Abstract This article studies how subsidized career breaks affect future labor marketperformance. The analysis uses a Swedish career break program where applicationswere accepted until local funds were exhausted. The rejected applicants serve as coun-terfactuals to derive estimates that are unaffected by selection or omitted variables. Theestimated wage effect of a 10-month-long break is negative and in the order of 3 % 1–2years after the interruption. The average applicant is estimated to have substantiallylower returns to experience than the average worker. The results thus show that careerbreaks are costly, even for groups with low expected returns to experience, and in anenvironment with very compressed wages. The career breaks also induced an increasein job and task mobility whereas post-leave labor supply remained unaffected exceptfor workers close to retirement.
Keywords Career interruptions · Wages · Experience · Job mobility
JEL Classification J31 · J22 · J24 · J26
1 Introduction
A substantial fraction of the workforce in most countries experience significant careerinterruptions during their labor market careers. Such career breaks may occur for sev-eral reasons such as own sickness, sick family members or parental leave. Interruptions
O. N. Skans (B) · L. LiljebergInstitute for the Evaluation of Labour Market and Education Policy (IFAU),Uppsala, Swedene-mail: [email protected]
O. N. SkansIZA and Uppsala Center for Labor Studies, Uppsala, Sweden
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are typically non-randomly distributed with an over-representation among females andlow-wage workers (see, e.g., Albrecht et al. 1999). The frequencies of interruptionsare in many cases affected by public policies both directly through various forms ofleave subsidies, and indirectly through counteracting policies which subsidize unin-terrupted work (e.g. publicly funded child care alternatives). In order to derive anoptimal mix of these policies, it is important to know the economic consequencesof the interruptions for covered workers. This article supplements the existing stockof knowledge by providing quasi-experimental evidence on the effects of a Swedishsubsidized nonthematic career-interruptions program.
There are several reasons as to why career interruptions may affect future wages.An interruption means forgone experience and thus lost accumulation of skills (Mincer1974; Mincer and Polachek 1974). Career interruptions may also send negative signalsto employers since workers going on leave may be considered as less motivated thanothers (see Albrecht et al. 1999). On the other hand, career breaks can, in theory,also be spent accumulating useful human capital through informal investments or animproved health status.
This article aims to estimate how the labor market outcomes changes for a workerwho, with all else being equal, takes a career break. The ideal situation for identifyingthis effect would be one of random assignment between a continued and an inter-rupted career. Obviously, such situations do not commonly occur, which is why auxil-iary identifying assumptions typically are needed to solve problems of identification.There are three main problems that need to be addressed: Unobserved heterogeneityproblems arise if the people taking breaks have specific unobserved characteristicsthat also affect the outcomes. One way of solving this problem is to rely on unob-served components models, such as fixed- or random-effects models (as in, Albrechtet al. 1999). Anticipation problems arise if wages are reduced already before an inter-ruption as suggested by, Gronau (1988). Importantly, anticipation effects invalidatethe assumptions underlying models relying on before-and-after comparisons (such asfixed- and random-effects models) leading to a negative bias if the anticipation effectsare negative.1 Finally, problems with time varying heterogeneity occur if the careerbreaks are associated with external events that have an independent effect on the futureoutcome: parental leave is associated with having children, and sick leave spells withpoor health, both of which may have a direct effect on future wages.
There is a substantial literature studying how maternal leave schemes affect thegender wage gap, where the typical study uses either cross-sectional variation, relyingon observed characteristics for identification, or fixed/random-effects estimation andfinds negative effects on future wages from women’s career interruptions.2 In addition,there are a number of articles using quasi-experimental designs to study the effectsof longer periods of subsidized absence for mothers (see Lalive and Zweimüller 2009or Liu and Skans 2010). There is also a small number of studies on the effects of
1 Note the similarity to what is referred to as “Ashenfelter’s dip” (see Ashenfelter 1978) in the evaluationliterature.2 Some examples include Arun et al. (2004), Baum (2002), Corcoran and Duncan (1979), Corcoran et al.(1983), Gronau (1988), Gupta and Smith (2002), Kim and Polachek (1994) and Waldfogel (1997, 1998a,b).Stafford and Sundström (1996) looks at the effects of maternity leave using Swedish data.
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sick leave spells on future labor market performance using similar methods.3 Finally,some articles, including Phipps et al. (2001) for Canada and Albrecht et al. (1999) forSweden, have tried to simultaneously identify the effects of different forms of careerinterruptions using cross-sectional and panel regressions. An interesting case is Edinand Gustavsson (2008) who show that nonemployed people in general perform worseon formal tests than they did before the nonemployment spell started, suggesting thathuman capital can be lost during periods of inactivity.4
This article studies the effects of participating in a Swedish nonthematic subsidizedcareer break scheme which resembles policies in Finland and Belgium (previouslyalso in Denmark). We compare the outcomes of participants to outcomes of applicantswho were denied to go on leave due to the providing agency’s lack of funds. Theinstitutional selection criteria generate an exogenous instrumental variable which hasa large effect on the probability of an interrupted career, but which we argue is unlikelyto be correlated with differences in expectations or unobserved heterogeneity of eitherpermanent or time-varying nature.
The career breaks are estimated to have reduced subsequent wages by about3 %. The wage effect is substantially larger than the average estimated returns topotential experience among workers with similar characteristics as the career-breakapplicants. We also find that career breaks increase the probability of changingboth jobs and tasks. Among those that do move, we see a tendency toward lowerwage growth, which should not be interpreted as a causal effect since the selectioninto mobility is likely to be affected by career breaks. Overall, however, the pat-terns indicate that the career interruptions either change the selection into mobil-ity, or change the wage impact more among those that actually move. We alsostudy effects on hours worked, but find no significant effects, except for a largebut imprecisely estimated increase in the probability of retiring among workers agedover 60.
This article is structured as follows. Section 2 presents the career-break schemeunder study and the empirical model. Section 3 presents the data and descriptivestatistics. Results are presented in Sect. 4. Section 5 gives concluding remarks.
2 The policy and the empirical strategy
We study the effects of subsidized career interruptions using data from a pilot schemethat ran in 12 Swedish municipalities between February 1, 2002 and December 31,2004. The Swedish Green Party put subsidized career breaks on top of their agendain their campaign for the general election of 2000, and the pilot was part of a broaderbudget compromise between the Green Party, the governing Social Democratic Party,
3 Hesselius (2004) uses fixed-effects estimation on Swedish data and finds a wage penalty of around 5 %for 1 year’s absence.4 There is also a large amount of empirical literature studying the effects of unemployment on future wagesand employment using a variety of methods. The applied methods range from OLS (Ellwood 1982), matching(Eliasson and Storrie 2004) and sibling comparisons (Skans 2004) to the use of aggregate instruments (Gregg2001) and distributional assumptions (Heckman and Borjas 1980).
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and the Left Party during the fall of 2001.5 Similar nonthematic leave schemes existin both Finland and Belgium and have been used also in Denmark.
In total 80 (out of 288) municipalities announced their interest to participate in thepilot. The 12 Municipalities were chosen so as to represent the Swedish “regionaldiversity” according to a press-release from the ministry of Industry, Employment andCommunication. The far north and the far south as well as (parts of) the two majorSwedish metropolitan areas were represented.
The career-break program subsidized workers on 3–12 months leave spells duringwhich they were granted 85 % of their unemployment insurance (UI) benefits. Sincethe UI replacement-rate was 80 %, the subsidy amounts to 68 % of previous earnings.However, the Swedish UI-system has two different maximum levels, or “caps,” result-ing in a maximum compensation at 13,662 SEK (e1,500) per month during the first100 days, and 12,716 SEK (e1,390) per month thereafter (numbers are for 2002).
There are no restrictions on how the time was spent during the break, with theexception of a requirement not to work during the leave.6 According to the surveyin Lindqvist (2004), 15 % of the people on leave were caring for family members,whereas 22 % were in some form of education; 6 % ran their own firm during theirleave; and 55 % were on leave for other reasons, such as recreation. It can be notedthat 75 % of those in education did not expect that this schooling would result in anychanges in the wage, suggesting that at least part of the education was consumptionrather than investment.
To qualify for the subsidy during the pilot, an employee had to have at least 2 yearsof tenure within an establishment situated in one of the 12 municipalities. The estab-lishment could be either in the private or the public sector. Furthermore, the subsidywas only granted if the employer approved of the career break and agreed to hiring apreviously unemployed replacement worker.7 The replacement worker did not haveto work at the same position as the person on leave. Survey results suggest the mainreason for the employer to approve of the application was simply to accommodatethe employees’ wishes (Lindqvist 2004). After receiving the employer’s approval, anemployee could apply for a subsidized leave at the local Public Employment Service(PES). The PES had a fixed annual budget for the program which was proportional tothe size of the labor force in the municipality.8 During the pilot, the (national) budgetwas expected to finance two thousand 12-month-long interruptions per year. Validapplications were approved until the funds had expired.9
5 From January 1, 2005, the program was available nationally until a change of government in late 2006led to the abolishment of the program.6 The only conditions under which a person on leave was allowed to work is if the worker continuing witha minor secondary job or became self-employed. These were extremely rare cases.7 In practice, however, half of the replacement workers were not unemployed when hired. Also, half ofthe replacement workers had worked at the establishment previously (Lindqvist 2004). For an evaluationof the effects on the replacement workers, see Hartman et al. (2010).8 After the national implementation in January 2005, the budget allows for 12,000 one year long breakseach year.9 Approval was either in the order of the intended start date, or in the order of the date of application,depending on municipality.
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Similar to subsidized parental leave systems in most countries, participants inthe career-break scheme retained job protection after the scheme. Notably, Swedishemployment protection legislation provides a fairly stringent protection for employ-ees on permanent contracts. Most applicants are likely on permanent contracts sinceeligibility requires at least two years of consecutive tenure. This means that mostworkers had the legal right to return to their original jobs after participating in thescheme.
2.1 Assigned and rejected applications, and the relationship to treatment
There were more applications than available funds in 10 of the 12 participating munic-ipalities. For ease of exposition, we will refer to the group of applicants whose initialapplications were approved as assigned and to the group whose initial applicationswere rejected as rejected.
Rejected workers (i.e., workers who applied for a leave without being assigneddue to lack of funds) had the opportunity to reapply for a break later on during thepilot since the budget was set on an annual basis so that a second chance appearedlater. One third of the rejected applicants chose to do so, receiving a subsidized breakin the next round (see Sect. 3 for details). Empirically, we are interested in esti-mating models which explain how wages are affected by taking part in the career-break program (which we refer to as Treatment). The reapplication opportunity does,however, turn actual Treatment into an endogenous variable since the subsampleof rejected workers who reapplied may be a selected sample of the initial pool ofapplicants.
Importantly, however, as long as the initial assignment was exogenous (conditionalon our model, see below for details) so that (initially) assigned and rejected workerswere comparable, we are able to derive estimates of the causal effects by using initialassignment as an instrument for (potentially endogenous) Treatment.
2.2 Empirical model
Our model explains outcomes (denoted by Y ) by a dummy for the Treatment of takinga subsidized career break (T = 0 or 1). However, since treatment can be endoge-nous (due to repeat applications, as explained above), our model draws identificationfrom the assignment (Z = 1) or rejection (Z = 0) of the initial application. Thisdecision depends on timing within each municipality since the slots were allocatedas long as there were available funds within the municipality. Since early applicantswere accepted first (within a municipality), we thus need to handle differences acrossmunicipalities as well as potential unobserved confounders that are related to the timeof application. We handle differences between municipalities by controlling for munic-ipality dummies (each denoted by M). And, to account for confounders related to thetime of application, we include municipality-specific linear functions of the intendedstart date (S) for each applicant (i). These linear functions capture all unobserveddifferences that are (linearly) related to the intended start date within each municipal-
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ity.10 In addition we use a set of individual-level covariates (X). Thus, we can writethe model as:11
Outcome regression : Yi = α + λTi + MiμM + Si Miγ
M + XiβX + εi
First stage regression : Ti = αFirst + δZi + MiμM,First + Si Miγ
M,First
+XiβX,First + εFirst
i (1)
The linear start date variables for each municipality are identified from differenceswithin the two groups (assigned and rejected). The model therefore properly identifiesthe causal effects as long as there are no “jumps” in unobservables depending on dateof application around the time when the cut-off appeared. In robustness checks we alsovary the model, by including quadratic terms of S and by excluding marginal cases(early rejections and late acceptances), in both cases without any discernible impacton the results.
Equation (1) is estimated using initial assignment (Z) as an instrument for finalTreatment. The difference between the two variables arises since some rejected appli-cants reapplied and received Treatment in the second round. We can only speculateas to why some but not all, reapplied. There is certainly a cost associated with apply-ing since it requires negotiations with the employer as well as some planning underuncertainty on behalf of the individual. Thus, it is more likely than not that those thatreapplied were a more “eager” subset of the original pool of applicants. Importantly,however, this has no impact on the causal interpretation of the estimates since it is doesnot affect the value of the instrument. However, the process does change the identify-ing population—if workers with a stronger urge to go on leave do so irrespective ofthe initial assignment, we will in effect draw inference from (i.e., the estimates willspeak about) the behavior of the more marginal two thirds (since one third reapplies)of the applicants. Since both the instrument and the explanatory variables are binaryit is straightforward to interpret the results as “local average treatment effects” (seeImbens and Angrist 1994). This means that the estimates measure the effects of acareer break for those that (would have) remained at work if their initial applicationhad been rejected but gone on leave if it was accepted.
In some specifications, we estimate a model using initial assignment as the covariateof interest (instead of as an instrument). Following the conventions of the evaluationliterature we denote this the “intention-to-treat effect”.
3 Data and descriptive statistics
We collected all applications from the different PES-offices in June 2003. We thenmerged information on applicants to data on all who started a subsidized career inter-
10 Potential confounders could include health or other factors related to how motivated the applicant wasto take a leave. Since some of the municipalities used the timing of application (for which we lack data),rather than the intended start date, as the selection criteria, Z will, however, not be a deterministic functionof S.11 The variable S is interacted with a full set of municipality dummies (i.e., with no left-out category) andhence not included without interactions in the model.
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ruption between the start of the pilot in February 2002 and March 31, 2003.12 Furtherdata on background characteristics, as well as the actual treatment status were madeavailable by the National Labour Market Board.13
We surveyed the accepted and rejected applicants through telephone interviewson two different occasions.14 The first survey during the fall of 2003 focused onbackground questions and questions regarding what the respondents did during theirleave, or the corresponding time-period for those who never went on leave. The secondsurvey during February 2005 (i.e., 1–2 years after the end of the intended leave) askedquestions regarding employment, working hours and wages after the leave. We usethese responses as outcomes. In total, we have 1,605 observations in our used sample.Appendix 1 shows the number of responses to the different surveys by the individualcharacteristics we have for both respondents and nonrespondents. Since used datacontain a subset of the original population, biased attrition provide a potential causefor concerns. We have addressed this issue in two ways, we first use the covariates wehave for the full sample and use them to predict the outcome (log wage, out-of-samplefor nonrespondents for obvious reasons). We then estimate if the nonrespondents weredifferently selected among the assigned and rejected groups. This does not appearto be the case (detailed results are available on request). Secondly, we analyze thesensitivity of our results to the inclusion of the rich set of covariates that we have forthe respondents in order to investigate if the responding assigned and rejected sampleshave systematically different background characteristics. This does not seem to be thecase (see Table 3, comparing columns 3 and 4). We are, however, as always, requiredto assume that the relationship between unobservables and attrition resembles therelationship between the observed characteristics and attrition.
Note that the data include some observations from municipalities where all appli-cants were assigned. These are only included to enhance the precision in the covariateestimates.
3.1 The intended start date and initial assignment
To show that the initial decision (assignment) was indeed determined by the time ofapplication Fig. 1 shows the distribution of (intended) start dates for assigned andrejected individuals. The first panel is for all municipalities pooled, but since everymunicipality had there own queue, there is some overlap between the groups. The
12 The initial descriptive surveys where commissioned by the Ministry of Industry, Employment andCommunications with a strict timeline. At the time of performing the surveys, we decided to focus onapplications prior to March 31, in order to grant us a sufficiently long follow-up period for writing the finalreport.13 In a strict sense, we therefore estimate the impact of subsidized career breaks relative to the alternativeof not receiving this subsidy. We are not able to measure the incidence of unpaid leave, which could biasthe estimates downward if interpreted as the impact of career interruptions overall. In a strict sense, wetherefore estimate the impact of subsidized career breaks relative to the alternative of not receiving thissubsidy.14 The surveys were executed by two different independent contractors, “Intervjubolaget” and “ARS-research.”
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0.0
02.0
04.0
06
01jan2002 01jul2002 01jan2003 01jul2003 01jan2004
Start date
Assigned Rejected
All
0.0
02.0
04.0
06
01jan2002 01jul2002 01jan2003 01jul2003 01jan2004
Start date
Assigned Rejected
Gothenburg
Fig. 1 Start dates for assigned (actual dates) and rejected (intended dates)
second panel is for Gothenburg (which is by far the largest participating municipality,see Appendix 2): in this figure we see a clear separation in the timing between thegroups, but also a small overlap. To address concerns that the overlapping parts of thesamples are selected, we will perform robustness checks where the earliest rejectionsand latest acceptances are dropped from the sample following the logic of the “donut”model (see Barreca et al. 2011).
3.2 Covariates
The survey based characteristics that we include as covariates are age, gender, laborearnings, part time work, sector, education, experience, tenure, marital status, presenceof children, municipality and intended start date. We also use indicators of registrationat the PES 2–4 years before the (intended) start of the leave (2 years of consecutiveemp-loy-ment with the same employer was a prerequisite for the application).15
Descriptive statistics are presented in Table 1.16 A number of additional variablesare described in Appendix 2. It is worth noting that there is a disproportionate shareof women and public employees among the applicants. Also the numbers for age andexperience are quite high relative to the overall work force. A more detailed inspectionof the most frequent occupations shows that, assistant nurses, mail delivery persons,and daycare personnel are over-represented. Overall, the evidence thus suggests that
15 The register data is consistent with this requirement for all but a very small number of individuals.16 Appendix 1 presents details regarding variable definitions.
Note See Appendix 2 for details about variable definitions and Appendix 2 for further comparisons betweenthe two groups. Corrected differences are from regressions on municipality dummies, in the last columninteracted with a linear function in the (intended) start datea See Appendix 2 for the distributionb Employment status is calculated 2 years before since employ-ment is required during the final 2 years* (**, ***) Significant at the 10 % (5 %, 1 %) level
the take up of the policy mainly is within occupations with low wage dispersion, evenby Swedish standards. The average length of the intended leave spell is 10 months andthe median is 12 months (see Appendix 2).
Table 1 presents differences between the assigned (column 1) and the rejected (col-umn 2) applicants.17 Note, however, that differences in the raw data are expected sincethe municipality distributions differ and each municipality had their own threshold.In the third column we present differences after controlling for systematic differ-ences between municipalities. We find that some of the differences in covariates are
17 Appendix 2 presents further comparisons and the municipality distribution. The multivariate relation-ships are shown in the results sections below.
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Table 2 Outcomes for assignedand rejected
Note Standard deviations inparenthesis. See Appendix 1 fordetails about variable definitionsa Only for those that changedtasks, equal to 1 if morequalified task, 0 if equallyqualified and –1 if less qualified
Assigned Rejected
Wages 19,745 (3,773) 20,177 (3,701)
Log wages 9.874 (0.180) 9.897 (0.174)
Hours worked last week 26.3 (17.1) 28.4 (16.4)
Retired if age 60+ 0.252 0.125
Full time retired if age 60+ 0.286 0.125
Same job 0.874 0.953
Same task 0.798 0.877
Change in qualificationsa 0.330 0.491
significant; most notably the accepted have somewhat more experience and tenurebut lower earnings before their application. There is also a lower fraction of publicemployees among the accepted applicants. These differences suggest that there aresome systematic differences between early applicants and later applicants even withinmunicipalities. However, once we control for the linear functions of the intendedstart date (by municipality) most of these differences disappear. The results pre-sented in column 4 of Table 1 shows that, with the notable exception of a dummyfor working in the public sector, remaining differences are now insignificant. Thispattern is not only reassuring in terms of selection into participation, but also speaksagainst biased sample attrition as a major cause for concern since the sample weanalyze here (and which is balanced in terms of observed covariates), is made upthe survey respondents. Thus, as far as the general pattern also hold for unob-served differences, the model should properly identify the causal effects of the careerbreaks.
3.3 Outcomes
All outcome variables are measured in February 2005 which means 1–2 years afterthe end of the intended leave. Our main focus is on how career breaks affect wages.Depending on the type of contract, people were asked to report their monthly or hourlywages. We convert the responses to monthly wages by multiplying hourly wages by165 (following Statistics Sweden 2004) and by correcting monthly wages for parttime work. For 93 % of the sample the estimate is based on a monthly wage. Thosewith atypical contracts ( self-employed) were asked about their earnings during thelast month, which was corrected for part time employment when applicable. To avoidinducing systematic wage differences from differences on contract type, we includedummies for type of wage information (i.e., hourly wages or monthly earnings), butthese dummies are not important for the results
Data also contain a number of additional outcome variables, such as hours workedin the previous week, retirement, sick leaves and whether the person changed jobs ortasks during the last three years (which, effectively means from before the application).Definitions of these variables can be found in Appendix 1. Descriptive statistics arepresented in Table 2 above.
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4 Results
For the formal analysis, we estimate a wage regression and incorporate the subsidizedleave as described in Eq. (1). Naturally, this specification does not exclude the possibil-ity of previous career breaks, in fact such breaks are likely to have occurred for manyin the sample since the majority are women and many have children. The estimatesthus capture the contribution of an additional career break.
Table 3 shows the results from different regressions based on Eq. (1). The first stageregression shown in column (1) shows that initial assignment is a strong predictorof treatment, as expected. The other included variables do not significantly predicttreatment, with the only exception of tertiary education.
Columns (2)–(4) estimate the effects on wages. The results suggest that the careerbreaks had negative and significant effects on future wages. The intention-to-treateffect (i.e., the direct effect of the initial assignment decision) is 2.2 % and the resultingIV-estimate is 3.1 %. If not controlling for any covariates except municipality and wagetype, we find a slightly larger estimate of 4.0 %. Note that going from column (4) to(3) we add covariates that increase the explanatory power of the model from 6 to 49 %which means that we add variables that capture about half of the relevant heterogeneity,yet the estimates are largely unaffected. Thus, the differences between the assignedand rejected applicants, at least in terms of measurable dimension, appear to be ofminor importance for the estimate of interest.18
Table 4 shows estimates from additional models to see how the covariates affectthe result. The municipality dummies increases the estimates, whereas the covariatesreduces them, while the start date plays no role conditional on the covariates. We alsoshow estimates from models with squared controls for the start date and the resultsremain negative and significant. Finally, we dropped the earliest rejections and thelatest assignments (5 % of each) finding unchanged point estimates but with somewhatlower precision. In Fig. 2 we show the full distributions of log wages in the outcomeperiod for assigned and rejected individuals, suggesting a consistent effect over theentire wage distribution.
Note that as always when studying wages, the quasi-experimental set-up is con-founded by the fact that we are unable to measure the wage effects on those thatdo not work (230 observations lack information on wages). However, as shown inSect. 4.3 below, there does not appear to be any systematic reduction in workinghours due to the career break. We have also estimated the models using a sam-ple excluding everyone aged 60 or above since we found a marginally significanteffect on retirement for this group (see Sect. 4.3 below), but the results were nearlyidentical.
18 Note that the estimates of the control variables may differ from standard wage regression results forseveral reasons. First, the samples differ, wages are lower and more compressed than for the random sampleof workers. Second, we introduce previous earnings as a covariate, thus we are controlling for much of thecross-sectional variation in wages. Both of these factors may explain the modest estimate for the genderdummy. Third, we introduce experience at the same time as age, as well as tenure.
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Table 3 Effects on log wages
Note Instrument is assignment.All regressions control formunicipality (12 dummies) andwage type. All except column(4) also control for (intended)start date interacted withmunicipality; unemployment,temporary employment andon-the job search (2-yearsbefore); marital status andhaving children. Income ismonthly earnings in 1000 SEKs(≈100 Euros). All controlvariables refer to before the(intended) leave. Robuststandard errors in parentheses* (**, ***) Significant at the10 % (5 %, 1 %) level
Note Wage type dummies for whether the wage comes from a monthly wage, an hourly wage or frommonthly income are included in all regressions. Other covariates are age (with square and cube), male,primary school, some tertiary or more, pre-application experience (with square), pre-application tenure(with square), full time employed, income (with square), public employee; unemployment, temporaryemployment and on-the job search (2-years before); married, and having children. All these covariates referto before the application. The final column excludes “border observations” which refers to the latest 5 %of rejections and the earliest 5 % of acceptances within each municipality. Robust standard errors are inparenthesis* (**, ***) Significant at the 10 % (5 %, 1 %) level
01
23
9 9.5 10 10.5ln(Wage)
Assigned Rejected
Fig. 2 Wages after intended leave for assigned and rejected (kernel density)
4.1 How specific is the study population?
Since the study population, i.e., the career-break applicants that we study in this articleare nonrandomly selected, it raises the issue of whether the results generalize to otherpopulations. In particular, the population under study contains older workers, more
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Table 5 Returns to potential experience in a matched sample
Average estimated return to potential experience 0.63 % 0.41 %
R2 0.35 0.37
N 118,251 82,913
[Weighted N] [1.605]Note: Wages are defined in logs. Covariates included in the wage regression are potential experience (withsquare), Gender, Schooling and dummies for sector (private, local or central government). Data are fromthe 3.35 random sample of the Swedish population in the Linda database. The matched sample is createdusing exact matching on the following career-break applicant’s characteristics in 2002: age categorized in1-year groups, gender, schooling (3 levels) and sector. All observations within each matched cell are usedbut weighted according to the applicants characteristics. Robust standard errors are in parenthesis* (**, ***) Significant at the 10 % (5 %, 1 %) level
females and more public sector employees than the average stock of employees. Inorder to get a more structured sense of the difference between the population understudy and the overall population of workers, we estimate how the returns to experiencediffer between individuals with characteristics that resemble those of the applicantsand a random sample of workers. In practice, we use register data on a representativecross-sectional sample and estimate the returns to experience on average and in asample that matches the career-break applicants with respect to gender, age, sector,and level of schooling.19 We calculate the returns to experience as averages from aquadratic function of potential experience in the two samples.20
The estimates in Table 5 show that the average returns to potential experience are0.41 % per year in the matched sample and 0.63 % for the average worker.21 Thus,the estimated returns to (potential) experience for those that apply for the career-breakprogram are not only much smaller than for the average worker; the returns are alsomuch smaller than the estimated wage effect of a career break of 3 %. The wage effectis more in the order of the average nominal wage increase between the years (themedian wage increase for individuals working both years is 3.4 %).22
19 The data are drawn from the 2002 version of the Linda data base, see Edin and Fredriksson (2000)for details. The matched sample is created using exact (one-to-many) matching on the characteristics. Theresulting sample is weighted so as to match the distribution among the applicants.20 We do not estimate the returns to experience within the main sample since selection into application candiffer between individuals with different levels of experience.21 The estimates for the overall sample are very close to those presented in Gustavsson (2006) using datafrom the same database for 2001.22 A further question regarding the interpretation of the results has to do with how the leave is spent. Sincewe do not know what the workers in the comparison group would have done if they had been granted aleave, we can only study this by comparing outcomes within the group of treated individuals. Experiments
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Table 6 Effects of career breaks on jobs and tasks
Note: Regressions control for covariates and intended start dates. Wage type dummies are for whether thewage comes from a monthly wage, an hourly wage or from monthly income. Other covariates are age(with square and cube), male, primary school, some tertiary or more, pre-application experience (withsquare), pre-application tenure (with square), full time employed, income (with square), public employee;unemployment, temporary employment and on-the job search (2-years before); married and having children.All these covariates refer to before the application. Robust standard errors are in parenthesis* (**, ***) Significant at the 10 % (5 %, 1 %) level
4.2 Jobs and careers
Given that most of the people taking the subsidized career breaks have occupationswith relatively modest within-job wage dispersion, it is perhaps surprising that wefind any wage effect at all. In order to further our understanding of the mechanismsat hand, we try to investigate the role of job and task mobility. Results are presentedin Table 6. We first re-estimate Eq. (1) using the probability of remaining at the pre-application job as the outcome variable. The results show that workers going on leaveare less likely to work at their old job 1–2 years after the end of the (intended) leave.We then show that a similar result emerges when analyzing the probability to returnto performing the same type of task as before. Thus, career breaks appear to causallyinduce mobility.
We further present some results from explorative models documenting the relation-ship between job/task mobility and wage growth. We do this by separately estimatingthe “wage effects” for movers and stayers using Eq. (1) for each of the two samples (atthe same job vs. changed job). Here, a word of caution is warranted since we split thesample according to an endogenous variable. It is therefore clear that this particularset of results also could be compatible with selection stories—it is not implausiblethat, e.g., “good workers” leave their jobs when not receiving a career break, whereas“bad workers” leave their jobs when actually receiving a career break, or vice versa.
Despite this caveat, we find this descriptive analysis enlightening. Taken at facevalue, the results seem to suggest that those who changed jobs had a strong negativewage impact from the career break, whereas those that remained in their old job whereunaffected. However, an alternative, and equally plausible, explanation is that workerswith bad wage prospects increased their mobility more if they were accepted into the
Footnote 22 continuedwith this strategy suggest that there is no difference in the effect for those enrolled in education (23 %) orself-employment (4 %) during the leave. This suggests that the types of education pursued during the leavewere mainly a form of consumption.
123
O. N. Skans, L. Liljeberg
career-break program. Although our setting does not allow us to disentangle the twostories (since mobility is endogenous), the results clearly suggest a link between thewage impact of career breaks and labor mobility, either through changes in the selectioninto mobility, or by a stronger negative wage impact among mobile workers (or both).
We also find that a similar pattern emerges when estimating a crude model explain-ing the direction of changes in the required qualifications (last column of Table 6),measured as a variable with the value 1 if the new task is more qualified (accordingto the respondent), 0 if equally qualified, and −1 if less qualified. The estimates areinsignificant23 but suggest that there is a move toward (relatively) less-qualified tasksfor the people going on leave. Inspection of the raw data shows that the assigned bothhave fewer cases of upward mobility and more cases of downward mobility.
Here it is important to note that these results are unrelated to the validity of the mainresults of the article, i.e., the finding that the average worker in the sample loses about3 % of wages relative to the estimated counterfactual wage. The reason is that the maineffect is estimated on the full sample including both mobile and immobile workers.Due to the possibility of selection effects, we cannot rule out that those that remainedat their old jobs also experienced a negative wage effect but that this is masked bychanges in the composition of remaining workers.
4.3 Effects on labor supply
By definition, career breaks imply a temporary reduction in labor supply. However,there is also a possibility that the interruptions affect future labor supply. As was arguedby the Green Party proponents of the subsidized career-break scheme studied in thisarticle, it is possible that a break may reduce the risk of subsequent sickness absence,in particular for example due to stress related illnesses. Similar effects can influencethe chosen age of retirement; if a subsidized leave is a substitute for early retirement,it may have positive effects on the labor supply in the future. Second, it is conceivablethat the preference for leisure (or similarly, the stock of information that affects thevalue of leisure) is increased by a career break. In addition, negative effects on futurecareer prospects could reduce the value of continued work. Thus, leisure today mayeither be an intertemporal substitute or complement to future leisure.
In order to analyze the question, we estimate Eq. (1) with weekly hours of work asthe dependent variable. In Table 7, we see that all point estimates are negative but farfrom significant. The estimates are very similar if we include or exclude the covariates.Including the covariates increases the R-squared from 2 to 17 % without affecting theestimates of interest, suggesting that systematic correlations between assignment and(at least the observed) characteristics of the applicants are of minor importance alsoin this case.
In order to check whether the wage estimates are affected by censoring, we explicitlyestimated the effect on the probability of not working at all. The results, presented inTable 8, are small and highly insignificant, suggesting that systematic drop outs fromthe sample should be a minor concern for the interpretation of the wage regressions.
23 Estimates are significant if we only include observations where the level of qualifications changed.
123
The wage effects of subsidized career breaks
Table 7 Effects on weekly hours worked
Variable First stage Intention to treat IV IV(1) (2) (3) (4)
Assignment 0.708∗∗∗ −1.186
(0.025) (1.267)
Treatment (career break) −1.674 −1.465
(1.792) (1.904)
N 1,605 1,605 1,605 1,605
R2 0.62 0.17 0.17 0.02
Other covariates Yes Yes Yes No
Note: Instrument is assignment. All regressions control for municipality (12 dummies). All except last col-umn also control for (intended) start date interacted with municipality; unemployment, temporary employ-ment and on-the job search (2-years before); marital status and having children. Income is monthly earningsin 1,000 SEKs (≈100 Euros). All control variables refer to before the (intended) leave. Robust standarderrors in parentheses* (**, ***) Significant at the 10 % (5 %, 1 %) level
Table 8 Other indicators of labor supply
All Aged 60 or more in outcome year (2005)
Not working Not working Full time retired Full or part time retired
With controls 0.023 0.230∗ 0.214∗ 0.218∗(0.046) (0.138) (0.112) (0.114)
R2 0.13 0.32 0.31 0.29
Without controls 0.033 0.169 0.163∗ 0.212∗∗(0.036) (0.109) (0.091) (0.093)
R2 0.01 0.08 0.02 0.02
N 1,605 270 270 270
Note: Hours and employment information refers to the previous week. All regressions include municipalitydummies. Controls are age (with square and cube), male, primary school, some tertiary or more, pre-application experience (with square), pre-application tenure (with square), full time employed, income(with square), public employee; unemployment, temporary employment and on-the job search (2-yearsbefore); married and having children. All these covariates refer to before the application. Robust standarderrors are in parenthesis* (**, ***) Significant at the 10 % (5 %, 1 %) level
We further experimented with analyzing the probability of not working for differentage groups and found a marginally significant effect only for workers aged over 60.When explicitly looking at retirement, we see that the subsidized break had a positiveeffect on the probability of retirement.24 The estimates suggest that a career breakincreases the probability of retirement by 21 %. Thus, for workers close to retirement,leisure appears to be an intertemporal complement to future leisure. It should, however,
24 We have also experimented with alternative measures of labor supply for the whole population withoutfinding any significant effects: We used a dummy if working 36 h or more and found negative but insignificantresults and studied the sick leave propensity and found no significant results.
123
O. N. Skans, L. Liljeberg
be noted that the estimates are based on a small sample and only statistically significantat the 10 % level.
5 Concluding remarks
This article studies labor market effects of being granted a subsidized career inter-ruption. The analysis compares workers whose applications were accepted with thosewhose applications were rejected due to lack of funds. The breaks were 3–12 monthslong with a mean of 10 months and a median of 12 months. The results show thathourly wages are reduced by approximately 3 % by the career break 1–2 years afterthe end of the break. This is in the order of an average yearly nominal wage increase butsubstantially larger than estimated returns to experience among workers with similarcharacteristics as the career-break applicants.
While interpreting the results, it is important to keep in mind that we are identifyingthe effect of being granted a career break; any effects through signaling toward theown employer (although perhaps not toward alternative employers) will be washedaway since both the treatment and the comparison group advertised their willingnessto take a break. Also, characteristics that we typically associate with small returnsto experience, such as being female, working in the public sector, and having longexperience are over-represented among the workers applying for the career breaks.Thus, it is likely that the presented estimates are lower bounds for the effects anaverage worker would experience if taking a career break. Therefore, we interpretthe results as showing that policies which encourage career interruptions will reducefuture earnings, even when they are directed toward groups with little wage dispersionand little expected returns to experience. Given that the take-up of most career-breakpolicies is much greater for women, such policies are also likely to contribute to thegender wage gap.
Further results suggest that career breaks induced additional changes in jobs andtasks. On average, workers who changed jobs after a career break also experiencedlarger relative wage drops which implies that either the selection into mobility, or thewage consequences of changing employer (or both), was altered as a consequence ofthe career breaks. Regardless of which of these explanations is the dominating force,the results suggest that the overall wage effect partly is related to a less favorable careerdevelopment across jobs, rather than a pure within-job effect. Although this particularset of results should be viewed as tentative, it suggests that the effects of career breakson future career mobility provide an important topic for future research.
Acknowledgments The authors wish to thank two anonymous referees, Per-Anders Edin, Magnus Gus-tavsson, and Eva Mörk, and seminar participants at SOFI, Göteborgs universitet and IFAU for their helpfulcomments; the Ministry of Industry, Employment and Communications for financing our surveys; Inter-vjubolaget and ARS for executing the surveys; and Katalin Bellaagh, Bo Melin, and Lotta Nylén for helpfuldiscussions on the survey design.
Appendix 1: Details about the data
See Tables 9 and 10
123
The wage effects of subsidized career breaks
Tabl
e9
Var
iabl
ede
finiti
ons
(con
tinue
son
next
page
)
Var
iabl
eSo
urce
Com
men
t/res
tric
tions
Ass
igne
d/re
ject
edPE
Sre
gist
eran
dap
plic
atio
nsPE
Sre
gist
ered
the
assi
gned
inof
ficia
lreg
iste
rs
App
licat
ions
for
thos
ere
ject
edfr
omPE
Sof
fices
wer
ega
ther
edm
anua
llyR
easo
nfo
rre
ject
edSu
rvey
1:W
hatw
asth
ere
ason
for
notb
eing
gran
ted
ale
ave?
Rem
ain
inda
taif
answ
erin
g“P
ES
ran
outo
fm
oney
”(8
4%
).O
ther
wis
edr
oppe
dM
ain
othe
rre
ason
sar
e“n
otfin
ding
asu
itabl
ere
plac
emen
twor
ker”
and
“unk
now
nre
ason
”T
reat
edPE
Sre
gist
ers
All
trea
ted
are
regi
ster
ed
Mun
icip
ality
ofw
orkp
lace
PES
regi
ster
sor
Surv
ey1
ifas
sign
ed.F
rom
appl
icat
ion
ifre
ject
edIn
som
eca
ses
impu
ted
from
area
code
sof
phon
enu
mbe
rsA
ctua
lSta
rtda
tePE
Sre
gist
ers
Inte
nded
Star
tdat
e(i
fre
ject
ed)
(1)
App
licat
ions
Not
alla
pplic
atio
nsin
clud
ed(r
eada
ble)
star
tdat
es.N
otal
lres
pond
ents
rem
embe
red
thei
rin
tend
edst
artd
ates
orun
ders
tood
the
ques
tion
corr
ectly
(2)
Surv
ey1
Q:W
hen
did
you
inte
ndto
star
tyou
leav
e?
(3)
Impu
ted
from
mea
nam
ong
reje
cted
inm
unic
ipal
ity
Mon
thly
Wag
eSu
rvey
2:B
ased
onm
onth
lyw
age
(93
%),
hour
lyw
age
orm
onth
lyea
rnin
gs(i
fco
ntra
ctot
her
than
mon
thly
orho
urly
wag
e)Q
1:M
onth
lyw
age,
hour
lyw
age
orot
her
cont
ract
?
Q2:
Wha
twas
your
mon
thly
(hou
rly)
wag
e(o
rea
rnin
gs)?
Mon
thly
wag
ean
dea
rnin
gsco
rrec
ted
for
part
time.
Hou
rly
wag
em
ultip
lied
by16
5(f
ollo
win
gSC
B,2
004)
Q3:
(If
noth
ourl
yw
age)
Isth
atfo
rfu
lltim
e?
Q4:
(If
notf
ullt
ime)
For
whi
chfr
actio
nof
full
time
was
your
cont
ract
last
wee
k?K
epti
f>
10,0
00an
d<
40,0
00:d
rops
17ob
serv
atio
ns
Wee
kly
hour
sSu
rvey
2If
reas
onfo
rw
orki
ngle
ssth
an36
hour
is“b
eing
onca
reer
brea
k”(1
4ca
ses)
then
use
repl
yfr
om“H
owm
any
hour
sdi
dyo
uw
ork
the
first
wee
kin
Dec
embe
r?”
123
O. N. Skans, L. Liljeberg
Tabl
e9
cont
inue
d
Var
iabl
eSo
urce
Com
men
t/res
tric
tions
Q1:
How
man
yho
urs
did
you
wor
kla
stw
eek?
Q2:
(if
nota
ble
toan
swer
,[im
pute
dva
lue]
):W
asit
0[0
],1–
14[8
],15
–24
[20]
,25–
35[3
0],3
6or
mor
e[4
0]Sa
me
job
Surv
ey2
Q:A
reyo
ucu
rren
tlyw
orki
ngfo
rth
esa
me
orga
niza
tion
orfir
mas
you
did
thre
eye
ars
ago?
Sam
eta
skSu
rvey
2
Q:A
reyo
urm
ain
task
sth
esa
me
asth
eyw
ere
thre
eye
ars
ago?
Mor
eor
less
qual
ified
task
sSu
rvey
2
Q:(
ifch
ange
dta
sks)
Are
your
curr
entt
asks
mor
equ
alifi
ed,e
qual
lyqu
alifi
edor
less
qual
ified
than
the
old
ones
?R
etir
edSu
rvey
2if
notw
orki
ngIf
answ
erin
g“r
etir
ed”
Q:M
ain
reas
onfo
rno
twor
king
?
Pre
appl
icat
ion
labo
rin
com
eSu
rvey
1Im
pute
dfr
omm
odel
base
don
the
cova
riat
esin
clud
edin
the
IVm
odel
ifno
repl
y(5
0ca
ses)
Q:W
hatw
asyo
urm
onth
lyla
bor
inco
me
(bef
ore
taxe
s)be
fore
appl
ying
?E
xper
ienc
e/Te
nure
Surv
ey1
Q:H
owm
any
year
sof
wor
kex
peri
ence
doyo
uha
ve?
‘…w
ithth
eem
ploy
erfr
omw
hich
you
appl
ied
for
ale
ave’
for
tenu
reU
nem
ploy
men
t(hi
stor
y)PE
Sre
gist
ers
(730
–1,4
60da
ysbe
fore
star
tdat
e)In
clud
esbo
thop
enun
empl
oym
enta
ndtr
aini
ngpr
ogra
ms
Tem
pora
ryem
ploy
men
t(hi
stor
y)A
sab
ove
Reg
iste
red
inte
mpo
rary
empl
oym
ent,
part
time
unem
ploy
ed,s
ubsi
dize
dem
ploy
men
tetc
On-
the-
job
sear
ch(h
isto
ry)
As
abov
e
Not
eSu
rvey
1in
fall
of20
03,s
urve
y2
inFe
brua
ry20
05.S
ome
ques
tions
have
been
abbr
evia
ted
inth
eta
ble
123
The wage effects of subsidized career breaks
Table 10 Responses to survey 1 and 2
Initial pop-ulation
Responses Responses Used sample
Survey 1 Surveys 1 and 2
Assigned
All 2,001 1,478 1,110 1,106
Age
<40 0.216 0.190 0.188 0.186
40–55 0.437 0.438 0.445 0.447
>55 0.347 0.372 0.367 0.367
Female 0.719 0.749 0.777 0.777
Status 730– 1,460 days before start*
Unemployed 0.016 0.013 0.013 0.013
Temporary or subsidized employment 0.036 0.034 0.037 0.037
On-the job search 0.010 0.012 0.013 0.013
Rejected
All 1,322 908 637 499
Age
<40 0.250 0.221 0.203 0.212
40–55 0.431 0.445 0.454 0.463
>55 0.319 0.334 0.344 0.325
Female 0.750 0.776 0.818 0.836
Status 730– 1,460 days before start*
Unemployed 0.020 0.017 0.017 0.017
Temporary or subsidized employment 0.066 0.061 0.065 0.069
On-the job search 0.007 0.008 0.010 0.009
Note The reason for the relatively low number of “rejected” people in the last column is that 135 observationswho stated that their application was rejected due to not fining a suitable replacement worker were dropped*Fraction of days. Employment status is calculated two years before since employment is required duringthe final two years, see Appendix 1 for details
The data derive from two telephone administrated surveys. 72 % of the 3,323 peoplethat were contacted for the first survey responded. The main reason for nonresponseswas failure to contact the respondent either because of incorrect phone numbers orsince the respondent was not accessible during the survey period (a more detailedanalysis of reasons for nonresponses is available upon request). Of those respondingto the first survey, 73 % also responded to the second survey. Thus, in total, we have53 %, or 1,747 individuals, who responded to both surveys.
We dropped 135 observations who stated other reasons than “lack of funds at thePES” for not being assigned. The other reasons were “unknown reason,” (68 cases)“no suitable replacement worker was found,” (46 cases) ‘Changed my mind’ (3 cases)and “Other reason’ (18 cases). Since we do not know whether these workers wouldhave been assigned or not if these alternative reasons had not occurred we drop them
123
O. N. Skans, L. Liljeberg
from the data. However, we have estimated the models under different assumptionsregarding these observations and the results are robust.We also drop a very smallnumber of observations with missing background information and end up with 1,605observations in our used sample.
Appendix 2: Comparisons of assigned and rejected
See Tables 11, 12, 13, 14 and 15
Table 11 Municipalitydistribution of applications
Municipality Assigned Rejected
Botkyrka 0.058 –
Gällivare 0.024 0.108
Göteborg (Gothenburg) 0.374 0.232
Hultsfred 0.025 0.046
Hällefors 0.011 0.042
Katrineholm 0.044 –
Landskrona 0.052 0.028
Lund 0.119 0.076
Piteå 0.070 0.182
Strömsund 0.020 0.052
Västerås 0.159 0.174
Åmål 0.045 0.060
Number of observations 1,105 500
Table 12 Children (beforeapplication)
Assigned Rejected
Having children 0.495 0.556
1 child 0–6 0.117 0.148
2 children 0–6 0.045 0.052
≥3 children 0–6 0.007 0.010
1 child 7–16 0.152 0.192
2 children 7–16 0.163 0.150
≥3 children 7–16 0.036 0.036
1 child 17 or older 0.116 0.154
2 children 17 or older 0.025 0.034
≥3 children 17 or older 0.005 0.006
Number of observations 1,105 500
123
The wage effects of subsidized career breaks
Table 13 Earnings (beforeapplication)
Assigned Rejected
Monthly pre-tax earnings (SEK)
Mean 17,157 17,513
Median 17,000 17,500
<10 000 0.033 0.034
10,000–14,999 0.206 0.204
15,000–19,999 0.524 0.508
20,000+ 0.236 0.254
Number of observations 1,105 500
Household income (if married or cohabiting)
Mean 36,821 36,057
Median 35,000 35,000
<20 000 0.060 0.044
20,000–29,999 0.171 0.184
30,000–39,999 0.395 0.429
40,000–49,999 0.252 0.236
50,000+ 0.122 0.106
Number of responses 820 385
Table 14 Sick-leaves (beforeapplication)
Assigned Rejected
Number of sick leave spells 12months before application
1.381 1.428
0 0.424 0.380
1 0.271 0.292
2–5 0.256 0.290
>5 0.049 0.038
Number of sick leave days 12months before application
14.794 12.768
0 0.424 0.380
<14 days 0.353 0.416
14–28 days 0.097 0.112
1–3 months 0.074 0.052
>3 months 0.052 0.040
Number of responses 1,105 500
Table 15 Length of break(actual and intended)
Assigned Rejected
Average career-break length (months) 9.9 9.1
Median career-break length (months) 12 12
Number of responses 1,105 500
123
O. N. Skans, L. Liljeberg
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