Did COVID-19 induce a reallocation wave?
Date: 2022-08-18
Institutions: European Central Bank
Subjects: United States, coronavirus disease, labour market, unemployment

Agostino Consolo, Filippos Petroulakis 

Working Paper Series 

Did COVID-19 induce a reallocation 
wave? 

No 2703 / August 2022 

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

Abstract

Recent research has argued that the COVID-19 shock has also brought about a real-
location shock. We examine the evidence for such an occurrence in the United States, 
taking a broad perspective. We ﬁrst consider micro data from CPS and JOLTS; there 
is no noticeable uptick in occupation or sector switches, nor churn, either at the ag-
gregate level or the cross-section, or when broken down by ﬁrms’ s ize. We then ex-
amine whether mismatch unemployment has risen as a result of the pandemic; using 
an off-the-shelf multisector search and matching model,  there is little evidence for 
an important role for mismatch in driving the elevated unemployment rate. Finally, 
we  employ  a  novel  Bayesian  SVAR  framework  with  sign  restrictions  to  identify  a 
reallocation shock;  we ﬁnd that it has played a relatively minor role in explaining 
labor market patterns in the pandemic,  at least relative to its importance in earlier 
episodes.

Keywords: Reallocation, COVID-19, mismatch. JEL Codes: E24, J63.

ECB Working Paper Series No 2703 / August 20221Non-Technical Summary

An important policy debate that has emerged in the wake of the COVID-19 pandemic is whether it also
induced structural changes in the labor market. The scale and nature of the shock has raised concerns that
the effects on the labor market in the United States were of a more permanent nature, calling for a large
job reallocation wave.

Whether a reallocation shock is already under way has important implications for the design of pol-
icy. Reallocation is a long process, as it involves search and matching costs and the need for upskilling,
generating mismatch unemployment. If certain types of jobs are permanently destroyed, then generous
unemployment insurance may disincentivize laid off workers from seeking other employment and build-
ing new skills, delaying the needed reallocation. Employee retention policies may also be inefﬁcient in
this context, as they provide temporary protection for jobs that will ultimately be lost. Finally, mismatch
unemployment implies a higher NAIRU, a smaller output gap and hence calls for less accommodating
monetary policy, in the lens of standard New Keynesian models.

In this paper, we critically examine the case for a pandemic-induced reallocation wave. We focus
on the United States and take a broad perspective. First, we gauge whether reallocation patterns can
already be seen in publicly available data, namely CPS and JOLTS. In the CPS, we show that there is
no discernible uptick in the share of workers switching occupations or sectors and, in general, job-to-job
transitions have remained stable. From JOLTS, we ﬁnd that hires have recovered and helped to reduce
unemployment following the initial spike in job separation. Taken together with the results of Barrero
et al. (2020), these ﬁndings imply that reallocation may have yet to take off, but if it has, it is taking place
within occupations and industries; however, employer transitions rates for employed workers also fail
to show much change. Across ﬁrms, we do not ﬁnd that the hiring behavior of large ﬁrms has changed
by poaching more workers from small ones. Still, this could be consistent with substantial reallocation
within these ﬁrms (via labor-saving technologies or polarizing hiring patterns), although it does not show
up in the indicators of job switchers.

We then take an off-the-shelf multisector search and matching model to examine whether mismatch
unemployment rose as a result of the pandemic. A reallocation shock should have led to a large and
persistent rise in mismatch unemployment (as a share of total), as workers move to new and growing
sectors. We ﬁnd little evidence to support this view; by all measures, mismatch unemployment has been
less than 1 percentage point during the whole of the pandemic. Just like Sahin et al. (2014) show for
the aftermath of the Great Recession, mismatch unemployment has not been an important factor for the
pandemic recovery either.

Finally, we look for evidence of a reallocation shock using a novel identiﬁcation strategy in a
Bayesian structural VAR model. We introduce a job reallocation shock, distinct from neutral technology,
matching efﬁciency, labor supply, or wage bargaining shocks. There is little evidence to suggest that the
job reallocation shock played an especially large role, relative to other episodes, in driving the labor mar-
ket through the ﬁrst year of the pandemic. Aggregate demand was the primary driver of unemployment,
hires and layoffs, with all other shocks having similar contributions. This is consistent with the view of
the COVID-19 shock as reﬂecting cycles in demand, lockdowns restrictions and supply bottlenecks.

ECB Working Paper Series No 2703 / August 202221

Introduction

COVID-19 rendered an unprecedented shock to the global economy.
In the United
States, 25 million jobs were lost from February to April 2020, especially in contact in-
tensive sectors, and even more switched to working from home. While the initial shock
quickly receded, over a quarter of job losses from the initial shock had not been recov-
ered a year later. As such, the scale and nature of the shock makes it possible that such
effects will be persistent, through, e.g. a steeper adoption of digitalization, or even a
switch of innovation towards teleworking technologies (Bloom et al. 2020).

The unequal sectoral effects of COVID-19 (Guerrieri et al. 2020) raise the question
whether a reallocation shock is already under way. The size of a reallocation shock (see
Barrero et al. 2020) has important implications for the design of policy. Reallocation
involves search and matching costs and the need for upskilling, generating mismatch
unemployment. If certain types of jobs are permanently destroyed, then generous un-
employment insurance (UI) may disincentivize laid off workers from seeking other em-
ployment and building new skills, hence delaying the needed reallocation. Employee
retention policies (such as short-time work or temporary unemployment schemes) may
also be inefﬁcient in this context, as they provide temporary protection for jobs that will
ultimately be lost.

In this paper, we examine whether such a phenomenon can already be detected by
publicly available data. As there is no unique way of tackling the issue, or even deﬁning
a reallocation shock, we take a holistic approach and rely on a variety of data sources
and methods. The main contribution of this paper is to show that there is little evidence
in publicly available data to suggest that COVID-19 has induced a major reallocation
shock. While reallocation of resources to their most productive use is an integral part
of a market economy, and a crucial channel for productivity growth (Decker et al. 2017,
Andrews et al. 2019), we do not ﬁnd that a reallocation shock has been an especially
important driver of the economy in the pandemic era, relative to earlier periods.

First, we note that after the ﬁrst few months of the pandemic, the forward-looking
expected reallocation rates measures of Barrero et al. (2020) have been revised substan-
tially downwards to numbers only slightly above historical averages.1 We then look for
evidence of reallocation in Current Population Survey (CPS) data. We ﬁnd no meaning-
ful change in the share of workers switching occupations or sectors within the month,

1See https://www.frbatlanta.org/research/surveys/business-uncertainty?panel=1.

ECB Working Paper Series No 2703 / August 20223suggesting that any major reallocation would be taking place within occupations or sec-
tors, which may not be consistent with the reallocation shock narrative. Job-to-job tran-
sitions (an indicator of within-sector reallocation) also remain close to historical average,
however, despite an increase in quits towards Summer 2021. The situation is much the
same when we consider sectoral data; the fraction of workers who stay in their sector is
quite close to pre-pandemic rates across sectors.

Furthermore, while we conﬁrm large relative creation and destruction magnitudes
in aggregate data for April 2020, by comparing the hire and separation rates in the Job
Openings and Labor Turnover Survey (JOLTS), we note that hires were back to pre-
COVID levels relatively soon after the initial shock, despite much higher unemploy-
ment, and lower separations. Hires picked up more broadly in 2021, along with a large
rebound in economic activity; the increase in quits also occurred alongside a reduction
in layoffs, as ﬁrms replenished their labor stock, and vacancies were at all-time highs.
Finally, newly-released size class data by JOLTS show that little of the new hires or sepa-
rations during the initial shock occurred at the very largest ﬁrms (over 5000 employees),
in line with pre-COVID data, but unlike expectations of large shifts of workers in large
ﬁrms who are taking advantage of the pandemic to increase their size. In a nutshell,
there is little evidence in CPS or JOLTS to suggest a reallocation shock is already occur-
ring; to the extent worker churn has increased, it is primarily occurring within sectors
and occupations.

We then take an alternative approach, by examining mismatch; in the presence
of frictions to reallocation, a large reallocation shock should have led to an increase in
labor market mismatch, as it takes time to move workers from declining sectors and oc-
cupations to booming ones, and hence an increase in mismatch unemployment. This is
reminiscent of similar discussions in the aftermath of the Great Recession. We take an
off-the-shelf search and matching model with heterogeneous sectors from Sahin et al.
(2014) and perform their analysis for the pandemic sample. We ﬁnd that, while mis-
match unemployment has increased since the beginning of the pandemic, it still ac-
counts for a small fraction of the total increase. As such, similar to the Great Recession,
we ﬁnd that the pandemic recession has not led to a substantial increase in mismatch
unemployment, and the high unemployment levels can only partially be attributed to
mismatch.

It should be noted that, in principle, our ﬁnding of a small role for reallocation
shocks in driving the labor market may stem precisely from the presence of generous

ECB Working Paper Series No 2703 / August 20224UI, which disincentivizes workers from switching to growing sectors. The evidence so
far do not point to such a mechanism. Under different research designs and data, Dube
(2021), Bartik et al. (2020), and Finamor & Scott (2021) all fail to ﬁnd that unemployment
insurance extensions reduced employment gains after lockdowns were loosened. On
the other hand, the presence of partially experience rated UI may work in the opposite
way; employers that tend to layoff a higher share of employees pay higher taxes, re-
sulting in large and economic meaningful variation across states (Auray & Fuller 2020).
Half of all states waived all employer UI charges as part of the CARES Act, which could
in theory lead increased layoffs.2 Bartik et al. (2020) examine this angle and similarly
ﬁnd no evidence that UI expansion raised layoffs. Marinescu et al. (2021) show that UI
did lead to reduced search effort, but did not affect vacancy creation; as labor markets
tightness was low, job creation was unaffected.

In a ﬁnal exercise, we gauge the different drivers of labor marker ﬂuctuations
during the pandemic, through the lens of a Bayesian Structural Vector Autoregression
(SVAR) framework. We employ a novel identiﬁcation strategy, using sign restrictions
disciplined from a New Keynesian search and matching model Abbritti & Consolo
(2022); in particular, we distinguish between reallocation shocks and other supply shocks.
The results point to a relatively minor role for a reallocation shock in driving hires and
separations over the pandemic period. Aggregate demand played a much larger role,
with other shocks having more nuanced effects. This is not to say that reallocation does
not matter; rather, our point is that if COVID did indeed induce a major reallocation
shock, we should expect for the effects of reallocation to stand out in this period relative
to previous ones. We instead ﬁnd the role of the reallocation shock to be broadly in line
with historical experience.

Relationship to the literature Our work is related to the new and burgeoning liter-
ature examining the employment and output effects of COVID-19. While our focus is
empirical, it is useful to brieﬂy consider theoretical work, particularly as it pertains to
optimal policy. This is because a strong reallocation shock would, as stressed in Barrero
et al. (2020), necessitate allowing creative destruction to run its course, limiting the use
of countercyclical policies. Most prominent papers ﬁnd diminished potency of mone-
tary and ﬁscal policy under pandemic conditions, but still advocate for strong policy
support. Guerrieri et al. (2020) provide conditions under which supply shocks can lead

2For details, see https://taxfoundation.org/unemployment-insurance-tax-hikes-covid19/.

ECB Working Paper Series No 2703 / August 20225to negative demand shocks that are larger than the supply shocks themselves, focusing
on complementarities in consumption; optimal policy requires extended UI for affected
sectors, while accomodative monetary policy prevents ﬁrm exits. Baqaee & Farhi (2020)
use a multisector framework to show that negative sectoral supply shocks are stagﬂa-
tionary, but negative sectoral demand shocks deﬂationary, accounting instead for com-
plementarities in production networks; in their model, each of these account for roughly
half the aggregate output decline from February to May 2020. They ﬁnd a large role for
UI in reducing the fall in output. Guerrieri et al. (2021) consider optimal policy speciﬁ-
cally under the possibility of a reallocation shock, shifting demand across sectors. Even
though the shock becomes endogenously equivalent to a cost-push shock and breaks di-
vine coincidence, the presence of downward nominal wage rigidity (preventing a wage
contraction in shrinking sectors) results in an optimally higher aggregate inﬂation rate.

On the empirical side, Brinca et al. (2021) draw sign restrictions from the frame-
work of Baumeister & Hamilton (2015) to disentangle demand and supply shocks at
the sectoral level during the initial shock, and ﬁnd that two-thirds of the drop in hours
growth was due to lower labor supply. Forsythe et al. (2020) augment CPS data with job
postings from Burning Glass, and ﬁnd that not only did labor market slack rise by less
than conventional measures suggested, but also that mismatch somewhat fell. While we
ﬁnd mismatch temporarily rose, our focus is on mismatch at the sector level; Forsythe
et al. (2020) instead consider mismatch at the occupation level, and they note that an ap-
parent within-ﬁrm reduction in job postings for college-educated workers has reduced
the variance of tightness across labor markets, hence signaling declining mismatch. This
is fully consistent with our results; depending on the speciﬁcation (i.e. the richness of
sectoral heterogeneity), we ﬁnd either a temporarily higher sectoral mismatch, or little
to no change in sectoral mismatch. The fact that occupational mismatch seems to have
declined makes the case for a reallocation shock even weaker.

This paper is also related to the extensive literature that considers labor realloca-
tion, and how this changes in the business cycle. The classic argument of Caballero &
Hammour (2005) presents the two opposing forces operating in recessions; the cleansing
force, which destroys unproductive ﬁrms and efﬁciently reallocates labor, and the weak
demand force, which prevents productive businesses from growing. Evidence remains
mixed; Chodorow-Reich & Wieland (2020) for instance, show that exposure to realloca-
tion shocks has no effects on local unemployment during expansions, but does increase
it in recessions. Foster et al. (2016), on the other hand, ﬁnd that recessions in general

ECB Working Paper Series No 2703 / August 20226lead to cleansing (in the sense that reallocation is productivity-enhancing), but that was
not the case for the Great Recession. More broadly, research in the wake of the Great
Recession (Hershbein & Kahn 2018) showed that job polarization, the switch away from
routine jobs towards non-routine cognitive and manual jobs, occurs predominantly in
downturns, when routine jobs are disproportionately destroyed. Jaimovich & Siu (2020)
show that polarization implies jobless recoveries, as routine jobs are permanently lower.
Nevertheless, evidence from Sahin et al. (2014) and Herz & van Rens (2020) indicate that
mismatch unemployment moves with the cycle, and is not the result of policy inhibiting
structural reallocation from taking effect.

Our work is also linked to the literature looking at how hires vary cyclically across
ﬁrms of different size. Moscarini & Postel-Vinay (2012) argue that large ﬁrms possess an
advantage in poaching workers who rise on the job ladder; this advantage is stronger
in tight labor markets. As such, employment growth of large ﬁrms is much more pro-
cyclical than it is for small ﬁrms i.e. in downturns, employment growth is stronger in
small ﬁrms. They show that this effect was strong in the Great Recession. Haltiwanger
et al. (2018) reﬁne this ﬁnding by showing that i) the higher employment procyclicality
of large ﬁrms is also due to higher nonemployment ﬂows; and ii) that even though large
ﬁrms contract faster when unemployment is high, it is smaller ﬁrms that contract more
when unemployment is rising. Our results show this was also the case for the COVID
episode; larger ﬁrms contracted substantially less in the initial shock, but then also grew
less in the recovery. By Summer 2021, when the labor market had substantially tight-
ened, it appears that larger ﬁrms started to grow faster, in line with the predictions of
the literature.

Finally, this paper is related to the literature looking at the impact of technology
shocks on the labor market (see Gal`ı 1999, Christiano et al. 2003). We augment a standard
Bayesian structural VAR model with a job reallocation shock with a view to capturing
the potentially large reallocation wave stemming from the pandemic crisis related to
the asymmetric sectoral effect and sector-speciﬁc shares of home working. In doing so,
we complement previous work on the role of technology shocks (Justiniano et al. 2011,
Fisher 2006, Canova et al. 2010) by enriching the model with labor market ﬂows (see also
Shimer 2012), to focus on how job turnover help to disentangle neutral and reallocative
effects during COVID. Canova et al. (2013) highlight the importance of job ﬂows as, after
a technology shock, the unemployment rate increases because of a large wave of layoffs
and because the job-ﬁnding rate taking times to adjust. The initial impact of the COVID

ECB Working Paper Series No 2703 / August 20227crisis, instead, had very little persistent effects on job separation and ﬁnding rates.

Structure of the paper

In section 2 we brieﬂy present the data used in the various ex-
ercises of the paper, which come from well-known publicly available sources. Then in
section 3 we discuss the evidence for reallocation from cross-sectional microdata. In sec-
tion 4 we present the mismatch analysis based on the multisector search and matching
model, while in 5 we lay out and analyze results from our Bayesian SVAR framework.
Section 6 concludes.

2 Data

Our primary data sources are the CPS and JOLTS. The CPS sample is composed of in-
dividuals aged 15 or over. We use the panelized version of CPS, using the approach
of Nekarda (2009).3 Job-to-job transitions are calculated as the share of workers who
remain employed in consecutive months but report they are not working for the same
employer, following the pioneering approach of Fallick & Fleischman (2004). A draw-
back of this approach is that a large number of respondents who are employed in con-
secutive months are reported as ”not in universe” for this question, but it is impossible
to know why. Since 2007, and due to changes in interviewing practices, the share of non-
respondents has been rising, potentially biasing the estimate. Fujita et al. (2021) develop
a method to address this issue, and show that the deviation levels off by 2010, and the
trends between the raw and corrected series co-move very well for the period we study.
Nevertheless, we use all three measures provided by them.

We work with two-level NAICS sectors, and group some similar sectors together,
as some have too few observations in each month to record transitions out of employ-
ment. We end up with 34 different sectors (down from 51 used in the detailed group
two-digit classiﬁcation in CPS).4 We complement this analysis using data for the Job
Openings and Labor Turnover Survey (JOLTS), a monthly survey of 16,000 establish-
ments designed to capture worker ﬂows in the US labor market, split by sector, and, as
of 2020, also by ﬁrm size. We consider both types of splits (by 17 major sectors, and 6
size classes).

3We panelize using the code of Kevin Rinz, available at kevinrinz.github.io/data.html.
4See Petroulakis (2020) for details.

ECB Working Paper Series No 2703 / August 20228For the mismatch exercise, we follow Sahin et al. (2014) closely and assemble data
from a variety of sources. We use sectoral vacancy data from JOLTS and unemploy-
ment counts from the CPS, at monthly frequencies, for 17 sectors. We also follow the
methodology of Sahin et al. (2014) to estimate sectoral matching efﬁciency parameters
and aggregate vacancy shares for a constant-returns to scale matching functions. We
measure labor productivity for each sector by the ratio of sector value added from the
Bureau of Economic Analysis to employment from the Bureau of Labor Statistics (BLS)
Employment Survey. Finally, sectoral job destruction rates are given as the ratio of gross
job losses to employment, with data from the former coming from the Business Em-
ployment Dynamics (BED) of the BLS. As Sahin et al. (2014) do, we impute monthly
destruction rates from quarterly BED data.

Finally, the SVAR exercise includes six monthly variables: the annualized growth
rate of industrial production, PCE inﬂation and nominal wage inﬂation (also annual), as
well as unemployment rate, hire and layoff rates (source is JOLTS). We take particular
care in choosing the wage measure; the unique nature of the COVID-19 shock led to
record-level unemployment but burdened disproportionately low-wage workers. As
such, conventional measures such as hourly earnings rose considerably, but exclusively
due to compositional effects, as also shown in Cajner et al. (2020). This would also
mean that calculating wage growth based on this measure would have led to a positive
spike in April 2020 and a negative one in April 2021 (through base effects). We instead
use wage growth data from the Atlanta Fed Wage Growth Tracker, which calculates
median hourly wage growth for continuing workers from CPS microdata.5 This way,
the measure implicitly addresses compositional concerns.

The choice of the sample period is determined by data availability of the job ﬂows,
which are only available starting in 2001, and the monthly data used in the VAR model
run from January 2001 to August 2021.

3

Is there a reallocation wave under way?

While the pandemic shock is transitory, a combination of search costs, defaults, uncer-
tainty and structural change may imply the permanent loss of many jobs, as well as
a long delay in creating new jobs. The results of Barrero et al. (2020) certainly point

5The Wage Growth Tracker reports a number of different measures; we use the weighted hourly series

for the aggregate economy.

ECB Working Paper Series No 2703 / August 20229towards such a story. Using survey data, they show an increase in expected excess real-
location (sum of creation and destruction minus net employment change) of 2.4 times
relative to pre-pandemic averages, at the ﬁrm level. They document that for every 10
jobs lost, 3 were created already within the ﬁrst 2 months of the pandemic. Combining
their results with historical data on recalls, they estimate that 42% of stafﬁng reduction
will lead to permanent job losses.

Since the publication of Barrero et al. (2020), expected job reallocation rates have
been revised substantially downwards.6 This is shown in the left-hand panel of ﬁgure 1.
After rising almost three-fold relative to historical averages (and six-fold for sales reallo-
cation) in the earlier stages of the pandemic, peaking in May 2020, job reallocation rates
fell to numbers only slightly above historical averages. The data come from the Survey
of Business Uncertainty, which surveys ﬁrms on their sales and stafﬁng expectations.
The measure of expected excess reallocation is constructed using expected employment
changes over 12 months for growing and shrinking ﬁrms minus the absolute aggre-
gate expected change. One possibility for the sharp revision in expectations of excess
reallocation is the unprecedented nature of the shock, which also substantially raised
uncertainty (measured as the weighted average standard deviation of growth forecasts
across ﬁrms), and hence reduced the value of forecasts. Once uncertainty started falling
(right-hand panel), so did expected reallocation, even though uncertainty remains high
by historical standards. Another possibility is the return of furloughed employees, as
temporary layoffs represented the bulk of employment losses early in the pandemic
(Hall & Kudlyak 2020). On the other hand, it is possible that expected reallocation went
down after the ﬁrst pandemic wave as a large reallocation already took place and ex-
pected reallocation reverted to normal levels.7

3.1 Reallocation in the cross-section

We then turn to the CPS to examine whether there is a noticeable shift of workers across
occupations or sectors. This could include employed workers who switch employers or
unemployed workers who take up a new job in a different occupation or sector relative

6See https://www.frbatlanta.org/research/surveys/business-uncertainty?panel=1.
7In a follow-up paper, Barrero et al. (2021) use 12 month forward and backward reallocation data
and show job reallocation at levels somewhat higher than historical values. Since this measure combines
realized and expected reallocation, it is consistent with the view that a reallocation wave has already
occurred. Moreover, a backward-looking measure will include the highly turbulent months of the early
pandemic period, and it is unclear how informative it is about expected reallocation.

ECB Working Paper Series No 2703 / August 202210Figure 1: Business Expectations

(a) Reallocation

(b) Uncertainty

Notes: The ﬁgures show 4-quarter ahead expectations of sales and job reallocation (left-hand
side) and uncertainty (right-hand side) for a representative sample of ﬁrms, deﬁned as in the
text. Source:Atlanta Fed/Chicago Booth/Stanford Survey of Business Uncertainty. The series
have been transformed to a 2-month moving average until February 2020. Sample is January
2017-August 2021.

to their previous employment. An increase in the fraction of workers who switch oc-
cupation or sector would point to increased reallocation. We calculate occupation and
sector switchers as the fraction of workers who switch in adjacent months, both for con-
tinuing workers and for entrants from unemployment (as CPS codes the last known
sector and occupation for unemployed workers). We also do this for switchers across
12 months, allowing us to also capture individuals present in the sample in later stages
of the pandemic. For this measure we use the coarse 22 CPS major occupation classiﬁ-
cation, to correct for the erroneous switches problem notes by Fujita (2018), which are
likely to be substantial across 12 months, especially since occupation codes changed in
2020.8 We extend the sample to July 2021, when employment had risen substantially,
and could give additional data points for reallocation. We exclude individuals recalled
from a temporary layoff; while Fujita (2018), in a similar exercise, does include them,
the spike in recalls would bias the switching rate downwards. We calculate job-to-job
transitions as the share of workers who are employed in consecutive months but switch
employers from one month to the other.

We plot these measures in ﬁgure 2-a. The red and blue lines show the (non-

8For the adjacent month comparison there is a resulting discrete jump in January 2020, which we
remove (as well as June and July 2015, which exhibit a very large spike in the share of switchers). The
12-month measure can also be corrected similarly, but the coarse measure is more appropriate. Note also
that focusing only on workers employed in consecutive months yields a qualitatively very similar picture.

02468Expected Reallocation (%)2017m12018m12019m12020m12021m1Expected Sales ReallocationExpected Job Reallocation3.544.555.56Employment Gr Uncertainty3456Sales Gr Uncertainty2017m12018m12019m12020m12021m1Sales Gr UncertaintyEmployment Gr UncertaintyECB Working Paper Series No 2703 / August 202211Figure 2: Share of workers switching occupations and sectors

(a) Occupation/Sector Switches

(b) Job-to-job transitions

Notes: The LHS chart shows the share of workers employed in consecutive months who switch
occupations (4-digit, red line) or sectors (2-digit, blue line) from one month to another. The
dashed gray line uses the CPS 22-occupation classiﬁcation and shows occupation switches over
12 months, and the black line does the same for sectors. The RHS chart shows the share of
workers who switch employers (job-to-job transitions); FF uses the Fallick & Fleischman (2004)
measure, FMP the Fujita et al. (2021) measure, and MAR a measure assuming missing obser-
vations are missing at random. The sample for the LHS chart includes all CPS Basic Monthly
Sample ﬁles from January 2012-November 2021. The data for the RHS chart come from Fujita
et al. (2021).

seasonally adjusted) shares of workers employed in a given month who switch occu-
pations (4-digit level) and sectors (2-digit level) relative to the previous month. There is
no clearly discernible increase for either series. Following an upward trend coinciding
with the recovery, the share of workers employed in consecutive months that switch oc-
cupations has hovered around 8% since 2016, and 5% for sectors.9 The dashed gray line
shows the occupation switching rates across 12 months, using the coarse measure; the
black line shows the corresponding measure for industries. These are naturally higher,
but the pattern is very similar, and also shows little movement during the COVID-19
shock. There is a visible short-lived fall in the share in April 2021, due to base effects
(workers who did not lose their jobs in the initial shock were likely more attached), but
the variation if overall small. This is especially interesting, since during later months of
the shock, employment had substantially risen, and so a large reallocation wave would
have been visible.

Figure 2-b plots the share of job-to-job transitions. We report three measures. FF
denotes the standard measure by Fallick & Fleischman (2004), who pioneered using the
CPS question on whether the interviewee is with the same employer to measure job-to-

9Seasonally adjusted series display an even more muted pattern.

.02.07.12.17.22.27.32.37Share of switchers (12m).03.06.09.12.15.18Share of switchers2013m22015m22017m22019m22021m2timeoccupation switchersindustry switchersoccupation switchers (coarse, 12m)industry switchers (12m)0.01.02.03.04Share of switchers2013m22015m22017m22019m22021m2dateFFFMPMARECB Working Paper Series No 2703 / August 202212job transitions. FMP is a recent correction of FF by Fujita et al. (2021), in order to account
for an increase in missing responses to this question in the CPS. Finally, MAR, also com-
puted by Fujita et al. (2021), is a naive correction assuming that missing responses are
missing at random. While the three measures do exhibit level differences in our sample,
the overall pattern is very similar.

Job-to-job transition rates fell early in the pandemic, as expected, but then reverted
close to historical patterns.10 A small, but shortlived, pickup is noticeable towards the
end of the sample (November 2021), which is consistent with the uptick in quits we doc-
ument below; however, when considering seasonally adjusted rates (shown in ﬁgure 9
in the appendix) the pickup is much smaller. As the purpose here is only to see whether
there is a spike in switching, not to analyze the cyclical properties of these series, we
prefer to focus on non-seasonally adjusted rates. In any case, given that quits are at his-
torical highs, we do eventually expect a pick-up in job-to-job transitions as well, but so
far the picture does not suggest large reallocations occurring along that margin.

3.1.1 Sectoral patterns

Finally, we consider whether the muted switching pattern on aggregate is masking dif-
ferences across sectors. A reallocation shock towards growing sectors could increase
the share of stayers in these sectors, all the while reducing them in shrinking sectors,
leaving the aggregate unaffected. To examine this, we show, in the top panel of ﬁgure 3,
the share of workers who remain employed in the same sector they were employed in
12 months before the survey month, in a sample which also includes individuals who
exit employment. The red bars show this average share for 2019, and also for part of
the second half of 2021 (May-August). We consider the period starting 13 months after
the initial shock to avoid having our estimates contaminated by base effects; since such
a high share of workers were laid off in April 2020, the share of stayers in April 2021
conditional on remaining employed in April 2020 is likely to be artiﬁcially high.11

10This measure can also include workers who are laid off and ﬁnd a job prior to the CPS interview. The
denominator consists only of individuals with a valid response to the relevant question, as a large number
of eligible interviewees have missing values for this question, for unknown reasons. However, the stable
value around 2% is consistent with estimates by Bosler & Petrosky-Nadeau (2016) with different data.

11In each case, we take the average across all months in the respective period. We do so because some
sectors have low coverage in the CPS, and averaging across months helps correct for the small sample. For
the same reason, we also group some groups of similar sectors together. These are: Finance, Insurance and
Real Estate and Leasing (FIRE); Arts and Entertainment and Accommodation and Food Services (Leisure);
Agriculture and Forestry and Mining (Agriculture and Mining).

ECB Working Paper Series No 2703 / August 202213As ﬁgure 3(a) shows, on aggregate, around 70% of workers remain employed in the
same sector after 12 months, even accounting for exits to non-employment. This ﬁgure
did not change between 2019 and 2021. Moreover, for almost all sectors, the change
In some sectors, such as Wholesale Trade and
in the share of stayers is very small.
Information, it is even slightly higher. The only sector with a noticeable decline, from
69% to 64%, is Leisure, which includes both Accommodation and Food Services and
Arts/Entertainment. This is to be expected, as this was by far the sector most affected
by the pandemic, with many establishments going out of business, and workers having
to seek work in other sectors.

Even in this case, however, evidence suggests that displaced workers transitioned
to sectors with similar skill requirements; in California, over 60% of UI claimants from
Accommodation and Food Services who transitioned found work in either Retail, Ad-
ministration Support and Waste Management, Healthcare and Social Assistance, or Trans-
portation and Warehousing.12

Focusing on the period after April 2021 corrects for base effects, but has the draw-
back of only looking at the period after the initial shock, hence missing the possibility for
persistent reallocation occurring during the initial shock. As such, the bottom panel of
ﬁgure 3 repeats the exercise for 2021-Q1 instead. In this case, we only include employed
individuals in the sample, as joblesness was still high at that time, with employment
to population ratio lower than the trough of the Great Recession. The situation is very
similar in this case; on aggregate and across sectors, the share of workers employed in
the same sector as a year earlier were very similar before and after the pandemic.

A shortcoming of the analysis above is the fact that it focuses exclusively on in-
dividuals employed 12 months before each given period. For April 2020 in particular,
despite the fact that job losses were so high, given the rotating panel nature of the CPS,
the sample contains too few workers present in both April 2020 and April 2021 and hav-
ing lost their jobs in April 2020. However, data suggests that even those who lost their
jobs in the initial shock had very high re-employment rates in the same sector. In par-
ticular, policy reports from the California Policy Lab13, using administrative evidence
from unemployment insurance data from California, show that 68% of UI claimants in
2020Q2 had returned to the same industry a year later. More interestingly, this share
was almost identical for both Accommodation and Food Services (65%) and for the Pro-

12Source: California Policy Lab, December 8th Analysis of Unemployment Insurance Claims.
13Source: California Policy Lab, ibid.

ECB Working Paper Series No 2703 / August 202214Figure 3: Sector stayers

(a) 2021-H2

(b) 2021 Q1, employed only

Notes: The ﬁgures show the share of workers employed in the same sector where they were employed
a year earlier. In the top panel, the sample also includes individuals who exit into non-employment; in
the bottom panel, it only includes employed workers. The bars show averages across all months for the
respective period (2021-H2 includes May-August 2021). Some sectors have been grouped due to small
sample size. Source: CPS.

0.2.4.6.8Educational servicesHealth & Social AssistanceFIREGovernmentConstructionLeisureTransportation and utilitiesDurables manufacturingProf & Buss ServicesRetail tradeAgriculture, Forestry, MiningOther ServicesNon-durables manufacturingInformationWholesale trade .Aggregate20192021-H20.2.4.6.8Educational servicesHealth & Social AssistanceFIREGovernmentConstructionLeisureTransportation and utilitiesDurables manufacturingProf & Buss ServicesRetail tradeAgriculture, Forestry, MiningOther ServicesNon-durables manufacturingInformationWholesale trade .Aggregate20192021-Q1ECB Working Paper Series No 2703 / August 202215fessional, Science and Technology sector (64%), even though these were two industries
with diametrically opposite outcomes in the initial COVID-19 shocks.

3.2 Hires and separations

We now consider hires and separations data from JOLTS. Figure 4-a shows seasonally
adjusted sectoral hire and separation rates for the non-farm private economy.

Figure 4: Churn rates

(a) Hire and Separation rates

(b) Layoff and Quit rates

Notes: The ﬁgures show hires and separations for the non-farm private sector, split by ﬁrm
size (number of employees). Data come from JOLTS. The hire and separation rates are deﬁned
as hires or separations over employment, multiplied by 100. The levels ﬁgures are plotted in
square root scale.

Hires fell a lot in April 2020 relative to February in absolute terms, though they
moved little relative to employment, due to a large denominator effect. Separation rates
and levels more than doubled. Hires spiked up and separations collapsed in May 2020
once lockdowns were lifted, and by the end of the year they were similar to pre-COVID
levels, despite employment being substantially lower (although separations were still
lower, so the net increase in employment was still larger). Hires picked up steam fur-
ther as the recovery broadened, along with the proliferation of vaccines in 2021, and
employment still substantially lower than pre-pandemic. Separations have also been
somewhat higher than historical averages in 2021.

While the recovery of the aggregate economy in 2021 could account for the increase
in the hiring rate, an elevated separation rate could indeed indicate an increase in churn.
The total separation rate, however, is masking a steep rise in quits, as large numbers of
US workers were quitting employment in the Spring and Summer of 2021. Figure 4-

246810122000m12005m12010m12015m12020m1dateHire rateSeparation rate02468102000m12005m12010m12015m12020m1dateLayoff rateQuit rateECB Working Paper Series No 2703 / August 202216b shows separations split into layoffs and quits.14 The average quit rate in 2021Q2, at
2.7%, was the largest recorded since JOLTS started in 2000. This can reﬂect both the
strength of the labor market, as vacancies were also at record highs during the same
period, while there is anecdotal evidence that workers are rethinking career trajectories
(Furman & Powell 2021). Layoffs, on the other hand, have been trending lower; for the
last three months of the sample (May-July 2021) they averaged 1.1%, over a third lower
than the 1.6% average of the 2001-2019 period. Firms are still ﬁlling positions lost due to
the pandemic, with employment 5 million lower than February 2020. Indeed, the layoff
rate has fallen precisely in the sectors that experienced large losses due to the pandemic
recession; the correlation of the change in the layoff rate (relative to historical standards)
and log change in employment relative to February 2020 is 0.61.15

Finally, aggregate hires may still mask important differences in the cross-section
that could indicate reallocation. Newly available size class data by JOLTS can shed light
on this. Figure 5 shows seasonally adjusted sectoral hire and separation rates and levels
for the non-farm private economy, by ﬁrm size (the levels are in square root scale), at
different points of the pandemic era.

For small and medium ﬁrms, hires resumed their pre-COVID levels relatively
quickly, after rising during summer 2020 following huge spikes in separations. In 2021,
hires for these ﬁrms picked up considerably, and they seem to be largely driving the em-
ployment recovery. The pickup for the smallest ﬁrms is also consistent with evidence of
a surge in new business applications, disproportionately found in internet sales16.

Throughout this period, and given the tighter borrowing constraints small ﬁrms
faced (particularly in the earlier stages), one would expect increased hiring from larger
ﬁrms (of the type detailed in the anecdotal evidence mentioned in Barrero et al. (2020)
and this paper), who take advantage of the recession and try to expand their market
shares. The data shows that while separations for these largest ﬁrms (over 5,000 work-
ers) were barely affected as a result of the pandemic, their hiring in fact fell and only
reached its pre-pandemic levels by Summer 2021. It is well-known that a disproportion-
ate fraction of hires comes from young ﬁrms, so a smaller contribution from the largest
ﬁrms is to be expected. Note also that this pattern of increasing churn for small ﬁrms

14Figure 4 in fact includes, in addition to layoffs and quits, other separations, which include retire-
ments, deaths, exits due to disability, and transfers to another location. These are an order of magnitude
lower than layoffs and quits, and have not risen appreciably.

15See also ﬁgure 10 in the appendix.
16See here.

ECB Working Paper Series No 2703 / August 202217Figure 5: Hires and separations by ﬁrm size, 2020-2021

(a) Hires (levels)

(b) Hires (rates)

(c) Separations (levels)

(d) Separations (rates)

Notes: The ﬁgures show hires and separations for the non-farm private sector, split by ﬁrm
size (number of employees). Data come from JOLTS. The hire and separation rates are deﬁned
as hires or separations over employment, multiplied by 100. The levels ﬁgures are plotted in
square root scale.

immediately after a large shock was also present after the Lehman shock, as shown by
Moscarini & Postel-Vinay (2016). On the other hand, we know from the literature that
smaller ﬁrms grow faster in recoveries, but larger one grow faster in tight labor mar-
kets (through poaching); as Summer 2021 is the time when labor market tightness (as
conventionally measured by unemployment over vacancies) had reached pre-pandemic
level, slower growth of the larger ﬁrms until then was to be expected. The upshot is that
the data does not seem to suggest heightened levels of reallocation towards the largest
ﬁrms.

Figure 6 shows the breakdown of separations into layoffs and quits by ﬁrm size.
The reduction in the layoff rate in 2021 is driven primarily by the smaller ﬁrms, so it
is possible some of this is due to composition effects, through new ﬁrms entering. For
ﬁrms with over 250 employees, layoff rates in July 2021 were at pre-pandemic levels or

100400900160025003600hire level (000s)1-910-4950-249250-9991K-5K5K+Feb 20Apr 20May 20Sep 20Feb 21Jul 210246810hire rate (%)1-910-4950-249250-9991K-5K5K+Feb 20Apr 20May 20Sep 20Feb 21Jul 21100400900160025003600100400900160025003600totsep level (000s)1-910-4950-249250-9991K-5K5K+Feb 20Apr 20May 20Sep 20Feb 21Jul 21051015totsep rate (%)1-910-4950-249250-9991K-5K5K+Feb 20Apr 20May 20Sep 20Feb 21Jul 21ECB Working Paper Series No 2703 / August 202218Figure 6: Layoffs and quits by ﬁrm size, 2020-2021

(a) Layoffs (levels)

(b) Layoffs (rates)

(c) Quits (levels)

(d) Quits (rates)

Notes: The ﬁgures show hires and separations for the non-farm private sector, split by ﬁrm
size (number of employees). Data come from JOLTS. The hire and separation rates are deﬁned
as hires or separations over employment, multiplied by 100. The levels ﬁgures are plotted in
square root scale.

higher. Similarly, the elevated quit rate is also concentrated at small and medium ﬁrms.

Accounting for where these quitters go is beyond the scope of this paper. We note,
however, that our results show that workers seems to move out of their sectors and
occupations, as well as to other jobs in general, in reasonably similar rates as previ-
ously. The situation is much the same when looking across sectors. This idea of workers
mostly moving to the same sector is strengthened when considering the correlation be-
tween job losses between February and April 2020, and job gains since then, is over 99%.
Combined with the fact that the employment-to-population ratio, while still at historical
lows, is rebounding quickly, there is enough evidence to suggest movement of workers
is mostly taking place across ﬁrms, not sectors or occupations.

Overall, the results of the preceding analysis do not point to a clear reallocation
wave. First, expected reallocation measures have come down substantially since the

100400900160025003600100400900160025003600100400900160025003600100400900160025003600layoff level (000s)1-910-4950-249250-9991K-5K5K+Feb 20Apr 20May 20Sep 20Feb 21Jul 210510layoff rate (%)1-910-4950-249250-9991K-5K5K+Feb 20Apr 20May 20Sep 20Feb 21Jul 21100400900160025003600100400900160025003600100400900160025003600quit level (000s)1-910-4950-249250-9991K-5K5K+Feb 20Apr 20May 20Sep 20Feb 21Jul 2101234quit rate (%)1-910-4950-249250-9991K-5K5K+Feb 20Apr 20May 20Sep 20Feb 21Jul 21ECB Working Paper Series No 2703 / August 202219Spring of 2020, suggesting a reevaluation of initial arguments signaling major reallo-
cation. CPS data show that occupational or sector switching stayed at normal levels,
across different deﬁnitions. JOLTS data point to an increase in the hire rate in 2021, as
ﬁrms rebuild their stock of labor; separations are also elevated, which could indicate
higher churn. However, a large part of the change in separations reﬂect an increase in
quits, which are at historical highs. Data by ﬁrm size indicate most of the action is com-
ing from small ﬁrms; in particular, the purported increase in hires from large ﬁrms who
tried to exploit the pandemic shocks is not borne out by the data.

4 Mismatch unemployment

An indirect way of studying the magnitude of the reallocation shock is by examining
the extent to which unemployment is frictional. A large reallocation shock implies that
workers need to be shifted across sectors and occupations, but matching frictions can
slow down this reallocation, creating mismatch unemployment over the short-run. De-
termining the level of mismatch unemployment can have important implications about
the scope of policy action. If a large fraction of unemployment is due to mismatch, then
demand-side policies (ﬁscal or monetary) may be ill-advised, and supply-side policies
(such as employment assistance or training programs) are more appropriate.

We estimate the extent to which the high levels of unemployment during the pan-
demic are due to mismatch, using the framework of Sahin et al. (2014). In their multi-
sector version of the canonical search and matching model, matching frictions lead to
a mismatch between vacancies and jobseekers across sectors, which impedes the ag-
gregate job-ﬁnding rate and leads to a higher unemployment rate. By comparing ac-
tual unemployment u with a counterfactual unemployment u∗ from a planner who dis-
tributes workers across all vacancies, one can gauge the portion of unemployment that
is exclusively due to mismatch. This framework, which casts mismatch (and mismatch
unemployment) as deviations from an optimal allocation, is ideally suited to the exer-
cise at hand, as we try to estimate the extent to which frictions are preventing realloca-
tion of workers from declining to booming sectors.17 The indicator measures mismatch
across, not within sectors, and as such the results should be benchmarked relative to
pre-pandemic levels. The measure also ignores employed individuals seeking employ-
ment (through on-the-job search); this is unlikely to have mattered crucially during the

17One caveat is that our data only allow construction of sectoral mismatch indices.

ECB Working Paper Series No 2703 / August 202220early parts of the pandemic, but may have become more important in recent months.

We present here the basic intuition, maintaining the original notation of Sahin et al.
(2014). For more details, see Sahin et al. (2014). The economy contains a set I of distinct
labor markets; unemployed workers search for jobs in one sector only, and labor markets
are frictional, with matches determined by the sectoral matching function:

hit = Φtφitνα

itu1−α
it

,

(1)

where Φt and φit are aggregate and sectoral matching efﬁciency parameters, re-
spectively, νit are vacancies, uit unemployed workers seeking jobs, and hit are hires, for
sector i in period t. As such, different labor markets are allowed to have different match-
ing efﬁciencies. Parameter α is between 0 and 1 and is the vacancy share, common across
all sectors. The mismatch index Mφ,t is then given by

Mφ,t = 1 −

ht
h∗
t

= 1 −

I
(cid:88)

i=1

(cid:18) φit
¯φt

(cid:19) (cid:18)νit
νt

(cid:19)α (cid:18) uit
ut

(cid:19)1−α

,

(2)

with h∗

t = ¯φtΦtνα

itu1−α
it

(cid:34) I

(cid:88)

and ¯φt =

(cid:19)(cid:35)α

1
α
it

φ

(cid:18) νit
νt

. h∗

t is the optimal number of hires,

i=1

and ¯φt is an aggregator of sectoral matching efﬁciency parameters, weighted by sectoral
vacancy shares. Note that the planner can only move workers across markets, but is
still bound by the within-market matching friction.18 As Sahin et al. (2014) note, the
index measures the fraction of hires lost due to mismatch. A planner would eliminate
it by placing unemployed workers across sectors by vacancies.
Intuitively, a higher
covariance of vacancies and unemployed workers (at the sector-time level) would imply
a lower mismatch index.19

Allowing productivity zit and job destruction rates δit to be heterogeneous across
sectors, let xit = φitzit/[1 − β(1 − δit)], where β is the discount rate. This measure, which
Sahin et al. (2014) call overall market efﬁciency, is the destruction-rate and productivity
adjusted matching efﬁciency of the sector. Under heterogeneity, the planner wants to
maximize output, and so sectors with higher productivity and lower destruction rates

18In other words, the planner cannot eliminate all unemployment, only that which arises from misal-

location of searchers across sectors. This setup allows one to capture frictional unemployment.

19Sahin et al. (2014) prove that i) the index is between 0 and 1; ii) it is invariant to aggregate shock
shifting total vacancies and unemployed but leaving the shares across sectors unchanged; and iii) it rises
in disaggregation, and so comparisons should be made keeping disaggregation ﬁxed.

ECB Working Paper Series No 2703 / August 202221are given higher weights. The index becomes

Mxt = 1 −

I
(cid:88)

i=1

(cid:18) φit
¯φxt

(cid:19) (cid:18)νit
νt

(cid:19)α (cid:18) uit
ut

(cid:19)1−α

,

(3)

where ¯φxt =

I
(cid:88)

i=1

φit

(cid:18) xit
¯xt

(cid:19)1/α−1 (cid:18) νit
νt

(cid:19)

and ¯xt =

(cid:19)(cid:35)α

(cid:34) I

(cid:88)

i=1

x1/α
it

(cid:18) νit
νt

. If we instead

assume homogeneous matching efﬁciency, productivity, and destruction rates across

sectors, then the index simpliﬁes to Mt = 1 −

I
(cid:88)

(cid:18) νit
νt

(cid:19)α (cid:18) uit
ut

(cid:19)1−α

. We also consider,

i=1

in line with Sahin et al. (2014), mismatch indices Mδ,t, and Mz,t, where heterogeneity
is only in the form of destruction rates and productivity, respectively (in addition to
unemployment and vacancies).

Focusing now on the homogeneous case (M ) for brevity, from (2), the job ﬁnding

rate can be written as

ft = 1 −

ht
ut

= (1 − Mt)Φt

(cid:19)α

.

(cid:18) νt
ut

(4)

Deﬁning f ∗

t as the ﬁnding rate when ut = u∗
t+1 = st + (1 − st − f ∗

dynamics of u∗
t are deﬁned by u∗
u∗
t gives mismatch unemployment. Notice that

t and st as the separation rate, the
t )u∗
t . The difference between ut and

f ∗
t =

(cid:19)α

(cid:18) νt
ut

= ft

1
1 − Mt
(cid:124) (cid:123)(cid:122) (cid:125)
direct

.

(cid:19)α

(cid:18) ut
u∗
t
(cid:124) (cid:123)(cid:122) (cid:125)
feedback

(5)

The optimal ﬁnding rate is related to the actual ﬁnding rate through two terms.
The ”direct effect” is simply related to the presence of mismatch. However, with lower
mismatch and a lower overall unemployment, it becomes easier for unemployed work-
ers to meet a vacancy, further reducing unemployment and giving rise to this ”feedback”
effect. Sahin et al. (2014) argue that the presence of the feedback effect implies that mis-
match unemployment may remain high for some time after mismatch has fallen, due to
delays in the reabsorption of unemployed workers.

We calculate the ﬁve measures for the period January 2012-June 2021, except for
those with heterogeneity in destruction rates (Mx,t and Mδ,t), where data stop at Decem-

ECB Working Paper Series No 2703 / August 202222ber 2020. Results are given in ﬁgure 7, where the left panel shows the different mismatch
indicators, and the one to the right shows the associated mismatch unemployment rates.
For clarity, we show, in the top panel, the baseline (Mt) and measures allowing for het-
erogeneity in matching efﬁciency (Mφ,t and Mx,t), and in the bottom one the ones with
homogeneous matching efﬁciency (Mδ,t and Mz,t).

Consider ﬁrst Mt and Mφ,t in ﬁg 7, the measures without heterogeneity and with
matching efﬁciency heterogeneity only. After a slowly diminishing trend after the Great
Recession, mismatch rose sharply at the start of the pandemic (shaded area), rising by
over 50% relative to February, before falling sharply once the ﬁrst part of the shock
passed and employment started to recover. The further pickup in economic activity
in early 2021 led to a further reduction in both measures, which, by Summer 2021,
were back to pre-pandemic levels, and in line with historical standards. The measure
with productive heterogeneity only, Mz,t, behaves similarly. We see instead a different
behavior for Mx,t and Mδ,t, where mismatch falls sharply on impact. As Sahin et al.
(2014) stress, when allowing for heterogeneity in productivity, the planner maximizes
the present discounted value of output, and not necessarily hires, and so in principle
optimal hires may be lower than equilibrium hires, resulting in negative mismatch. A
similar reasoning holds for the case where destruction rates are allowed to be heteroge-
neous. The nature of the shock in 2020Q220 when output exhibited its sharpest monthly
reduction on record, led to large swings in these measures, because the correlation of
δi,t with φi and xi,t fell substantially. In any case, the measure recovered to levels close
to the other measures once the initial anomaly passed, and overall all measures show a
very similar qualitative behavior, indicating little to no change in overall mismatch by
late 2020.

Turning over to mismatch unemployment, let mismatch unemployment be given
by ˆu = u − u∗, with appropriate subscripts for each measure. We see that estimated mis-
match unemployment pre-pandemic was quite similar across measures, but diverged
since. For each measure, mismatch unemployment dipped initially, likely due to the
initial fall in vacancies, but rose thereafter. The rise was modest for all measures, how-
ever. Mismatch unemployment for the measures without heterogeneity in destruction
rates (ˆu, ˆuφ, and ˆuz) rose by around 0.4-0.5 percentage points (pp) relative to their pre-
pandemic average by summer 2020 (measured from 2019 to February 2020), but has
since fallen to roughly 0.3-0.4pp relative to the pre-pandemic era. As excess unemploy-

20Since output data are quarterly, this fall is also apportioned to May and June.

ECB Working Paper Series No 2703 / August 202223ment due to the pandemic (relative to February 2020) has fallen from highs of over 10%
to 2.4% by June 2021, the share of mismatch unemployment out of total has slightly
risen, but mismatch remains a marginal contributor to overall unemployment; for Mφ,
which exhibits the largest mismatch, ˆuφ accounts for 14% of total and less than a ﬁfth
of the increase. It is also important to note that mismatch unemployment has remained
higher far longer than mismatch itself, conﬁrming the importance of the ”feedback”
mechanism.

Figure 7: Mismatch

(a) Mismatch index - heterogeneous φi

(b) Mismatch Unemployment-
heterogeneous φi

(c) Mismatch index - homogeneous φi

(d) Mismatch Unemployment -
homogeneous φi

Notes: The ﬁgures show the mismatch index (left-hand side) and the implied mismatch un-
employment (right-hand side), according to the method of Sahin et al. (2014). The sample is
January 2012-July 2021.

The measures that take into account heterogeneity in destruction rates, ˆux and
ˆud, after the initial fall to slightly negative territory described previously, rose to levels
very close to pre-pandemic by the end of 2020, and much less than the other measures,

ECB Working Paper Series No 2703 / August 202224though ˆud was almost 0.2pp higher at the end of the sample. Recall that ˆud allows only
for heterogeneity in destruction rates, ˆuz only for productivity heterogeneity, and ˆux for
both, as well as matching efﬁciency heterogeneity. We see that ˆux is lower than the other
two measures, which is because of the negative covariance between matching efﬁciency
and destruction-adjusted productivity, which, while smaller than in the initial shock,
has persisted until the end of 2020.

The analysis has so far implicitly assumed constant matching efﬁciency for each
sector. Given the size of the shock, and to allow more ﬂexibly for the possibility that a
reallocation shock may have changed matching efﬁciency differentially across sectors,
it may be useful to also allow for matching efﬁciency φ to be different during the pan-
demic, for those measures incorporating matching heterogeneity (Mφ,t and Mx,t). To do
so, we estimate matching functions using the procedure outlined in Sahin et al. (2014),
with a separate slope parameter for the pandemic period. The results are given in ﬁgure
8, together with the simple baseline measure Mt for comparison purposes. We see that
the differences between Mt and Mφ,t are somewhat larger pre-pandemic; mismatch un-
employment with matching heterogeneity, in particular, is almost always about 0.2pp
higher than the baseline measure, but the trends remain very similar throughout the
sample, and this does not change in the pandemic.

Figure 8: Mismatch unemployment - varying matching efﬁciency

(a) Mismatch index- varying φi

(b) Mismatch Unemployment - varying φi

Notes: The ﬁgures show the mismatch index (left-hand side) and the implied mismatch un-
employment (right-hand side), according to the method of Sahin et al. (2014). The sample is
January 2012-July 2021.

Overall, it appears that little of the increase in unemployment can be explained
by mismatch. As the recovery broadens, it is likely that mismatch will account for an
ever larger share of total unemployment, but the evidence so far suggests that frictions

ECB Working Paper Series No 2703 / August 202225potentially slowing down the labor market adjustment to the shock have not been a
large driver of unemployment during the pandemic recovery.

5 Structural identiﬁcation of reallocation shocks

We directly examine for the possibility of a job reallocation shock in driving the aggre-
gate labor market patterns during the pandemic, through the lens of a Bayesian SVAR
model with sign restrictions. Such models are often used to study the effects of tech-
nology on labor, where a positive technology shock is typically assumed to have pro-
growth and disinﬂationary effects.

The macro-econometric literature has widely considered the role of technological
changes on growth, productivity and employment. Gal`ı (1999), Christiano et al. (2003),
Uhlig (2004), Francis & Ramey (2005), Dedola & Neri (2007), Canova et al. (2010) and
Mumtaz & Zanetti (2012) use different identiﬁcation strategies to measure the effects
of neutral or investment-speciﬁc technology shocks in the economy. The former type of
shock refers to exogenous changes in TFP that equally affect the marginal productivity of
capital and employment in the aggregate production function. The investment-speciﬁc
technology shock, instead, is deﬁned as directly affecting the price of investment, espe-
cially of ICT (information and communication technology) capital. That is, higher efﬁ-
ciency in producing capital goods leads to stronger investment patterns in machinery
and equipment, which is indirectly followed by higher economic growth and stronger
labor demand. A feature of our identiﬁcation approach is that we aim at identifying
a number of structural shocks which is equivalent to the number of our endogenous
variables in the VAR.

Job reallocation shock In this paper, we impose a novel identiﬁcation scheme, whereby
we differentiate between two types of technology shocks: a standard factor neutral ag-
gregate supply shock (ASTech) and a reallocation shock (ASJRe). We introduce job real-
location as a shock that captures the efﬁciency gains from the current capital and labor
allocation via an increase in job turnover, measured by higher hiring and destruction
rates. Output responds positively to both types of shocks, but they differ substantially
in their labor market effects. While ASTech is assumed to raise wages and reduce layoffs,
ASJRe induces churn in the labor market: we assume it raises both hires and layoffs, as
well as unemployment.

ECB Working Paper Series No 2703 / August 202226Table 1: Matrix of Sign Restrictions

Shock

Variable AggDem ASTech ASJRe WagBag MatEff LabSup

IndPro

PCEy

Wages

URate

Hires

+

+

-

+

+

-

+

+

+

+

+

-

-

-

+

-

-

+

+

-

+

-

-

-

Layoffs

+
Notes: The table shows the sign-based identiﬁcation restrictions imposed by our SVAR frame-
work. A ”+” indicates that we restrict the sign of the on-impact response of the variable to the
shock to be positive. Similarly for the negative ”-” sign. An empty cell indicates that no restric-
tion has been imposed. IndPro refers to Industrial Production, PCEy to personal consumption
expenditure inﬂation, Wages to Atlanta Fed measure of nominal wages, URate to the unemploy-
ment rate. Similarly, AggDem refers to Aggregate Demand, ASTech to the neutral technology
shock, ASJRe to the job reallocation shock, WagBag to the wage bargaining shock, MatEff to the
matching efﬁciency shock, and LabSup to the Labor Supply shock.

+

-

Differently from other technology shocks, job reallocation shocks are expected to
weaken labor demand and lead to an on-impact surge in the unemployment rate. This
is because it takes time to create new jobs when ﬁrms decide to switch jobs away from
certain occupations/tasks and to create new ones with a higher embedded technologi-
cal content. The reallocation is thus expected to enhance productivity and will support
higher job posting. As displaced workers will face adverse labor demand conditions
and higher mismatch in the labor market, a job reallocation shock which has positive ef-
fects on output is likely to have non-positive effects on wages.21 In general, the reaction
of price inﬂation is not pinned down as it is negatively affected by the increase in efﬁ-
ciency from job reallocation, while higher marginal costs could arise from investments
in capital goods and high-skilled workers.

A job reallocation shock differs from a job matching efﬁciency shock because –
given similar responses of job ﬂows - the response of the unemployment rate and wages
will be different. An improvement in matching efﬁciency will ameliorate the matching
and hiring process and decrease unemployment, while job reallocation will lead to a
temporary increase in the unemployment rate. Regarding wages, while the gains from

21Our baseline set of identiﬁcation restrictions does not constrain the on-impact effect of ASJRe on

wages.

ECB Working Paper Series No 2703 / August 202227higher matching efﬁciency will partly be shared between employers and employees, the
job reallocation effect is expected to exert temporary downward pressure on wages.

Neutral shock In the standard Mortensen & Pissarides (1994) model, an aggregate
(neutral) technology shock beneﬁts all workers. The endogenous probability of sepa-
ration decreases because the value of the marginal job beneﬁts from a right-shift of the
productivity distribution. At the same time, the response of the job ﬁnding rate de-
pends on the degree of price ﬂexibility in the economy. In a New-Keynesian model with
a high degree of price stickiness, output does not adjust enough after a neutral technol-
ogy shock to accommodate an increase in employment, and the job ﬁnding rate may
decrease. In the class of ﬂexible price models, instead, the response of output is stronger
and that leads to higher labor demand and higher job ﬁnding rate. In this latter case,
the combined effects from the job ﬁnding and separation rates clearly identify the sign
of the employment response. In general, given the uncertainty about the sign of the
response, we keep the sign of the identiﬁcation restriction of a technology shock on the
unemployment rate and hires unconstrained.

Other shocks Canova et al. (2010) ﬁnd that neutral technology shocks explain only a
small portion of labor market ﬂuctuations (measured as per-capita hours) and a much
larger portion of output ﬂuctuations. Canova et al. (2013) instead argue that neutral
shocks increase unemployment and explain a substantial portion of its volatility, while
Foroni et al. (2018) ﬁnd an important contribution from both labor supply shocks and
wage bargaining shocks. We thus augment our identiﬁcation structure with additional
shocks that are more tailored to explain ﬂuctuations in the labor market.

We include aggregate demand, labor supply, matching efﬁciency and wage bar-
gaining shocks to complete our model. Anzoategui et al. (2019) provide a rationale for
looking at aggregate demand shocks, as they may lead to more persistent effects on
output and labor market variables, which could help to explain the fallout from the
Global Financial Crisis (GFC). Labor supply shocks are also relevant in view of the stark
contraction of labor force participation after the GFC. This decline, which blurs devel-
opments in the unemployment rate is, together with the wage bargaining shock, rele-
vant to explain relatively moderate wage dynamics during the recovery (Daly & Hobijn
(2013), Yellen (2014)). In the context of the COVID-19 pandemic shock, the labor supply
shock (LabSup) is ideally suited to capture the direct effects of lockdowns and move-

ECB Working Paper Series No 2703 / August 202228ment restrictions on aggregate variables. We restrict LabSup to have a positive sign on
IP, negative on wages, and positive on unemployment. The rationale is that higher labor
supply (entry from inactivity) increases the labor force and labor incomes; unemploy-
ment mechanically rises as it takes time for new entrants to be absorbed, while wage fall
due to increase slack. We also introduce a negative sign on layoffs, which is necessary
to separately identify LabSup from ASJRe. The intuition is that the expansionary effect
of the shock will tend to reduce layoffs, ceteris paribus. In turn, the matching efﬁciency
shock (MatEff) relates to shifts in the Beveridge curve which are affecting by exoge-
nous changes in the aggregate matching function. Consolo et al. (2021) ﬁnd that a job
matching efﬁciency shock is quantitatively important to explain ﬂuctuations in the un-
employment, separation and job ﬁnding rate. MatEff raises churn and reduces inﬂation,
just like ASJRe, but reduces unemployment by improving matching.

Estimation We estimate a six-variable model for the US economy in monthly frequen-
cies. Our model includes industrial production, inﬂation, wage growth, unemployment,
layoffs, and hires. We estimate the model using a Bayesian SVAR model with six lags,
using the methodology of Arias et al. (2018).22 We use a ﬂat (normal-diffuse) prior with
standard hyperparameters. The identiﬁcation process is based on sign restrictions for
the impact period, and we use 1000 draws for the burn-in sample, and 2000 draws for
the estimation sample. All shocks and respective sign restrictions are summarized in
Table 1. Figure 12 shows the impulse responses of all variables for each of the shocks.

Results We provide results across all identiﬁed shocks to gauge their respective rel-
evance for business cycle ﬂuctuations. We then look at the recent COVID crisis to see
which class of shocks has been more important.

Figure 11 summarizes, for each of the variables in our VAR model, the share of the
volatility explained by each shock. Structural shocks drive endogenous variables in dif-
ferent ways. This implies that the conditional correlation among endogenous variables
may get stronger or weaker depending on the main business cycles forces. Technology
and job reallocation shocks appear to be mostly relevant for wages; the share of variance
explained by these two aggregate supply shocks is limited for the rest of the endogenous
variables – including output. The evidence in the literature is rather mixed on the issue,
which to some extent can be explained by differences in sample period, variable choice,

22We use the BEAR 5.0 Toolbox of Dieppe et al. (2016).

ECB Working Paper Series No 2703 / August 202229and shocks. Closest to ours is the setup of Foroni et al. (2018), who also include a de-
mand shock, and also ﬁnd a relatively small role of neutral technology shocks for the
labor market.

Figures 13-18 provide the historical decomposition of the shocks for each endoge-
nous variable, separately for the full sample, and the sub-sample since 2018. We focus
our discussions on how the shocks affected the endogenous variables during the COVID
shocks, distinguishing between initial effects, when output collapsed, and longer-term
effects during 2021, when the economy made up for the pandemic shock and recov-
ery accelerated. The initial effects would primarily manifest in how ﬁrms adapted to
lockdowns and the switch to work from home, while the recovery phase should reﬂect
higher efﬁciency from adoption of new technologies and resource reallocation, together
with a pickup in inﬂation associated with supply shortages.

During the initial phase, the dominant factor behind the fall of Industrial Produc-
tion was the collapse in AggDem. This is consistent with the idea that supply shocks
affecting speciﬁc sectors can affect demand and hence output even in other sectors,
through sectoral complementarities, as workers for one sector are consumers for an-
other; see Guerrieri et al. (2020), Woodford (2020) for formal derivations of these argu-
ments. The other shocks had smaller effects, with negative contributions from ASTech,
LabSup and WagBag; the ﬁrst two reﬂect supply bottlenecks and lockdowns, while the
latter likely reﬂects the role of wage rigidities (see below). There were small positive
contributions from ASJRe and MatEff but were too trivial to matter. As the initial effect
receded and movement restrictions subsided, industrial production rose, also mostly
driven through the positive contribution of AggDem, as restrictions were lifted and vac-
cinations allowed the opening of contact-intensive activities. Here there is a larger role
for a reallocation shock in driving the growth of production, though the effect is tran-
sient (while AggDem more persistent). However, the role of ASJRe is not exceptionally
high by historical standards, as there was an important contribution in earlier episodes
as well.

For inﬂation, the results are quite similar (ﬁgure 14). The bulk of the variation in
the initial shock is explained by aggregate demand on the negative side, and ASTech on
the positive side. This is a fairly straightforward and classic result, and consistent with
most narratives of the pandemic. Conversely, the large increase in inﬂation towards
summer 2021 is primarily driven by AggDem, even though the other shocks also con-
tribute positively. ASJRe also has a positive contribution towards the end of the sample,

ECB Working Paper Series No 2703 / August 202230but much smaller than AggDem, and also not especially large relative to other episodes.

Moving to the labor market, we ﬁrst look at layoffs (ﬁgure 15). The model ascribes
the initial steep increase to all shocks, with the largest contributions coming through
AggDem, LabSup, and ASTech, as well as WagBag. This is again consistent with the
collapse of aggregate demand, stay at home orders and supply bottlenecks. The role
of wage bargaining in this context is understood through nominal rigidities in the face
of a steep reduction in production, which prevented matches from being preserved.
This inefﬁcient labor shedding aspect of the pandemic has been documented also in
Giupponi & Landais (2020) and Petroulakis (2020). The reduction in layoffs in 2021 was
initial driven by AggDem, and then by a combination of all shocks. In 2021Q2, ASJRe
had a positive contribution to layoffs, which could reﬂect the reopening of large parts of
the sectors of the economy.

AggDem was also the major driver of the initial fall in hires, but their subsequent
rise was the result of positive contributions from essentially all variables, throughout
the pandemic episode. The contribution of ASJRe was at its largest in 2021Q2, just as
with layoffs. And while the overall hire rate has been exceptionally large by historical
standards, due to the very large initial reduction in the employment pool and the need
to ﬁll vacancies, the relative contribution of reallocation to this elevated hire rate does
not stand out. In the hiring slump after the GFC, in the expansion of the mid-2010s,
as well as in the pre-GFC period, there is always a meaningful role for reallocation in
driving hires. Overall, ASJRe does play a role in driving churn, but it is minor relative
to the other shocks, despite a pickup late in the sample. As a result, ASJRe has a rel-
atively minor role in the evolution of unemployment, especially in the early phase of
the pandemic, when unemployment was at all-time highs, and any effect should have
been particularly strong then. The role of ASJRe is stronger in 2021Q2, where it has a
positive effect; note that this is consistent with the ”feedback” effect of Sahin et al. (2014)
analyzed in the mismatch section. Episodes of high unemployment may result in pro-
longed higher mismatch unemployment, even if mismatch itself has subsided, due to
delays in ﬁlling vacancies.

For completeness, we also consider wages. In the short run, both aggregate and
labor market-speciﬁc shocks play a role for wage growth, while over the long run wages
are mostly driven by aggregate shocks, AggDem and ASTech. In the pandemic period,
the main drivers were AggDem, WagBag and LabSup shock. The positive contribution
of WagBag reﬂects the role of nominal downward wage rigidity in preventing a fall in

ECB Working Paper Series No 2703 / August 202231nominal wages in the face of the spike in unemployment (see Abbritti et al. 2021 and
Grigsby et al. 2021). LabSup also played a very important role in wage formation as the
participation margin was a very important channel of labor market adjustment during
the pandemic because both the lockdown restrictions and the degree of home working
were heterogeneous across sectors, ﬁrms and worker types.

5.1 Sensitivity checks

We test the sensitivity of our results to our speciﬁcation and data choices in a number of
ways. First, instead of our baseline choice of a normal-diffuse prior, we use the optimal
prior selection procedure of Giannone et al. (2015). This approach treats the informa-
tiveness of these priors as additional hyperparameters, and estimates these parameters
by maximizing their posterior distribution under a ﬂat hyperprior. The authors show
that this is equivalent to maximizing the one-step ahead forecasting ability. Imposing
this optimal prior selection prior has little effect on our results.

Second, we try different restrictions for the effect of ASJRe on wages and of LabSup
on aggregate demand. In the former case, the sign is theoretically ambiguous, while in
the latter, the restriction maybe redundant. We conﬁrm that any relevant permutation
has little bearing on the main results.

A ﬁnal concern relates to the choice of the wage variable. The focus on continuing
workers to correct for compositional effects may be a concern if the wages of continuing
workers and new hires behave differently across the cycle. The literature remains quite
mixed on this issue; Haefke et al. (2013) argue that wages of new hires respond strongly
to productivity, and Hall & Milgrom (2008) that continuing workers are more insulated
from cyclical shocks. On the other hand, Gertler et al. (2020) posit that this procycli-
cality is only relevant for job-to-job transitions, and reﬂects procyclical improvement in
matches. They do not ﬁnd excess cyclicality for hires from unemployment. We note that
the vast majority of job losses in the initial COVID shock were temporary layoffs Hall &
Kudlyak (2020), and it is likely that these workers were rehired at their previous wages.
It is not possible to check this directly in a statistically meaningful way, but we repeat the
SVAR exercises using wage growth for job switchers (instead of all continuing workers),
and ﬁnd little difference in the results.23

23The reason why it is not possible to check directly the wages of these individuals is the nature of the
CPS rotating. CPS is a rotating panel, where workers are observed for four consecutive months, dropped
for eight months, and then observed for another four consecutive months. Moreover, they only report

ECB Working Paper Series No 2703 / August 2022326 Conclusion

In this paper, we critically examine the case for a pandemic-induced reallocation wave in
the United States, taking a broad perspective. First, we consider direct evidence for CPS
and JOLTS, focusing in particular on standard measures of churn, as well as movements
by ﬁrm size. In the CPS, we show that there is no discernible uptick in the share of
workers switching sectors or occupations, be it in the aggregate or in the cross-section.
Other data corroborate that even workers who lost their jobs when the pandemic shock
hit tend to be re-employed in the same sector a year later, in similar fashion as before the
pandemic. From JOLTS, we ﬁnd that the hire rate moved much less than the separation
rate in the early stages, despite high unemployment; it rose later on, especially in 2021,
in line with the pickup in the aggregate economy, but has also been accompanied by a
reduction in layoffs.

We also do not ﬁnd evidence of large hiring sprees for the very large ﬁrms, who
were expected to gain substantially from the pandemic. Instead, and primarily until
Summer 2021 (when the labor market had started to tighten substantially), we ﬁnd
evidence consistent with well-known patterns of cyclical behavior by ﬁrm size (Halti-
wanger et al. 2018, Moscarini & Postel-Vinay 2012), with large ﬁrms contracting less in
the initial shocks, but growing more slowly during the expansion. The absence of large
movements for the largest ﬁrms could of course still be consistent with substantial re-
allocation within these ﬁrms (who for instance could increase their use of labor-saving
technologies and workers in abstract tasks, at the expense of other employees), but it
fails to reﬂect the popular narrative of massive hires by the largest ﬁrms. Taken together
with the results of Barrero et al. (2020), these ﬁndings imply that reallocation may have
yet to take off, but if it has, it is taking place within occupations and sectors.

We then take an off-the-shelf multisector search and matching model to examine
whether mismatch unemployment rose as a result of the pandemic. A reallocation shock
should have led to a large and persistent rise in mismatch unemployment (as a share of
total), as workers move to new and growing sectors. We ﬁnd little evidence to support
this view; by all measures, mismatch unemployment has been less than 1 percentage
point during the whole of the pandemic, accounting for 14% of total unemployment in

wages in the fourth and eighth month of observation. As such, we can only capture 12-month wage
growth for these workers only if the April 2020 was their 6th or 7th month. The sample remaining to
complete this calculation (using the tools provided by the Atlanta Fed Wage Growth Tracker) is only a
few dozen observations for each month, rendering precise estimation impossible.

ECB Working Paper Series No 2703 / August 202233February 2021, and less than a ﬁfth of the total increase. Just like Sahin et al. (2014)
show for the aftermath of the Great Recession, mismatch unemployment has not been
an important factor for the pandemic recession either.

Finally, we look for evidence of a reallocation shock using a novel identiﬁcation
strategy in a Bayesian Structural VAR setup. We introduce a job reallocation shock, dis-
tinct from standard neutral technology shocks, matching efﬁciency shocks, labor supply
shocks, or wage bargaining shocks. There is little evidence to suggest that the job real-
location shock played an especially large role, relative to other episodes, in driving the
labor market through the ﬁrst year of the pandemic. Aggregate demand was the pri-
mary driver of unemployment, hires and layoffs, with all other shocks having similar
contributions. This is consistent with the view of the COVID-19 shock as reﬂecting low
demand, lockdowns, supply bottlenecks, and low capacity utilization.

The results of the preceding analyses fail to provide evidence of a reallocation
wave. It should be acknowledged that it is possible that highly stimulative policies may
have prevented such a wave from materializing, for instance by reducing incentives of
workers to look for jobs in growing sectors. At the same time, the reallocation argu-
ment would also affect job-to-job transitions. We have shown that, across sectors, there
is little change in reallocation relative to pre-pandemic times. Reports by the Califor-
nia Policy Lab further showed that workers who lost their job in April 2020 had similar
same-sector re-employment rates 12 months later across both hard-hit sectors and less
affected ones. As such, we believe that, on the margin, UI generosity is unlikely to have
affected our results. On the other hand, it is also conceivable that policy, through lock-
downs and diversion of economic activity, combined with fear of the disease, induced
itself some reallocation (Goolsbee & Syverson 2021); this would then imply a mostly
temporary nature for such reallocation, which would vanish upon the removal of these
policies and the eradication of the disease. It could possibly be too soon to tell whether
COVID-19 induced a reallocation wave.

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ECB Working Paper Series No 2703 / August 2022397 Additional Figures

Figure 9: Job-to-job transitions, seasonally adjusted

Notes: The chart shows the share of workers who switch employers (job-to-job transitions); FF
uses the Fallick & Fleischman (2004) measure, FMP the Fujita et al. (2021) measure, and MAR a measure
assuming missing observations are missing at random. The data for the RHS chart come from Fujita et al.
(2021) and have been seasonally adjusted using the ratio-to-moving-average method.

0.01.02.03.04Share of switchers2013m22015m22017m22019m22021m2dateFFFMPMARECB Working Paper Series No 2703 / August 202240Figure 10: Change in layoff rate

Notes: The y-axis shows, for each sector, the change in the average layoff rate from the 2001-
February 2020 period to the May 2021 - July2021 period. The x-axis shows the log change in employment
from February 2020 to the average of the May 2021 - July2021 period. Source: JOLTS and CES.

artsrecconstrdurableeducfininsfoodhcsainfominelognondurothserveprobusrerlretailtransutilwholesale-1.5-1-.50Δ layoff rate-.2-.15-.1-.050Δ log employmentECB Working Paper Series No 2703 / August 202241Figure 11: Forecast Error Variance Decomposition

Notes: The table shows, for each of the variables in our VAR model, the share of the volatility
explained by each shock. The vertical axis is in %, and the horizontal axis in months. IP refers
to Industrial Production, PCE to personal consumption expenditure inﬂation, Wages to Atlanta
Fed measure of nominal wages, UR to the unemployment rate. Similarly, AggDem refers to
Aggregate Demand, ASTech to neutral technology, ASJRe to job reallocation, WagBag to wage
bargaining, MatEff to matching efﬁciency, and LabSup to Labor Supply shock.

ECB Working Paper Series No 2703 / August 202242Figure 12: Impulse Responses

Notes: The ﬁgure shows impulse responses of all variables to all shocks in the SVAR model.

ECB Working Paper Series No 2703 / August 202243Figure 13: Industrial Production

Notes: The ﬁgures show the historical decomposition of shocks in the SVAR framework. The top ﬁgure
shows the full sample (2001m1-2021m8), while the bottom one focuses on the sample since 2018.

ECB Working Paper Series No 2703 / August 202244Figure 14: Price Inﬂation

Notes: The ﬁgures show the historical decomposition of shocks in the SVAR framework. The top ﬁgure
shows the full sample (2001m1-2021m8), while the bottom one focuses on the sample since 2018.

ECB Working Paper Series No 2703 / August 202245Figure 15: Layoffs

Notes: The ﬁgures show the historical decomposition of shocks in the SVAR framework. The top ﬁgure
shows the full sample (2001m1-2021m8), while the bottom one focuses on the sample since 2018.

ECB Working Paper Series No 2703 / August 202246Figure 16: Hires

Notes: The ﬁgures show the historical decomposition of shocks in the SVAR framework. The top ﬁgure
shows the full sample (2001m1-2021m8), while the bottom one focuses on the sample since 2018.

ECB Working Paper Series No 2703 / August 202247Figure 17: Unemployment Rate

Notes: The ﬁgures show the historical decomposition of shocks in the SVAR framework. The top ﬁgure
shows the full sample (2001m1-2021m8), while the bottom one focuses on the sample since 2018.

ECB Working Paper Series No 2703 / August 202248Figure 18: Wages

Notes: The ﬁgures show the historical decomposition of shocks in the SVAR framework. The top ﬁgure
shows the full sample (2001m1-2021m8), while the bottom one focuses on the sample since 2018.

ECB Working Paper Series No 2703 / August 202249Acknowledgements 

We would like to thank an anonymous referee, Danilo Cascaldi-Garcia, Andrea Colciago, Renato Faccini, Matthias Hertweck, Ioannis 
Kospentaris, Theodora Kosma, Nicholas Kozeniauskas, Dimitris Louzis, Myrto Oikonomou, Plutarchos Sakellaris, Boriss Siliverstovs, 
Eugenia Vella and Ernesto Villanueva, as well as participants at the 5 th ESCB Research Cluster 2 workshop, 20th CRETE, and 26th 
ICMAIF for helpful comments and discussions. 
The views expressed in this paper are those of the authors alone and may not necessarily represent those of Bank of Greece, the 
European Central Bank or the Eurosystem. All errors are ours. 

Agostino Consolo 
European Central Bank, Frankfurt am Main, Germany; email: agostino.consolo@ecb.europa.eu 

Filippos Petroulakis 
Bank of Greece, Athens, Greece; email: fpetroulakis@bankofgreece.gr 

© European Central Bank, 2022 

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