ijems | scientific article
TheEffectsofJobRetentionSchemes
onEmploymentPreservation
duringthe covid-19Epidemic
inEuroAreaCountries
anton rop
rop.anton@yahoo.com
Inthispaper,weanalysetheeffectsofdifferentjobretention(jr)
schemestake-upsonthepreservationofemploymentduringthe co-
vid-19pandemicineuroareacountries.Wefindthat jr schemesin
euroareacountrieshelpedreducejoblossesduringthepandemic.The
mosteffectiveinpreservingemploymentweretake-upsofthemost
extensivelyupdatedpre-existingshort-timework(stw)schemesthat
weremoregenerousandincludednonstandardworkers.However,the
impactof jr schemeswaslessthantheoverallemploymentpreser-
vationachieved.IncontrasttotheGreatRecession,macroeconomic
measuresofeconomicsupportalsohelpedpreservejobsduringthe
pandemicaswell.Correspondingdifferencesinsectoralemployment
preservationeffectsshowthatsuchmacroeconomicsupportledto
morejobsbeingkept,especiallyinthegroupofvulnerableservicesec-
tors.
KeyWords:covid-19pandemic,jobretentionschemes,short-time
workschemes,macroeconomicmeasures
https://emuni.si/ISSN/2232-6022/17.115-140.pdf
introduction
Jobretention(jr)schemesfeatureamongthekeyinstrumentsformit-
igating the effects of the lockdowns on employment and social hard-
ship introduced or extended by different countries in response to the
crisis. Such schemes provided strong income support to workers with
reducedworkinghours,reducedincomelosses,bolsteredaggregatede-
mand,andsignificantlyloweredthenumberofjobsatriskofbeingter-
minatedduetoliquidityconstraints(oecd 2021).
jr schemes can take the form of short-time work (stw) schemes
thatdirectlysubsidisehoursnotworked,suchasGermany’sKurzarbeit
or France’s Activité partielle. They can also include wage subsidy (ws)
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schemes that subsidise hours worked, and can in addition be used
to top up the earnings of workers with reduced hours, such as the
Netherlands’ Emergency Bridging Measure (Noodmatregel Overbrug-
ging Werkgelegenheid, now ) or the JobKeeper Payment in Australia.
A crucial aspect of all these jr schemes is that employees keep their
contracts with the employer even if their work is fully suspended. Ac-
cordingtoanoecdassessment(oecd2020a),inthesecondquarterof
2020,whentake-upratespeaked, jrschemeswerebeingimplemented
in almost all oecd countries, covering around 60 millionworkers. In
comparison, during the Great Recession, jr schemes only included
some6millionworkers, eventhough16 oecd countrieslaunched jr
schemesorhadimplementedtheschemesalreadyearlyoninthecrisis
and7 oecd countrieshadintroducednewschemesduringthatperiod
(HijzenandVenn2011).
However, research on the impact of jr schemes on employment
preservationinthepandemichasbroughtmixedresults.Adams-Prassl
et al. (2020) find that in Germany, with a well-established short-time
work(stw)scheme,34ofemployeesinworkattheonsetofthepan-
demichadbeenaskedtoreducetheirhourstobenefitfromthisscheme.
InApril2020,only5ofGermanworkershadlosttheirjobscompared
tothe usa andthe uk wheretherespectivefigureswere20and17
ofindividuals(Adams-Prassletal.2020).Similarly,theJobKeeperPay-
ment scheme in Australia is estimated to have saved one in five jobs
(BishopandDay2020).Incontrast, oecd analysisbasedonthetake-
up¹ of jr schemes in oecd countries shows jr schemes had a rela-
tively small effect on employment (compared to data on employment
inhoursdecline),accordingtowhichremovingthe jr schemeswould
haveledtoadropinemploymentofbetween6and11(oecd 2021).
Besidesthehighcosts,anintriguingaspectofrunningintensive jr
schemesmightbethe‘deadweighteffects,’namely,theriskofsupport-
ingjobsthatactuallydonotneedsupport(oecd 2021).Thus,govern-
mentscouldbereluctanttousetheexistingsupportschemesoncethey
discovertheirlimitedreach.Thismightexplainwhyinmost eu (devel-
oped) countries scheme cover grew strongly in the second quarter of
2020 buteasedconsiderablyalreadybythenextquarterto fallbehind
the second quarter level in almost every country. This was also seen
¹Take-up rates refer to actual use and are calculated as a share of total employees in
short-timework(oecd 2020a).
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duringthesecondandthirdwavesofepidemicwhenthepick-upineco-
nomic activity continued, even though in the most of these countries
schemecoverremainedavailableuntilthemiddleof2022.
Inadditiontojrschemes,eucountriessoughttoreducetheharm-
ful effects of the lockdowns on economic activity, employment, and
socialhardshipbylaunchingotherpowerfulfiscalandmonetarymea-
sures,whichhadstronganddifferenteffectsinvarioussectorsofeco-
nomicactivityandhencealsoonemployment.Giventhat jr schemes
were simultaneously operating alongside those policy measures, eval-
uating the effects of jr schemes by only considering firm-based em-
piricalevidenceonthepure take-up of jr schemes’covercouldbebi-
ased.Moreover,whileevaluatingtheemploymentpreservationeffects
of such take-ups, one must also consider the sectoral different inten-
sity of the impact of the economic support measures. Sectoral use of
jrschemesduringthecovid-19pandemicisquiteunlikethatseenin
the Great Recession. During the first three waves of the epidemic, jr
schemesaffectedemploymentacrossmanysectorsandtypesoffirms,
whereasintheGreatRecessionalmost80ofjrschemetake-upswere
concentratedinmanufacturing(oecd 2020a).
In this paper, we analyse the effects of different job retention (jr)
schemes’take-ups onemployment preservation duringthe covid-19
pandemicineuroareacountriesconsideringthecompleteportfolioof
policymeasuresandsectoraleffectsasacrucialnon-policy-relatedfac-
tor.Wefindthat jr schemesintheeuroareacountrieshelpedreduce
job losses during the pandemic. The most effective in retaining em-
ployment weretake-ups ofthemostextensivelyupdated,pre-existing
short-timework(stw)schemesthatweregenerousandincludednon-
standard workers. However, the impact of jr schemes take-ups was
lessthantheachievedlevelofemploymentthatwaspreserved.Corre-
spondingdifferencesinsectoralemploymentpreservationeffectsshow
that macroeconomic support eased the loss of employment especially
inthegroupofvulnerableservicesectors.
Our study complements previous research (Hijzen and Venn 2011;
Aiyar and Dao 2021) since assessments of what determines the size
andquarterlydynamicsofjrschemestake-upssupport,andtheirem-
ploymentpreservationeffectsbysectorsandbasedonmacroeconomic
dataarerareoronlypartial.Thestudycontributestotheliteratureon
the implementation and effectiveness of various jr scheme take-ups
indifferentcountriesregardingseveralcovid-19wavesandsectors.In
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addition, the effects of other macroeconomic (non-jr schemes) mea-
sures on employment as well as potential sectoral differences in such
macroeconomicemploymentsupportarealsopresented.
literature review
Although in the Great Recession jr schemes covered ten times fewer
workers thanintherecentpandemic,theirimplementationandeffec-
tiveness soonbecameasubjectofacademicresearch.Asearlyas2011,
theoecdconductedadetailedanalysisofjrschemes’impactandrole
duringtheGreatRecession(HijzenandVenn2011).Thestudydescribes
the characteristics of the schemes implemented (albeit, it deals solely
withstwschemes)andevaluatestheireffectivenessinpreservingem-
ploymentintheshortandlongrun(inbustandrecovery).Itunderlines
two important potential shortcomings of these schemes. First, it as-
sessedthattheimpactonjobswassmallerthanthepotentialnumber
ofjobssaved,indicatingweaktargetingand,second,thattheschemes
led to greater labour segmentation if limited to workers holding per-
manentcontracts.
Similar ambition and results may be seen in a study by Boeri and
Bruecker(2011).Theyfoundthat stw helpedreducejoblossesduring
theGreatRecession.Still,accordingtotheirmacroeconomicestimates,
thenumberofjobssavedwaslessthanthefull-timeequivalentjobsin-
volved in these programmes, in some cases pointing to sizeable dead-
weightcostsentailingthesamemoralhazardproblemsasthosearising
withtheprovisionofunemployment insurance.Workers andemploy-
ersmightcolludetoextractpaymentsfromthestateevenwhenincen-
tives for reductions in hours would notbe required to avoid layoffs as
thefirmwasnolongerfacinganegativedemandshock.
The performance of jr schemes during the recovery period of the
Great RecessionepisodewasanalysedinbyHijzenandMartin (2013).
They foundthat stw raisesthe output elasticity ofworking time and
helpspreservejobsinthesizeablecontextofarecessionbymakingem-
ploymentlesselasticwithrespecttooutput.Akeyfindingwasthatthe
timingof stw wascrucial.
One can also find several papers dealing with jr schemes’ per-
formance during the Great Recession in specific countries. See, for
example, Bellmann, Gerner, and Upward (2015) regarding Germany,
Calavrezo,DuhautoisandWalkoviak(2009) analysingthesituation in
France, and Siegenthaler and Kohl (2019), describing the Swiss expe-
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TheEffectsofJobRetentionSchemesonEmploymentPreservation
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riences with jr schemes during the Great Recession and afterwards.
Asthepandemicdeveloped,manystudiestrackedtheinitialimpact
ofthecovid-19inducedcrisisontheusaandEuropeancountriesre-
gardingemployment,hoursworked,andincome(InternationalLabour
Organization 2020; 2021; Zimpelmann et al. 2021; Cotofan et al. 2021;
Andertonetal.2020).GangopadhyayaandGarrett(2020)comparedthe
levelofunemploymentinbothcrises:theGreatRecessionandcovid-
19. They found that during the Great Recession unemployment in the
usa reached 10, while during the pandemic unemployment spiked
at12.8.Andertonetal.(2020)analysedthecovid-19pandemic’s
impactontheeuroarealabourmarketfromtheperspectiveofthecu-
mulative contribution of four specific economic shocks to changes in
total hours worked and the labour force: a technology or productivity
shock,ashockinthelaboursupply(viaashocktolabourforcepartic-
ipation), a shock giving rise to an increase in the demand for labour,
andawagebargainingshock.
The oecd (2020a) analyses the jr schemes that oecd countries
reliedonduringthe(firstwaveof)thecovid-19pandemic.Theoecd
estimates that stw schemes typically allow reduced working time at
zero cost to firms, while ws schemes generally permit larger reduc-
tionsinlabourcoststhanstwschemes,yetareassociatedwithgreater
fiscal costs or weaker income protection for workers. Due to the bet-
ter targeting of stw subsidies to firms likely to experience financial
difficulties, they are probably more effective at saving jobs than ws
schemes. Accordingto oecd simulations based on the single-hit sce-
nario, stw subsidiesreducetheshareofjobsatriskby10percentage
points from 22, whereas this is only 7 percentage points under ws.
Asmallersectionofthestudyalsodiscussesthesectoraldimensionof
the jr schemes’effects.
Another study for g20 countries (oecd 2020b) finds that diverse
working arrangements offered less security and were concentrated in
affected sectors. Workers in a range of employment forms that vary
from a full-time wage and salary work under a permanent contract –
such as self-employed workers, those on temporary, on-call or part-
time contracts, and informal economy workers – have been very vul-
nerabletothejobandincomelossestriggeredbythepandemic.
An oecd (2020c) study stresses that the sectors most directly af-
fectedbythecovid-19containmentmeasuresaccountforaround40
oftotalemploymentandthesesectorsemployalargeshareofnonstan-
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dardworkers,i.e.,part-timeworkers,self-employed,andworkershired
underfixed-term contracts. Relative topermanentemployees, tempo-
rary workers have a higher risk of losing their jobs and less chance of
beingenrolledinshort-timeworkschemes.
The oecd (2021) devotes a separate chapter to the jr schemes in
place during the first three waves of epidemic. The paper tackles the
sizeandvolatilityofthe jr schemestake-ups, dealswithsectoraldif-
ferencesintake-ups,aswellasthedependenceofemploymentsupport
onthesizeofworkersincome.
Several studies of jr scheme effectiveness have looked at pro-
grammesinparticularcountries,yettheirresultsarealsoinconclusive.
Smaller estimates than expected are also evident in an imf study for
Germany (Aiyar and Dao 2021), whereas estimated effects of the Job-
Keeper Payment scheme in Australia show just the opposite – much
higher effects, with the jr scheme being estimated to have saved the
jobofoneinfiveemployees(BishopandDay2020).Resultsalsodiffer
significantlyforstudiesofthesameschemeandcountry,suchasstud-
ies ofthe PaycheckProtectionProgram (ppp)used inthe usa (Autor
etal.2020;HubbardandStrain2020).
data, descriptive statistics of main variables
and hypotheses
Data
We can generally capture the observations made in the previous sec-
tionsinthefollowingequation:
Jobpreservation = f(jr schemes,macroeconomiceffects
oftheportfoliooffiscalandmonetary
measures,sectors)( 1 )
Weexplainthesourcesofdataandconstruction ofthemainvariables
below.
Ouranalysisisbasedonquarterlydatafromthefirstquarterof2019
until the second quarter of 2021 (q1 2019–q2 2021) for 19 euro area
countriesand9sectors.Foreachsectorandeuroareacountry,wetake
seasonallyadjusted dataonemployment inhours andemployment in
persons from Eurostat (https://ec.europa.eu/eurostat). We normalise
employmentdatabysettingtheaverageemploymentlevelachievedin
2019foreachcountryas1.Thisallowsustoconstructtheemployment
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preservationindicatorasaratioofemploymentinpersonsandemploy-
mentinhours,whichservesasthemaindependentvariableinthere-
search.Withtheemploymentpreservationindicator,foreachquarter,
country and sector, we measure the level of employees per number of
hoursusedrelativetotheaveragein2019soastocapturetherelative
level of employees who remained in employment despite the decline
inthenumberofworkinghours.Forinstance,inthesecondquarterof
2020,i.e.duringthefirstcovid-19wave,theratioforGermanyis1.10.
ThisindicatesthatinthisquarterGermanyrecordedalevelofemployed
personsthatwas10higherthanthelevelobservedinworkinghours.
It is thus evident that the number of employees in Germany dropped
considerablylessthanthenumberofworkinghours.Thiswasactually
thecaseforalleuroareacountries.
To assess the impacts of different jr schemes, we use the oecd
classification whereby countries use five types of jr schemes, name-
ly,besidesthefour stw schemesalso ws (oecd 2020a).Accordingto
the oecd study,23countrieswithapre-existing stw schemerapidly
adjustedtheirstwschemetocopewiththecovid-19crisis(oecd
2020a). They applied different combinations of three key changes: (1)
simplifying access and extending coverage; (2) extending coverage to
non-permanent workers; and (3) making them more generous. Boeri
andBruecker(2011)arguethatmakingthebenefitsmoregenerouspro-
vides the subject workers with stronger support whilegranting access
fornonstandardjobsmeansthatbettertargetingcanbeachievedsince
workersholdingnonstandardjobs–i.e.theself-employedandworkers
withtemporaryorpart-timedependentemployment–areveryvulner-
able to job and income losses.² However, employers have little or zero
incentivetouse stw fornonstandardjobsastheyknowtheseworkers
can be fired at little or no cost, meaning access for nonstandard jobs
shouldprobablybecombinedwithmoregenerous stw benefits.
A number of countries have introduced temporary ws in response
tothecovid-19crisisthatcanbeusedbyfirmsforhoursworked(like
standard wage subsidies) as well as for hours not worked (like stw
²On average, across the oecd countries, the sectors most directly affected by the
covid-19containmentmeasuresaccountforaround40oftotalemployment.These
sectorsemployalargeproportionof‘nonstandardworkers,’i.e.part-timeworkers,self-
employed and workers hired under fixed-term contracts. This proportion is generally
highestinentertainmentindustries,hotelsandrestaurants(oecd 2020c).
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table1 TypesofjrSchemesUsedduringthecovid-19PandemicintheEuro
Area
Stw,Leastupdated,pre-existing stw scheme:Increased
accessandcoveragewithmoregenerousbenefits
Austria,Belgium,Luxem-
bourg,SlovakRepublic
Stw,Updated,pre-existing stw scheme:increasedaccess
andcoverageandaccessforworkersinnonstandardjobs
Italy,Portugal
Stw,Mostextensivelyupdated,pre-existing stw scheme:
increasedaccess,coverage,benefitgenerosityandaccess
forworkersinnonstandardjobs
Germany,Spain,Finland,
France
Stwn,New(notpreviouslyexisting) stw scheme Greece,Lithuania,Latvia,
Slovenia,Cyprus
Ws,Newwagesubsidyscheme Estonia,Ireland,
Netherlands,Malta
notes Basedondatafrom oecd (2020a).
schemes), e.g., Australia, Canada, Estonia, Ireland, New Zealand. ws
arereservedforfirms experiencingasignificantdeclineinrevenue.In
somecountries,thesizeoftheactualsubsidyonlydependsonthewage
bill (before programme participation) and not the decline in business
activity(oecd 2021).
Table 1 presents different types of jr schemes used during the
covid-19crisisintheeuroarea.Itrevealsimportantcross-country
differences in the jr schemes used: 10 countries that adjusted their
pre-existing stw schemes;5countrieswithnew stw schemes,and4
countrieswithwagesubsidyschemes.
Data on the total portfolio of economic support measures were
collectedfromtheOxfordcovid-19GovernmentResponseTracker
(GitHub2022).TheOxford covid-19GovernmentResponseTrack-
er (oxcgrt)provides a systematic setofcross-national, longitudinal
measures of government responses for more than 180 countries since
1stJanuary2020(Haleetal.2021).Atpresent,itincludes19policyindi-
catorscoveringclosureandcontainment,healthandeconomicpolicies.
Tomake iteasierto describe government responses inaggregate, ox-
cgrt calculates simple indices that combine individual indicators to
provide an overall measure of the intensity of government response
acrossafamilyofindicators.Theseindicesare:(1) gri (allcategories);
(2) stringency index (containment and closure policies sometimes re-
ferredtoaslockdownpolicies);(3) chi (containmentandclosureand
healthpolicies);and(4) esi (economicsupportmeasures).The esi in-
dexiscomposedofeconomicpolicyresponseindicatorswhichinclude
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income support, debt/contract relief for households, fiscal measures
and giving international support indicators.³ We used the esi index
as an aggregate measure of the economic support for the period q1
2019–q2 2021for19euroareacountries.
Tobeabletodeterminehowmuchthemacroeconomiceffectsofthe
portfolio of fiscal and monetary measures (used to mitigate the dam-
agecausedbythelockdownmeasures)helpedpreserveemploymentin
addition to the actual jr scheme take-up effects, we must control for
macroeconomic effects and their sectoral dimension on the trajectory
of jr take-ups.Lockdowneffectsandthecorrespondingemployment
lossvaried considerably between sectors. Hence, we use employment
dataforthea10sectionsofthebroadnacestructureofeucountries.
Lockdown measures were quantified by using the corresponding
stringencyindexwhichembracesallindicatorsoncontainmentandclo-
sure policies (school closure, workplace closure, cancellation of public
events, restrictions on the size of gatherings, halting of public trans-
port, stay-at home requirement, limitations on internal movement,
restrictionsoninternationaltravel),constructedandpublishedonthe
Oxford covid-19GovernmentResponseTracker(Haleetal.2021).
Specific npi indicators (restrictions on the size of gatherings and
school closure) used while constructing the instruments are collected
fromthesamesource.
DescriptiveStatisticsofMainVariablesandOperativeHypotheses
Table 2 presents descriptive statistics of the most important model
variablesforeuroareacountries:employmentpreservationratio,nor-
malised employment in persons, normalised employment in hours,
government’seconomicsupportmeasures,andgovernment’scontain-
mentmeasuresforeachquarterfrom q1 2020until q2 2021.
The levels of employment in persons and employment in hours
in euro area countries were at their lowest in the second quarter of
2020,andwhilethemaximumdropintheaveragelevelofemployment
perpersonpercountrydidnotexceed7.6(Spain),unemploymentin
hours dropped substantially more, notably on average by 12.7 com-
paredtotheaveragefor2019,toreachamaximumdecreaseof27.2in
the case of Greece. For the entire period under observation, the nor-
malised level of employment in persons was higher than the level of
³ThewaycompositeindicesarecalculatedisdescribedinHaleetal.(2021).
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table2 DescriptiveStatistics
Item 
q

q

q

q

q

q
Mean Employmentpreservationratio . . . . . .
Employmentinperson = . . . . . .
Employmentinhours = . . . . . .
(a) Economicsupportmeasures . . . . . .
stwn_take-up . . . . . .
ws_take-up . . . . . .
stw_take-up . . . . . .
stw_take-up . . . . . .
stw_take-up . . . . . .
(b) stringency . . . . . .
gatherings . . . . . .
school . . . . . .
sd Employmentpreservationratio . . . . . .
Employmentinperson = . . . . . .
Employmentinhours = . . . . . .
(a) Economicsupportmeasures . . . . . .
stwn_take-up . . . . . .
ws_take-up . . . . . .
stw_take-up . . . . . .
stw_take-up . . . . . .
stw_take-up . . . . . .
(b) stringency . . . . . .
gatherings . . . . . .
school . . . . . .
Continuedonthenextpage
employment in hours. This difference is most pronounced in the sec-
ondquarterof2020andshrinksslowlyafterwards.
The employment preservation ratio was at its highest during the
peak of both epidemic waves. Still, there is quite a high cross-country
heterogeneity in the preservation ratios, reflecting differences in the
intensityofpolicyresponsestothepandemicandthesectoralcompo-
sitionoftheeconomies(Andertonetal.2020).
The jr supportmeasureswereattheirlowestin q1 2020,increased
considerablyinq22020andremainedatelevatedlevelsfortheremain-
ing quarters of the observation period. For all types of jr schemes,
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[125]
table2 Continuedfromthepreviouspage
Item 
q

q

q

q

q

q
min Employmentpreservationratio . . . . . .
Employmentinperson = . . . . . .
Employmentinhours = . . . . . .
(a) Economicsupportmeasures . . . . . .
stwn_take-up . . . . . .
ws_take-up . . . . . .
stw_take-up . . . . . .
stw_take-up . . . . . .
stw_take-up . . . . . .
(b) stringency . . . . . .
gatherings . . . . . .
school . . . . . .
max Employmentpreservationratio . . . . . .
Employmentinperson = . . . . . .
Employmentinhours = . . . . . .
(a) Economicsupportmeasures .     
stwn_take-up . . . . . .
ws_take-up . . . . . .
stw_take-up . . . . . .
stw_take-up . . . . . .
stw_take-up . . . . . .
(b) stringency . . . . . .
gatherings . . . . . .
school . . . . . .
notes Row headings are as follows: (a) government’s support measures, (b) gov-
ernment’s containment measures. Based on data from Eurostat (https://ec.europa.eu
/eurostat),oecd(2000a),andOxfordcovid-19GovernmentResponseTracker(GitHub
2022).Employmentvariablesarenormalisedonthebasis  2019=1;indexofeconomicsup-
port variable, Stringency index, School closing and Gatherings are used as defined in the
Oxfordcovid-19GovernmentResponseTracker(GitHub2022).Typesofjrschemesde-
finedassuggestedinthe oecd (2020a)study.
thehighestleveloftake-upswasreachedinthesecondquarterof2020
while afterwards they declined and stayed quite stable. In the second
quarterof2020,thelevelofschemeuse(take-ups)reachedamaximum
level of 31 (Italy) for scheme type stw2 (a pre-existing stw scheme
withupdatedaccessandcoverageaswellasaccessforworkersholding
nonstandard jobs) with a mean value of 25 of scheme take-ups. The
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lowest level of take-ups was reached in all quarters in those countries
withnew stw schemes(stwn),eventhoughtheyhadanaveragelevel
(7.8) of take-up in q2 2020 with a corresponding minimum level of
almost2.8(Latvia).
The containment and closure policies index (stringency, measured
from 1 to 100) is at its lowest in q1 2020 and its highest in q2 2021.
Overall, the mean value increased after q3 2020 and remained high
through the other periods. A similar pattern occurred with the indi-
cator Restriction on the size of gatherings (ordinal values, 1–5), while
the indicator School closure (ordinal values 1–5) was at its highest in
q2 2020(2.37),butlaterrelaxed.
Againstthisbackgroundandtheliteraturereviewed,wetestthefol-
lowinghypotheses:
1 Changesintheemploymentpreservation ratioovertimecanbe
to a larger degree explained by changes in jr scheme take-up
rates.
2 Changes in the preservation indicator over time are also influ-
enced by changes in other support measures (fiscal and mone-
tary)thatgovernmentshaveimplementedduringthepandemic.
3 Among different jr schemes, the most effective at preserv-
ing employment levels were take-ups of already existing stw
schemesthathadbeenmostextensivelyupdated.
model
In normal times, Okun’s law⁴ suggests that employment in persons
depends on employment in hours, the cyclical phase of economic ac-
tivity (Burggraeve, de Walque, and Zimmer 2015), as well as sectoral
andcountrycharacteristics(Crivelli,Furceri,andToujas-Bernaté2012).
However, inthepandemic,policymakershavesupportedemployment
preservation (our dependent variable) directly by using jr schemes
andindirectlythroughmacroeconomicpolicysupporttotheeconomy.
We start the description of the equation composing our model of
employmentpreservationwiththeequationforemploymenttoappro-
priately encompass the relationship between employment in persons
and employment in hours (Burggraeve et al. 2015). Explanatory vari-
ables of the model for employment in persons are therefore employ-
⁴Okun’sLawisanempiricallyobservedrelationshipbetweenunemploymentandlosses
inacountry’sproduction(Prachowny1993).
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TheEffectsofJobRetentionSchemesonEmploymentPreservation
[127]
mentinhours, jr schemetake-uprates,economicsupportmeasures,
andtimefixedeffects.Toencompasslargedifferencesinthepotential
effects of economic support between sectors, economic support vari-
able effects are specified separately for five groups of sectors. Corre-
spondingexplanatoryvariablesaredefinedasaproductofthesectoral
groupindicatorvariableandeconomicsupportvariable.Timefixedef-
fects areincludedtoaccountforanyothertime-specificeffects onthe
employment in persons variable thatmighthave affected penetration
ratesinthecountriesunderstudy.
If ep
ijt
is employment in persons and eh
ijt
employment in hours,
jr
it
·dum_jr
ik
thetake-upofjrscheme k(forcountryitake-upjr
it
and
schemedum_jr
ik
),es
it
·dum_es
il
sectoraleconomicsupport(forcountry
ieconomicsupportes
it
andsectordum_es
il
),dum
t
timeindicatorsem-
bracingpotentialother(undisclosed)yetsystematicfactors’effectson
persons employed dynamics, U
i
unobservable country effects, U
j
un-
observable sector effects andε
ijt
the error term, then the conceptual
versionofthemodelforemploymentmaybeformallywrittenas
ep
ijt
= F(eh
ijt
,jr
it
·dum_jr
ik
,es
it
·dum_es
il
,
dum
t
,U
i
,U
j
,ε
ijt
), (2)
where index i stands for country, j for sector, t for time, k for type of
schemeandlforsectoralgroup.
Regarding the specification of the function F,itisassumedthat
thereisalineardependenceofep
ijt
ontheelasticityofeh
ijt
andthein-
crementsofothervariablesstatedsuchthatthecompletespecification
of the estimable operative version of the model for ep
ijt
isthe follow-
ing:
ep
ijt
= eh
α
ijt
·exp(
 k
β
k
jr
it
·dum_jr
ik
+
 l
γ
l
es
it
·dum_es
il
+
 δ
t
dum
t
+U
i
+U
j
+ε
ijt
). (3)
Sectoral groups are defined by sectors of the a10 sections of the
broadest nace sectoral classification. These groups of sectors are
defined according to the potential extent of their lockdown expo-
sure (manufacturing, construction, utilities, vulnerable services, non-
vulnerable services, public sector). Types of jr schemes are specified
accordingtotheclassificationusedin oecd (2020a).
Weanalysetheperiod q1 2019–q2 2021. Theperiodisextendedto
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AntonRop
thebeginningof2019toidentifytheeffectsofmodificationsmadeto
jr schemes at the start of the epidemic (14 euro area countries mod-
ified an already existing stw scheme in q1 2020, as well as potential
other systematic (but undisclosed) time-specific impacts on employ-
mentduringtheepidemicepisode(parametersσ
t
)aswellastoincrease
the accuracy of the estimated dependence of the employment preser-
vationindicator.
Sinceforestimatedrelation(3)parameterαdidnotsignificantlydif-
ferfrom1,⁵theestimablestartingoperativeversionofthemodelspec-
ificationforemploymentpreservationisdefinedasfollows:
log
ep
ijt
eh
ijt
=
 k
β
k
jr
it
·dum_jr
ik
+
 l
γ
l
es
it
·dum_es
il
+
 t
σ
t
dum
t
+U
i
+U
j
+ε
ijt
,( 4 )
where index i stands for country, j for sector, t for time, k for type of
schemeandlforsectoralgroup.
Thisstartingversionofthemodelisestimatedandanalysedinthree
steps;ineachstep,specificationofthepreviousstepisfurthersimpli-
fiedtoallowspecificcharacteristicsofthemodeltobeanalysed.
In step one, the starting version of the model specification (4) is
usedtocheckthepotentialexistenceofspecifictimeeffectsinfluencing
employmentpreservationintheepidemicepisode.
Insteptwo,themodelisestimatedinitsbasicspecificationas
log
ep
ijt
eh
ijt
=
 k
β
k
jr
it
·dum_jr
ik
+
 l
γ
l
es
it
·dum_es
il
+U
i
+U
j
+ε
ijt
.( 5 )
Notably, the basic specification differs from the starting specifica-
tiononlyin(missing)timedummies.Sinceitencompassesboththeo-
reticallyimportantfactors–thetake-upofdifferent jr schemeeffects
aswellasthesectoralmacroeconomiceffects,adiscussionofthebasic
modelestimatesrepresentsthecoreoftheanalysisinthispaper.
Arobustness checkofthemainbasicmodelconclusionsismadein
step three when the model is estimated without any explicit specifi-
cation of the sectoral differences, therefore formally in the following
specification:
⁵Correspondingestimatesareavailablefromtheauthoruponrequest.
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TheEffectsofJobRetentionSchemesonEmploymentPreservation
[129]
table3 HausmanTest
Model χ
2 P
Startingmodel Asymptoticassumptionsviolated
Basicmodel . .
Robustmodel . .
notes Hausmantestvaluesandsignificance;thedataforthestartingmodelviolatethe
asymptoticassumptionsofthetest.
log
ep
ijt
eh
ijt
=
 k
β
k
jr
it
·dum_jr
ik
+γ
l
es
it
+U
i
+U
j
+ε
ijt
.( 6 )
Thepresentedstepwisesimplificationofthemodelspecificationen-
ables the explicit focusing on (testing of) the crucial questions (hy-
potheses)ofthestudyembracedinthestatedpaperresearchquestion.
results
Themodel(4)isestimatedonpaneldata(wheretheobservationunitis
country,sectorinaquarter)for19eurocountriesand9sectorsinthe
period q1 2019–q2 2021.Duetomissingdata,thereare1,330complete
observations.
Becauseofthehighpossibilitythatunobservableindividualeffects
for country and sectors are present,⁶ a fixed effects estimator should
beusedasitexcludescountryandsectortime-invariantvariables’im-
pacts and gives consistent parameter estimates. Nonetheless, Haus-
man’s test is conducted to test for the presence of fixed effects and
whetherthemoreefficientrandomeffectsestimatorcouldalsobeused.
Table3presentsvaluesofHausman’stestforallthreemodelspecifica-
tions analysed (starting specification, basic specification, robust spec-
ification).Hausman’s test does notenablethe use ofa randomeffects
estimatorinanymodelvariantandthusallthreemodelsareestimated
withfixedeffects.
The possible endogeneity of economic support measures as well as
the jr schemetake-upratesleadsustorunaninstrumentalversionof
thefixedeffectsregression(theinstrumentalestimatorgmmisused).⁷
⁶Crivelli et al. (2012) suggest a set of determinants of cross-country variations of
employment growth consisting of the following variables: (a) Structural and Policy
Variables (labour market policies, product market policies, and government size), (b)
Productmarketregulations,likelabourmarketregulations,(c)governmentsize,(d)
macroeconomicvariables,and(e)demographicvariables.
⁷Economicsupportmeasuresandtheassociatedjrschemetake-upsarehighlyendoge-
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table4 StartingModelEstimates
Explanatoryvariables Coefficient t-stat P
support(–) . . .
support_con(–) –. –. .
support_vul(–) .*** . .
support_nvul(–) –. –. .
support_uti(–) –. –. .
support_pub(–) –. –. .
takeup_ws .*** , .
takeup_stw .*** . .
takeup_stw .*** . .
takeup_stw .*** . .
takeup_stwn .*** . .
dum_stwo .*** . .
dum_q . . .
dum_q . . .
dum_q . . .
dum_q –. –. .
dum_q –. –. .
dum_q –. –. .
dum_q –. –. .
dum_q –. –. .
dum_q –. –. .
_cons –.*** –. .
Continuedonthenextpage
The instruments used are a stringency index, lockdown variables
forpublicgatheringsandschoolclosures,dummiesforsectorsandthe
typeof jr schemesaswellasthecombination(products)ofthesevari-
ables. We used instrument variables representing pandemic contain-
ment measures as they are defined in relation to the state of the pan-
nous to labour market conditions since they were mainly used to alleviate the short-
term effects of the covid policy measures constraining social mobility on employ-
ment and temporary unemployment (Bole, Prašnikar, and Rop 2021). For instance,
firms tend to place workers in jr schemes when the underlying conditions are poor
and,correspondingly,reducetheshareoftheworkforcein jr schemeswhenbusiness
conditions improve. Such pro-cyclical behaviour strongly biases the estimate of our
variableofinterestbecausetheunobservablebusinessconditionswouldbepartofthe
residualandnegativelycorrelatedwiththe jr schemetake-upvariable(forGermany,
seeAiyarandDao2021).
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TheEffectsofJobRetentionSchemesonEmploymentPreservation
[131]
table4 Continuedfromthepreviouspage
Explanatoryvariables P
Andersoncanoncorrelationtestofunderidentification .
SarganHansentestofoveridentification .
notes Calculations based on Eurostat (https://ec.europa.eu/eurostat) and Oxford
covid-19GovernmentResponseTracker(GitHub2022).Thedependentvariableisem-
ploymentinpersonsperemploymentinhours,normalisedsothattheaveragein2019is
1. Explanatory variables are: support_con(–1)– economic supportpolicy index multiplied
byadummyforconstruction,lag1;support_vul(–1)– economicsupportpolicyindexmul-
tipliedbyadummyforvulnerable,lag1;support_nvul(–1)–economicsupportpolicyindex
multipliedbyadummyfornon-vulnerable,lag1;support_uti(–1)–economicsupportpol-
icy index multiplied by a dummy for utilities, lag1; support_pub (–1) – economic support
policyindexmultipliedbyadummyforpublicsector,lag1;takeup_ws–take-upsmultiplied
by a dummy for a ws scheme; takeup_stw3 – take-ups multiplied by a dummy for an up-
dated stw scheme (increased access and coverage; increased generosity; increased access
for workers in non-standard jobs); takeup_stw2 – take-ups multiplied by a dummy for an
updated stw scheme(increasedaccessandcoverage;increasedaccessfor workersinnon-
standardjobs);takeup_stw1–take-upsmultipliedbyadummyforanupdatedstw scheme
(increased access and coverage; increased generosity); takeup_stwn – take-ups multiplied
byadummyforanew stw scheme;dum_stwo–adummyforapre-covid stw scheme.
Sargan-Hansenoveridentificationtest(significance);Andersontestofunderidentification
(significance).***,**,*significant,respectivelyat0.01,0.05and0.1.
demicandnotthestateofthelabourmarketandtheeconomy,butmay
well impactthe levelofthegovernment’s economicsupport measures
and jr schemes.
Table4presentsestimatesofthemodelinitsstartingspecification
(5)foreuroareacountries.TheSargan-HansenandAndersontestscon-
firmthattheinstruments’ qualityisacceptable.Thecoefficientsof jr
schemetake-uptypesaresignificantandhavetheexpectedsign,while
among the sectoral economic support variables only the support for
vulnerablesectorsissignificant.Othershavetheexpectedsignbutare
not significantly different from the corresponding effect of manufac-
turing, which represents the basis of the sectoral economic support
variablescomparisonandwhichinitselfisnon-significant.Giventhat
alltimedummiesinthecovid-19periodarenon-significantandare
not significantly different from the time dummies in the pre-covid-
19period,⁸therearenootherdecisivefactorsmitigatingemployment
lossesduringthelockdownepisode.
Table 5 displays estimates of the model in its basic specification. It
⁸Correspondingestimatesareavailablefromtheauthoruponrequest.
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table5 BasicModelEstimates
Explanatoryvariables Coefficient t-stat P
support(–) . . .
support_con(–) . . .
support_vul(–) . .*** .
support_nvul(–) . –. .
support_uti(–) –. –. .
support_pub(–) –. –. .
takeup_ws . .*** .
takeup_stw . .*** .
takeup_stw . .*** .
takeup_stw . .*** .
takeup_stwn . .*** .
dum_stwo . .*** .
Cons –. –.*** .
Andersoncanoncorrelationtestofunderidentification .
SarganHansentestofoveridentification .
notes N = 5130. The dependent variable is employment in persons per employment in
hours,normalisedsothattheaveragein2019is1.Explanatoryvariablesare:support(–1)
– economic support policy index, lag1; support_con(–1) – economic support policy index
multiplied by a dummy for construction,lag1; support_vul(–1)– economic supportpolicy
index multiplied by a dummy for vulnerable, lag1; support_nvul (–1) – economic support
policy index multipliedby a dummyfor non-vulnerable, lag1; support_uti(–1) – economic
supportpolicyindexmultipliedbyadummyforutilities,lag1;support_pub(–1)–economic
support policy index multiplied by a dummy for public sector, lag1; takeup_ws – take-ups
multiplied by a dummy for a ws scheme; takeup_stw3 – take-ups multiplied by a dummy
foranupdatedstw scheme(increasedaccessandcoverage;increasedgenerosity;increased
accessforworkersinnon-standardjobs);takeup_stw2–take-upsmultipliedbyadummyfor
anupdatedschemestw(increasedaccessandcoverage;increasedaccessforworkersinnon-
standardjobs);takeup_stw1–take-upsmultipliedbyadummyforanupdatedstw scheme
(increased access and coverage; increased generosity); takeup_stwn – take-ups multiplied
by a dummy for a new stw scheme; dum_stwo a dummy for a pre covid stw scheme.
Sargan-Hansenoveridentificationtest(significance);Andersontestofunderidentification
(significance);***,**,*significant,respectivelyat0.01,0.05and0.1.
differs from the model’s starting specification in the absence of time
dummies. The model is estimated with instrumentalised fixed effects.
In estimating the basic model, the same instruments are used as for
estimatingthestartingmodel.
The coefficients of the jr scheme take-up rates are highly signif-
icant and have the expected sign. A positive impact was especially
strong in pre-existing stw schemes with increased access and cov-
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table6 EffectsofEmploymentPreservationPolicy
Period Actualvalues Allmodelmeasures Macroec.measures
q  . . .
q  . . .
q  . . .
q  . . .
q  . . .
q  . . .
Average . . .
notes Employment preservation; basic model simulation of employment preservation
effects; actual values;totalsimulatedeffects; simulationof theeffects of only themacroe-
conomicsupportmeasures.
erage, increased generosity, and increased access for workers holding
non-standardjobs.
Theeffectsofthemacroeconomiceconomicsupportbywayofmit-
igated sectoral employment loss are positive for manufacturing, con-
struction,vulnerableandnon-vulnerableservices,butonlysignificant
forthe group ofvulnerable sectors.
To reveal the structure of policy contributions to the retained em-
ployment levels, table 6 presents actual values of employment in per-
son per working hours, model-simulated common effects of the jr
schemesandthemacroeconomicsupportmeasures,aswellasthecon-
tributionofonlythemacroeconomicsupportmeasures.Averagevalues
forallsectorsandcountriesaregiven.
Employment preservation effects were quite volatile in the first
threewavesoftheepidemic.Theyreachedtheirpeakin q2 2020when
slightlymorethan10oftheemployedwerenotworking.Thebiggest
contributiontosuchemploymentpreservationeffectswasmadebythe
takingupofjrschemes.Still,thecontributionmadebythemacroeco-
nomicmeasurestocurbingemploymentlosseswasalsonotnegligible.
Itwassmallonlyinthefirsttwoquartersofthecovid-19pandemic.
After that, thecontribution was quite sizeable; in the wholeperiod of
the first three epidemic waves, it accounted for around one-quarter
(0.011/0.048=0.23)ofthetotalmitigationeffects.
Overall, it may be concluded that in line with hypotheses 1 and 2
changes in the employment preservation ratio over time can be to a
largerdegreeexplainedbychangesin jr schemetake-upratesandby
changesinothergovernmentsupportmeasures.
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table7 MacroeconomicSupportEffectsonEmploymentPreservation–Sectoral
Differences
Sector support_con support_vul support_nvul support_uti support_pub
support_man . .*** –. –. –.
support_con .*** –. –. –.
support_vul –.*** –.*** –.***
support_nvul –.* –.
support_uti .
notes Differencesinmacroeconomicsupporteffectsonemploymentpreservation;sec-
toral differences (column sector less row sector item) are multiplied by 1,000; macroeco-
nomicsupporteffectsonemploymentpreservationin:manufacturing(support_man),con-
struction (support_con), vulnerable service sectors (support_vul), nonvulnerable service
sectors(support_nvul),utilities(support_uti),andpublicsector(support_pub);***,**,*sig-
nificantat0.01,0.05,and0.1,respectively.
The estimated effects of macroeconomic support on sectoral em-
ployment preservation presented in table 5 also enable a comparison
ofthoseeffectsbetweensectors.Correspondingdifferencesinsectoral
employment preservation effects are given in table 7. Macroeconomic
supportisshowntohavemitigatedemploymentlossbyfarthemostin
thegroupofvulnerableservicesectors.Theemployment-preservation
impact of economic support measures for the vulnerable service sec-
tors is several times (significant at p = 0.00) larger than in the other
sectors.However,asshownintable7,theeffectsofthosesectors(with
oneexception)didnotsignificantlyexceedtheeffectsinanyothersec-
tor; only the effects in non-vulnerable service sectors differ from the
effectsinutilitiesbythelowestmarginofsignificance(p=0.10).
We may conclude that the evidence presented in table 8 confirms
thattheimpactofeconomicsupportmeasuresonemploymentpreser-
vationvariesacrosssectors,especiallybetweenvulnerablesectorsand
others.
Estimatesofthebasicmodel(intable5)alsoindicatethattheeffec-
tiveness ofdifferent types of jr schemetake-ups varies considerably.
To enable a more detailed comparison, table 8 presents differences in
the effects on employment preservation of the analysed types of jr
schemetake-ups.
The evidence presented in table 8 shows that the most successful
countrieshavebeenthosewithaprevious stw schemewhichtheyex-
tended most extensively by applying all three key changes to it (the
mostextensivelyupdated stw schemes–denotedbytakeup_stw3):
• simplifyingaccessandextendingcoverage;
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[135]
table8 EmploymentPreservationEffects–Differencesbetween jr Schemes
Take-Ups
takeup_stw takeup_stw takeup_stw takeup_stwn dum_stwo
takeup_ws .*** .*** .*** . –.***
takeup_stw –.*** –.*** –.*** –.***
takeup_stw .*** –.** –.***
takeup_stw –.*** –.***
takeup_stwn –.
notes Differencesinemploymentpreservationeffectsfortypesof jr schemetake-ups;
effect of jr scheme take-ups in column less the effect of a jr scheme in row item; anal-
ysed types of jr schemes: takeup_ws – take-ups multiplied by a dummy for ws scheme;
takeup_stw3 – take-ups multiplied by a dummy for an updated existing stw scheme (in-
creased access and coverage; increased generosity; increased access for workers in non-
standardjobs);takeup_stw2–take-upsmultipliedbyadummyforanupdatedexistingstw
scheme(increasedaccessandcoverage;increasedaccessforworkersinnon-standardjobs);
takeup_stw1 – take-ups multiplied by a dummy for an updated existing stw scheme (in-
creasedaccessandcoverage;increasedgenerosity);takeup_stwn–take-upsmultipliedbya
dummyforanew stw scheme;dum_stwoadummyforapre-covid stw scheme;***,**,
*significant,respectivelyat0.01,0.05and0.1.
• extendingcoveragetonon-permanentworkers;and
• makingtheirbenefitsmoregenerous.
The take-ups of this jr scheme had the strongest impact on em-
ployment preservation, outperforming other types of jr schemes in
thecovid-19pandemicby25–70.Thelowesteffectonemployment
preservation was seen for take-ups of the jr scheme ws, which sup-
ported employment bysubsidising all employees in the firm (denoted
by takeup_ws). For every 1 of take-ups, this type of jr scheme re-
duced employment losses almost four times less effectively than the
mostsuccessful stw scheme.
Onemayconcludethattheempiricalevidencegivenintable9val-
idates the last (3rd) research hypothesis, namely, that among the dif-
ferentjrschemesinthecovid-19pandemicthemosteffectiveat
preserving employment levels were take-ups of already existing stw
schemesthathadbeenmostextensivelyupdated.
robustness test
Tochecktherobustnessoftheestimatedbasicmodelwithregardtoits
estimates, consistencyand lessons, themodel is also estimated in the
robustspecification(6).Themodelisestimatedinasimplifiedversion
withoutanexplicitsectoraldimension.Again,weemployedaninstru-
mentalversion ofthefixedeffects regression (instrumental estimator
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table9RobustModelEstimates
Explanatoryvariables Coefficient t-stat P
support(–) .*** . .
takeup_ws .*** . .
takeup_stw .*** . .
takeup_stw .*** . .
takeup_stw .*** . .
takeup_stwn .*** . .
dum_stwo .*** . .
Cons –.*** –. .
Andersoncanoncorrelationtestofunderidentification .
Sargan-Hansentestofoveridentification .
notes Thedependentvariableisemploymentinpersonsperemploymentinhours,nor-
malisedsothattheaveragein2019is1.Explanatoryvariablesare:support(–1)–economic
supportpolicyindex,lag1;takeup_ws–take-upsmultipliedbyadummyfora ws scheme;
takeup_stw3–take-upsmultipliedbyadummyforanupdated stw scheme(increasedac-
cessandcoverage;increasedgenerosity;increasedaccessforworkersinnon-standardjobs);
takeup_stw2–take-upsmultipliedbyadummyforanupdated stw scheme(increasedac-
cessandcoverage;increasedaccessforworkersinnon-standardjobs);takeup_stw1–take-
upsmultipliedbyadummyforanupdated stw scheme(increasedaccessandcoverage;in-
creasedgenerosity);takeup_stwn–take-upsmultipliedbyadummyforanewstwscheme;
dum_stwoadummyforapre-covid stw scheme.Sargan-Hansenoveridentificationtest
(significance); Anderson test of under identification (significance); ***, **, * significant, re-
spectivelyat0.01,0.05and0.1.
gmm).Instrumentsusedinestimatingtherobustmodelareagaincon-
structed according to the same principles as for the previous model
variants.Theestimatedmodelispresentedintable9.
Themacroeconomiceffectsonemploymentpreservationarehighly
significantandlargerthanthesimpleaverageofthecorrespondingsec-
toraleffectsinthebasicmodel.Namely,thesimpleaverageofthesec-
toraleffectsinthebasicmodel(seetable5)was0.00016( p=0.06)ver-
sus0.00019( p=0.00)intherobustversionofthemodel.
This fact further confirms the sectoral differences in the economic
supportmeasureseffectsand,inparticular,thesizeandimportanceof
the vulnerable service sectors support for the success in limiting the
lossofjobsduringthecovid-19pandemic,asalreadyseenintable7.
conclusion
The covid-19pandemichashadanunprecedentedimpactonthe
labour market across world economies. The key instruments for mit-
ijems

TheEffectsofJobRetentionSchemesonEmploymentPreservation
[137]
igating the effects of the lockdowns on employment and social hard-
shipthatdifferentcountriesintroducedorextendedinresponsetothe
crisis include different job retention (jr) schemes. jr schemes were
implementedinalleuroareacountries,althoughdifferentcountriesin-
troduced orextendedarangeof jr schemes.Usingdataforeuroarea
countries, this paper has analysed the effects of various jr scheme
take-ups on employment preservation during the covid pandemic.
Toassesstheimpactsofdifferent jr schemes,weusedthe oecd clas-
sification whereby the countries use five types of jr schemes (oecd
2020a).
Ourpapersupportsliteraturefindings(HijzenandVenn2011;Boeri
andBruecker2011;HijzenandMartin2013;oecd2020a;2020b;2020c;
2021) that jr schemes have been the most important instrument for
reducingthelossofemploymentfollowingtheimpactsofthenonphar-
maceuticalinterventionsduringthe covid-19crisis.Suchschemes
were able to relatively successfully limit excessive layoffs in the situ-
ation of a temporary reduction in business activity. Our results also
showthatcountries(France,Germany,Spain,Finland)whichextended
aprevious stw schemebyincreasingitsaccess,coverageandgeneros-
ity, and also integrated workers holding non-standard jobs (denoted
by takeup_stw3) into the scheme had the most successful jr scheme
take-ups.
Our study reveals that jr schemes contributed less than the over-
allemploymentpreservationachievedduringdifferentepidemicwaves,
and that other macroeconomic measures (non-jr schemes) contrib-
uted around one-quarter to the employment preserved. Correspond-
ingdifferencesinsectoralemployment preservation effects showthat
macroeconomic support mitigated the loss of jobs by far the most in
thegroupofvulnerableservicessectors,wherethecorrespondingnon-
pharmaceuticalintervention (npi)losseswerethehighest,andwhich
was the crucial driver of the high indirectnet effects in other sectoral
groups(Bole,Prašnikar,andRop2021).Bettertargetingbyusing stw
fornon-standardjobs(i.e.self-employedworkers andthoseintempo-
rary or part-time dependent employment) and providing more gen-
erous benefits have no doubt helped to improve the situation (oecd
2020b). Still, since employers have little or zero incentive to use stw
fornon-standardjobswhentheyknowthattheseworkerscanbefired
at little or no cost, and governments are reluctant to subsidise these
jobsduetothemoralhazardproblem(BoeriandBruecker2011),other
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macroeconomic measures (non-jr schemes) mightalso do a good job
atpreservingjobs,inparticular,inthesesectorsoftheeconomy.
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