impacts  of  the 
COVID-19 shock on productivity is already emerging in the economic literature.  

In their recent analysis, Andrews et al. (2021) show that labour turnover declined in response 
to the pandemic1 for three countries of the OECD. However, high productivity firms seem to 
have expanded, while less productivity firms were contracting. Furthermore, their  evidence 
shows  that  firms  that  intensively  used  e.g.  e-commerce,  digital  cash  flow  reporting  or 
management software applications to run their business were more resilient, strengthening 
the case for the digitalization. More so, working culture and managerial practices might have 
already  played  a  role  in  how  successful  firms  were  in  tackling  the  pandemic.  For  example, 
Lamorgese  et  al.  (2021)  find  a  sizable,  positive  effect  of  management  practices  on  firm 

1 Similar findings are also reported in a similar paper for Austria. See Andrews, D., Hambur, J., Bahar, E. (2021).  

3 

 
 
                                                           
performance  for  Italian  companies.  It  seems  that  better-managed  firms  were  able  to 
implement important organizational changes, including remote work.  

Bloom et al. (2020), estimate that COVID-19 will reduce TFP in the private sector by up to 5% 
in 2020 Q4, falling back to a 1% reduction in the medium term in the UK. This impact is driven 
by  a  significant  reduction  in  ‘within-firm’  productivity,  as  measures  to  contain  the  virus  are 
likely  to  increase  intermediate  costs.  Importantly,  the  negative  ‘within-firm’  impact  is 
somewhat  offset  by  a  positive  ‘between-firm’  effect.  Here,  the  least  productive  firms  are 
disproportionately affected by the pandemic and consequently make a smaller contribution at 
the economy-wide level.  

Indeed,  Barrero  et  al.  (2020)  use  the  Survey  of  Business  Uncertainty  (SBU)  and  construct  a 
novel,  forward-looking  reallocation  measures  for  jobs  and  sales  for  the  US.  They  find  that 
COVID-19  is  a  major  reallocation  shock,  crystalizing  in  several  permanent  job  losses, 
“confirming anecdotal evidence of large pandemic-induced demand increases at some firms, 
with  contemporaneous  evidence  on  gross  business  formation,  and  with  a  sharp  pandemic-
induced rise in equity return dispersion across firms.”   

This  notion  is  confirmed  by  a  recent  article  of  Akcigit  et  al.  (2021).  The analysis  shows  that 
corporate market power has been on the rise in recent decades. Importantly, authors also find 
that the pandemic will strengthen market concentration further as several small and medium-
sized enterprises are likely to end up in bankruptcies.  

Nevertheless,  due  to  substantial  policy  support,  bankruptcies  have  so  far  been  largely 
prevented.  Cros  et  al.  (2021)  show  that  factors  that  predicted  firm  failures  (primarily 
productivity and debt) before the pandemic have also played a key role during the pandemic. 
Furthermore, public support seems to have distorted the sectoral component in predicting the 
probability to fail.   

Furthermore, Kozeniauskas et al (2020) examine how firms were impacted by the COVID-19 
and  which  firms  have  benefited  from  the  government  support.  Using  a  panel  survey  of 
Portuguese  firms  conducted  during  the  pandemic,  which  they  match  with  pre-crisis 
administrative data, authors report very heterogeneous responses across firms. Most of the 
firms  experienced  declines  in  sales,  but  high  productivity  firms  were  more  likely  to  remain 
open, less likely to lay off employees and used government support to a lesser extent.  

Some papers have already been published also with the Enterprise Survey data. Bosio et al. 
(2020)  estimate  the  survival  time  of  firms  in  a  dozen  high-income  and  middle-income 
economies under the extreme shock. The authors estimate the median survival time across 
industries between 8 and 19 weeks, while firms have the liquidity to survive between 12 and 
38 weeks on average. These estimates hold under the assumption of no incoming revenues 
and coverage of fixed cost only. It follows from the analysis that the pandemic should have 
strong repercussions for the reallocation of resources even if the authors do not investigate 
Schumpeter's  theory  of  creative  destruction,  as  the  firm  exit  did  not  account  for  firms’ 
characteristics or their productivity. 

4 

 
Using partly the COVID-19 module of the Enterprise Survey, which is key for our analysis, Cirera 
et al. (2020) discuss the impact of COVID-19 policy measures on enterprises. Authors find a 
clear mismatch between policies needed and policies provided, questioning the effectiveness 
of these measures in addressing liquidity constraints and preventing layoffs. The mismatch has 
been more pronounced in more vulnerable firms and countries. While this research does not 
provide direct implications for firm productivity, it might provide evidence that policymakers 
did not internalize much the productivity aspect in the initial phase of the crisis but acted as 
firefighters to prevent the worst.    

With  the  COVID-19  module,  Apedo-Amah  et  al.  (2020)  confirm  the  severe  impacts  of  the 
pandemic  on  enterprises.  It  follows  from  their  analysis  that  the  COVID-19  shock  has  been 
severe and widespread across firms, with a persistent negative impact on sales. As regards the 
labour adjustment, authors find that the adjustment on the intensive margin has been much 
stronger.  They  also  find  evidence  that  smaller  firms  were  disproportionately  impacted, 
particularly  via  financial  constraints.  In  addition,  firms  seem  to  have  speeded  up  their 
digitalization  agendas.  Authors  also  document  persistent 
impacts  of  the  heightened 
uncertainty.  Authors  believe  that  “productivity  growth  could  be  an  especially  important 
channel to analyse as the crisis could effectively impair productivity growth through different 
mechanisms, by reducing incentives or resources for investment in innovation as well as by 
worsening misallocation of resources between firms and sectors.”  

In  our  paper,  we  confirm  and  extend  the  previous  findings.  We  support  the  hypothesis 
postulated in the Apedo-Amah et al. (2020) paper by showing that the COVID-19 shock has had 
a  strong  impact  not  only  between,  but  also  within  various  sectors.  Firms  that  were  more 
productive before the crisis suffered less in terms of output, employment and were more likely 
to extent remote work, likely amplifying the difference between laggards  and winners. This 
finding is like the one in Kozeniauskas et al (2020) or Andrews et al. (2021a) but extended to a 
much broader set of countries. In this sense, Bloom et al. (2020) was right in judging that the 
least productive firms have been disproportionately affected by the pandemic and therefore 
the  drop  in  productivity  was  smaller  than  it  would  have  otherwise  been.  Nevertheless,  our 
findings  support  the  view  that  the  market  power  of  dominant  firms  is  likely  to  increase  as 
discussed  in  Akcigit  et  al.  (2021)  leaving  the  overall  long-term  impact  undetermined  as 
stipulated  in  the  paper  by  Fuentes  &  Moder  (2021),  calling  for  further  investigation.  Our 
findings on firms’ responses to policy measures remain rather inconclusive. On the one hand, 
massive and universal, but mostly untargeted, support was paramount for firms to survive the 
extreme COVID-19 shock as confirmed by an overly broad utilization of these packages by firms 
in our dataset. On the other hand, there is little evidence for a targeted support to address the 
productivity  gap,  which  is  likely  to  become  an  even  more  important  topic  as  the  recovery 
proceeds.  

5 

 
 
 
 
3  Data and variables 

Our analysis builds on the World Bank Enterprise Survey (ES) datasets. Our primary sources of 
information are the COVID-19 follow-up questionnaires conducted in 2020 and in 2021. These 
surveyed the same firms engaged in the general modules of 20182 and 2019 Enterprise Surveys 
and come in several waves. We merge the two sets of information at the enterprise level and 
also capitalize on the information from the World Bank Productivity dataset (see Francis et al. 
2020)  that  was  constructed  for  the  same  enterprises.3  The  resulting  sample  provides 
information on 18,765 firms from 32 countries. Out of these, 13,791 firms were interviewed 
again in the first wave of the Covid-19 follow-up surveys. In 22 countries, the second or even a 
third wave of surveys are available, allowing us to collect additional information on 9,173 and 
3,873 firms, respectively. See detailed information in Table 1 below. 

Table 1. Number of firms in the Enterprise and the COVID-19 follow-up surveys by country 

Albania 

Armenia 

Azerbaijan 

Belarus 

Bosnia and Herzegovina 

Bulgaria 

Croatia 

Cyprus 

Czech Republic 

Estonia 

Georgia 

Greece 

Hungary 

Italy 

Jordan 

ES 

survey 

377 

546 

225 

600 

362 

772 

404 

240 

502 

360 

581 

600 

805 

760 

601 

I 

344 

460 

101 

530 

234 

521 

342 

167 

398 

272 

501 

530 

619 

419 

498 

follow-ups 

II 

III 

501 

324 

181 

435 

551 

412 

499 

327 

172 

396 

289 

482 

543 

643 

424 

448 

ES 

follow-ups 

Latvia 

Lebanon 

Lithuania 

Malta 

Moldova 
  Mongolia 
  Montenegro 
  Morocco 
  North Macedonia 

Poland 

Portugal 
  Romania 

survey 

I 

359 

532 

358 

242 

360 

360 

150 

1,096 

360 

1,369 

1,062 

814 

240 

364 

213 

193 

283 

284 

136 

781 

291 

975 

743 

514 

Russian Federation 

1,323 

1,145 

Serbia 

Slovak Republic 

361 

429 

313 

325 

II 

265 

222 

193 

282 

233 

704 

243 

1018 

770 

468 

III 

187 

246 

500 

301 

315 

Kazakhstan 
221 
Note: The number of firms includes the completed follow-up interviews only either by CATI or online and the firms that could not have been 
interviewed fully as they have closed down or refused to answer are excluded from the above statistics. Closing date on survey collection: 
June 24, 2021. 

Slovenia 

1,446 

806 

409 

249 

251 

The  COVID-19  follow-up  surveys  were  carried  out  at  different  points  in  time  in  various 
countries. In Figure 1 we show the duration of the survey by country and across various waves 
(when  applicable)  against  a  timeline  and  the  Google  mobility  indices.  The  Google  mobility 
indices provide a useful benchmark to place the timing of the interviews with respect to the 
COVID-19  associated  social  distancing  measures.  For  example,  Figure  1  shows  that  the  first 
interviews of the COVID-19 follow-up surveys were conducted during the initial wave of the 
pandemic in in Moldova, Italy and Georgia. Later interviews of the first wave, e.g. in Estonia 
and Lithuania, took place near the end of 2020 when most countries started to open-up again. 

2 All Enterprise Survey interviews were carried out in 2019, except in Greece and Belarus (both 2018).  
3 The Enterprise Survey is an establishment-level dataset.  

6 

 
 
 
 
 
  
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
                                                           
The final interviews of the first wave were conducted only in early 2021 when the second wave 
of  the  pandemic  already  started.  In  the  context  of  the  analysis,  such  characteristics  of  the 
dataset caution against a direct cross-country comparison of the results. 

Figure 1. The timing of COVID-19 follow-up surveys by country. 

Note: Figure shows the interval between the first and the last COVID-19 follow up interviews by country in horizontal bars. The figure, on its 
y-axis shows the values of Google mobility index time-series for all countries in the surveys, where the mobility index is available. 

The Enterprise survey covers a wide range of sectors classified with NACE rev 1. Sectors in our 
analysis include the following: Manufacturing (15-37), Construction (45), Wholesale and retail 
(50,  51  and  52),  Hotels  and  restaurants  (52  and  55),  Transport  (60-63)  and  other  services 
including Telecommunication, IT and Computer services (64 and 72). 

7 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 2. Description of variables 

N 

mean 

min 

max 

sd 

PANEL A: 
Productivity (sales/employment in logs) 
Labour productivity (value added/empl. in logs) 
Total factor productivity 

11990 
6739 
4275 

10.47 
9.89 
3.08 

3.43 
-3 
-1.35 

17.52 
15.49 
11.17 

PANEL B: 
Indicator variables for firm size: 
   small (<20) 
   medium (20-99) 
   large (100 and over) 
firm age (in logs) 
Indicator: exporter 
Indicator: foreign ownership 

PANEL C: 
Indicator variables for COVID-19 response 
   permanent closure 
   temporary closure 
   drop in yoy sales 
   drop in yoy sales more than 30% 
   filed for bankruptcy or insolvency 
   decreased demand for product / services 
   increased demand for product / services 
   decreased supply of materials  
   increased supply of materials  
   firm converted product or services 
   decrease of working hours 
   employment layoff 
   employment unpaid leave 
   increased or introduced remote work 
   increased or introduced online sales 
   increased or introduced delivery 

PANEL D: 
Indicator variables for Policy support 
   firm received government support 
   Cash transfer 
   Deferral of credit / mortgage payments 
   Access to new credit 
   Fiscal exemption 
   Wage subsidies 

13791 
13791 
13791 
13570 
13581 
13564 

18765 
13776 
12731 
12731 
13791 
13425 
13425 
13425 
13425 
13789 
13425 
13791 
13791 
13791 
13791 
13791 

13791 
13238 
13371 
13374 
13365 
13398 

45 
34 
21 
2.76 
22.88 
8.95 

3.38 
41.23 
64.39 
34.86 
2.28 
59.34 
10.21 
49.82 
8.23 
33.58 
39.86 
10.19 
26.2 
31.96 
23.99 
21.09 

35.06 
12.34 
10.9 
7.28 
11.82 
28.38 

0 
0 
0 
0 
0 
0 

0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 
0 

0 
0 
0 
0 
0 
0 

100 
100 
100 
5.09 
100 
100 

100 
100 
100 
100 
100 
100 
100 
100 
100 
100 
100 
100 
100 
100 
100 
100 

100 
100 
100 
100 
100 
100 

1.36 
1.3 
1.91 

50 
47 
41 
0.74 
42.01 
28.55 

18.07 
49.23 
47.89 
47.65 
14.94 
49.12 
30.28 
50 
27.48 
47.23 
48.96 
30.25 
43.97 
46.63 
42.71 
40.8 

47.72 
32.89 
31.16 
25.99 
32.29 
45.09 

In  these  surveys,  the  surveyed  enterprises  provided  information  about  various  actions, 
including  closures  (temporary  or  permanent),  employment  adjustment,  as  well  as  financial 
responses and expectations, perceived barriers, and utilization of policy measures. Here, we 
focus  on  firms’  characteristics  that  are  related  to  productivity  and  firms’  operations.  There 
variables are collected in Table 2 and discussed in the following subsection. 

8 

 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3.1  Productivity measures 

We measure firm-level productivity as real sales per employee. The reason for this choice is 
twofold. First, real sales per employee statistics is available for a much larger number of firms 
in a wider range of sectors than other, more conventional measures at our disposal, such as 
labour productivity or total factor productivity (TFP). In addition, the latter is only available for 
the manufacturing firms and not for those in the service sector. Since the services sectors have 
been  strongly  impacted  by  social  distancing  measures,  its  inclusion  is  important  for  the 
purposes of the analysis. Figure 14 (in the Appendix) shows the share of observation in each 
country that have available productivity measures in both, manufacturing and services sectors. 

Figure 2. The share firms with missing labour productivity or TFP by sales over employment. 

Note: The figure shows the share of firms by the percentile values of sales over employment that have no labour productivity measure (services 
sector) or no total factor manufacturing. The error bars correspond to a ten percent significance. 

The second reason for using sales over employees as a proxy for productivity is that labour 
productivity or TFP measures are not missing randomly across observations. In fact, missing 
data is more likely to be associated with lower sales per employee for both of them. Figure 2 
shows that for both sectors, the share of firms with missing TFP or labour productivity measure 
are significantly higher in the lowest percentile of the sales per employment distribution than 
at the middle or at the top of the distribution. 

This suggests that we would miss important variation from the lower ends of the productivity 
distribution if we had only used labour or total factor productivity measure and thereby bias 
the  results.  Within  the  restricted  samples,  however,  our  preferred  measure  of  productivity 
correlates very strongly with the labour productivity, with the correlation coefficient of over 
80%. Consequently, the correlation of our measure with TFP mimics the correlation of the TFP 
and labour productivity. 

9 

 
 
 
 
 
3.2  Variables for COVID-19 response 

Our  analysis  relies  on  a  wide  range  of  variables  available  from  the  ES  to  evaluate  firms’ 
operational responses to the COVID-19 shock (see Panel C of Table 2). 

Firm closures: In the first waves of the ES COVID-19 follow-up survey, we find that only a small 
fraction of firms closed down permanently (about 3% of the sample), consistent with other 
surveys  and  hard  economic  indicators  that  bankruptcies  were  rather  limited  during  the 
pandemic. Nevertheless, a significant share of firms had to stop operations and close business 
temporarily. This happened in about 40 percent of firms in the manufacturing sectors, while in 
the Services sectors the percentage is even higher, at 45%. 

Losses: Due to the COVID-19 shock, most firms also reported a loss in sales. When comparing 
their year-on-year sales losses with respect to the month at the time of the interview, more 
than 64 percent of firms, on average, reported to have suffered losses. Contrasting these losses 
against  the  last  available  pre-COVID  sales  figures  allows  us  to  create  a  continuous  variable, 
measuring the relative size of the loss and an indicator variable for a loss of more than 30% in 
sales. As Table 2 suggests, on average, about a third of the enterprises experienced such severe 
losses. 

Change in supply and demand: The social distancing and preventive measures created business 
disruptions.  Firms  were  not  only  unable  to  supply  consumers  or  other  firms,  but  also  the 
consumption has shifted away from some goods towards others (i.e. online services, delivery 
etc.). Table 2 suggests that, on average, nearly 60 percent of the surveyed firms experienced 
loss in demand and half of them had problems in their supplies. 

Employment  response:  Adaptation  of  the  firms  to  the  pandemic  also  affected  their  labour 
decisions. Many firms had to lay off employees or send them on unpaid leave. In our sample, 
on average across countries, about one in ten firms reduced employment permanently and a 
less  than  a  third  sent  workers  on  unpaid  leave.  This  latter  option  was  also  facilitated  with 
furlough  schemes  by  the  governments  in  many  economies.  In  addition,  several  firms  have 
decided to lower production and employment costs by reducing working hours. 

Remote  work  and  online  sales:  Increased  use  of  digital  technologies  was  key  to  continue 
business with fewer interruptions as the social distancing measures kicked in. The ES COVID-
19 follow-up surveys allow us to concentrate on two such measures. First, firms report on the 
introduction or relying more on remote working solutions. On average, more than 30 percent 
of firms report to have increased remote working. Naturally, not all industries and business 
models allow for remote work, hence the average propensity varies across sectors. Second, we 
look at the introduction or increased utilization of online sales. Table 2 shows that, on average, 
about  24%  of  the  firms  increased  online  sales  activity,  suggesting  that  digitalization  was  an 
important aspect of the adjustment. In addition, we find that about a quarter of firms in the 
sample have also increased or introduced the delivery of their products, which also could have 
been driven by the increase in online sales. 

10 

 
 
 
3.3  Variables for policy support 

Firms  in  the  ES  Follow-up  surveys  were  also  asked  about  access  to  the  policy  support.  On 
average,  35  per  cent  of  firms  received  at  least  one  of  the  following  government  support 
measures  enterprises  were  asked  about:  cash  transfers,  deferral  of  credit  and  mortgage 
payments, access to new credit, fiscal exemption and wage subsidy. As Table 2 shows, the most 
common  policy  instrument  were  the  wage  subsidies  (28  percent  of  firms)  to  reduce 
unemployment, while the subsidized credit was used much less frequently (7 per cent of firms).  

4  Empirical Strategy 

Our key aim is to evaluate firm level outcomes, taking into account their productivity levels.  

 Eq 

𝑃𝑃𝑃𝑃(𝑌𝑌𝑖𝑖 = 1) = 𝛽𝛽𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖 + Γ𝑿𝑿𝒊𝒊 + 𝜓𝜓𝑐𝑐 + 𝜎𝜎𝑠𝑠

(1) 

where  the  outcome  variable  Y  is  a  binary  variable,  describing  the  COVID-19  response  of 
establishment i in country c and sector s. PROD is a measure of productivity and X is a vector 
 represent country and sector fixed effects. We estimate Eq. (1) by 
of firm controls, 
taking into account that Enterprise survey is a stratified survey.4 

 and 

𝜓𝜓

𝜎𝜎

To  compare  firms  across  the  productivity  distribution  we  evaluate  the  predicted  outcome 
probabilities at various percentiles of the productivity distribution. 

While we refrain from interpreting these relations in a causal way, it is important to note that 
the reverse causality can be ruled out. That is, our productivity measure and control variables 
are  not  affected  by  the  COVID-shock,  because  the  dependent  variable  describes  the 
establishment’s  behaviour  in  2020  and  2021,  while  the  independent  variables  are  obtained 
from the surveys taken in 2018 and 2019. 

We  focus  primarily  on  the  first  wave  of  the  COVID-19  survey.  However,  when  and  where 
available, we also include information from the second and the third wave of the follow-up 
surveys.  In  these  cases,  we  restrict  our  sample  to  the  countries  where  multiple  waves  are 
available.  

4 Specifically, we use the following STATA command: svyset idstd [pweight = W], strata(strata_all) single(scaled). Where W 
are establishment level weights provided with the COVID-19 follow-up survey rescaled at country level. For firms that are 
permanently closed, such weight is not available and we therefore rely on the weights from the ES structural surveys of 
2018-2019 instead. 

11 

 
 
 
        
 
 
 
 
 
                                                           
 
5  Results  

The results section will investigate the following channels of adjustment and firms’ responses 
with  respect  to  firm-level  productivity:  size  of  the  shock  (loss  in sales,  temporary  closures), 
employment  responses  and  changes  in  firms’  operational  framework  (remote  work,  online 
sales and delivery). 

5.1  Firm closures and disruptions in operations 

Less  productive  firms  are  more  likely  to  exit.  Figure  3  shows  the  predicted  fraction  of  firms 
permanently closed at the time of the survey across the productivity quantiles. In the lowest 
productivity  quintile,  permanent  closure  is  about  twice  as  likely  as  for  firms  in  the  most 
productive quintile.  

Figure 3. Predicted probability of permanent firm closure. 

Note:  The  columns  show  the  predicted  share  of  firms  that  close  down  by  productivity  moments.  The  results  are  obtained  from  survey 
regressions on quintiles with pre-2020 weights of the Enterprise Survey. Regressions are derived from Eq. 1 and control for country and sector 
fixed effects and firm size, age, ownership and exporting status. Error bars correspond to 10 percent significance level  

Note that we do not know how many firms would have closed down had the pandemic not 
taken place; Figure 3 shows the share of firms that were closed down due to the pandemic and 
of those who would have closed down anyway . There are two effects at work. On the one 
hand, a cleansing effect that forces out the least competitive and least productive companies. 
There is also a “scarring” effect present particularly in industries where business models did 
not allow easy adaptations to continued business operation when social distancing measures 
were in place. 
Here, even the most productive companies can be forced to stop their operations.  

In addition to the heterogeneity by productivity, the regressions underlying Figure 3 reveal that 
younger firms, larger firms (median sized) and exporting firms are less likely to close down. See 
Table 3 and Table 4 in the appendix for a full set of results. 

12 

 
 
 
 
 
 
Table 3 also highlights the marked differences of the Covid-19 shock across the sectors. Results 
confirm  that  industries  depending  on  face-to-face  interactions  (e.g.  Hotels  and  restaurants) 
and  those  effected  more  by  social  distancing  measure  were  more  likely  to  close  down 
temporarily and suffer greater losses. 
Less productive firms are more likely to file for bankruptcy or insolvency. As Figure 4 suggest, 
the least productive firms are about twice as likely to file for bankruptcy compared to the most 
productive firms (see also Table 3). 

Figure 4. Predicted probability of bankruptcy and insolvency 

Note: The columns show the predicted share of firms that file for bankruptcy by productivity moments. The results are obtained from survey 
regressions with COVID-19 Follow-up survey weights of the Enterprise Survey. Regressions are derived from Eq. 1 and control for country and 
sector fixed effects and firm size, age, ownership and exporting status. Error bars correspond to 10 percent significance level. 

Sales decline is less likely among firms that are more productive. Figure 5 shows the predicted 
probability  of  sales  declines  compared  to  the  same  month  of  last  year.  We  find  that 
overwhelming  majority  of  firms  have  experienced  sales  losses.  In  the  case  of  the  least 
productive decile, the probability of losses are above 70 percent, while in the top decile they 
are significantly lower, at close to 60 per cent. We find that the results are unchanged when 
we concentrate on the larger losses, defined at 30 percent. See Table 3 in Appendix. 

13 

 
 
 
 
 
 
Figure 5. Change of sales due to COVID-19 compared to last year 

Note: The columns show the predicted share of firms that report sales losses by productivity moments. The results are obtained from survey 
regressions with COVID-19 Follow-up survey weights of the Enterprise Survey. Regressions are derived from Eq. 1 and control for country and 
sector fixed effects and firm size, age, ownership and exporting status. Error bars correspond to 10 percent significance level 

Losses in sales are negatively correlated with temporary closures: more productive firms are less 
likely to close their business temporarily. The fact that firms had to close, either because of the 
lock-down  measures  or  to  accommodate  their  businesses  to  comply  with  social  distancing 
regulations,  had  significant  effects  on  their  sales.  On  average,  firms  that  closed  down 
temporarily are 18 percent more likely to suffer losses, and 22 percent more likely to report 
losses of above 30%.  

The relationship between temporary closure, sales loss and productivity is illustrated in Figure 
6.5 First, the leftmost panel shows that the probability of sales decline is higher for enterprises 
that  closed  their  businesses  temporarily.  Second,  the  middle  panel  shows  that  the  more 
productive firms are less likely to close down temporarily. The predicted probability of losses 
for the most productive firms (90th percentile) is 39 percent, while for an enterprise in the least 
productive decile, this probability increases to 47 percent. Third, we find that more productive 
firms are less likely to report significant losses in sales, despite the close connection between 
temporary  close-downs  and  losses  in  sales:  the  negative  relationship  between  losses  and 
productivity prevails even after controlling for the temporary closures. 

5 The graphs are produced by STATA-s binscatter command, where average values variables on the vertical axis are displayed 
against the percentile groups of the variable on the horizontal axis. Each of the 30 dot represents the same number of 
observations. 

14 

 
 
 
 
 
 
                                                           
Figure 6. Temporary closure, decrease in sales and productivity 

Note: The figures are obtained by combining binscatter plots with weights from the COVID-19 follow-up of the Enterprise Survey, they include 
country and sector (ISIC 2 digit) fixed effects. From left to right: The relationship between loss in sales and temporary closure, the relationship 
between temporary closure and productivity and the relationship between a decrease in sales and productivity conditional on temporary 
closure. 
Binscatter calculates averages over residualised variables obtained from multivariate regression, hence outside range values can occur. See 
leftmost graph x-axis on temporary close indicator variable [0,100]. 

In addition, not only the demand for firms’ products and services were affected but also the 
availability of inputs from suppliers. These both trigger a strong impact on business-to-business 
interactions. In fact, most firms (60% on average) report decreased demand for their products 
or services. Not surprisingly, we find that all these disruptions were most frequent in the Hotels 
and Restaurant and in the Textiles and Apparel manufacturing (see Table 3). 

The most productive companies are less likely to report a decrease in demand or in the supplies 
of intermediate inputs. As Panel a) of Figure 7 shows, in the lowest productivity decile, 67 per 
cent of the firms report a decrease in demand. At the top quintile, the percentage is at 54. 
Similarly,  in  the  lowest  quintiles,  57  per  cent  of  enterprises  report  having  experienced  a 
reduction in the supplies for their product or service, in contrast to the 45 per cent reported, 
at the top quintile. These results do not imply that the shock was smaller for more productive 
firms, but rather hint at the resilience and capability of those firms. 

15 

 
 
 
 
 
Figure 7. Predicted probability for change in demand or in the supply of inputs 

(a) Decrease in demand 

(b) Increase in demand 

(c) Decrease in supply                                              (d) Increase in supply 

Note: The columns show the predicted share of firms that report (a) decrease or (b) increase in demand for their product or services or that 
report (c) decrease by productivity moments. The results are obtained from survey regressions with COVID -19 Follow-up survey weights of 
the Enterprise Survey. Regressions are derived from Eq. 1 and control for country and sector fixed effects and firm size, age, ownership and 
exporting status. Error bars correspond to 10 percent significance level. 

5.2  Employment responses and changes in business operations 

Firms that are more productive are less likely to shed jobs. Our results show that the pandemic 
had a rather strong impact on employment. In fact, as much as 7% of firms in the sample laid-
off their workers.  

Figure  8  investigates  the  relationship  between  the  share  of  employment  losses  and  firms’ 
productivity. We find that over 10 percent of the least productive firms reduced employment, 
while the corresponding estimate for the most productive firms is below 7%.  

16 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 8. Predicted probability of employment layoff 

Note: The columns show the predicted share of firms that report employment layoff by productivity moments. The results are obtained from 
survey  regressions  with  COVID-19  Follow-up  survey weights  of  the Enterprise  Survey. Regressions  are  derived from  Eq.  1 and control  for 
country and sector fixed effects and firm size, age, ownership and exporting status. Error bars correspond to 10 percent significance level. 

Less productive firms are also more likely to reduce working hours. As Figure 9 suggest, about 
35  per  cent  of  the  firms  in  the  highest  productivity  bracket  reported  reduction  in  working 
hours,  compared  to  above  45  per  cent  in  the  lowest  productivity  bracket.  In  addition,  less 
productive firms are more likely to use unpaid leave possibilities (furlough scheme). This result 
seems  to  be  strongly  associated  with  temporary  closures.  We  find  that,  on  average,  low 
productivity firms sent more than 20 per cent of their employees on unpaid leave and/or took 
advantage  of  the  furlough  scheme6  (See  Figure  10.)  The  corresponding  average  for  high 
productivity  firms  is  significantly  lower,  but  not  zero.  This  is  not  surprising  and  confirms 
previous findings that even some of the most productive firms had to close down their business 
temporarily. As the right panel of Figure 10 suggests, the share of workers using these schemes 
is strongly associated with temporary closures.  

6 In the Enterprise survey, furlough schemes are country specific. Either it means unpaid leave or it can mean furlough 
scheme as a form of government measure where a share of wage of the workers is paid by the local or central government. 
We find that in the dataset furlough schemes do not necessarily coincide with wage subsidy. 

17 

 
 
 
 
 
                                                           
Figure 9. Reduction of working hours 

Note:  The  columns  show  the  predicted  share  of  firms  that  report  reducing  the  working  hours  by  productivity  moments.  The  results  are 
obtained from survey regressions with COVID-19 Follow-up survey weights of the Enterprise Survey. Regressions are derived from Eq. 1 and 
control for country and sector fixed effects and firm size, age, ownership and exporting status. Error bars correspond to 10 percent significance 
level. 

Figure 10. Workers in furlough schemes  

s
e
a
s

l

n

i

e
n

i
l

c
e
D

0
0
1

0
9

0
8

0
7

0
6

0
5

-50

0

50
Temporary closure indicator

100

150

Note: The left pane shows the average share of employees sent on unpaid leave (furloughed) in response to the C-19 crisis by productivity 
quintiles. The results are obtained from survey regressions with COVID-19 Follow-up survey weights of the Enterprise Survey. Regressions are 
derived from Eq. 1 and control for country and sector fixed effects and firm size, age, ownership and exporting status. Error bars correspond 
to 10 percent significance level. The right panel shows a binscatter plot on the relationship between the share of employees sent on unpaid 
leave (furloughed) and the variable indicating firms’ temporary closure. Binscatter calculates averages over residualised variables obtained 
from multivariate regression, hence outside range values can occur. See graph x-axis on temporary close indicator variable [0,100]. 

More  productive  firms  are  more  likely  to  introduce  or  increase  remote  work  even  after 
controlling for sector heterogeneity. As the left panel of Figure 11 suggests, the most productive 
firms  are  twice  as  likely  to  introduce/extent  remote  work  as  the  least  productive  ones. 
However, this could be a results of sector-specificities as face-to-face interaction differs from 
sector to sector, limiting the extent to which remote work can be introduced (See Dingel and 
Neiman  (2020)  or  Koren  and  Pető  (2020)  for  details).  However,  even  after  controlling  for 

18 

 
 
 
  
 
 
 
 
 
sectoral  differences,  we  find  that  the  fraction  of  firms  increasing  remote  work  is  positively 
associated with productivity.  

Figure 11. Introduction or increase of remote work by productivity. 

Note: The columns show the predicted share of firms that report Introduction or increase of remote work by productivity. The results are 
obtained from survey regressions with COVID-19 Follow-up survey weights of the Enterprise Survey. Regressions are derived from Eq. 1 and 
control for country and sector fixed effects and firm size, age, ownership and exporting status. Error bars correspond to 10 percent significance 
level. 

Finally,  we  also  investigate  whether  firms  converted  products  and  services,  increased  or 
introduced online sales or increased or introduced delivery. We find that these adjustments 
were in fact very frequent. Interestingly, our results show that none of these responses are 
strongly connected to productivity (seeTable 4 for regression results in the Appendix).  

5.3  Longer run responses of enterprises  

In this subsection, we look at the sample of countries (see Table 1) where multiple waves of 
COVID-19  follow-up  surveys  are  available  to  investigate  firms’  adjustment  strategies  as  the 
time passes. With the multiple information on the same enterprises from various points in time 
over the pandemic available, we can assess how firms’ responses changed over time and how 
successful the enterprises were once their productivity distribution is considered. 

We highlight four responses: permanent closure, filing for bankruptcy or reporting insolvency, 
online sales activity and introducing or increasing remote work.7 The predicted probability of 
these responses at various percentiles of the productivity distribution is depicted in Figure 12. 
Each panel looks at a separate response probability based on the enterprise response from the 
first  wave  and contrasts it  to  probabilities  estimated  based  on  the  information  from  all  the 
available waves together. 

7 Not all questions of those in the first wave of COVID-19 follow-up questions are available in later waves. 

19 

 
 
 
 
                                                           
As Panel a) shows, the probability of a permanent closure increases over time throughout the 
full range of the distribution: the probability increases for low and high productivity firms alike. 
In contrast, the evolution of bankruptcies varies across the productivity distribution over time. 
As Panel b) suggests, the initial clear advantage with respect to the probability of bankruptcy 
of the more productive firms decreases, as the probability of bankruptcy increases over time 
relatively more for more productive firms. However, the probability difference between the 
most and the least productive enterprises remains significant. 

Panel c) shows that the share of firms engaging in online sales increased over time by more 
than  10  percent  on  average.  This  implies  that  in  the  full  pandemic  period  examined  (all 
available survey waves together), about 25 percent of the least productive firms and more than 
30 percent of the most productive firms increased of introduced online sales. We also show 
that the gap between the most and the least productive firms – in terms of use of online sales 
- increased further.  

20 

 
 
 
Figure 12. Longer term responses to COVID-19 shock by productivity 

Panel (a): Permanent closure  

           Panel (b): Bankruptcy 

Panel (c): Online sales 

 Panel (d): Remote working 

Note: The columns show the predicted share of firms that close down permanently (panel a) or file for bankruptcy (panel b), the share of firms 
that report Introduction or increase of online sales (panel c) and remote work (panel d) by productivity. The results are obtained from survey 
regressions with COVID-19 Follow-up survey weights of the Enterprise Survey. Regressions are derived from Eq. 1 and control for country and 
sector fixed effects and firm size, age, ownership and exporting status. Error bars correspond to 10 percent significance level. 

Panel  d)  looks  at  the  remote  work  responses  over  time.  We  find  that  the  probability  of 
introducing or increasing remote work solutions increased by 10-15 percentage points at each 
point of the productivity distribution. The clear advantage of more productive firms prevails 
and remains significant. 

21 

 
 
 
 
 
 
 
 
 
 
 
 
5.4  Government measures 

This section looks at the availability of government measures, either at the central or the local 
level,  depending  on  firms’  characteristics,  taking  into  account  firms’  responses  to  the 
pandemic.  We  look  at  several  types  of  measures  reported  in  the  Enterprise  Survey,  their 
prevalence and their availability.  

The use and need for policy support shows heterogeneity both across the type of the support 
and  across  sectors.  As  Table  2  shows,  the  most  common  policy  instrument  were  the  wage 
subsidies to reduce unemployment, while the subsidized credit was used much less frequently. 
Support  was  also  more  channelled  towards  sectors  that  were  more  affected  by  the  social 
distancing  measures.  Results  from  logistic  regressions  on  government  support  presented  in 
Table  5  (Appendix)  suggest  that  firms  operating  in  hospitality  sectors  were  more  likely  to 
receive support, while those in the Construction and Telecom sectors were less likely to obtain 
it. 

In  general,  we  do  not  find  strong  evidence  that  policy  support  was  channelled  to  less 
productive, less viable firms. However, looking at the specific policy measures, one can find 
some heterogeneity. As Figure 13 (derived from Table 5 regressions) shows, cash transfer and 
wage subsidies are the policy instrument that most productive firms were less likely to receive. 
However, we also find that this negative correlation between public support and productivity 
is not significant any more once the size of sales loss or temporary closure is controlled for. See 
Table 6 in the Appendix. Such findings mainly confirm our prior that policy support was largely 
untargeted due to the emergency situation caused by the pandemic. Our results also confirm 
that sales loss channel was one of the crucial elements considered as regards the allocation.  

Figure 13. Allocation of policy measures by productivity 

Note: The columns show the predicted share of firms that report receiving policy measures. P10 and P90. The results are obtained from survey 
regressions with COVID-19 Follow-up survey weights of the Enterprise Survey. Regressions are derived from Eq. 1 and control for country and 
sector fixed effects and firm size, age, ownership and exporting status. Error bars correspond to 10 percent significance level.  

22 

 
 
 
 
 
6  Discussion 

In this paper, we show how firm across the productivity spectrum reacted to the COVID-19 
pandemic  and  how  they  fared  so  far.  While  the  pandemic  induced  strong  country 
heterogeneity in terms of 2020 recession and the projected recovery, it is far less clear how 
within- and cross-sectoral firm-level productivity differentials were affected and what are the 
major implications for the total factor productivity as the recovery kicks in.  

Furthermore,  while  it  is  evident  that  enormous  policy  support  prevented  numerous 
bankruptcies  and  cautioned  against  further  lay-offs,  it  is  far  less  straightforward  how 
implemented policy measures interacted with firms and how, if at all, these actions effected 
firm-level productivity distribution. As these measures are gradually phased out, it will become 
increasingly important to re-introduce competition and level playing field so that productivity 
is fostered in coming years.  

In  our  paper,  we  try  to  provide  some  insight  into  these  complicated  relations  by  extending 
previous findings from the economic literature. First, we confirm the hypothesis regarding the 
impact on productivity postulated in the Apedo-Amah et al (2020) paper by showing that the 
COVID-19 shock has had a strong impact not only between, but also within various sectors.   

More  productive  firms  generally  weathered  the  pandemic  better  in  terms  of  lost  output  or 
employment. In addition, they seem to have introduced or extended remote work more than 
their less productive peers. As such, this is likely to amplify the difference between the less 
productive  laggards  and  more  productive  winners.  Such  a  finding  is  similar  to  the  one  in 
Kozeniauskas et al (2020) but extended to a much broader set of countries, also supporting 
findings  of  Bloom  et  al.  (2020).  It  also  comes  clear  from  our  results  that  sectors  that  were 
subject to social-distancing and other containment measures, suffered more regardless of their 
productivity  levels.  This  points  to  a  potential  cross-sectoral  reallocation  as  the  recovery 
proceeds.  

While it follows from our results that the least productive firms are disproportionately affected 
by the pandemic and therefore the drop in productivity was smaller than it would otherwise 
have been, we also find that the re-allocation took place even among the top productive firms, 
irrespectively  of  the  sector  within  which  they  operate.  This  suggests  that  even  the  most 
productive firms were impacted strongly by the pandemic.  

Overall, it is still too early to conclude about the overall long-term impact on productivity. First, 
adjustment is still ongoing. Second, policy measures are still in place and are likely to mask the 
true impact that the crisis has had on the productivity. Indeed, our findings on firms’ responses 
to policy measures remain rather inconclusive. On the one hand, massive and universal, but 
mostly untargeted, support was paramount for firms to survive the extreme COVID-19 shock 
as confirmed by a broad utilization of these packages by firms in our dataset.  

It  follows  from  our  results  that  the  impact  on  productivity  was  not  a  major  concern  for 
policymakers, as most of utilization of government measures can be explained by sales losses. 
This was likely made for a good reason. Nevertheless, more targeted measures are likely to be 

23 

 
 
paramount in coming years, particularly to   address the green, digital and other gaps for a 
sustainable recovery, where firm-level competition is reintroduced.  

24 

 
 
 
 
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VoxEU.org. URL https://voxeu.org/article/scarring-effects-covid-19-global-economy  

Granja, J. and Moreira, S., 2019., Product Innovation and Credit Market Disruptions. Available 
at SSRN: https://ssrn.com/abstract=3477726 or http://dx.doi.org/10.2139/ssrn.3477726 

ILO, 2021. ILO Monitor: COVID-19 and the world of work. Seventh edition. Updated estimates 

and analysis 

IMF, 2021a, World Economic Outlook, IMF, January 2021.  

IMF, 2021b. Fiscal Monitor Update, January 2021 

Koren, M., Pető, R., 2020. Business disruptions from social distancing. PLoS ONE 15, 

e0239113. https://doi.org/10.1371/journal.pone.0239113 

Kozeniauskas, N. and Moreira, P. and Santos, C, 2020. COVID-19 and Firms: Productivity and 
Government Policies (August 1). CEPR Discussion Paper No. DP15156, Available at 
SSRN: https://ssrn.com/abstract=3674945 

Lamorgese, A., Linarello, A., Patnaik, M., Schivardi, F., 2021. Management practices and 
resilience to shocks: Evidence from COVID-19. Centre for Economic Policy Research. 
https://cepr.org/active/publications/discussion_papers/dp.php?dpno=15987 

Mauro, F. di, Syverson, C., 2020. The COVID crisis and productivity growth. VoxEU.org. URL 

https://voxeu.org/article/covid-crisis-and-productivity-growth 

26 

 
 
 
 
7  Appendix 

7.1  Figures 

Figure 14. Availability of firm level productivity measures.  

Note: The figure shows the availability of three productivity measures, TFP, labour, productivity and sales/employment per country by sectors 
in (unweighted) percentage of firms. The bars are to be read additively: the share of firms that have all three measures available (TFP), only 
labour productivity and sales/employment (TFP+labprod), only sales per employment available (TFP + labprod + sales/employment). TFP is 
measures see Francis et al 2020 for details. Labour productivity is defined as real value added over number of employees. The value added is 
defined in sales net of the cost of raw materials and intermediate goods used in production in Manufacturing and as sales net of cost of 
finished goods/materials bought to resell in the services sectors. 

27 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 3. Logit regressions on COVID-19 response – Part I. 

VARIABLES 

productivity 

enterprise age (in logs) 

 Medium size (20-99) 

Large (100 and over) 

Foreign ownership 

Exporter  

Textile, Apparel, Leather 

Wood, Paper, Publishing  

Coke, Petrol 

Rubber, Minerals, Metals 

Machinery, Electronics 

Motor Vehicles 

Furniture, Recycling 

Construction 

Wholesale 

Retail 

Hotels, restaurants 

Transport 

Telecom, IT, Computer  

(1) 
closed 

(2) 
temp.  
closed 

(3) 
sales 
decline 

(4) 
s. decline 
 (<-30%) 

(5) 
bank- 
ruptcy 

(6) 
demand 
decrease 

(7) 
demand 
increase 

(8) 
supply 
decrease 

(9) 
supply 
increase 

0.81** 
[0.08] 

0.89*** 
[0.04] 

0.85*** 
[0.04] 

0.90** 
[0.04] 

0.65*** 
[0.08] 

0.89*** 
[0.03] 

1.13** 
[0.07] 

0.89*** 
[0.03] 

1.06 
[0.07] 

0.74** 
[0.10] 
0.58** 
[0.16] 
0.57 
[0.21] 
1.55 
[0.75] 
0.58* 
[0.19] 

1.79 
[0.74] 
0.76 
[0.56] 
0.83 
[0.91] 
0.61 
[0.30] 
0.88 
[0.56] 
0.24 
[0.26] 
0.95 
[0.61] 
1.57 
[0.65] 
0.92 
[0.39] 
1.27 
[0.48] 
1.84 
[0.75] 
1.43 
[0.72] 
1.26 
[0.64] 

0.86** 
[0.05] 
0.76*** 
[0.07] 
0.57*** 
[0.08] 
1.11 
[0.21] 
0.68*** 
[0.09] 

5.52*** 
[1.25] 
2.26*** 
[0.51] 
1.05 
[0.35] 
1.91*** 
[0.38] 
1.56* 
[0.39] 
3.32*** 
[1.55] 
3.73*** 
[0.92] 
1.67** 
[0.34] 
1.90*** 
[0.35] 
2.27*** 
[0.41] 
9.27*** 
[2.06] 
1.82** 
[0.43] 
0.91 
[0.26] 

1.06 
[0.07] 
0.82* 
[0.08] 
0.71** 
[0.10] 
0.88 
[0.17] 
0.91 
[0.12] 

1.49* 
[0.35] 
1.87*** 
[0.45] 
0.43** 
[0.14] 
0.86 
[0.16] 
1.42 
[0.32] 
1.27 
[0.61] 
0.93 
[0.29] 
0.76 
[0.14] 
1.20 
[0.22] 
0.73* 
[0.13] 
3.26*** 
[0.86] 
2.14*** 
[0.53] 
0.56** 
[0.15] 

0.95 
[0.06] 
0.66*** 
[0.07] 
0.54*** 
[0.08] 
0.94 
[0.18] 
1.01 
[0.14] 

2.55*** 
[0.57] 
1.89*** 
[0.43] 
0.49 
[0.21] 
1.16 
[0.23] 
1.81** 
[0.47] 
1.76 
[0.97] 
1.46 
[0.42] 
1.34 
[0.27] 
1.21 
[0.23] 
0.92 
[0.16] 
4.50*** 
[0.96] 
3.41*** 
[0.81] 
0.74 
[0.21] 

0.59*** 
[0.12] 
0.81 
[0.30] 
0.88 
[0.39] 
1.02 
[0.81] 
0.70 
[0.34] 

0.59 
[0.50] 
1.67 
[1.33] 
0.03*** 
[0.04] 
0.88 
[0.60] 
1.90 
[1.53] 
0.16 
[0.26] 
0.65 
[0.73] 
1.03 
[0.70] 
0.59 
[0.38] 
0.40 
[0.25] 
0.97 
[0.56] 
1.74 
[1.19] 
0.02*** 
[0.03] 

1.07 
[0.07] 
0.93 
[0.09] 
0.81* 
[0.10] 
0.86 
[0.16] 
0.82* 
[0.10] 

1.85*** 
[0.42] 
2.09*** 
[0.48] 
0.54* 
[0.19] 
0.86 
[0.16] 
1.22 
[0.27] 
1.67 
[0.74] 
0.98 
[0.29] 
0.84 
[0.15] 
1.09 
[0.19] 
0.76* 
[0.12] 
2.45*** 
[0.54] 
1.94*** 
[0.43] 
0.69 
[0.17] 

0.87 
[0.09] 
0.98 
[0.14] 
1.45** 
[0.25] 
1.01 
[0.22] 
0.92 
[0.18] 

0.48** 
[0.17] 
0.36** 
[0.14] 
1.95 
[1.07] 
0.97 
[0.28] 
0.88 
[0.29] 
0.45 
[0.30] 
0.86 
[0.35] 
0.59* 
[0.18] 
0.68 
[0.19] 
0.84 
[0.22] 
0.20*** 
[0.09] 
0.50** 
[0.17] 
0.74 
[0.28] 

1.08 
[0.07] 
0.97 
[0.09] 
0.86 
[0.11] 
0.89 
[0.17] 
0.87 
[0.10] 

1.77*** 
[0.38] 
2.05*** 
[0.45] 
0.84 
[0.33] 
0.98 
[0.18] 
1.51** 
[0.32] 
2.34* 
[1.16] 
1.09 
[0.31] 
0.88 
[0.15] 
1.33* 
[0.23] 
0.83 
[0.13] 
2.15*** 
[0.44] 
0.96 
[0.21] 
0.47*** 
[0.12] 

0.94 
[0.10] 
0.84 
[0.13] 
1.41* 
[0.27] 
1.07 
[0.26] 
0.96 
[0.22] 

0.39** 
[0.15] 
0.37** 
[0.16] 
1.96 
[1.06] 
0.86 
[0.26] 
0.42** 
[0.17] 
0.33 
[0.27] 
0.71 
[0.30] 
0.56* 
[0.18] 
0.59* 
[0.17] 
0.78 
[0.21] 
0.24*** 
[0.11] 
0.33** 
[0.14] 
0.34** 
[0.18] 

Observations 

15,321 

12,966 

10,801 

10,801 

9,434 

11,306 

11,306 

11,306 

11,306 

*** p<0.01, ** p<0.05, * p<0.1 
Odds rations are reported. Regression includes country fixed effects (not reported) 

28 

 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
Table 4. Logit regressions on COVID-19 response – Part II. 

VARIABLES 

productivity 

enterprise age (in logs) 

 Medium size (20-99) 

Large (100 and over) 

Foreign ownership 

Exporter  

Textile, Apparel, Leather 

Wood, Paper, Publishing  

Coke, Petrol 

Rubber, Minerals, Metals 

Machinery, Electronics 

Motor Vehicles 

Furniture, Recycling 

Construction 

Wholesale 

Retail 

Hotels, restaurants 

Transport 

Telecom, IT, Computer  

(10) 
convert 
production 

(11) 
decrease 
w. hours 

1.00 
[0.04] 

0.87** 
[0.06] 
1.03 
[0.10] 
1.09 
[0.16] 
1.09 
[0.21] 
0.95 
[0.12] 

1.20 
[0.28] 
0.79 
[0.21] 
0.56 
[0.20] 
0.57*** 
[0.12] 
0.50*** 
[0.12] 
0.36* 
[0.19] 
0.69 
[0.19] 
0.40*** 
[0.08] 
0.77 
[0.14] 
0.62*** 
[0.11] 
0.94 
[0.19] 
0.60** 
[0.13] 
0.73 
[0.18] 

0.93* 
[0.04] 

1.06 
[0.07] 
0.90 
[0.09] 
0.93 
[0.13] 
0.95 
[0.19] 
0.88 
[0.11] 

1.98*** 
[0.43] 
1.89*** 
[0.44] 
0.63 
[0.24] 
0.71* 
[0.13] 
1.19 
[0.27] 
2.59** 
[1.08] 
0.74 
[0.21] 
0.88 
[0.16] 
0.64** 
[0.11] 
0.71** 
[0.12] 
2.36*** 
[0.47] 
1.55** 
[0.33] 
0.46*** 
[0.12] 

(12) 
empl. 
layoff 

0.88** 
[0.05] 

0.89 
[0.10] 
1.28* 
[0.19] 
1.44** 
[0.26] 
0.82 
[0.22] 
0.83 
[0.16] 

1.29 
[0.42] 
1.15 
[0.40] 
1.03 
[0.67] 
0.74 
[0.21] 
1.10 
[0.42] 
2.22 
[1.67] 
0.89 
[0.35] 
1.00 
[0.30] 
0.91 
[0.25] 
0.86 
[0.23] 
1.68* 
[0.46] 
2.03** 
[0.62] 
0.86 
[0.36] 

(13) 
unpaid  
leave 

(14) 
remote 
work 

0.90*** 
[0.04] 

1.25*** 
[0.05] 

1.07 
[0.07] 
1.11 
[0.12] 
1.41** 
[0.21] 
0.95 
[0.21] 
0.77* 
[0.11] 

1.29 
[0.31] 
1.11 
[0.31] 
0.73 
[0.26] 
0.96 
[0.20] 
0.73 
[0.20] 
2.40** 
[0.99] 
1.06 
[0.32] 
0.85 
[0.18] 
0.91 
[0.18] 
0.93 
[0.17] 
1.97*** 
[0.43] 
1.20 
[0.29] 
0.37*** 
[0.14] 

0.98 
[0.07] 
1.58*** 
[0.15] 
3.29*** 
[0.42] 
1.83*** 
[0.31] 
1.39*** 
[0.17] 

0.75 
[0.20] 
2.06*** 
[0.50] 
1.53 
[0.44] 
1.16 
[0.23] 
1.62** 
[0.36] 
1.49 
[0.72] 
0.95 
[0.29] 
1.23 
[0.25] 
1.89*** 
[0.33] 
1.25 
[0.22] 
0.76 
[0.18] 
2.01*** 
[0.42] 
7.35*** 
[1.93] 

(15) 
online 
sales 

1.07 
[0.05] 

0.86** 
[0.06] 
1.25** 
[0.13] 
1.59*** 
[0.23] 
1.04 
[0.18] 
0.97 
[0.13] 

0.93 
[0.25] 
1.16 
[0.33] 
1.09 
[0.33] 
0.50*** 
[0.11] 
0.64* 
[0.17] 
0.89 
[0.62] 
0.63 
[0.19] 
0.49*** 
[0.11] 
1.24 
[0.25] 
1.34 
[0.26] 
0.93 
[0.21] 
0.69 
[0.17] 
2.55*** 
[0.67] 

(16) 
delivery 

0.97 
[0.04] 

0.90 
[0.06] 
1.11 
[0.13] 
1.28 
[0.23] 
0.96 
[0.22] 
0.90 
[0.15] 

0.46*** 
[0.13] 
0.70 
[0.19] 
1.77 
[0.63] 
0.50*** 
[0.12] 
0.42*** 
[0.13] 
0.17** 
[0.14] 
0.48** 
[0.16] 
0.24*** 
[0.06] 
0.77 
[0.16] 
0.97 
[0.18] 
1.02 
[0.23] 
0.29*** 
[0.10] 
0.70 
[0.21] 

Observations 

11,373 

11,306 

12,624 

12,027 

12,966 

12,624 

12,966 

*** p<0.01, ** p<0.05, * p<0.1 

29 

 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
Table 5. Logit regressions on probability of policy support 

(17) 
Govt support 
indicator 

(18) 
Cash  
transfer 

(19) 
Credit pay.  
deferral 

(20) 
Access 
new credit 

(21) 
Fiscal  
exemption 

(22) 
Wage  
subsidies 

0.95 
[0.04] 

0.94 
[0.06] 
1.13 
[0.11] 
1.37** 
[0.18] 
0.80 
[0.14] 
0.89 
[0.10] 

1.29 
[0.30] 
1.37 
[0.30] 
1.01 
[0.35] 
0.99 
[0.19] 
0.89 
[0.20] 
1.70 
[0.90] 
1.01 
[0.30] 
0.67** 
[0.13] 
1.10 
[0.19] 
0.85 
[0.14] 
1.99*** 
[0.40] 
1.39 
[0.29] 
0.62* 
[0.17] 

0.90* 
[0.05] 

0.89 
[0.08] 
0.71** 
[0.11] 
0.74 
[0.16] 
0.51 
[0.22] 
1.11 
[0.20] 

1.81 
[0.69] 
1.43 
[0.46] 
1.00 
[0.56] 
0.95 
[0.28] 
1.39 
[0.41] 
1.33 
[0.90] 
1.38 
[0.65] 
0.96 
[0.29] 
1.51 
[0.40] 
1.00 
[0.24] 
2.10*** 
[0.57] 
1.46 
[0.45] 
0.66 
[0.27] 

1.04 
[0.05] 

0.98 
[0.09] 
0.88 
[0.13] 
1.41 
[0.31] 
0.57** 
[0.16] 
1.14 
[0.20] 

0.90 
[0.33] 
1.48 
[0.53] 
0.74 
[0.34] 
1.20 
[0.39] 
0.67 
[0.26] 
1.16 
[1.05] 
0.92 
[0.49] 
0.54* 
[0.18] 
0.98 
[0.29] 
0.79 
[0.23] 
1.54 
[0.48] 
1.50 
[0.53] 
0.36** 
[0.15] 

1.10 
[0.08] 

0.89 
[0.11] 
1.10 
[0.19] 
1.01 
[0.27] 
0.51* 
[0.21] 
1.11 
[0.27] 

0.72 
[0.32] 
1.68 
[0.66] 
0.12*** 
[0.06] 
1.44 
[0.54] 
1.33 
[0.54] 
1.72 
[1.35] 
1.29 
[0.81] 
0.60 
[0.26] 
1.19 
[0.43] 
0.81 
[0.28] 
0.95 
[0.37] 
1.29 
[0.56] 
0.94 
[0.49] 

1.01 
[0.07] 

0.93 
[0.09] 
0.82 
[0.12] 
1.00 
[0.21] 
1.13 
[0.30] 
0.86 
[0.16] 

1.63 
[0.62] 
1.31 
[0.44] 
0.94 
[0.54] 
1.05 
[0.32] 
1.19 
[0.45] 
0.32 
[0.23] 
1.01 
[0.42] 
1.18 
[0.38] 
1.35 
[0.40] 
1.15 
[0.34] 
2.37*** 
[0.76] 
1.30 
[0.46] 
0.55 
[0.31] 

0.92* 
[0.04] 

0.98 
[0.07] 
1.22** 
[0.12] 
1.53*** 
[0.23] 
0.92 
[0.18] 
0.81* 
[0.10] 

1.31 
[0.32] 
1.56* 
[0.37] 
0.86 
[0.31] 
1.09 
[0.22] 
1.14 
[0.28] 
2.34* 
[1.21] 
1.74 
[0.61] 
0.63** 
[0.13] 
1.26 
[0.24] 
0.92 
[0.16] 
2.23*** 
[0.48] 
1.65** 
[0.39] 
0.54** 
[0.16] 

12,624 

10,050 

11,266 

10,772 

11,263 

10,654 

VARIABLES 

productivity 

enterprise age (in logs) 

 Medium size (20-99) 

Large (100 and over) 

Foreign ownership 

Exporter  

Textile, Apparel, Leather 

Wood, Paper, Publishing  

Coke, Petrol 

Rubber, Minerals, Metals 

Machinery, Electronics 

Motor Vehicles 

Furniture, Recycling 

Construction 

Wholesale 

Retail 

Hotels, restaurants 

Transport 

Telecom, IT, Computer  

Observations 
*** p<0.01, ** p<0.05, * 
p<0.1 

30 

 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
Table 6. Logit regressions on probability of policy support with sales loss controls 

Panel A 
productivity 

Panel B 
productivity 

temporary closure 

sales loss more than 30% 

(1) 

(2) 

(3) 

(4) 

(5) 

(6) 

Public   support 

Cash 
transfer 

Credit payment 
deferral 

Access to 
new credit 

Fiscal 
exemption 

Wage subsidy 

0.990 
[0.007] 

1.00 
[0.01] 

1.18*** 
[0.02] 
1.05** 
[0.02] 

0.990* 
[0.005] 

0.99 
[0.01] 

1.08*** 
[0.02] 
1.02 
[0.01] 

1.003 
[0.004] 

1.01 
[0.00] 

1.03** 
[0.01] 
1.05*** 
[0.01] 

1.006 
[0.004] 

1.01 
[0.00] 

1.02** 
[0.01] 
1.02** 
[0.01] 

1.000 
[0.006] 

1.00 
[0.01] 

1.06*** 
[0.01] 
1.02* 
[0.01] 

0.986* 
[0.008] 

0.99 
[0.01] 

1.20*** 
[0.02] 
1.08*** 
[0.02] 

Observations 

10,962 

9,759 

10,929 

10,442 

10,924 

10,399 

31 

 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
ECONOMICS – WORKING PAPERS 2022/09

Productivity and responses  

to the pandemic

Firm-level evidence 

Economics Department
economics@eib.org

www.eib.org/economics 

European Investment Bank
98 -100, boulevard Konrad Adenauer
L-2950 Luxembourg
+352 4379-22000
www.eib.org – info@eib.org

© European Investment Bank, 06/2022   EN

PDF:

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