Productivity-enhancing reallocation during the Covid-19 pandemic.
Date: 2024-06-19
Institutions: European Central Bank
Subjects: coronavirus disease, economic recession, employment statistics, labour market, productivity

Working Paper Series 

Tibor Lalinsky, Jaanika Meriküll, 
Paloma Lopez-Garcia 

Productivity-enhancing reallocation 
during the Covid-19 pandemic 

No 2947 

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

 
 
 
Abstract

This paper studies how the Covid-19 pandemic and the extensive job retention support that

accompanied it affected productivity in Europe. The focus is on the reallocation channel

and productivity-enhancing reallocation of jobs, following Foster et al., 2016. An exten-

sive micro-distributed analysis of firm-level data for 11 euro area countries is used. The

unique firm-level datasets are constructed by merging balance-sheet and income-statement

data with policy support data. The paper exploits variation in employment responsive-

ness to productivity over time, particularly examining the relationship between changes in

employment responsiveness and the job retention support in 2020 and studying how well

the support was targeted by firm productivity. Acknowledging limitations of a small set of

countries covered and occasionally large confidence bounds around estimates, the findings

suggest that (1) productivity-enhancing reallocation was weaker in the pandemic than in

the Great Recession; (2) The countries that were more generous with job retention support

and countries where more support was allocated to low-productivity firms showed weaker

productivity-enhancing reallocation in 2020.

Keywords: Productivity-enhancing reallocation, Covid-19, adjustment of firms, job reten-

tion support, cross-country analysis

JEL codes: D22, H25, J38, L29

ECB Working Paper Series No 29471Non-technical summary

The Covid-19 pandemic prompted widespread lockdown measures and subsequent disrup-

tions in supply chains, resulting in significant declines in firm sales. Despite endeavours to

mitigate revenue shortfalls through cost adjustments, a discernible decrease in value added was

evident across various sectors during the initial year of the pandemic. With adjustments in

employment comparatively minor in relation to those made in value added, a decline in labour

productivity ensued.

Our study builds upon prior research concerning the impact of crises on productivity growth

and labour reallocation, contributing to the comparative analysis of Covid-19’s effects on produc-

tivity. We offer empirical insights into several pivotal inquiries. What constituted the primary

conduit for the productivity downturn during the Covid-19 pandemic? Were heightened lev-

els of labour reallocation observed amidst the economic downturn of the pandemic? Did such

reallocation yield productivity gains, such that more proficient enterprises experienced greater

employment expansion? Did the trajectory of productivity and reallocation amid the pandemic

diverge from patterns observed during other crises? Moreover, did the extensive policy in-

terventions aimed at supporting firms during the pandemic adversely affect productivity and

productivity-enhancing reallocation?

We present empirical findings for 11 euro area countries: Croatia, Estonia, Finland, France,

Italy, Latvia, the Netherlands, Portugal, Slovakia, Slovenia, and Spain. Our analysis draws upon

firm-level data sourced from balance sheets and income statements, supplemented by information

pertaining to the distribution of pandemic-related job retention support at the firm level.

The methodology underpinning our data preparation and analysis aligns with the established

CompNet framework. Employing a micro-distributed approach, we deploy a standardised code

disseminated to data providers, who execute it independently on their respective national firm-

level datasets. This approach ensures broad coverage and facilitates cross-country comparability

of the underlying data while safeguarding its confidentiality.

Our study demonstrates the significant impact of the Covid-19 pandemic on productivity

dynamics in the euro area, primarily manifesting through adverse effects at the within-firm

level. However, the between-firm dimension is also noteworthy, contributing positively to overall

productivity growth.

Our comparative examination shows that higher-productivity firms generally exhibited greater

employment expansion or lesser employment contractions during the pandemic than their less

ECB Working Paper Series No 29472productive counterparts. Across most euro area countries, we ascertain that productivity-

enhancing labour reallocation accelerated amidst economic contraction, indicative of the ac-

tivation of mechanisms such as creative destruction or cleansing during recessionary phases.

However, a longitudinal perspective reveals that productivity-enhancing reallocation, on aver-

age, exhibited somewhat subdued trends during the Covid-19 pandemic compared to the Great

Recession.

To assess how productivity declines and the unprecedented scale of government support

influenced subdued reallocation during the pandemic, we leverage granular data on employment

support and conduct a thorough investigation into its distribution among firms.

Our findings indicate that most subsidies were directed towards productive firms, which

were more likely to receive support across most countries. Nonetheless, the quantum of support

relative to revenue diminishes with increasing firm productivity.

Despite variations in the design and implementation of job retention schemes and employ-

ment subsidies across countries, our analysis suggests that productivity-enhancing reallocation

was less pronounced in 2020 in nations where support participation was more widespread or

skewed towards low-productivity firms.

ECB Working Paper Series No 294731 Introduction

The Covid-19 pandemic caused deep disruption to global economic activity and affected resource

allocation and productivity. The response to the pandemic was massive policy support from

governments. Fiscal support was given to firms in order to limit bankruptcies, capital disruption

and job losses. The rapid policy support provided to firms prevented a wave of bankruptcies

and it supported the rapid economic recovery of the euro area economy following the pandemic-

induced crisis. However, it is not straightforward to assess the scale of its effect on productivity

across individual economies and firms. This paper adds to the debate by providing comparable

firm-level evidence for a number of euro area countries.

In this paper, we ask what the impact of the pandemic was on labour reallocation and thereby

on productivity. We first provide comparative evidence about implications of job reallocation

on productivity, the productivity-enhancing reallocation, for 11 euro area countries: Croatia,

Estonia, Finland, France, Italy, Latvia, the Netherlands, Portugal, Slovakia, Slovenia and Spain.

We compare productivity-enhancing reallocation during the pandemic with that observed during

the Great Recession. We then ask whether job retention support affected productivity-enhancing

reallocation during the pandemic. To that end, we use cross-country variation in the intensity

of job retention support. Finally, and to complement the analysis of the link between policy

support and productivity-enhancing reallocation, we merge wage support with productivity at

the firm level in the few countries with available data and study whether participation in the

support and the intensity of it depended on firm productivity.

Our analysis adds to the comparative studies on Covid-19 and productivity. It builds on the

early work by di Mauro and Syverson, 2020 highlighting the main channels through which the

crisis might affect productivity growth. Following Schumpeter, 1939, the recessions accelerate

the process of labour reallocation from low-productivity firms to high-productivity ones through

creative destruction, and activate the cleansing effect of recessions predicted by Caballero and

Hammour, 1994. The substantial policy measures that were taken to mitigate the shock of

Covid-19 might have reduced productivity-enhancing reallocation. One of the first estimates

published by Andrews, Charlton, and Moore, 2021 suggests that job reallocation remained

connected to firm productivity during the pandemic, so high-productivity firms were more likely

to expand and low-productivity firms were more likely to contract. However, they also suggest in

their three-country comparative study that productivity-enhancing reallocation was suppressed

ECB Working Paper Series No 29474in New Zealand and not in Australia or the UK because the job retention support was much

more widespread there. We follow this line of research and contribute by studying productivity-

enhancing reallocation and job retention support in a much larger set of 11 European countries.

We apply the approach of Foster et al., 2016 by estimating productivity-enhancing realloca-

tion as the sensitivity of a firm’s employment growth to its lagged relative productivity. The rel-

ative productivity is taken as firms’ productivity relative to average productivity in the industry,

hence the effect of relative productivity on employment growth shows how the between-firm re-

allocation contributes to aggregate productivity. The approach is a regression-based alternative

to various decomposition methods that disentangle the contribution of within-firm productivity

growth and between-firm reallocation of resources to aggregate productivity, see the method-

ological discussion in Dosi et al., 2015. The between-firm term is showing how effectively the

resources move from low- to high-productivity firms, being a proxy for Schumpeterian creative

destruction or cleansing process. There are other ways how to estimate the contribution of

reallocation to aggregate productivity, most notably the evolutionary accounting approach by

Dosi et al., 2015 where the firm sales growth is explained by relative productivity growth, and

marginal product approach by Hsieh and Klenow, 2009 where within a detailed industry varia-

tion in firms’ marginal products are compared to hypothetical counterfactual of no variation in

marginal products.

We take the approach of Foster et al., 2016 in this paper as it allows direct comparison of

our results with many empirical papers on productivity-enhancing reallocation. These papers

focus on the productivity-enhancing reallocation during the Great Recession in the euro area

countries1, but the evidence is inconclusive as some studies find the recession to have been

cleansing, while others do not. The evidence from the US found by Foster et al., 2016 is that

while the Great Recession was cleansing, the cleansing effect was weaker than that in earlier

recessions.

Much less is known about productivity-enhancing reallocation during the Covid-19 pandemic,

though Andrews, Charlton, and Moore, 2021, Andrews, Hambur, and Bahar, 2021, Kozeniauskas

et al., 2022 and Merik¨ull and Paulus, 2024 provide the first evidence about it. The results are

again inconclusive, but the findings suggest that the extensive policy support may have played

a part in muting productivity-enhancing reallocation by supporting low productivity firms that

1See Carreira and Teixeira, 2016, Bartelsman et al., 2019, Dias and Marques, 2021, Mina and Santoleri, 2021,

Domini and Moschella, 2022, Merik¨ull and Paulus, 2024 and Garcia-Louzao and Tarasonis, 2023.

ECB Working Paper Series No 29475otherwise would have exited. In general, there is evidence that growth in jobs has become less

responsive to productivity in recent decades, showing that productivity-enhancing reallocation

is contributing less to aggregate productivity (Decker et al., 2020, Andrews and Hansell, 2021).

These findings point to the importance of studies that use a longer timespan and underline the

shortcomings of drawing inferences from a single crisis episode.

A related line of literature studies whether it was high-productivity or low-productivity

firms that were most likely to take up support, and derives conclusions from this about how

support affected productivity. There is evidence that the support during the pandemic was

concentrated among low-productivity firms, and this may have muted productivity-enhancing

reallocation (Kozeniauskas et al., 2022, Harasztosi et al., 2022). However, there is also evidence

that whether firms received support was not linked to their productivity (Merik¨ull and Paulus,

2023) or that the relationship with firm productivity was positive or negative dependent on the

country (Bighelli et al., 2023). Altomonte et al., 2021 find that the support was allocated in line

with firm productivity in Italy and Germany, but it was productivity-neutral in France.

Fern´andez-Cerezo et al., 2022 show that the Covid-19 shock had a stronger impact on small,

young and less productive firms, and that those firms resorted relatively more to all the available

support schemes, including furlough. Traditionally low-productivity industries such as hotels

and restaurants were affected most by the Covid-19 containment measures, so controlling for

industry or for eligibility for support is crucial if the relationship between firm productivity and

take up of the support is to be understood. As shown by Fern´andez-Cerezo et al., 2022, the link

between firm productivity and the severity of the Covid-19 shock weakens substantially once

the industry that firms work in has been controlled for. Similarly, Merik¨ull and Paulus, 2023

demonstrate that the link between firm productivity and participation in support disappears

after eligibility for the support is controlled for. We contribute to this literature by providing

comparative evidence for this link in a sample of six euro area countries for which we are able

to merge data on the support with balance-sheet data.

Our data preparation and analysis benefit from the established CompNet infrastructure2. We

distribute a common code that is independently executed by data providers on their national

firm-level datasets. This method is often called a micro-distributed exercise and ensures that

there is high coverage and cross-country comparability, while preserving confidentiality (Bartels-

man et al., 2004). Like Demmou et al., 2021, Lalinsky and P´al, 2022 or Bighelli et al., 2023, we

2The Competitiveness Research Network https://www.comp-net.org/.

ECB Working Paper Series No 29476use micro data originating from balance sheets and income statements to compute developments

in productivity during the pandemic. The administrative data used in this paper have a high

degree of coverage and span several decades for the majority of our sample countries, allowing

us to compare the two crisis episodes of the Great Recession and the Covid-19 pandemic.

We show that the Covid-19 pandemic affected developments in productivity in the euro area

significantly, mostly through the within-firm margin, though the between-firm margin is also

important and is larger than shown by industry-level estimates. We find that productive firms

experienced larger employment gains or smaller employment losses on average during the recent

economic cycle. However, productivity-enhancing reallocation was somewhat weaker on average

during the Covid-19 pandemic than during the Great Recession. The relationship between the

intensity of the job retention support and the change in productivity-enhancing reallocation is

negative at the country level in 2020, suggesting that reallocation contributed less to productivity

in countries that gave more widespread support for jobs.

Firms from the lowest productivity decile were the least likely to participate in job retention

support. After conditioning on participation, the relationship between firm productivity and rel-

ative support size is negative in all the sample countries. Combining the results for participation

in support with the size of the support and productivity-enhancing reallocation suggests that

the countries that supported relatively more low-productive firms recorded lower increases in

productivity-enhancing reallocation during the pandemic. This means that the characteristics

of the job retention support, and in particular its extent and effectiveness in targeting high-

productivity firms, played a part in productivity-enhancing reallocation during the pandemic.

Our findings on the link between weaker productivity-enhancing reallocation in the pandemic

and the potential role of job retention support are subject to many limitations. We can pro-

vide the long-run comparative evidence on reallocation and productivity for only eight sample

countries and estimate the targeting of the support for only five countries. The estimates of

the productivity-enhancing reallocation have occasionally large confidence bounds and changes

in the reallocation pattern are rarely statistically significant. However, various identification

strategies taken in this paper point to the direction that the contribution of between-firm real-

location on productivity was weaker during the pandemic than during the Great Recession and

likely due to job retention support.

The paper proceeds as follows. In Section 2 we describe the aggregate and micro-aggregated

developments in productivity in our sample countries. Section 3 presents the firm-level data

ECB Working Paper Series No 29477and the methodology. Section 4.1 shows the results of productivity-enhancing reallocation, Sec-

tion 4.2 connects the reallocation with pandemic job retention support and Section 4.3 provides

details on the distribution of job retention support by firm productivity. Section 5 concludes.

2 Productivity developments during the Covid-19 pandemic

The Covid-19 pandemic affected developments in productivity in the euro area significantly.

The euro area experienced a sharp decline in aggregate GDP per person employed, followed by

a partial recovery in 2021; see Figure 1. The decline in labour productivity was more severe

than that seen during the Great Recession. Developments in the euro area during the Covid-19

period also contrasted starkly with those in the United States, probably largely because the

approaches to policy support for firms and households were different. Policy support in Europe

focused on job retention schemes and on preserving job matches, while support in the US focused

on unemployment insurance and preserving the incomes of those who were laid off (Giupponi

et al., 2022). There is ongoing academic debate over the productivity and welfare effects of the

different approaches taken by policymakers in the US and Europe. Job retention schemes are

costly for productivity if they go mostly to low-productivity firms and mute reallocation from

low-productivity firms to high-productivity ones, while they are an effective tool for guarding

against excess layoffs during temporary shocks (Giupponi et al., 2022).

Despite the extensive policy support given out to sustain employment during the Covid-19

pandemic in the euro area, firms recorded a decline in the number of employees; see Figure A1.

Firms reduced employment in response to the Covid-19 economic shock across the countries

analysed, and they switched from job creation in 2019 to job destruction in 2020. Our baseline

analysis for the pandemic period focuses on surviving firms. We cannot study firm exits in

this paper because of difficulties in identifying firm entries and exits properly in 2020. There

is evidence that firm entry and exit had only a very small effect on productivity during the

Covid-19 pandemic (Bloom et al., 2020) and that business exits remained at a lower level than

usual (Wang et al., 2020, Crane et al., 2022, Cros et al., 2021 and Lalinsky et al., 2024).

The decline in value added was accompanied by a decline in the number of people employed,

but the adjustments in employment were often smaller, with a negative impact on labour pro-

ductivity. All of our sample countries witnessed negative growth in real value added in the

first year of the pandemic, and the micro-aggregated growth in the labour productivity of the

ECB Working Paper Series No 29478Figure 1: Labour productivity dynamics in the euro area and the US, year-on-year changes
1996-2021
Note: Labour productivity is measured as GDP per person employed.
Source: OECD.

surviving firms varied between -11.8% in Portugal and 1.5% in Finland. Productivity growth

remained positive in only two sample countries, in which the reduction in employment was larger

than the drop in value added. The majority of our sample countries experienced a decline in

productivity in 2020, which is in line with the average decline of 4% in productivity in the euro

area in 2020. The decline in measured labour productivity resulted to some extent from labour

hoarding, which was induced by policy support and the expectation that the crisis would be

short-lived (di Mauro and Syverson, 2020).

The decomposition of productivity growth shows a negative within-firm margin and a positive

between-firm reallocation margin; see Figure 2. We follow Baily et al., 1992 and decompose

overall productivity growth into within-firm growth and the reallocation between firms3. The

between-firm labour reallocation channel lifted aggregate productivity in the euro area by 1-3

percentage points in 2020. The contribution made to productivity growth by reallocation during

the pandemic is remarkable, and was larger than that made during the Great Recession in most

3We omit the adjustment margins from entering and exiting firms and decompose the change in aggregate
labour productivity ∆lnPt for the set of surviving firms, S, as follows: ∆lnPt = (cid:80)
i∈S(θit −
θit−1)lnpit, where i denotes firm, t year, pit firm i productivity and θit firm i employment share in total employ-
ment.

i∈S θit−1∆lnpit + (cid:80)

ECB Working Paper Series No 29479Figure 2: Margins of labour productivity growth in 2009 and 2020, balanced panels of firms
Notes: Micro-aggregated growth in real value added per employee from a balanced panel of firms in 2005-2010
and in 2015-2020.
Source: Authors’ calculations from administrative data.

ECB Working Paper Series No 294710of the euro area countries. More detailed analysis confirms that only three countries recorded a

stronger between-firm margin during the Great Recession than during the pandemic (Figure A2

in Appendix). The caveat in comparing these two recession episodes is the large heterogeneity

in the shape of the recession during the Great Recession. While the pandemic was a relatively

homogenous V-shape recession and hit all our sample countries the hardest in 2020, the Great

Recession had its trough in 2009 for all of our sample countries, but there was a large variation

in the shape of the recession.

It was a V-shaped fast decline and fast recovery recession for

Estonia, France, Latvia and Slovakia and a slow recovery recession for the rest of the countries,

some of which double-dipped into the sovereign debt crisis.

Although the within-firm component predominantly drives the developments in the pro-

ductivity of continuing firms, the contribution of between-firm reallocation is relatively larger

than what is found for aggregate between-sector reallocation during the pandemic (see e.g.

Lopez-Garcia and Sz¨orfi, 2021). Bloom et al., 2020 estimate that only half or two thirds of the

contribution of between-firm reallocation to productivity came from inter-industry reallocation,

so that intra-industry reallocation also played a substantial role. Further analysis is needed

to understand the role of within-sector reallocation and the extent of productivity-enhancing

reallocation, and we conduct this analysis in the forthcoming sections.

3 Data and methodology

We use firm-level administrative data on firm performance, and for some countries we also have

firm-level information on the subsidies received by each firm. The data originate from national

administrative sources and are representative of all non-financial firms; see Tables A1 and A2.

The universe of firms is covered for the majority of sample countries, while for larger countries

also firm surveys have been used. The firm surveys are usually representative to the whole

population, while larger firms are over-represented in samples for France, Italy and Netherlands.

All the databases are workhorse tools for firm-level analysis at national central banks (CompNet,

2018). The data cover the two recessions in the Great Recession and the Covid-19 pandemic

in ten of the eleven sample countries. The data are harmonised using the CompNet approach

(CompNet, 2021) and are combined with firm-level data on pandemic subsidies.

We follow Foster et al., 2016 to study productivity-enhancing reallocation between firms.

This approach regresses the employment growth at a firm, gi,t, on the firm’s relative productivity

ECB Working Paper Series No 294711a year earlier, Relprodi,t−1:

gi,t = α + βRelprodi,t−1 + γCycler,t + δ(Relprodi,t−1 × Cycler,t) + θZ′

i,t−1 + τt + ϵi,t,

(1)

where gi,t is the mid-point average growth of the number of employees in firm i in year t, and

Relprodi,t−1 is the relative productivity of firm i in year t − 1. Relative productivity is measured

as the deviation of the firm’s log labour productivity from the median value for its NACE 2-digit

industry. Labour productivity is measured as value added divided by the number of employees,

where GDP deflators are applied at the level of NACE 2-digit industries to give real values. The

median of relative productivity is zero by construction, but the mean is not; see Table 1. The

business cycle, Cycler,t, is measured as growth in the unemployment rate in the NUTS2 level

region4, r. We control for additional firm-level characteristics in year t − 1, which are captured

by Z′

i,t−1 and include the log of firm employment and the NACE 2-digit industry. Time fixed

effects are also controlled for by τt, while ϵi,t is an error term with conventional properties.

Table 1: Descriptive statistics over the sample period

Country
(ISO code)

Croatia (HR)
Estonia (EE)
Finland (FI)
France (FR)
Italy (IT)
Latvia (LV)
Netherlands (NL)
Portugal (PT)
Slovenia (SI)
Slovakia (SK)
Spain (ES)

Employment growth Relative productivity Unemployment growth

Mean

0.020
0.005
0.024
0.016
0.012
-0.009
0.015
0.018
0.050
-0.006
0.018

St.dev.

0.302
0.285
0.459
0.209
0.251
0.390
0.270
0.285
0.402
0.223
0.364

Mean

0.020
-0.043
0.039
-0.040
-0.100
-0.108
-0.028
-0.088
-0.003
-0.063
-0.040

St.dev.

0.302
0.968
0.755
0.698
0.874
1.135
0.728
0.977
0.705
1.273
0.750

Mean

-0.017
0.001
-0.001
-0.004
-0.100
-0.004
-0.006
-0.012
-0.008
-0.009
-0.013

St.dev.

0.006
0.000
0.000
0.001
0.874
0.000
0.003
0.000
0.001
0.004
0.000

No of
obs

1,058,779
503,416
2,294,214
3,744,600
9,523,530
798,124
2,803,212
3,512,165
479,731
489,677
8,501,020

Notes: Employment growth is the mid-point average growth rate of the number of employees; relative
productivity is derived as the log difference between a firm’s value added per employee and its industry median;
and unemployment growth is the regional growth in the unemployment rate. The number of observations
corresponds to those firm-year observations where there is non-missing information on employment over the
sample period. The timespan covered is shown in Table A1.
Source: Authors’ calculations from administrative data.

This approach allows us to investigate whether more productive firms tend to have higher

employment growth than less productive firms, meaning that β is positive, and whether this

4The NUTS is the nomenclature of territorial units for statistics, the EU’s hierarchical regional classification,
and NUTS level 2 defines “basic regions for the application of regional policies”; see https://ec.europa.eu/eurostat/
web/nuts/background. There is no regional variation in some countries Estonia and Latvia at this level of
disaggregation.

ECB Working Paper Series No 294712relationship strengthens during economic downturns, meaning that δ is positive. We expect to

observe productivity-enhancing reallocation towards more productive firms, and we expect it to

happen faster during economic downturns as predicted by the hypothesis of creative destruction

or cleansing. The coefficient γ is expected to be negative, showing that growth in employment

is lower when unemployment is growing or during an economic downturn. The descriptive

statistics of the variables used in the estimates of productivity-enhancing reallocation are shown

in Table 1 and the size of sample firms in Table A2.

We estimate two alternative specifications to study the robustness of our results. The first

robustness specification uses year dummies as a proxy for the business cycle instead of growth in

the regional unemployment rate, by replacing Cycler,t with time dummies τt in the interaction

term of the baseline specification:

gi,t = α + βRelprodi,t−1 + τt + δ(Relprodi,t−1 × τt) + θZ′

i,t−1 + ϵi,t,

(2)

The advantage of this specification is that it describes productivity-enhancing reallocation

year by year and lets us study whether the sensitivity of reallocation to the business cycle has

changed over time. Equations 1 and 2 are estimated by pooled OLS, following Foster et al.,

2016. A fixed effects estimator is also used as a robustness test in order to control for firm-

specific, time-invariant effects, and in this case αi is estimated instead of α in equations 1 and

2.

For the second robustness specification, we run another set of regressions where separate

regressions are estimated for each sample year t instead of the panel setting:

gi,t = α + βRelprodi,t−1 + θZ′

i,t−1 + ϵi,t,

(3)

This approach differs from those taken by equations 1 and 2 as it uses a cross-section of firms.

This specification puts the focus on the relationship between firm productivity and employment

growth in a given year and so provides an alternative to the estimates from equation 2. As

a robustness test, we also derive the firms’ relative productivity using a revenue-based TFP

instead of labour productivity for this specification. The TFP is estimated as a residual from

the Cobb-Douglas production function. All the estimates are weighted by firm size, where the

weights are calculated as the average employment at the firm over the whole timespan.

We also have data on government support that cover the job retention support paid during

ECB Working Paper Series No 294713the Covid-19 pandemic in Croatia, Estonia, Latvia, Portugal, Slovakia and Spain. The size of

the support given and the share of firms supported, differ across countries, as does the type of

support; see Table A3. The share of firms that the support reached ranged between 29% in

Slovakia and 59% in Croatia; the share of workers on support ranged from 6% in Latvia to 40%

in Croatia. The largest relative job retention subsidies were recorded in Croatia at 5% of the

revenue of the firms; which corresponds to 2.1% of GDP in 2020. We focus on the first year of

the pandemic and study the allocation of job retention support dependent on the characteristics

of the firm receiving it in the year before the pandemic.

To study the distribution of the support by firm characteristics, we start by defining produc-

tivity clusters based on firm performance in the year before the pandemic, and then compute the

share of subsidies allocated to each cluster in 2020. We continue by estimating logit regressions

to assess the relationships between firm characteristics and the support received. We regress

the dependent variable, which is a binary dummy variable set to 1 for a firm that received sup-

port and 0 otherwise, P articipationit, on firm relative productivity, the log of firm employment

and the NACE 2-digit industry in a similar vein to the specifications of productivity-enhancing

reallocation:

p(P articipationi,t) =

1

1 + e−(α+βRelprodi,t−1+θZ′

i,t−1+ϵi,t)

,

(4)

where t refers to the year 2020 and the probability of a firm receiving job retention support

in 2020 depends on the characteristics of that firm in 2019.

Finally, we estimate OLS regressions to analyse how the amount of support given at the

firm level depends on the same set of characteristics of firms. The dynamics of the specification

are similar to those of participation, and we investigate how the pre-pandemic characteristics in

2019 affect the amount of support given in 2020, Supportit:

Supporti,t = α + βRelprodi,t−1 + θZ′

i,t−1 + ϵi,t,

(5)

where Supportit is measured as the share of support in revenue in 2020.

ECB Working Paper Series No 2947144 Results

4.1 Productivity-enhancing reallocation

The results from our baseline analysis for productivity-enhancing reallocation using the specifi-

cation in equation 1 are presented in Table 2. These results confirm that high-productivity firms

had higher employment growth on average than low-productivity firms from 2015 to 2020, and

the reallocation remained productivity-enhancing. There are, however, noticeable differences

between countries, as employment growth is more sensitive to firm productivity in Slovenia

and Spain, but less sensitive to it in France and Italy5. This suggests that the between-firm

reallocation channel contributes more to aggregate productivity in Slovenia and Spain and less

in France and Italy6. Alternative estimates for the pandemic period employing a fixed-effects

model (Table 3) confirm our baseline OLS estimates and suggest that there is a statistically

significant cleansing effect in a larger number of countries.

Supplementary estimates from an unbalanced panel of firms for 2015-2020 (Table A4) show

a similar degree of productivity-enhancing reallocation. A comparison between the balanced

and unbalanced panel results also suggests that the firm extensive margin made a relatively

small and heterogeneous contribution to labour productivity growth during the pandemic. Fur-

ther robustness estimates that use the full sample of firms across all the available years in the

unbalanced panel (Table A5) and a comparison of those estimates with the unbalanced panel

estimates for 2015-2020 (Table A4) indicate that productivity-enhancing reallocation was weaker

and intensification was less frequent during the economic downturn in the pandemic than in the

long run across the overall country-specific time spans.

How much productivity-enhancing reallocation contributes to aggregate productivity can

also be estimated by deriving the weighted average productivity of firms in alternative em-

ployment growth scenarios, following Foster et al., 2016, Decker et al., 2020 and Andrews and

Hansell, 2021. For the counterfactual scenario, the coefficients β and δ are set equal to zero in

5There is no reason to believe that these cross-country differences in responsiveness are driven by differences
in sample structure, within the samples where large firms are over-represented, France and Italy stand out by low
responsiveness and the Netherlands by high responsiveness.

6How much reallocation contributes to aggregate productivity depends on the frequency or size of shocks and
on the sensitivity of firm employment growth to shocks; see Decker et al., 2020. The sensitivity of firm employment
growth to shocks is denoted as responsiveness and this is the channel that we study in this paper. The shocks
to firms are presumably similar in our sample of euro area countries and the slowdown in job reallocation and
productivity is related to lower responsiveness and not to change in economic shocks, as shown by Decker et al.,
2020 with US data.

ECB Working Paper Series No 294715equation 1 so that firm growth is set not to depend on productivity. This investigates whether

the weighted average productivity produced by the employment predicted by such a restricted

model would be substantially lower than that from the model with the estimated β and δ. We

derive predicted firm employment from the full model and from the counterfactual model with-

out the coefficients β and δ. The contribution made by productivity-enhancing reallocation to
aggregate productivity can be derived as ∆Pt = (cid:80)N
× pi,t−1, where ˆli,t
i=1
and ˆLt are the employment predicted for firm i and total employment from the full model and
ˆlc
i,t and ˆLc
Firm productivity pi,t−1 is taken from the previous year, so this decomposition ignores the con-

t are from the restricted model without reallocation towards more productive firms.

× pi,t−1 − (cid:80)N
i=1

ˆlc
i,t
ˆLc
t

ˆli,t
ˆLt

tribution of within firm productivity between years t and t − 1, and as productivity is measured

by taking the logarithm of labour productivity in our conditional estimates, the ∆Pt shows the

difference in aggregate productivity between the two scenarios in log points. Figure A3 presents

these contributions for each country and each sample year.

The results are similar to those in Table 2, and show that productivity-enhancing reallocation

contributes least to aggregate productivity in France and Italy, as productivity is 0.7 log point

higher each year in France because of reallocation towards more productive firms, and 1.9 log

points higher in Italy. The reallocation to productivity contributes more in other countries and

is highest in 2015 to 2020 in Latvia, where it contributes 5.2 log points. There is also evidence

that the contribution of reallocation to productivity has declined over the decades, as the cross-

country average contribution was 3.4 log points before 2011 and 2.5 log points after 2011. The

decline is most noticeable in the Netherlands, Estonia and Spain, and the trend is also negative

in other countries.

Evidence of reallocation making an increasing contribution to productivity is only found in

Finland and Latvia. Given that the yearly aggregate growth in productivity was 0.8% before

2011 and 0.4% in 2011-2020 in the euro area, see Figure 1, the decline in reallocation is in line

with the decline in aggregate productivity growth. Back-of-the-envelope calculations show that

the slow-down in reallocation is in the magnitude of half of the decline in aggregate productivity

growth. Aggregate growth in labour productivity halved between these periods and the contri-

bution of productivity-enhancing reallocation to aggregate productivity declined by a quarter

in our sample of euro area countries. These findings overlap with the evidence from the US and

Australia (Decker et al., 2020, Andrews and Hansell, 2021), where the decline in productivity-

enhancing reallocation also contributed negatively to aggregate productivity growth.

ECB Working Paper Series No 294716K
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ECB Working Paper Series No 294717Next, we examine the cyclicality of productivity-enhancing reallocation, taking the increase

in productivity-enhancing reallocation during the recessions as evidence of creative destruction

or the cleansing effect. The sensitivity of employment growth to productivity in firms increases

during recessions in half of our sample countries, but not in the other half from 2015-2020; see

the positive and statistically significant interaction term in Tables 2 and 3. This is in line with

the related literature, in which there is no consensus about the presence of the cleansing effect

during recessions.

A broader look at the evolution of productivity-enhancing reallocation shows that it was

somewhat weaker on average during the Covid-19 pandemic than it was during the Great Re-

cession, see Table 4. The table summarizes results for alternative specifications in equation 2

where the interaction term between firm productivity and the year fixed effects were used as

a cycle proxy and equation 3 that provided estimates year-by-year. The full set of years for

these estimates are presented in Figures A4, A5 and A6. This approach allows us to describe

the changes in the strength of productivity-enhancing reallocation in the individual years and

compare the situation in 2020 with that in 2009. As discussed above, while the timing and

shape of the recession were very similar during the pandemic in our sample countries, this was

not the case during the Great Recession. The economy started to decline already in 2008 in

some of the sample countries and the shape of the recession differed. Given that the trough of

the recession was in 2009 for all the sample countries, we focus on this year to see whether the

Great Recession increased productivity-enhancing reallocation.

The results for the countries that have sufficiently long data show productivity-enhancing

reallocation to be weaker in 2020 than it was in the past for some sample countries. The

sensitivity to productivity went up to a statistically significant degree during the Great Recession

in three of the nine sample countries in the panel estimates, which were Estonia, Italy and Latvia.

There are also signs of increased sensitivity to productivity during the Great Recession in Spain

and France, though the change in those countries is statistically insignificant. The Slovenian

results also suggest that the Great Recession may have made job growth more responsive to

productivity, but any conclusions about this need some more data on the years before the

recession.

The results of the Covid-19 pandemic show that productivity-enhancing reallocation in-

creased in 2020, which was the first and most severe year of the pandemic. The change in

sensitivity is statistically significant in Finland, the Netherlands and Slovakia, while there was

ECB Working Paper Series No 294718also an increase in sensitivity in Spain and Slovenia, but it was statistically insignificant. The

sensitivity of employment growth to productivity did not change much in 2020 in six of the

eleven sample countries or even declined from what it was before the pandemic.

Table 4: Productivity-enhancing reallocation, Great Recession versus Covid-19 pandemic,
summary of panel and year-by-year estimates

Country

(ISO code)

Croatia (HR)
Estonia (EE)
Finland (FI)
France (FR)
Italy (IT)
Latvia (LV)
Netherlands (NL)
Portugal (PT)
Slovenia (SI)
Slovakia (SK)
Spain (ES)

Covid-19

Full panel estimates
Great
Recession
Labour
productiv-
ity

Labour
productiv-
ity

↑

(↑)
↑
↑

NA
NA
(↑)

(↓)

↑

(↓)
↑

(↑)
↑
(↑)

Year-by-year estimates

Great Recession

Covid-19

Labour
productiv-
ity

TFP

Labour
productiv-
ity

↑

(↑)

(↑)
NA
(↑)
NA
NA
↑

(↑)

↑

↑
NA
(↑)
NA
NA

(↓)

(↑)

↓
NA

↑

TFP

(↓)

(↓)
(↓)

(↓)
NA

(↓)

(↑)

Notes: The table summarizes the estimates of equation 2 (Full panel estimates) and equation 3 (Year-by-year
estimates). The detail results are shown in Figures A4, Figures A5 and A6. ↑ shows a statistically significant
increase and ↓ a statistically significant decrease in productivity-enhancing reallocation during the recession, the
arrows in parenthesis show increases and decreases that are statistically insignificant, the empty cell shows no
change in productivity-enhancing reallocation, and NA indicates not available estimates.
Source: Authors’ calculations from administrative data.

The year-by-year estimates from equation 3 confirm these findings, as the productivity-

enhancing reallocation strengthened around the Great Recession in most of the sample coun-

tries, but not during the first year of the pandemic. We also test the sensitivity of our re-

sults to the productivity measure using alternative specifications: one with labour productivity

and another with total factor productivity (TFP). These estimates have two messages. First,

the estimates with labour productivity or TFP provide similar results about the change in

productivity-enhancing reallocation; occasionally, one of the productivity measures shows sta-

tistically significant results, and the other does not, but usually, there are no conflicting findings

between the productivity measures. Second, similarly to the estimates with the panel data and

year dummies as business cycle proxy, there is evidence that productivity-enhancing reallocation

increased in the Great Recession in more countries than in the Covid-19 crisis. There are four

out of eight countries where reallocation towards more productive firms increased statistically

significantly in the Great Recession, either by labour productivity or TFP measure. While there

ECB Working Paper Series No 294719is only one country out of ten where the productivity-enhancing reallocation increased during

the Covid-19 crisis7.

The cross-country heterogeneity may arise from the scale of the pandemic policy support and

how efficiently it was allocated, and also from differences in the country-specific characteristics

of the cycle. 8

4.2 Employment subsidies and productivity-enhancing reallocation

The key question is whether productivity-enhancing reallocation was muted for some countries

because of the extensive policy support during the pandemic. It remains unclear whether the

productivity-enhancing reallocation was muted more because the pandemic had an unusually

strong effect on productive firms, or because policy support sustained relatively more employ-

ment in firms with low productivity. One way to address the causality problem and to draw

some inferences about how the extent of the policy support affected productivity-enhancing re-

allocation is to look at the cross-country variation in the participation rates for the support and

the change in productivity-enhancing reallocation during the pandemic. This follows the line of

argument in Andrews, Charlton, and Moore, 2021, who use fewer countries than our baseline

estimates. Figure 3 suggests that the countries that supported more workers had a smaller

increase in productivity-enhancing reallocation in 2020. The change in the point estimates of

responsiveness are shown on y-axis in Figure 3, the statistical significance of the change is shown

in Figure A4.

The design of the job retention support may also explain why the reaction of reallocation was

different in different countries. The design of the support scheme had different implications for

how long job matches were preserved, as the main furlough schemes that were used in Finland

and Spain allowed workers covered by the scheme to start looking for a job while still receiving

the support, whereas the short-time work or wage subsidies used in other countries implied that

workers getting the support could not be fired even several months after the support had ended,

see Table A3. The cap on support may have had a role as well: Netherlands had a very high

7Unfortunately, we do not have these rolling window estimates available for the Netherlands, where there was

also signs of increasing reallocation during the Covid-19 crisis.

8The job retention support schemes were used in many sample countries already during the Great Recession,
however, the scale of the support used at the time was substantially smaller. A quarter of workers received job
retention support during the peak month of Covid-19 (M¨uller et al., 2022); while 3.2% of workers in Italy, 1.7%
in Finland, 1% in Spain, 0.8% in Slovakia and France, 0.7% in Netherlands and 0.1% in Portugal received the
support on average month of 2009 (Hijzen and Venn, 2011).

ECB Working Paper Series No 294720cap compensating full wages of workers with wages up to 5.8 times the minimum wage; the cap

in France was also set very high, up to 4.5 times the minimum wage, while workers received

70% of their former gross wage. These high caps made the support relatively more attractive

for high-wage / high-productivity firms and may have contributed to the preservation of jobs at

high-productivity firms.

Figure 3: Share of workers getting support and change in productivity-enhancing reallocation
Notes: Change in productivity-enhancing reallocation between 2019 and 2020 origins from the estimates from
equation 2, the full set of coefficients on productivity-enhancing reallocation are shown in Figure A4.
Sources: Authors’ calculations from administrative data, job retention support data from Table A3.

In Finland and Slovakia, where productivity-enhancing reallocation increased statistically

significantly, the intensity of support was very low and only 8% of workers participated in it.

Similarly, firms from Croatia, France and Portugal, which were the countries where the support

was most widespread, did not experience any increase in productivity-enhancing reallocation in

2020. However, the intensity of support cannot explain the majority of the variation in cross-

country differences in productivity-enhancing reallocation. The correlation between the two

indicators for the intensity of the support and the increase in productivity-enhancing reallocation

ECB Working Paper Series No 294721is -0.34. The most notable exceptions to this are Latvia, which had a lower sensitivity to

productivity than predicted, and the Netherlands, where the sensitivity to productivity was

higher than predicted9. One of the reasons why these two countries stand out as outliers in

this relationship is the role of other measures taken to support firms during pandemic. While

the job retention support was the main measure taken to support firms in the Netherlands; in

Latvia it was firm liquidity aid and the job retention support played marginal role during the

first wave of pandemic (see the share of workers on job retention scheme by M¨uller et al., 2022

vs the share of firms benefiting from policy support by Harasztosi et al., 2022).

In addition to the intensity of the support, accurate targeting of the support may also have

played a role. We study the allocation of support by firm productivity in the following subsection,

but we use a smaller set of countries for which the data are available.

4.3 Take up of job retention support by firm productivity

This subsection studies whether the amount of job retention support and the participation in

it depends on firm productivity. We proceed from a sample of six countries – Croatia, Estonia,

Latvia, Portugal, Slovakia and Spain – for which we can link job retention support with balance-

sheet data at the level of the firm. 10

We find several common patterns in the distribution of the pandemic job retention support.

The group of the most productive firms received a much larger aggregate value of support than

the group of firms with the lowest productivity, as shown by the amount of support received by

firm productivity quintiles in Table 5. Around one third of the amount paid in job retention

support was allocated to productive firms, which are defined as firms in the highest quintile

of the labour productivity distribution in 2019. Only a small share of the support went to

low-productivity firms, which are defined as firms in the lowest quintile of the productivity

distribution. The distribution of job retention support was proportional to the number of work-

ers employed in Croatia and Slovakia, while the support received by low-productivity firms in

Estonia, Latvia and Portugal was half their share in employment.

The relationship between the support and productivity is non-linear and concave in all six

countries, and the concavity is much more pronounced in Croatia, Slovakia and Portugal than it

9These two countries remain outliers in this relationship also when using spending on job retention as a share

of GDP as a proxy for support; see Figure A7.

10The take up analysis for Spain is limited to the probability of being supported, as the firm-level information

on the amount of support given is not available for that country.

ECB Working Paper Series No 294722is in Estonia, Latvia and Spain; see Figure A8. The probability of a firm receiving the support

is lowest in all the countries in the lowest productivity decile, which is the reference category.

Firms from the highest productivity decile were similarly unlikely to receive support in Spain,

while the relationship between productivity and support was still high in the top productivity

decile in Estonia, Croatia, Portugal and Slovakia. There is a noticeable upward trend in the

probability of receiving support as the productivity of firms rises in Croatia, Portugal and

Slovakia. As suggested by Fern´andez-Cerezo et al., 2022 or Merik¨ull and Paulus, 2023, the

pandemic affected more firms from low-productivity industries, so controlling for industry or for

eligibility for support is essential.

Table 5: Allocation of employment subsidies and employment by firm productivity quintiles

Croatia

Estonia

Latvia

Portugal

Slovakia

Highest productivity quintile
Lowest productivity quintile

Highest productivity quintile
Lowest productivity quintile

33.9
7.0

30.3
7.9

26.0
4.2

Share of allocated support in %, 2020
34.0
3.4
Share of employed workers in %, 2019
39.1
6.6

30.7
10.8

28.8
8.0

32.1
5.2

32.2
5.8

27.8
5.4

Notes: Highest and lowest quintile firms are firms in the highest or lowest quintile of labour productivity
distribution in 2019. Data on the size of the support are unavailable for Spain.
Source: Authors’ calculations from administrative data.

We find that the country-specific patterns remain unaltered after the industry mix is con-

trolled for and the same regressions are run for within-sector productivity deciles; see equation 4

and Table 6. Conditional on sector and firm size, the probability of receiving job retention

support increased with firms’ relative productivity in five out of six sample countries; the link

between participation and productivity was negative only in Spain.11

The probability of a firm being supported is only one part of the story. To appreciate the

overall effect of subsidies, it is important to analyse how the amount of support given is related

to the productivity of firms. After conditioning on whether a firm received support, there is

clearly a negative relationship between the amount of support received and the productivity of

the firm; see equation 5 and Table 7. We find that more productive firms received relatively

smaller subsidies. The size of the effect varies over countries, and the sensitivity is highest in

11The annual frequency of the balance-sheet data does not allow us to address the eligibility issue directly,
because the eligibility for the Covid-19 employment support was assessed on monthly basis. The decisive eligibility
criterion in all countries was a significant decline in monthly turnover (exceeding at least 20%). Our alternative
estimates based on the equation 4 extended with the industry-level eligibility dummy variable controlling for the
annual decline revenue suggest somewhat weaker, but positive and statistically significant relationship between
the probability of receiving job retention support firms’ relative productivity. Results are available upon request.

ECB Working Paper Series No 294723Croatia, followed by Portugal, Latvia, Estonia and Slovakia. Relative productivity being 10

log points higher in 2019 is related to the share of the support in revenue in 2020 being 0.007

percentage points smaller in Croatia, and the elasticity is barely a tenth of that in Slovakia12.

Table 6: Participation in job retention support by productivity, 2020

Croatia

Estonia

Latvia

Portugal

Slovakia

Spain

Labour productivity
in 2019
Sector
Size class

0.0113***
(0.0020)
Yes
Yes

0.0017
(0.0012)
Yes
Yes

0.0021*
(0.0012)
Yes
Yes

0.0168***
(0.0010)
Yes
Yes

0.0153***
(0.0021)
Yes
Yes

-0.0085***
(0.0007)
Yes
Yes

Observations

71,344

36,036

55,538

227,502

84,652

400,153

Notes: Dependent variable is participation in support in 2020. The table shows the marginal effects of the logit
regressions for all the firms in 2020. Continuous variables in logarithm are used. Standard errors in parentheses,
*** p<0.01, ** p<0.05, * p<0.1.
Source: Authors’ calculations from administrative data.

Table 7: Amount of job retention support by productivity, 2020

Croatia

Estonia

Latvia

Portugal

Slovakia

Labour productivity
in 2019
Sector
Size class

-0.0744***
(0.0015)
Yes
Yes

-0.02382***
(0.01519)
Yes
Yes

-0.03704***
(0.02496)
Yes
Yes

-0.0631***
(0.0029)
Yes
Yes

Observations
R2

44,523
0.103

9,249
0.037

6,678
0.042

59,869
0.089

-0.0078***
(0.0010)
Yes
Yes

23,986
0.076

Notes: Dependent variable is the share of support in revenue in 2020. The table shows the coefficients of the
OLS regressions for firms receiving support with the share of subsidies on revenue as the dependent variable.
Continuous variables in logarithm are used. Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1.
Source: Authors’ calculations from administrative data.

Taken together, the results for the extensive and intensive margins of the support suggest

that the relationship between firm productivity before the pandemic and job retention support

during the pandemic may have been negative in Croatia, Estonia, Latvia and Portugal, where the

elasticity in the intensive margin was substantial. The results for Slovakia are ambiguous because

a positive relationship is shown by the extensive margin and a weak negative relationship by

the intensive margin. We find for most of the sample countries that firms with low productivity

were supported more, which is in line with Kozeniauskas et al., 2022 and Harasztosi et al., 2022.

12It does not seem that this relationship is driven by the cap on support. While the support in Croatia was
a lump-sum wage subsidy per worker capped at a low level so that it likely made a higher share of wage costs
in low-wage / low-productivity firms; the design of the support was similar in Estonia, Latvia, Portugal and
Slovakia, and the negative relationship captures rather targeting of the support and not its’ design mechanism.
See the overview of support schemes in Table A3.

ECB Working Paper Series No 294724These results complement our findings in Subsections 4.1 and 4.2 on productivity-enhancing

reallocation and job retention support. The productivity-enhancing reallocation did not strengthen

in Croatia, Estonia, Latvia or Portugal, but it did in Slovakia. These findings support the view

that employment subsidies played a role in muting productivity-enhancing reallocation during

the Covid-19 pandemic. The productivity of the firms that received the support is important,

as well as the intensity of the support as discussed in Section 4.2. In those countries where the

support was more generous for less productive firms, the cleansing mechanism was weaker and

the reallocation of jobs towards high-productivity firms did not strengthen in 2020.

5 Conclusions

This paper uses firm-level data from the balance sheets and income statements and the firm-level

information on the distribution of the pandemic job retention support to understand how the

Covid-19 pandemic impacted productivity-enhancing reallocation and what was the role of job

retention support in it. We show empirical evidence for 11 euro area countries.

Our comparative analysis shows that high-productivity firms experienced larger employ-

ment gains or smaller employment losses on average than low-productivity firms over the recent

economic cycle. In some countries we observe that productivity-enhancing labour reallocation

accelerated during the period of contraction or that the creative destruction or cleansing mech-

anism was activated in recessions. A broader look at the evolution of productivity-enhancing

reallocation suggests that it was, on average, somewhat weaker during the Covid-19 pandemic

than it was during the Great Recession.

Although, the conditions for job retention support varied over countries, the rapid policy

support prevented a wave of bankruptcies and it supported the rapid economic recovery of the

euro area economy following the pandemic. However, we find that productivity-enhancing real-

location was weaker in 2020 in countries where the participation in support was more widespread

or was concentrated towards low-productivity firms. Most subsidies were allocated to productive

firms, and productive firms were more likely to be supported in most of the countries. Yet, the

amount of support received relative to revenue decreases together with the productivity of firms.

In countries where this effect dominated, and the subsidies covered a significantly larger share

of the revenue of firms with low productivity, we observe that more subsidies were related to

lower productivity-enhancing reallocation during the pandemic.

ECB Working Paper Series No 294725This study is limited to firm performance and support in 2020, to a small set of coun-

tries covered and to occasionally large confidence bounds around estimates. Future studies on

linked employer-employee level data are encouraged to reveal the longer-term implications of

the pandemic on productivity. The key questions are whether the job retention support became

more concentrated to low-productivity firms over the duration of the support, as suggested by

Andrews, Hambur, and Bahar, 2021; or who were the new employers of these workers who

became unemployed during the pandemic. As the design of job retention support also varied

substantially across countries, it calls for focused case studies that would zoom into the details

of support and search for identification strategies that would allow more elegant identification

than taken in our cross-country study.

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capitalist process. McGraw Hill.

Wang, J., Yang, J., Iverson, B., & Kluender, R. (2020). Bankruptcy and the COVID-19 Crisis

(Harvard Business School Working Paper No. No. 21-041). Harvard Business School.

ECB Working Paper Series No 294730Appendix

Figure A1: Annual labour productivity growth in 2020, balanced panel of firms
Notes: Micro-aggregated growth in real value added per employee from a balanced panel of firms in 2015-2020.
Source: Authors’ calculations from administrative data.

ECB Working Paper Series No 294731Table A1: Sources of firm-level data

Country
No of firms

Croatia

Estonia
Finland
France
Italy

Latvia
Netherlands
Portugal
Slovenia

Slovakia

Spain

Data source

Financial Agency (FINA), Croatian Employment
Service (HZZ)
Business Register, Tax and Customs Board
Statistics Finland
Fiben
Cerved Centrale dei Bilanci, Istituto Nazionale
Previdenza Sociale (INPS)
Central Statistical Bureau and State Revenue Service
Statistics Netherlands
Central Balance Sheet Database
Slovenian Business Register (AJPES), Monetary
financial institutions reporting (PORFI)
Statistics Slovakia and Bisnode, Transparency
International Slovakia
Central Balance sheet database

Period

2002-2021

2004-2020
1999-2020
2000-2020
2001-2020

2007-2020
2000-2021
2006-2020
2008-2021

2015-2020

1995-2020

Table A2: Firm size and coverage

Country

Employment, sample years Employment,

No of firms in 2020

Comment

Mean

St.dev.

2020
Eurostat
mean

Sample

Eurostat

Share
covered

Croatia
Estonia
Finland
France
Italy
Latvia
Netherlands
Portugal
Slovenia
Slovakia
Spain

11.0
6.7
11.0
43.1
16.2
8.2
20.6
7.9
11.7
11.2
13.3

92.6
39.4
115.1
670.1
270.1
60.0
328.6
93.3
91.1
117.2
246.3

5.0
4.7
5.1
4.0
2.2
4.3
2.9
3.0
3.8
2.3
3.5

63,557
31,842
78,649
159,573
376,682
48,713
109,494
204,671
32,762
75,703
326,196

180,537
85,842
236,038
3,084,048
3,640,489
111,153
1,362,947
916,292
150,261
518,497
2,683,786

0.352
0.373
0.333
0.052
0.103
0.438
0.080
0.223
0.218
0.146
0.122

Census
Census
Census
Survey∗
Survey∗
Census
Survey∗
Survey∗
Census
Census
Survey∗

Notes: Number of firms in sample is reported as those with non-missing information on growth of employment
and value added in 2020.
France: Covers almost all firms that employ more than 500 employees, 20% of firms with 20-500 employees and
10% of firms with less than 20 employees (Sauv´e and Ortega, 2002).
Italy: Sample of firms with 20 or more employees and construction firms with 10 or more employees (see
https://www.bancaditalia.it/pubblicazioni/indagine-imprese/index.html).
Netherlands: Survey for large non-financial firms and tax register for small non-financial firms (CompNet, 2018).
Portugal: Coverage of sample is 98% of enterprises (CompNet, 2018).
Spain: Covers 22% corporations at the beginning of sample, while large firms are slightly over-represented
(Sauv´e and Ortega, 2002).
Sources: Authors’ calculations from administrative data; Eurostat table SBS NA SCA R2.

ECB Working Paper Series No 294732Table A3: Job retention scheme characteristics, 2020

Country

Type of support

Who
received

Share of
workers,
%

Share in
GDP, %

Cap on
support

Pre-
existing
scheme

Croatia

Wage subsidy

Employer

Estonia
Finland
France
Italy
Latvia
Netherlands
Portugal
Slovenia
Slovakia
Spain

Short-time work Worker
Worker
Furlough
Employer
Short-time work
Short-time work
Employer
Short-time work Worker
Employer
Employer
Employer
Employer
Worker

Wage subsidy
Short-time work
Short-time work
Short-time work
Furlough

40

22
8
34
22
6
28
28
21
8
19

2.1

1.0
0.3
1.0
1.6
0.2
1.9
0.5
0.9
0.3
1.8

Lump-sum
1.24×MW
1.71×MW
No cap
4.50×MW
1130eur
1.63×MW
5.77×MW
3.00×MW
1.86×MW
1.90×MW
0.99×MW

No

No
Yes
Yes
No
No
Yes
Yes
No
Yes
Yes

Notes: The characteristics of the support refer to the peak month of the pandemic in 2020, for most of the
countries it was April. We do not cover support schemes introduced later, e.g. short-time work scheme
introduced in June in Croatia.
There are three main types of job retention schemes used during the pandemic (Drahokoupil and M¨uller, 2021
and M¨uller et al., 2022):
(1) Short-time work – some work-time is reduced and compensated to employers by the support; employers
share some costs; workers share some costs by getting lower wage for hours not worked; workers cannot take up
other employment.
(2) Furlough – workers laid off temporarily, but can return to their job; employers pay some part of benefit;
benefit paid directly to workers; workers integrated into unemployment assistance system and can start a new
job.
(3) Wage subsidy – employers in financial difficulty receive support per employee, regardless of whether the
working hours of workers have been reduced.
The source for share of workers in the scheme is M¨uller et al., 2022 for all the countries, except the Netherlands
where the source is Scarpetta et al., 2020. M¨uller et al., 2022 report the share of workers in support at quarterly
frequency for the Netherlands and for the sake of comparability, we have switched to Scarpetta et al., 2020 for
this country, because they report workers supported at the end of May in 2020.
MW – denotes minimum wage. Italy does not have national minimum wage. If the cap at support depends on
the number of children in the family, the cap for worker without children is used.
Sources: https://www.etui.org/publications/job-retention-schemes-europe; Hijzen and Venn, 2011; Scarpetta
et al., 2020; Drahokoupil and M¨uller, 2021; M¨uller et al., 2022; national minimum wage by Eurostat table
EARN MW CUR

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ECB Working Paper Series No 294734Figure A2: Between-firm reallocation, the Great Recession and the Covid-19 pandemic
Notes: The figure plots between-firm productivity growth margins during the Great Recession and the Covid-19
pandemic. Time t equals 2009 for the Great Recession and 2020 for the Covid-19 pandemic.
Source: Authors’ calculations from firm-level data.

ECB Working Paper Series No 294735Figure A3: Contribution of productivity-enhancing reallocation to aggregate productivity by
sample years, full sample
Notes: Each bar shows how large the contribution of productivity-enhancing reallocation to aggregate produc-
tivity is, compared to the counterfactual where employment growth at a firm would not depend on that firm’s
productivity. The contribution is found as the difference between the employment-weighted productivity from
equation 1 and the employment-weighted productivity from the same equation, but with the coefficients β and δ
set equal to zero.
Source: Authors’ calculations from firm-level data.

0.05.119962002200820142020EE0.05.119962002200820142020ES0.05.119962002200820142020FI0.05.119962002200820142020FR0.05.119962002200820142020HR0.05.119962002200820142020IT0.05.119962002200820142020LV0.05.119962002200820142020PT0.05.119962002200820142020NL0.05.119962002200820142020SI0.05.119962002200820142020SKECB Working Paper Series No 294736Figure A4: Productivity-enhancing reallocation by sample years, marginal effects from the full
sample
Notes: The figure plots the marginal effects of an increase of one unit in relative productivity in each sample year
using equation 2 and their 90% confidence bounds. The economic cycle is proxied by year dummies instead of
regional unemployment for this specification and each marginal effect shows the sensitivity of employment growth
at the firm to its lagged productivity in a particular year.
Source: Authors’ calculations from firm-level data.

.02.04.06.08.1.1219962002200820142020EE-.050.05.1.1519962002200820142020ES0.05.1.1519962002200820142020FI0.01.02.0319962002200820142020FR.02.04.06.0819962002200820142020HR.01.02.03.04.05.0619962002200820142020IT.02.04.06.08.119962002200820142020LV.02.03.04.05.0619962002200820142020PT0.05.1.15.2.2519962002200820142020NL.02.04.06.08.119962002200820142020SI.01.02.03.04.0519962002200820142020SKECB Working Paper Series No 294737Figure A5: Productivity-enhancing reallocation by sample years, coefficients from year-by-year
estimates
Notes: The figure plots the coefficients of relative productivity from separate estimations for each sample year
using equation 3 and their 90% confidence bounds. Each coefficient shows the sensitivity of employment growth
at the firm to its lagged productivity in a particular year. The results for the Netherlands are not shown for these
estimates because of the limited coverage of the sample years.
Source: Authors’ calculations from firm-level data.

.02.04.06.08.1.1219962002200820142020EE.02.04.06.08.1.1219962002200820142020ES0.02.04.06.08.119962002200820142020FI-.010.01.02.0319962002200820142020FR.02.04.06.0819962002200820142020HR.01.02.03.04.0519962002200820142020IT.03.04.05.06.07.0819962002200820142020LV.02.03.04.05.0619962002200820142020PT.02.04.06.08.119962002200820142020SI.01.02.03.04.0519962002200820142020SKECB Working Paper Series No 294738Figure A6: Total factor productivity-enhancing reallocation by sample years, coefficients from
year-by-year estimates
Notes: The figure plots the coefficients of relative total factor productivity from separate estimations for each
sample year using equation 3 and their 90% confidence bounds. Each coefficient shows the sensitivity of employ-
ment growth at the firm to its lagged productivity in a particular year. The results for the Netherlands are not
shown for these estimates because of the limited coverage of the sample years.
Source: Authors’ calculations from firm-level data.

-.010.01.02.03.0419962002200820142020EE-.010.01.02.0319962002200820142020ES-.010.01.02.03.0419962002200820142020FI-.0050.005.01.01519962002200820142020FR-.010.01.02.0319962002200820142020HR-.004-.0020.002.00419962002200820142020IT-.010.01.02.0319962002200820142020LV0.005.01.015.02.02519962002200820142020PT-.010.01.0219962002200820142020SI-.01-.0050.005.0119962002200820142020SKECB Working Paper Series No 294739Figure A7: The costs of job retention support as a share of GDP and the change in
productivity-enhancing reallocation
Notes: Change in productivity-enhancing reallocation between 2019 and 2020 origins from the estimates from
equation 2.
Sources: Authors’ calculations from administrative data, job retention support data from Table A3.

ECB Working Paper Series No 294740Figure A8: Probability of a firm receiving employment support by productivity deciles, 2020
Note: Firms are assigned to deciles of productivity by the country-level distribution of labour productivity in
2019.
Source: Authors’ calculations from firm-level data.

ECB Working Paper Series No 294741Acknowledgements 

This output builds on close cooperation with several country teams, and it has benefited from their inputs and comments. For these we 
thank Konstantins Benkovskis and Olegs Krasnopjorovs (Latvijas Banka), Simon Bunel (Banque de France), Matjaž Volk (Banka 
Slovenije), Juuso Vanhala (Suomen Pankki – Finlands Bank), Tetie Kolkaiti and Maurice Bun (De Nederlandsche Bank), Dmitry 
Khametshin and Beatriz Gonzalez Lopez (Banco de España); Davide Fantino (Banca d’Italia), Fernando Martins (Banco de Portugal), 
Josip Raos and Domagoj Selebaj (Hrvatska Narodna Banka). 

Tibor Lalinsky 
National Bank of Slovakia, Bratislava, Slovakia; email: Tibor.Lalinsky@nbs.sk 

Jaanika Meriküll 
Bank of Estonia, Tallinn, Estonia; University of Tartu, Tartu, Estonia; email: Jaanika.Merikyll@eestipank.ee 

Paloma Lopez-Garcia (Corresponding author) 
European Central Bank, Frankfurt am Main, Germany; email: Paloma.Lopez-Garcia@ecb.europa.eu 

© European Central Bank, 2024 

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All rights reserved. Any reproduction, publication and reprint in the form of a different publication, whether printed or produced 
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This paper can be downloaded without charge from www.ecb.europa.eu, from the Social Science Research Network electronic library or 
from RePEc: Research Papers in Economics. Information on all of the papers published in the ECB Working Paper Series can be found 
on the ECB’s website. 

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ISBN 978-92-899-6757-0 

ISSN 1725-2806 

doi:10.2866/340251 

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