The individual cost of sick leave

J Popul Econ (2012) 25:1287–1306DOI 10.1007/s00148-011-0390-8

ORIGINAL PAPER

The individual cost of sick leave

Simen Markussen

Received: 22 March 2010 / Accepted: 26 September 2011 /Published online: 26 October 2011© Springer-Verlag 2011

Abstract This paper aims to estimate the causal effect of sick leave onsubsequent earnings and employment, using an administrative dataset forNorway. To obtain experiment-like variation in sick leave among otherwisesimilar workers, the leniency of these workers’ physicians—certifying sicknessabsences—is used as an instrumental variable for sick leave. A 1 percentagepoint increase in a worker’s sick leave rate is found to reduce his earnings by1.2% 2 years later. Around half of the reduction in earnings can be explainedby a reduction of 0.5 percentage points in the probability of being employed.

Keywords Sickness absence · Wage formation · IV estimation

JEL Classification J22 · J31 · J71

Responsible editor: Erdal Tekin

Electronic supplementary material The online version of this article(doi:10.1007/s00148-011-0390-8) contains supplementary material, which is available toauthorized users.

This paper is part of the project “A viable welfare state” financed by the NorwegianResearch Council. I am grateful to Knut Røed, Oddbjørn Raaum, Bernt Bratsberg,Espen Henriksen, Ole Røgeberg, Erik Biørn, Kjell Arne Brekke, Magne Mogstad,Harald Dale-Olsen and Per Johansson for their comments. I am also grateful to twoanonymous referees for the comments and suggestions.

S. Markussen (B)Ragnar Frisch Centre for Economic Research, Gaustadalléen 21, 0349 Oslo, Norwaye-mail: [email protected]

http://dx.doi.org/10.1007/s00148-011-0390-8

1288 S. Markussen

1 Introduction

From a series of studies, it is well known that workers’ sickness absence isresponsive to economic incentives. The more costly sick leave is for the worker,the less he is away from work. These costs may have several origins: Workersmay receive less pay while sick than while working (Henrekson and Persson2004; Johansson and Palme 2005; Ziebarth and Karlsson 2010), or the sickleave period may have a maximum duration such that the job contract is ter-minated if this period is exceeded (See e.g. Markussen et al. 2011). However,also more subtle and less visible mechanisms could be at work and make sickleave costly for the individual. By glancing at workers’ absenteeism records,employers can over time uncover their employees’ inherent productivity andlet this influence their wage offers. By doing so, employers may provideworkers with (stronger) incentives for absence reduction, and by encouraginglow-absence workers to stay through higher wage offers; they may improve thecomposition of their employees.1

If empirically relevant, such a wage penalty would make sickness absencemore costly for the individual worker than what is generally known. Still, if asick leave facilitates recovery from health problems and helps workers resumework; such a wage penalty is perhaps worth paying? However, recent medicalresearch points toward a less optimistic view on the possible therapeutic effectsof taking sickness leave. For musculoskeletal pain and common mental disor-ders, which are the most common diagnoses leading to sick leave and disabilityin many countries, research indicates that remaining in regular activity—nottaking sick leave—helps promote recovery and rehabilitation (Malmivaaraet al. 1995; Waddel 2004; Waddell and Burton 2006; OECD 2008). Takenliterally, sick leave could thus have negative health effects. And one couldsuspect sickness leave—an institution intended to help workers with healthproblems remain employed—of having the opposite effect, i.e., push workersout of the labor force and into permanent disability.

This paper aims to estimate the individual cost of taking sick leave, con-ditional on health status, in regards of possible wage penalties and subsequentlabor market participation. A causal mechanism from sick leave to future earn-ings and employment outcomes are of relevance for at least three reasons: first,it may explain parts of the strong correlation between health and economicstatus (see Smith 1999, for an overview). Secondly, if sick leave has a negativeeffect on future employment—conditional on health status—physicians shouldexert great caution when certifying sick leave. Third, if taking sick leave todayreduces workers’ future earnings, policy makers may want to take such a wagepenalty into consideration when making decisions regarding the provision ofpaid sick leave.

To estimate the potential effects of sick leave on subsequent earnings andemployment one needs to isolate the causal effect of the sick leave per se

1For a recent study of performance-related pay, see Gielen et al. (2010).

The individual cost of sick leave 1289

from confounding factors, such as health and job motivation, which are mostlikely correlated with sick leave uptake. In other words, one needs to createan experiment-like setting where otherwise similar workers have differentamounts of sick leave. In the present paper, such a quasi-experiment is createdfrom the substantial variation between Norwegian primary care physicians inthe amount of sick leave certified. In Norway, all sick leave episodes lastingmore than 3 or 8 days must be certified by a physician. These certificates,as well as the patient lists for all primary care physicians, are collectedin administrative data and made available for research by the NorwegianSocial Security Administration (NAV) and Statistics Norway (SSB). For eachprimary care physician, a strictness/leniency measure is estimated based onthe amount of sick leave they certify for their patients. This proxy for thephysicians’ judgment or practice style is then used as an instrumental variablefor workers’ amount of sick leave in order to estimate the individual cost oftaking sick leave. The estimates should be seen as a comparison of two similarworkers with the same health status taking sick leaves of different length dueto differences in their physicians’ judgment.

Sick leave uptake is found to have a substantial impact on earnings 2 yearslater. On average, a 1 percentage point increase in an individual’s sick leaverate reduces this person’s earnings 2 years later by 1.2%. Taking into accountthe fact that earnings are fairly persistent over time, these effects make itrelevant to ask whether sickness insurance really is insurance—or if it is a loanagainst future earnings. A 1 percentage point increase in sick leave also reducesthe probability of full-time employment 2 years later by 0.5 percentage points.Consequently, the effects on earnings are partly caused by reduced employ-ment. Still, also for workers who remain employed sick leave is followed by awage penalty.

The estimated effects differ among different workers. First of all, men’searnings are more affected by sick leave than what is the case for women. Thiscan at least partly be explained by a gender-segregated labor market. In jobsdominated by women, the effects of taking sick leave on earnings are smallerthan what is the case in jobs dominated by men. Within jobs however, thecost of taking sick leave differ much less between men and women than forthe labor market as a whole. The findings also indicate that wage effects arestrongest at the top of the income distribution (or education distribution) whileemployment effects are strongest at the bottom.

2 Related literature

Previously, a few studies only have aimed to identify causal effects of sick leaveuptake on wages. The study most comparable to this paper is Hansen (2000),which exploits a policy change in Sweden as an instrumental variable for sickleave uptake and finds substantial costs of taking sick leave for women, butnot for men. Hansen (2000) finds that, for women, each additional day on sickleave reduces the wage rate by 0.2%. He finds no effects on wages, however, of

1290 S. Markussen

staying at home with a sick child, and interprets this as support for a signalingargument rather than having to do with human capital accumulation (Hansen2000, p. 51). The approach of Hansen (2000) differs from this paper’s approachin some important aspects. First, Hansen (2000) studies the effect of sick leaveon current wages, such that only immediate wage responses are captured byhis model. Often wages are adjusted through a regulated bargaining process(e.g., once a year) and by focusing on immediate changes only one mightunderestimate the effects as wage changes also may occur after the sick leaveepisode. Second, the policy shift changes sick leave rates for all workers fromone year to another.2 The amount of information used to identify the causaleffects is thus fairly limited. Finally, his dataset is relatively small resulting inimprecise estimates, which perhaps explains why effects are found for femaleworkers only.

In a recent study, Ichino and Moretti (2009) find that absences of womenbelow the age of 45 tend to follow a 28 day cycle not present for olderwomen or for men. They interpret this as absenteeism caused by the menstrualcycle, reflecting biological differences and not different propensities for takingoccasional days off. From a model of statistical discrimination they hypothesizethat the relationship between absence and earnings should be weaker forfemales than for males—because biologically caused cyclical absences makessick leave uptake a less informative signal of productivity and effort for femalesthan for males—a proposition they find support for in their data. They estimatethat one additional day of cyclical absence costs male workers about 2.5%in earnings, whereas the cost for female workers is 1.5%. Finally, they findthat biological differences in cyclical absence can account for at least 14% ofthe gender wage gap.3 Differences in cyclical absences among workers shouldbe interpreted as permanent—or at least persistent—worker heterogeneity.When workers with one additional day of cyclical absence earn 2.5% less, it isnot the causal effect of one additional day of sick leave—but a manifestation ofhow heterogeneity in health (or effort) affects earnings over time. Ichino andMoretti (2009) convincingly illustrate the importance of biological differencesand their importance in the labor market. However, their question is differentfrom the question of this paper.

Sick leave uptake may also affect workers’ employment status later on. Sickleave is—by construction—the main gateway towards permanent disabilitypension. As a main rule, employment is needed to qualify both for sick leaveand disability pension. Since disability pension typically is offered workersunable to return to work from sickness absence, possibly after also completingother rehabilitations programs, disability pension will nearly always start witha sick leave episode. There is consequently a strong correlation between sick

2Hansen (2000) uses several instrumental variables of which the policy reform is arguably the mostcredible.3Even if cyclical absenteeism is more costly for males than females, the “quantity effect” domi-nates the “price effect” as females have more cyclical absences than males.


leave and the probability of leaving the labor market. Such a correlation canhave several origins—many of which are not causal in the sense that takingsick leave is the reason why workers leave employment and often end up asdisabled. Markussen et al. (2011) estimates the probability of returning towork after sick leave, and find strong negative duration dependence, despitecontrolling for unobserved heterogeneity in a very flexible manner.4 However,this result depends critically on the assumption that all unobserved differencesacross individuals are time-invariant. A related finding is that workers ongraded (part time) sick leave instead of full-time sick leave return faster towork (Høgelund et al. 2010; Andrén and Andrén 2008, 2009) and have betterfuture employment prospects (Markussen et al. 2010). On the face of it, thissuggests that being on sick leave has a negative causal impact on the probabilityof returning to work.

Sick leave may also increase the probability of being fired or laid off. This isstudied by Hesselius (2007) who shows that workers with high sickness absencerates are more likely to become unemployed later on. The Hesselius study isnot able, however, to separate the possible causal effects of sick leave frompossible unobserved characteristics correlated with both productivity and sickleave. Supporting evidence is found by Henningsen and Hægeland (2008) whostudy mobility in downsizing firms and find that workers with a history of sickleave are less likely to remain in the firm after downsizing.

3 Institutional setting and data

All Norwegian workers, part from self-employed, are entitled to a 100%replacement rate during sick leave, for a period up to 1 year, and an amount upto 6G (approximately 88,000 USD in 2011).5 Employers pay the first 16 dayswhile the social security administration foots the bill from day 17 and onwards.If lasting longer than a few days, all sick leave spells must be certified by aphysician. In most firms, such a certificate is needed after 3 days of absence,while in other firms the limit is 8 days. The certificate is typically given for alimited period of time (1–4 weeks) after which it must be renewed unless theworker returns to work.

Norway has implemented a panel doctor system in which all citizens areformally connected to a primary care physician. One can freely choose whichphysician to be linked up with using an internet service, provided that he/herhas free capacity. Normally, primary care physicians are the ones providingsickness certificates. There are however exceptions to this. In some cases,workers receive their first sickness certificate from a medical emergency center,

4Since health is unobserved, controlling for unobserved heterogeneity is crucial when estimatingduration dependence. The workers who first return to work are the ones with best health, meaningthat as duration increases, the remaining population on sick leave becomes more and moredisadvantaged. This results, almost mechanically, in negative duration dependence.5G is a unit for calculation of social benefits in Norway that is adjusted for inflation.

1292 S. Markussen

e.g., if their medical condition is acute or if their primary care physician isunavailable (e.g., during weekends, holidays etc.). The primary care physicianwill then normally be responsible for all later treatment and renewal of thesickness certificate. In other cases the medical condition is such that specialisttreatment is needed. Then the primary care physician will refer the patient to aspecialist who will take care of both treatment and any renewals of the sicknesscertificate.

This paper makes use of Norwegian administrative register data providedby the Social Security Administration (NAV) and Statistics Norway. It con-tains start dates and end dates for all certified sick leave spells in Norwayfrom January 2001 through 2004. It also includes the (encrypted) identity ofthe physician responsible for certifying each sick leave spell as well as theregistered primary care physician for each patient on a monthly basis. Dataon earnings, employment, education, sector of work, residency etc. stem fromvarious sources but are all administrated and provided by Statistics Norway.

The dataset used for estimation covers the period 2001–2004 and containsall person/year records of registered full-time employees earning at least 1.85G(≈27,000 USD in 2011) and being between 25 and 59 years of age. The lowerage limit is set to exclude most students whereas the upper limit is set to avoidthe age groups that qualify for early retirement programs—starting at age 62.The data set is presented in Table 1.

In total, the dataset contains 3.94 million employee/years, 60% of whichhave no registered sick leave episodes in year t. Workers with certified sickleave are more predominantly females, they are less educated, and they earnsubstantially less than those without any sick leave episodes.

In the years following the sick leave episode, the gap in earnings betweenworkers with and without sick leave widens further. Note that earnings are

Table 1 Data descriptions

All workers Without sick leave With sick leavein year t in year t

No. of worker/year obs. 3,938,744 2,355,859 1,582,885Percent females 38.5 33.0 46.7Age (mean) 42.6 42.9 42.2Education<10 years 6.2 5.6 7.1>13 years 37.2 41.3 31.0Sick leave rate 6.0 0 15.0Earnings in t 389,132 414,757 350,992Earnings in t + 1 396,704 428,390 349,544Earnings in t + 2 404,856 441,891 349,735Employed in t + 1 91.5 93.6 88.5Employed in t + 2 87.8 91.1 82.9

Included in the data sample are all person/year records of full-time employees and earning at least1.85G (approx: 27,000 USD-2011). t = 2001–2004. Earnings in t + 1 and t + 2 are unconditionalon employment in these years. Employment in the years t + 1 and t + 2 are determined using thesame definition as in year t (registered full-time employee and earning at least 1.85G)


measured unconditional on employment. One and 2 years later, workers withsick leave in year t also have substantially lower employment rates thanworkers without sick leave. This employment gap thus seems important tounderstand the widening gap in earnings. Whether or not these observationsreflect anything causal is to be investigated below.

4 Empirical model

Normally wage changes follow from annual coordinated wage negotiationsbetween labor unions and trade unions followed by individual wage bargainingat the workplace. To ensure that individual wage bargaining have taken placeafter the sick leave episode, earnings must be observed at some time afterthe sick leave episode. In the data earnings are observed on a yearly basis(calendar year). Generally, wage changes following from annual negotiationsare however not implemented precisely at the turn of the year. The first yearlyearnings’ observation entirely based on the “new” wage rate is thus 2 yearsafter the year the sick leave episode started. The equation of interest is givenby Eq. 1, where k = employment, earnings, Si,t is the sick leave rate of workeri in year t, and xi,t is a vector containing relevant control variables.

yki,t+2 = αk

OLSSi,t + xi,tβk + εk

i,t+2 (1)

Note that all the covariates included are measured in year t to ensure thatthese are unaffected by sick leave in year t.6 The model is not to be estimatedwith individual fixed effects since this would exclude all workers not presentin the data for more than one year. Such exclusion would create a potentiallyserious sorting problem, as we condition on future employment—which is anendogenous variable.

Ex ante it is unclear whether earnings in year t + 2 should be measuredlinearly in NOK or as the logarithm of earnings. The logarithmic specification,which arguably is the most common in economics, makes the error termscloser to normally distributed and makes the coefficients easier to interpret.However, it also creates a sorting problem as workers with zero earnings int + 2 must be excluded from the sample. In practice, a fairly limited fractionof workers exits the dataset when log earnings are computed. The number ofobservations is reduced from 3.9 to 3.8 million. Below the model is estimatedusing both the linear and the logarithmic specification. To estimate effects onemployment in t + 2, a linear probability model (LPM) is used. The coefficientsof interest are αk

OLS, capturing the effect of sick leave in year t on employmentand earnings two year later.

6There is one exception, current earnings, which is measured in year t − 1. Earnings in year t maybe affected by sick leave in year t from reduced overtime payment and lost bonuses for absentworkers. Earnings exceeding 6G will also not be replaced by the sickness insurance (unless coveredby the employer) which also may make earnings in year t dependent on sick leave in year t.

1294 S. Markussen

4.1 An instrumental variables model

Ex ante there is no reason to believe that estimating Eq. 1 using OLS willprovide an unbiased estimate of the effect of sick leave on subsequent earningsand employment status. Individual factors such as motivation and health,unobserved to the econometrician, will probably affect sick leave as well assubsequent earnings and employment status. To omit such endogeneity bias, Ifollow an instrumental variables approach. One possible instrumental variablefor sick leave uptake could be the leniency of the primary care physicians—responsible for certifying sick leave. Lenient primary care physicians certifysick leave more often and/or for longer periods of time. Clearly, a measure ofprimary care physicians’ leniency is not available directly. However, using datafrom the panel doctor system such a leniency measure can be estimated fromthe observed sick leave episodes of all the patients connected to each primarycare physician. The leniency of a primary care physician is then measuredas the average sick leave rate of the workers having he/her as primary carephysician after controlling for individual characteristics.

Using an extraordinarily rich set of Norwegian administrative data for 2001–2005 such a leniency measure is estimated by Markussen et al. (2011).7 In theirpaper, individual sickness absence propensity is modeled by means of a mul-tivariate hazard rate model. In short, they explain individuals’ transitions intoand out of sick leave by a rich set of individual factors, workplace fixed effectsand physician fixed effects. These physician effects are then transformed intoa single measure, which is the leniency indicator used as instrumental variablein this paper.

The instrumental variable, denoted by z, is available for 3,205 physicians.There is substantial variation in physicians’ leniency, causing variations inthe predicted sick leave rates of a worker—conditional on physician only—from below 4% and up to 14%. However, most workers are linked up withphysicians providing their patients expected sick leave rates of 6% to 10%.The variation is illustrated in the upper panel (a) of Fig. 1. Panel (b) showsthe distribution of individual sick leave rates for 2003. The maximum value,100%, implies that the worker is continuously on sick leave for an entire year.Most workers have no sick leave and the distribution is very different from theinstrumental variable z.

Substantial variation in sickness certification between physicians is alsofound in medical studies, typically studying artificial case vignettes which istested on different physicians (see, e.g., Englund et al. 2000; Gorter et al. 2001).

One unfortunate implication of estimating the leniency measure is that sucha measure also captures all sick leave differences between physicians’ patients,not related to the physicians’ leniency but to unobserved attributes of theirpatients. To a large extent the same workers are used both to estimate theinstrumental variable and to assess the effect of sick leave on future outcomes.

7A detailed description of their estimation strategy is provided in the Appendix.


Fig. 1 Distribution of the instrumental variable “physician leniency” and the instrumented vari-able “the individual sick-leave rate”. Panel a displays the instrument variable physician leniency(z) estimated by Markussen et al. (2011). This variable should be interpreted as the expected sickleave rate of a worker conditional on nothing but his physician. Panel b displays the distribution ofindividual sickness absence rates in 2003 for the workers in the sample. The distribution is highlyskewed as most workers have no sick leave

This creates a mechanical violation of the conditions for a valid instrumentalvariable related to the reflection problem (Manski 1993). We should howeverexpect the resulting bias to diminish as the groups become large. To addressthis problem the instrumental variable is estimated in an alternative mannerreferred to as the split sample approach. First, each physician’s patient lists arerandomly split into two parts. The first group is used to estimate physicians’leniency using a simple linear regression model where Si,t is the sick leave rateof worker i in year t, xi.t is a set of individual control variables such as age,gender, education, sector or employment, calendar time etc., and Pit is a set ofdummy variables, one for each physician.

Si,t = xi,tλ + Pi,tzSplit + ei,t

The set of coefficients zSplit = {z1, z1, . . . , zp

}is a physician leniency indicator

estimated for all the physicians using a random half of each physician’s pa-tients. When using this leniency indicator as instrumental variable for workers’sick leave on the remaining half of the dataset, not used when constructing it,the mechanical link between individual unobserved factors and the instrumen-tal variable is broken. Below, both estimates of physician leniency are usedin turn.

1296 S. Markussen

The instrumental variable model, estimated using two stages least squares(2SLS) is given by Eqs. 2 and 3.

yki,t+2 = αk

IV Si,t + xi,tβkIV + εk

i,t+2 (2)

Si,t = xi,tμ + θzi,t + υki,t (3)

The first-stage equation in the 2SLS procedure is given by Eq. 3. Predictedvalues from Eq. 3 are then used in Eq. 2, the second stage equation, toobtain αk

IV .

Si,t = xi,tμ + θzi,t

Unless specified otherwise, the vector of individual control variables consist ofthe following variables, mostly dummy coded: Earnings t − 1, age (3 variables,polynomial), years of schooling (5 var.), marital status (3 var.), calendar year(4 var.), sex (2 var.), country of origin (3 var.), sector of employment (11 var.).

4.2 Robustness tests

A series of robustness tests are carried out in order to test whether theidentifying assumption is seriously violated. In particular, three possible prob-lems are examined: (a) Confounding factors related to geography (area ofresidency—neighborhood) wrongly attributed to the physicians’ estimatedleniency, (b) physician differences arising from heterogeneity in treatmentquality not leniency, and (c) endogenous matching of physicians and workers.

Confounding factors related to geography may wrongly be captured by thephysician fixed effects since people tend to choose primary care physicians intheir neighborhood. Controlling for observable characteristics such as incomeand education will help, but only to some extent as differences between neigh-borhoods also are related to unobservable characteristics such as lifestyle. Ifpeople with, say, a “taste for exercise” tend to live near the recreational areaswhile those with a “taste for bars and nightlife” tend to live in the city centrewe encounter a problem as long as people also tend to have a physician that islocated in their neighborhood. These workers, with different preferences andlifestyles, may very well work at the same place and be similar along all otherobservable dimensions. To control for this, a set of neighborhood indicatorsare included. The workers in the dataset used for estimation live in around13,500 such areas and the model is estimated with neighborhood fixed effects.

An alternative interpretation of the leniency indicator is that differencesin sick leave uptake across patients stem from differences in the quality ofthe medical services provided. In order to estimate the effect of physicianleniency conditional on treatment quality, I follow Biørn and Godager (2009)and Markussen et al. (2010) which estimate age- and gender-adjusted mortalityrates for each physician’s patients and use these as a proxy for physician qual-ity. Markussen et al. (2010) also include neighborhood fixed effects when es-timating physician quality, to remove geographical sorting otherwise wrongly


attributed to the physician. And it is the quality measure of theirs that is usedalso in this paper. The proxy for treatment quality is estimated using a linearprobability model where mortality is the left hand side variable and where wecontrol for patient characteristics, neighborhood of residence and physician.The dataset contains around 4 million person-year observations for elderlyNorwegian retirees in the years 2001–2005. The (inverse of the) coefficientsfor the set of physician dummies are used as the proxy for treatment quality.

A third potential problem with using physicians’ leniency as an instrumentalvariable is that there might be a sorting of employees into physicians. Thismay lead to a situation where poorly motivated workers choose the mostlenient physicians. If such a mechanism is empirically important it makes theinstrumental variable unsuitable. The importance of this kind of sorting wasstudied by Markussen et al. (2011). Using a variance decomposition exercise,they find no evidence of sorting into physicians along observable variables.Close to none of the variation in sick leave among physicians was related todifferences in education, workplace, income etc. We should expect unobserv-able variables such as motivation and health to be correlated with individualcharacteristics such that the result, that these observables are unimportantfor explaining variation between physicians, is encouraging. However, to testfurther for such sorting the model is estimated on two different subsamples.First, the sample is restricted to workers having the same physician duringall the years 2001–2005.8 Only person-year observations in 2003 are thenused in order to make the conditioning (same physician in years t − 2 up tot+2) identical for all workers. Secondly, the model is estimated on a smallsubsample consisting only of workers who were collectively (and involuntarily)transferred to a new primary care physician such that these workers did notchoose their physician themselves. Such situations occur when a physicianretires, moves to another part of the country or for some other reason decidesto end his practice. Surely, if these workers are dissatisfied with their newphysician, they can change. To avoid sorting problems I thus assume thatworkers are connected with the physician to which they were transferred,regardless of whether they change later on. Clearly, this implies a measurementerror problem which is minimized by only including observations followingdirectly after the involuntarily transfer to a new physician.

5 Results

The main results, along with the robustness checks, are presented in Table 2.Note first that the instrumental variable, physicians’ leniency, has a significantimpact on individuals’ sick leave rates. Across all specifications the F-valueis well above the conventional criterion of 10 indicating that the instrumentis strong.

8I am grateful to one of the referees for suggesting this approach.

1298 S. Markussen

Tab

le2

The

mai

nre

sult

s,al

ong

wit

hth

ero

bust

ness

chec

ks

OL

SIV

1IV

2IV

3IV

4IV

5IV

6IV

7

I.F

irst

stag

eE

ffec

tofe

xclu

ded

inst

rum

ent;

–0.

750

0.73

60.

733

0.83

40.

608

0.36

60.

642

phys

icia

nle

nien

cy,(

z i,t)

(0.0

12)

(0.0

06)

(0.0

08)

(0.0

20)

(0.0

12)

(0.0

22)

(0.0

99)

Fex

clud

edin

stru

men

t–

3,75

8.6

4,88

3.1

3,40

3.7

1,62

0.4

903.

521

4.0

40.5

II.S

econ

dst

age

Ear

ning

st+

2Si

ckle

ave

rate

−1,9

23.2

−3,4

27.2

−2,0

46.5

−2,1

07.4

−1,7

30.6

−2,1

92.0

−2,2

94.9

−2,6

99.3

(per

cent

age

poin

ts)

inye

art,

(Si,t

)(1

1.1)

(222

.0)

(174

.4)

(222

.5)

(325

.0)

(307

.4)

(585

.9)

(201

0.2)

No.

obs.

3,93

8,72

53,

938,

725

3,93

7,68

72,

759,

006

439,

158

1,38

0,66

121

9,61

018

,663

Mea

nea

rnin

gsin

t+2

404,

856

404,

856

404,

846

403,

911

407,

499

403,

744

403,

742

385,

710

Log

earn

ings

t+2

Sick

leav

era

te−0

.012

−0.0

12−0

.012

−0.0

12−0

.010

−0.0

13−0

.012

−0.0

10(p

erce

ntag

epo

ints

)in

year

t,(S

i,t)

(0.0

00)

(0.0

01)

(0.0

01)

(0.0

01)

(0.0

01)

(0.0

01)

(0.0

02)

(0.0

08)

No.

obs.

3,84

0,92

43,

840,

924

3,84

0,91

22,

691,

221

432,

077

1,34

7,07

321

6,09

418

,118

Em

ploy

men

tin

t+2

Sick

leav

era

te−0

.005

−0.0

08−0

.005

−0.0

05−0

.005

−0.0

05−0

.006

−0.0

08(p

erce

ntag

epo

ints

)in

year

t,(S

i,t)

(0.0

00)

(0.0

00)

(0.0

00)

(0.0

00)

(0.0

01)

(0.0

01)

(0.0

01)

(0.0

04)

No.

obs.

3,93

8,72

53,

938,

725

3,93

7,68

72,

759,

006

439,

158

1,38

0,66

121

9,61

018

,663

Indi

vidu

alch

arac

teri

stic

sY

esY

esY

esY

esY

esY

esY

esY

esN

eigh

borh

ood

F.E

.Y

esY

esY

esY

esY

esP

hysi

cian

qual

ity

Yes

Yes

Yes

Yes

Sam

eph

ysic

ian

2001

-05

Yes

Yes

Split

sam

ple

Yes

Yes

For

ced

new

phys

icia

nY

es

Rob

ust

stan

dard

erro

rscl

uste

red

onph

ysic

ian

are

repo

rted

inbr

acke

ts.

Indi

vidu

alch

arac

teri

stic

sar

eea

rnin

gst−

1,ag

e(3

vari

able

s,po

lyno

min

al),

year

sof

scho

olin

g(5

var.

),m

arit

alst

atus

(3va

r.),

cale

ndar

year

(4va

r.),

sex

(2va

r.),

coun

try

ofor

igin

(3va

r.),

sect

orof

empl

oym

ent

(11

var.

).R

esul

tsfo

rth

efir

stst

age

esti

mat

esar

efr

omes

tim

atio

non

earn

ings

t+

2an

dem

ploy

men

tt+

2.F

irst

stag

ees

tim

ates

from

esti

mat

ion

onlo

gea

rnin

gst+

2ar

ehi

ghly

sim

ilar

but

not

repo

rted


Column IV2 shows the results from the preferred model specificationwhich in addition to individual characteristics also includes neighborhood fixedeffects. Predicted sick leave is measured in percent (from 0 to 100) such thatits coefficient should be interpreted as the change in the dependent variable ifa worker’s sick leave rate increases with 1 percentage point (almost 2.5 work-days). The column OLS displays the results when Eq. 1 is estimated directlyusing OLS, while Column IV1 displays the results when neighborhood fixedeffects are not included. Columns IV3–IV7 displays the different robustnesstests. A 1 percentage point increase in sick leave is found to reduce subsequentearnings by 2046.5 NOK, approximately 341 USD. Using the log specificationthe same cost is estimated to 1.2%.

The probability of leaving employment is found to increase by 0.5 percent-age point per percentage point sick leave. Hence, this paper confirms thattaking sick leave has a negative causal effect on future employment—using acompletely different identification strategy than e.g. Markussen et al. (2011).9

The estimated effects are fairly similar across the different specificationsand robustness tests.10 Neither including physician quality (IV3), excludingworkers changing physician in any of the years (IV4), splitting the sample toensure that the instrument is estimated on different workers than the sampleused to estimate effects (IV5), nor using workers whose physician is the resultof an involuntarily change (IV6), alters the point estimates much. Note alsothat the OLS estimates are very similar to the results from IV. This couldindicate that endogeneity bias in this case is unimportant and that estimatingEq. 1 using OLS should be the preferred model. However, since IV estimatesa local average treatment effect (LATE) while OLS estimate the averagetreatment effect—possibly with a bias—the similarity between αk

OLS and αkIV

could also be incidental. If LATE is larger than ATE this could counteract theremoval of selection bias and result in coefficients of similar size.

In an attempt to separate the effects on earnings from the effects onemployment, the model is also estimated on a subsample consisting onlyof those that is employed also in year t + 2. Such a conditioning (on anendogenous variable) may bias the estimated costs towards zero as the group ofworkers that remain employed despite high sick leave is a (positively) selected

9There is a potential caveat regarding the results on employment. If physicians lenient in sicknesscertification also are lenient when certifying disability insurance, this may lead to the same results,but with a different interpretation. However, normally it takes more than 2 years before a workerreceives disability pension, and to qualify for such a pension one must also receive approval froma “neutral” physician, employed by the social security administration.10When Markussen et al. (2011) estimatedzi,t they used workplace fixed effects for all workplaceswith at least 100 employees. In unreported results the sample is restricted to only include workersin firms large enough to be represented by a separate dummy variable when estimatingzi,t . Thesample is reduced by around 50%, but the estimated coefficient is not significantly different fromthe one in column (IV1) where all workers were included. In addition to the reported resultsthe model is also estimated with fixed effects for all combinations of jobs (same 12 categories asin Table 5) and neighborhoods, in total more than 75,000 combinations. The results are almostidentical to those reported in column IV2

1300 S. Markussen

sample. The estimated coefficient is then probably biased towards zero asworkers that remain employed despite having sick leave are positively selected.The estimated effects in this group using IV are −1,131.4 NOK and −0.0029log points. Both estimates are precisely estimated. This indicates that theemployment effects account for no more than half the estimated earnings costs.

5.1 Heterogeneous effects

Hansen (2000) reports that while females’ wages are reduced following a sickleave episode; male workers’ wages are not affected. In contrast, Ichino andMoretti (2009) find that cyclical sick leave is less costly for females than formales. Hence, it seems relevant to examine whether taking sick leave havedifferent consequences for men than for women. The consequences of sickleave may also vary for different age groups. If wage penalties from sick leaveare persistent, a much lower pro anno cost of taking sick leave would suffice todiscipline younger workers than their older colleagues. In order to learn moreabout how these effects differ between workers, the model is also estimatedon subgroups grouped by education, earnings and jobs.11 These results aredisplayed in Table 3.

In line with the results from Ichino and Moretti (2009), taking sick leave ismore expensive for males than for females, regardless of whether we measureearnings in kroner or logs. A 1 percentage point increase in the sick leave ratefor males leads to 1.4% lower earnings 2 years later. For females, the sameincrease in the sick leave rate reduces earnings by 1.0%. Since males tend toearn more than females, the relative difference using the linear estimator iseven larger. Interestingly, the effect on employment is opposite, i.e. larger forwomen than for men, and consequently cannot explain the gender differencein the effect on earnings. This is explored further below.

The pro anno cost of taking sick leave increases with age—regardless ofoutcome. This fits nicely with the idea that the cost is permanent. Youngworkers are disciplined by a smaller cost per year because they have more timeleft in the labor market. However, the estimates for employment also indicatethat reduced employment can explain at least part of this difference. Whiletaking sick leave has no effect on young workers’ subsequent employmentpropensity, it is increasingly important as workers get older.

Dividing the dataset after education or earnings provides roughly similarpictures. Earnings are most sensitive to sick leave uptake for workers in eitherend of the skill distribution or earnings distribution—but for different reasons.At the lower end subsequent employment is strongly affected by workers’ sickleave uptake. This is not the case at the upper end. In other words, sick leavereduces future employment prospects more than wages for low skilled and lowearners, while it is the opposite for high skilled and high earners.

11Unfortunately, jobs are not observed directly in the data. The jobs described in Table 3 aredefined using a combination of sector of employment and education.


Table 3 Different effects for different workers

No. obs. Mean Linear Log Employmentearnings model modelin t + 2

Gender Males 2,421,397 447,828 −2,600.7*** −0.014*** −0.0044***Females 1,516,290 336,227 −1,562.7*** −0.010*** −0.0056***

Age <30 251,286 363,285 −1,495.8*** −0.005* −0.002430–40 1,358,836 393,579 −1,647.9*** −0.010*** −0.0037***40–50 1,258,598 425,632 −1,935.1*** −0.011*** −0.0048***50–60 1,068,967 404,508 −2,468.5*** −0.013*** −0.0060***

Education <High school 1,348,084 339,006 −2,077.4*** −0.013*** −0.0052***High school 1,125,697 391,108 −1,824.1*** −0.011*** −0.0049***Some college 113,019 446,567 −2,261.1*** −0.009*** −0.0048***Full college 350,887 569,686 −5,285.8*** −0.020*** −0.0043**

Earnings Quartile 1 984,206 250,699 −1,451.0*** −0.012*** −0.0060***in t Quartile 2 984,504 329,295 −1,600.2*** −0.011*** −0.0052***

Quartile 3 984,538 402,807 −1,619.8*** −0.009*** −0.0030***Quartile 4 984,439 636,623 −3,876.1*** −0.012*** −0.0026***

Jobs Teacher 282,761 393,261 −2,276.5*** −0.009*** −0.0055***Nurse assistant 245,328 281,407 −1,432.5*** −0.010*** −0.0060***Nurse 215,117 348,471 −793.3*** −0.004*** −0.0058***Physician 39,903 616,009 −1,865.0 −0.012* −0.0006Civil servant 184,902 427,027 −3,300.5*** −0.011*** −0.0044***Production/ 515,027 367,203 −2,095.1*** −0.014*** −0.0042***

industry,low skill

Production/ 123,707 563,194 −3,856.3 −0.012*** −0.0027industry,high skill

R&D, high 23,245 492,116 −2,768.6 −0.005 −0.0022education

Trade & 495,026 353,344 −1,873.9*** −0.014*** −0.0055***Commerce,low skill

Trade & 105,139 504,667 −980.8 −0.013** −0.0032Commerce,high skill

Banking & 69,288 452,647 −2,058.2 −0.013*** −0.0062***Finance,low skill

Banking & 53,849 638,883 −16,458.1** −0.029*** −0.0029Finance,high skill

All results are estimated with neighborhood fixed effects. The numbers of observations arereported for the linear model and are slightly lower for the log specification because workers withzero earnings are excluded. Individual characteristics earnings t − 1, age (3 variables, polynomi-nal), years of schooling (5 var.), marital status (3 var.), calendar year (4 var.), sex (2 var.), countryof origin (3 var.), sector of employment (11 var.)*p > 0.1, **p > 0.05, ***p > 0.01

There are large differences across jobs. A nurse’s earnings are only reducedby 793 kroner, or 0.4%, from a 1 percentage increase in sick leave, whilethe corresponding figure for a civil servant is 3,300 kroner (1.4%). These

1302 S. Markussen

differences do not seem to be driven by differences in how taking sick leaveaffects employment.

Taking a sick leave is found to affect earnings for female workers less thanfor male workers. It is also more costly for high earners and private sectoremployees. An obvious question is then whether the reason why sick leaveis more costly for males is just because men are overrepresented among highearners in the private sector. Such a hypothesis can be tested by estimating themodel on subsamples where we compare females and males belonging to thesame group. The results from such a strategy are displayed in Table 4.

The upper half of Table 4 displays the estimated effects for men andwomen in four different groups divided by level of education and sector ofemployment. In all four groups, the estimated effects on earnings are largerfor men than for women, but the difference is generally smaller than for theunconditional comparisons in Table 3. The effects on log earnings are identicalbetween men and women for three of the groups. The exception is highskilled workers in production, banking and finance—jobs typically dominatedby men. Among these workers, the effect of sick leave uptake on earnings issubstantially higher for men than for women, regardless of model specification.The effects on employment are comparable to the ones in Table 3 also whenwe condition on different kinds of jobs.

Altogether, it seems like a substantial part of the reason why males’ earningsare more by sick leave than females’ is simply because men and women havedifferent jobs. In jobs dominated by men, both male and female earnings aremore affected by sick leave than what is the case in jobs dominated by females.

Another potential explanation as to why sick leave is less costly for femalesis that females’ absences are more tolerated because they are partly relatedto pregnancies and taking care of small children. For these reasons sick leaveuptake may be less of a signal of low effort for females, and should thereforenot be punished the same way as for males. This is also argued by Hansen(2000) who finds that own sickness reduces wages, but not sick leave due tosick children. Unfortunately, the data used in this paper do not cover sick leavedue to sick children.12 To investigate whether children can be the reason whysick leave is less costly for females, the models are estimated separately onfemales below 40 years of age, with and without children. The results from thelinear model indicate that earnings of females with children are less affectedthan earnings of those without children. This difference disappears, however,when we estimate on log earnings. The estimates on employment are alsosimilar regardless of the presence of children. These results suggest providethus mixed evidence on whether the reason why females’ earnings are lessaffected by sick leave is related to children.

Ichino and Moretti (2009) explain that females’ earnings are less sensitive tosick leave than males’ earnings because of statistical discrimination. Employers

12Workers with small children have a quota of ten sick leave days a year to take care of sickchildren. These absence spells are not covered by this dataset.


Tab

le4

The

gend

erpu

zzle

No.

obs.

Mea

nL

inea

rL

ogE

mpl

oym

ent

earn

ings

mod

elm

odel

int+

2

Hig

hsc

hool

educ

atio

nF

emal

edo

min

ated

jobs

Mal

es54

,391

322,

992

−1,7

36.6

***

−0.0

09**

−0.0

03(N

ursi

ngas

sist

ants

)F

emal

es19

0,93

726

9,55

7−1

,207

.0**

*−0

.009

***

−0.0

06**

*M

ale

dom

inat

edjo

bsM

ales

727,

238

387,

563

−2,1

30.6

***

−0.0

14**

*−0

.004

***

(Pro

duct

ion

and

trad

e)F

emal

es28

2,81

529

0,58

4−1

,706

.5**

*−0

.014

***

−0.0

06**

*C

olle

ge/u

nive

rsit

yed

ucat

ion

Fem

ale

dom

inat

edjo

bsM

ales

156,

676

421,

150

−1,8

58.3

**−0

.008

**−0

.001

(Tea

cher

san

dnu

rses

)F

emal

es34

1,20

235

4,70

2−1

,298

.3**

*−0

.007

***

−0.0

05**

*M

ale

dom

inat

edjo

bsM

ales

192,

488

614,

589

−5,1

81.2

*−0

.020

***

−0.0

04*

(Pro

duct

ion

and

finan

ce)

Fem

ales

90,2

0743

1,19

7−1

,191

.8−0

.010

***

−0.0

02

Age

<40

,fem

ales

No

child

ren

216,

734

347,

701

−1,4

91.8

***

−0.0

07**

*−0

.004

***

W.c

hild

ren

400,

621

317,

729

−839

.7**

*−0

.008

***

−0.0

04**

*A

ge<

30M

ales

151,

304

395,

535

−2,4

58.5

**−0

.010

**−0

.006

**F

emal

es99

,982

314,

479

−309

.6−0

.002

0.00

2A

ge30

–40

Mal

es84

1,46

343

2,10

1−2

,115

.1**

*−0

.012

***

−0.0

03**

*F

emal

es51

7,37

333

0,91

9−1

,152

.1**

*−0

.008

***

−0.0

05**

*A

ge40

–50

Mal

es76

7,87

347

4,30

7−2

,307

.8**

*−0

.013

***

−0.0

04**

*F

emal

es49

0,72

534

9,46

0−1

,781

.6**

*−0

.011

***

−0.0

06**

*A

ge50

–60

Mal

es66

0,75

744

9,07

0−3

,309

.1**

*−0

.016

***

−0.0

06**

*F

emal

es40

8,21

033

2,37

6−1

,664

.5**

*−0

.011

***

−0.0

06**

*

All

esti

mat

esin

clud

ene

ighb

orho

odfix

edef

fect

s.T

henu

mbe

rsof

obse

rvat

ions

are

repo

rted

for

the

linea

rm

odel

and

are

slig

htly

low

erfo

rth

elo

gsp

ecifi

cati

onbe

caus

ew

orke

rsw

ith

zero

earn

ings

are

excl

uded

.Ind

ivid

ualc

hara

cter

isti

csar

eea

rnin

gst−

1,ag

e(3

vari

able

s,po

lyno

min

al),

year

sof

scho

olin

g(5

var.

),m

arit

alst

atus

(3va

r.),

cale

ndar

year

(4va

r.),

sex

(2va

r.),

coun

try

ofor

igin

(3va

r.),

sect

orof

empl

oym

ent(

11va

r.)

*p

>0.

1,**

p>

0.05

,***

p>

0.01

1304 S. Markussen

have a preconception of females having higher absence rates than males.13

The result of this preconception is that they pay females less and make theirearnings less dependent on sick leave. With time they get to know theiremployees better, and as they learn more about the individual they put lessweight on their group-based prior, and as a consequence they treat men andwomen more similarly. A familiar argument, in the context of education, ismade by Altonji and Pierret (2001) who hypothesize and find support fora claim that statistical discrimination decreases over time, as employers getto know their employees. The results for males and females in different agegroups support this claim. The gender difference in how sensitive earnings areto sick leave uptake is by far greatest for young workers. The estimated cost ofa 1 percentage point increase in the sick leave rate for males below 30 yearsis 1%, whereas the corresponding figure for females is 0.2%. The latter isnot statistically different from zero, however. The relative gap in the cost ofabsence decreases with age, for all but the oldest group. Also note that thesegender differences are not driven by differences in the effects of sick leaveon employment. This observation provides some support for the statisticaldiscrimination hypothesis argued by Ichino and Moretti (2009).

6 Conclusion

Taking sick leave may be costly, and not just because of reduced pay duringabsence. This paper estimates the individual cost of taking sick leave in termsof reduced earnings and reduced employment prospects 2 years later. Usingthe leniency of primary care physicians, mandated to certify sick leave, as aninstrumental variable for individual sick leave uptake, strong negative effectson subsequent earnings and employment are found. A 1 percentage pointincrease in a workers’ sick leave rate is found to reduce earnings by 1.2%2 years later. Also, the probability of being employed at all is lowered by0.5 percentage points. The effects on employment can partly explain the effectson earnings, but not fully. Even for employees who remain employed there arenegative effects on earnings, indicating that also wages are affected.

There are large differences between different groups of workers along di-mensions such as: gender, job type, education and earnings. These differencesare interesting in their own right because they shed light on wage settingmechanisms, implicit contractual arrangements and statistical discriminationin the labor market.

From a policy perspective, the findings in this paper are worrying. Sickleave is supposed to help workers recover in order to return to their jobs.These results indicate that sick leave in some cases is like a trap—from wherethe chance of escaping decreases by the day. Just from studying raw data or

13In Ichino and Moretti (2009) employers expect females to have higher sick leave rates than malesbecause of biological differences—the menstrual cycle.


non-experimental settings it is well known that the fraction of workers return-ing to employment strongly decreases as the time spent on sick leave increases.By comparing otherwise equal workers who differ only in the strictness of theirprimary physician, the causal effect of being on sick leave can be isolated fromthe underlying reasons that caused the sick leave. The results indicate thatphysicians mandated to certify sick leave should think twice before certifyingsick leave since this may have serious and undesired consequences in thelong term.

Even if policymakers may consider such wage penalties following sick leaveepisodes undesirable, they are probably difficult to avoid as they are subtlydetermined in a market. Legislation intended to protect workers from adverseconsequences of taking sick leave can probably only protect workers in themost obvious cases, such as preventing workers from being sacked while onsick leave. As long as wage bargaining remains partly individualized—forwhich there may be many good reasons—employers may always claim othermotives for their wage or promotion offers, even if their ulterior motives areto reward workers whose sick leave rates are low.

References

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Andrén D, Andrén T (2008) Part-time sick leave as a treatment method? HEDG Working Paper08/011, University of York

Andrén D, Andrén T (2009) How to evaluate the impact of part-time sick leave on the probabilityof recovering. Working Paper 13/2009, Örebro University

Biørn E, Godager G (2009) Does quality influence choice of general practitioner? An analysis ofmatched doctor–patient panel data. Econ Model 27(4):842–853

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Documents

The individual cost of sick leave