The Impact of Furloughs on Emotional Exhaustion, Self-RatedPerformance, and Recovery Experiences

Jonathon R. B. HalbeslebenUniversity of Alabama

Anthony R. WheelerUniversity of Rhode Island

Samantha C. Paustian-UnderdahlUniversity of Alabama

The notion that strain can result as employees’ resources are threatened or lost is well established.However, the transition from resource threats to resource losses is an important but understudied aspectof employee strain. We argue that the threat-to-loss transition triggers accelerated resource loss and ashift in how employees utilize their remaining resources unless employees engage in recovery experi-ences during the transition. Using a discontinuous change framework, we examine employee furloughs—the placement of employees on leave with no salary of any kind—in terms of the transition from resourcethreat to loss: Resources may be threatened when the furlough is announced and lost when the furloughoccurs. Using 4 data collections with 180 state government employees, we found mean levels ofemotional exhaustion increased and mean levels of self-reported performance decreased following thefurlough. The discontinuous changes in exhaustion and performance were significantly impacted byemployees’ recovery experiences during the furlough. We discuss the implications of these findings forother threat-to-loss and recovery research as well as for organizations implementing furloughs.

Keywords: furlough, exhaustion, performance, recovery, conservation of resources

Employees have limited physical, mental, emotional, and mate-rial resources that they use to meet the demands of their workenvironment; the inability to meet resource demands contributes toemployee strain (Shirom, 2003). We propose that employee strainwill vary under conditions associated with resource threat andresource loss. While these processes have been discussed exten-sively in the literature on employee strain and performance (e.g.,Hobfoll, 2001, 2011), there have been no studies to empiricallyexamine how the threat of loss and actual loss independentlycontribute to employee outcomes. We utilize the process of amandatory furlough—the placement of employees on leave withno pay of any kind for the period of the leave—to examine thistransition (United States Office of Personnel Management, 2012).

Furloughs present a unique context to examine the effects of athreat of loss and an actual loss because the announcement of apossible furlough remains a threat (and not loss) to employees upto the day that the furlough takes place. This threat process occurs

because of the tendency for organizations to decouple announce-ments with actual policies (e.g., a high number of “false alarms”;Fiss & Zajac, 2006; Yang & Zheng, 2011). Thus, the ongoingthreat of the furlough, which precedes the actual furlough day,creates demands on employees that can lead to increased employeestrain and decreased performance. Furloughs may be particularlyrelevant experiences to employees and organizations today giventhe state of the current global economy.

In response to the 2008 Great Recession and its ongoing effects,many companies have sought methods for cutting costs that wouldenable continued competitive advantages or fiscal solvency. Sinceemployee costs often represent the largest line item in service-based organizations’ budgets, many organizations have looked toemployee costs as a source of cost savings. Although private sectorcompanies also use the practice to cut costs, most recently at theGannett Company-owned newspaper USA Today (Gannett Blog,2012), government organizations more frequently furlough em-ployees to save costs without engaging in layoffs (Vestal, 2009). Infiscal years 2009 and 2010, at least 21 states implemented afurlough program for state employees (Vu, 2009). In the case ofgovernment organizations, most furloughs are single-day eventsintermittently occurring throughout the year to minimize disrup-tion of services. Despite the recent surge of such programs (Bar-tlett, 2009; Vu, 2009), the limited extant academic research fo-cuses on longer-term furloughs (e.g., Rich, 1986), which provideslittle understanding of the impact of contemporary furloughs onemployees.

Because organizations typically notify employees of furloughsin advance of the actual furlough day, the announcement of thefurlough provides a threat to resources while the actual furlough

This article was published Online First March 18, 2013.Jonathon R. B. Halbesleben, Department of Management & Marketing,

University of Alabama; Anthony R. Wheeler, Schmidt Labor ResearchCenter and College of Business Administration, University of RhodeIsland; Samantha C. Paustian-Underdahl, Department of Management &Marketing, University of Alabama.

We acknowledge the valuable contributions of William Miller andRussell Hammond to this article.

Correspondence concerning this article should be addressed to JonathonR. B. Halbesleben, Department of Management & Marketing, Universityof Alabama, Box 870225, Tuscaloosa, AL 35487-0225. E-mail:jhalbesleben@cba.ua.edu

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Journal of Applied Psychology © 2013 American Psychological Association2013, Vol. 98, No. 3, 492–503 0021-9010/13/$12.00 DOI: 10.1037/a0032242

492

day manifests the resource loss. While employee pay is likely themost salient loss of resources in a furlough context, employeesmight also feel as though they lose control over their work (sincethe furloughs were dictated to them), the ability to complete workassociated with deadlines causing tasks to “pile up,” job security,time with friends at work, and status when compared to theirprivate-sector counterparts. However, the valence of resources islargely idiosyncratic (Hobfoll, 2001). For example, whereas theperception of less job security may be a major threat to oneemployee, it may be less of a threat to another who believes otheropportunities exist outside of the company. As a result, in thisarticle we focus on emotional exhaustion as a consequence ofresource loss rather than trying to theorize and test the individualinterpretation of resource losses for each employee (Janssen, Lam,& Huang, 2010; Lam, Huang, & Janssen, 2010).

The furlough context also enables us to test employees’ motivesto invest resources to mitigate resource loss in the face of workdemands (Hobfoll, 2001, 2011). Employees are motivated to investwhat resources they have to gain additional resources but willbecome more strategic and defensive in those investments as theirresources are threatened or lost (Hobfoll, 2001). Researchers havestudied resource investment in the face of strain (e.g., Halbesleben& Bowler, 2007; Halbesleben & Wheeler, 2011); however, re-searchers have not examined how employees alter resource invest-ment decisions in light of changes from threat to loss of resources.The loss itself is important, in that it leaves the employee withfewer resources (e.g., motivation and energy) to invest in theirwork (Binnewies, Sonnentag, & Mojza, 2009, 2010; Fritz & Son-nentag, 2005) and is a source of escalated threat to the employee.In other words, when the furlough occurs, the employee losesresources, but it may also signal that the organization is willing totake resources away in the future. This suggests that employeesmight reduce resource investment strategies in the face of a con-tinued threat.

Finally, we are able to examine the way employees mightmitigate resource losses during the furlough by engaging in recov-ery experiences (Sonnentag, 2001). Recovery describes the pro-cesses by which depleted resources are replenished and restored(Meijman & Mulder, 1998). If an employee chooses, a furloughrepresents an opportunity to recover. Therefore, we examine howemployees use their time during the furlough to examine how thatmay impact the threat-to-loss transition.

Furloughs and Employee Strain

Emotional Exhaustion

In the present study, we examine strain in terms of emotionalexhaustion, which is defined as a state of depleted work-relatedemotional and motivational resources (Halbesleben & Buckley,2004; Hobfoll & Freedy, 1993) and is considered to be an impor-tant marker of employee strain (Fritz & Sonnentag, 2006; Shirom,2003). Over the years, emotional exhaustion has garnered consid-erable research interest, largely because of its relationships withnegative outcomes in the workplace, including deleterious mentaland physical health outcomes, negative work attitudes, turnover,and job performance (cf. Cordes & Dougherty, 1993; Demerouti,Bakker, Nachreiner, & Schaufeli, 2001; Green, Walkey, & Taylor,

1991; Halbesleben & Buckley, 2004; Lee & Ashforth, 1996;Maslach, 2001; Shirom, Westman, Shamai, & Carel, 1997).

While both threats and losses contribute to strain, we proposethat losses should have a greater impact on strains because that iswhen the threat of a loss manifests to an actual loss of resources(Hobfoll, 2001). For example, while the threat of an impendingfurlough may increase an employee’s emotional exhaustion (due totheir increased worry and sense of uncertainty), the occurrence ofa furlough should lead to a greater increase in emotional exhaus-tion because the employee’s perceived threat of loss becomes anactual experienced loss. Instead of worrying about whether or nota furlough will occur, once a furlough has taken place, employeesexperience a loss of financial stability, a break in their time withcoworkers, and perceived job insecurity. In the furlough context,when employees receive announcements of a possible future fur-lough, the possibility exists in their minds that the threat may notactually manifest to a loss (e.g., decoupling). Thus, when the actualloss occurs, the resources lost (as described above) become par-ticularly salient, which enhances strain.

Further, we propose that the loss signals that future losses arelikely to occur. Implementing the furlough demonstrates that theorganization will use strategies that lead to resource loss foremployees as a mechanism for addressing organizational resourcelosses (e.g., financial well-being). Thus, the furlough is simulta-neously a loss of resources and a threat to future resources. Weshould expect to witness a combined effect of the realization of theresource loss coupled with the signal that future losses are apossibility. As a result, we predict that following the furlough therewill be an increase in emotional exhaustion.

Hypothesis 1: Emotional exhaustion will increase following afurlough day.

Job Performance

While the transition from resource threat to resource loss im-pacts the experience of emotional exhaustion, it also shifts theways in which employees address the resource losses (Halbesleben& Bowler, 2007; Halbesleben & Wheeler, 2011). Resource invest-ment tends to be strategic and based on the situation within whichthe individual is working (Hobfoll, 2001). Performance involvesemployees’ investment of resources (e.g., time and effort) intowork tasks with the expectation that organizations will reward theinvestment with other resources (e.g., pay, enjoyment of the work,etc.; Halbesleben & Bowler, 2007). Employees seek to maximizereturns on their investments (P. B. Baltes, 1997; M. M. Baltes &Baltes, 1990), so it is unlikely that employees will invest inbehaviors benefiting an organization that has enacted a policythreatening employee resources.

The shift from threat to loss of resources may trigger a changein the resource investment strategy of employees in the form of jobperformance. As the furlough approaches and merely threatensemployee resources, employees still have resources (e.g., motiva-tion and energy) to invest into performance. However, once thefurlough arrives and resource losses are realized, employees willhave fewer resources to invest into job performance. Further, therealization of the furlough as a loss in resources will lead theemployees to become more defensive in their resource investmentstrategies (Hobfoll, 2001) because furloughs signal instability in

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493IMPACT OF FURLOUGHS

the work environment. We expect employees to invest less inperformance behaviors when they know that doing so may leadthem to lose resources anyway; this should be particularly true ofbehaviors that are not expected of them, such as organizationalcitizenship behaviors (OCBs) directed at the organization (Halbes-leben & Bowler, 2007; Siegall & McDonald, 2004).

We examine self-reported performance because shortly follow-ing a furlough, employees themselves are most likely to noticechanges in their own performance behaviors before others do(Bolino, Turnley, Gilstrap, & Sauzo, 2010). However, we ac-knowledge self-reported performance can introduce the possibilityof an upward bias due to self-inflation (Heidemeier & Moser,2009). While self-reported performance may be subject to self-inflation, our model is focused less on the level of performancethan on the change in performance following the furlough. Thus, ina pattern similar to but opposite of exhaustion following a threat-to-loss shift, we expect that self-reported performance will alsoshift following the furlough as employees invest fewer resourcesinto their work in response to fewer resources and an escalation offuture threats to resources.

Hypothesis 2: Performance (in-role and organizational citizen-ship behavior) will decrease following a furlough day.

The Role of Recovery

As noted above, employees have a motive to seek resources tomitigate future resource loss (Hobfoll, 2001), which suggests thatindividuals can slow the rate of change of resource loss throughrecovery experiences (Eden, 2001; Sonnentag, 2001; Sonnentag &Fritz, 2007). In their work conceptualizing recovery experiences,Sonnentag and Fritz (2007) found recovery experiences can bedifferentiated into four distinct forms: psychological detachmentfrom work, relaxation, mastery experiences, and control. Whileeach works through somewhat different mechanisms, they sharethe common effect of reducing employee strain by giving employ-ees an opportunity to recover lost resources (Etzion, Eden, Lapi-dot, 1998; Fritz & Sonnentag, 2006; Larson, 1989; Sonnentag &Bayer, 2005; Sonnentag & Natter, 2004).

If those on furlough take the opportunity to engage in recoveryexperiences, we would expect the negative impact of a furloughwould be reduced. They can regenerate resources that were lost asa result of the furlough. For example, employees could engage involunteer work on the side that would reflect detachment fromwork, mastery, and control or they could gather with coworkers ina social setting so as not to lose the resource of the time withfriends at work and to recover from the loss via relaxation (Fritz &Sonnentag, 2005; Potocnik & Sonnentag, in press). As a result, weexpect recovery experiences to reduce strain (Fritz & Sonnentag,2006). Combined, these patterns suggest a negative relationshipbetween recovery experiences and changes in mean levels ofemotional exhaustion.

Hypothesis 3a: Recovery experiences will moderate the im-pact of furloughs on emotional exhaustion such that those whoengage in more positive recovery experiences will experienceless of an increase in emotional exhaustion.

By generating resources to make up for those that are lost,employees have more resources to draw from to invest in work

(Binnewies et al., 2009, 2010; Fritz & Sonnentag, 2005). Bydetaching from work during a furlough, employees may feel theyare in a better position to focus clearly on their work and maxi-mally invest resources into their work after the furlough (Beal,Weiss, Barros, & MacDermid, 2005; Sonnentag, 2003). Thus, ifemployees lose resources as a result of the furlough, those (orother) resources could be gained through recovery experiences thatwould buffer the negative impact that the furlough has on perfor-mance. Using the furlough as an opportunity to detach from workor relax may make the future threat of furloughs less salient (orreframe the furloughs as an opportunity to recover), such thatemployees take a less defensive position in resource investment.All told, these arguments suggest that recovery experiences mod-erate the impact of furloughs on performance, reducing the fur-lough’s negative impact.

Hypothesis 3b: Recovery experiences will moderate the im-pact of furloughs on performance such that those who engagein more positive recovery experiences will experience less ofa decrease in performance (in-role performance and organiza-tional citizenship behavior).

Our hypotheses suggest a discontinuous change model (Singer& Willett, 2003). Such models have gained popularity over thepast few years (e.g., Lang & Bliese, 2009) in large part because,via testing of discontinuous mixed-effects growth modeling, onecan model transitions in work processes and individual differencesin how those transition processes play out. Such models representan excellent way to conceptualize the transition from resourcethreat to loss, since that transition implies a discontinuous change.Further, using the mixed-modeling approach, we can examine howrecovery impacts the nature of that transition, offering a valuableextension of the strain and recovery literature.

Method

Study Context

We tested our hypotheses among a group of state governmentemployees that experienced mandatory furloughs. The state wasamong the first in the nation to implement furloughs; as a result,the announcement was a surprise to most employees. The furloughin the present study was the first that was not otherwise associatedwith a typical paid holiday (e.g., the prior furlough had been theFriday after Thanksgiving). Further, though furloughs had beenannounced several months prior to the furlough date in the presentstudy, there had been consistent messages that the furloughs maynot occur if the state’s financial situation improved or if it wasdetermined that the furloughs were a violation of the state employ-ees’ contracts (e.g., signaling decoupling; Fiss & Zajac, 2006;Yang & Zheng, 2011). As a result, while the announcement was ashock, the furloughs remained a possible threat (and not loss) up tothe day that they occurred. Thus, we believe the announcement andoccurrence of a furlough for these employees served as an excel-lent opportunity to study the transition from threat to loss.

Participants and Procedures

The furlough day under study occurred on a Monday. We chosethis day because it was the first that did not extend an already

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494 HALBESLEBEN, WHEELER, AND PAUSTIAN-UNDERDAHL

existing holiday break (e.g., the Friday after Thanksgiving wasalso used as a furlough but would confound the furlough and theholiday, as well as the potential preference not to work that dayanyway). All of the furloughs in the state program were associatedwith a weekend in some way (e.g., Fridays or Mondays). To avoidconfounding the weekend and the furlough together, we examinedthe variables on the following Tuesday to capture the variablesfollowing a non-furlough weekend. In total, our study spanned 19days.

Two Fridays before the furlough, high-ranking representativesof each state agency sent e-mails to all employees of their respec-tive agency with a recruitment e-mail from the researchers andtheir own encouragement to participate in the study. The recruit-ment e-mail included a link to an online survey. This procedurewas repeated on the Friday just prior to the furlough, the Tuesdayafter the furlough, and the following Tuesday. In accordance withthe parameters established between the four state agencies and us,the surveys were completely anonymous. The participants gener-ated a code based on demographic information (e.g., first twoletters of their high school, date of mother’s birthday) that wouldallow us to track the data across measurement occasions withoutcollecting identifying information.

The survey was distributed to 927 employees. Two hundred fiftyfour employees responded to the initial survey (initial responserate: 27%). We could match complete data from 180 participantsover the course of the four data collections for a retention rate of72% and a final response rate of 19%. We compared scores on allsurvey variables between those who responded to only the Time 1survey with those who responded to all four surveys, findingno significant differences in any of the substantive variables in thestudy or demographics.

The sample included 96 males and 81 females (three partici-pants did not provide a gender) with a mean age of 46.11 years(SD � 9.19). Participants had worked for their current organiza-tion for a mean of 6.46 years (SD � 5.09). They worked anaverage of 39.51 hr per week during typical (non-furlough) weeks.Given the highly political nature of the state furlough program andconcerns about identifying the agencies participating, we agreednot to collect data regarding occupations or job titles of partici-pants, and we do not report the domain of the government eachagency covered. That said, the four agencies covered a broadspectrum of employees (managerial, professional, and non-professional) in varied state government services.

Measures

Emotional exhaustion. We assessed emotional exhaustionusing the exhaustion subscale of the Oldenburg Burnout Inventory(OLBI; Demerouti, Bakker, Vardakou, & Kantas, 2003; Halbes-leben & Demerouti, 2005). It is an eight-item measure; a sampleitem is “There are days that I feel already tired before I go towork.” Given the design, with repeated measures over relativelyshort time lags between measurements, we rephrased the items tobe more appropriate for this type of setting (e.g., “Today I felt tiredbefore I went to work.”). Items were scored on a 5-point, Likert-type scale from strongly disagree (1) to strongly agree (5); higherscores indicate higher exhaustion. This scale was included in allfour data collection times.

Performance. We assessed performance using the in-role per-formance (seven items, sample item: “adequate completed myassigned duties”) and organizational citizenship behaviors-organization (seven items, sample item: “preserved and protectedorganizational property”) subscales of the performance measure ofWilliams and Anderson (1991). We administered this scale at allfour data collection periods. In the instructions, we asked theparticipants to consider how often they had engaged in the behav-iors since the previous survey (the first data collection periodasked the participant to consider performance over the previousweek). Items were scored on a 5-point, Likert-type scale fromstrongly disagree (1) to strongly agree (5); higher scores indicatehigher performance.

As discussed above, we acknowledge self-reported performancemay not be ideal, as it introduces the possibility of an upward biasdue to self-inflation and common method variance because all ofthe measures were from the same source. However, these concernsare tempered somewhat by four factors. First, we implemented arepeated measures design, which should reduce common methodvariance (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). Sec-ond, research on source of performance ratings finds self-reports,particularly OCBs, may not be any more biased than supervisorreports (Vandenberg, Lance, & Taylor, 2004). Third, recent re-search on performance reports that some performance behaviorsmay be difficult for others to observe, particularly in the relativelyshort intervals that we utilized in this study (Bolino et al., 2010;Ilies, Fulmer, Spitzmuller, & Johnson, 2009). Fourth, while self-reported performance may be subject to self-inflation, our model isfocused less on the level of performance than on the change inperformance following the furlough. As a result, we would expectself-inflation to be similar across time periods. If inflation wasplaying a role in the data, it should be more difficult to detect theshift toward poorer self-rated performance that our hypothesespredict, leaving us with a conservative test of the model. Given thetimeframe of our study, the sample setting (particularly the needfor complete anonymity), and the myriad jobs included in thesample, we could not have asked supervisors or coworkers tocomplete assessments of performance.

Recovery experiences. We assessed post-furlough recoveryexperiences using Sonnentag and Fritz’s (2007) Recovery Expe-riences Questionnaire. It is a 16-item measure that assesses fourrecovery experiences (with four items each): detachment fromwork, relaxation, mastery, and control. We slightly rephrased theitems to the past tense and asked the participants to think abouthow they had spent the previous 3 days. Example items included“I forgot about work” (detachment), “I kicked back and relaxed”(relaxation), “I sought out intellectual challenges” (mastery), and“I took care of things the way I wanted them done” (control). Itemswere scored on a 5-point Likert-type scale from strongly disagree(1) to strongly agree (5); higher scores indicate higher incidence ofeach type of recovery experience. We aggregated the four kinds ofrecovery into one scale because we had no theoretical reason toexpect differences between the experiences, similar to other stud-ies (e.g., Binnewies et al., 2009; Sonnentag & Kruel, 2006; Trou-gakos, Beal, Green, & Weiss, 2008). This scale was included onthe Tuesday post-furlough (Time 3) survey administration.

Control variables. Because tenure may have impacted reac-tions to the furlough (e.g., long enough experience with the state tonot react as strongly) and emotional exhaustion, we controlled for

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495IMPACT OF FURLOUGHS

it in all analyses. It was measured with a single continuouslyscored item asking how long in years the participant had worked intheir current agency.

Additionally, we acknowledge that some employees might nothave followed the furlough requirements as delineated by the state.Fifty-four participants reported they had worked during the fur-lough day. Since this likely would impact the trajectory of strainexperienced by those individuals, we controlled for it by creatinga dummy code for working during the furlough (1 � workedduring the furlough, 0 � did not work during the furlough). Theseemployees work in the same agencies, do the same type of work,and experience the loss of pay from the furlough yet maintain theirwork as though the furlough was not occurring.

Finally, we treated the organization they worked in as a controlvariable. Since there were four agencies, we coded the first threeagencies with a 1 (worked in that agency) or 0 (did not work in thatagency). Zero on all three indicates that the person worked in thefourth agency.

Data Analysis

Our hypotheses predict shifts in exhaustion and performance asa result of a distinct experience: a furlough. As such, the hypoth-eses imply a model of discontinuous change. We analyzed the datausing random coefficient modeling testing a mixed-effects modelfor discontinuous change (Singer & Willett, 2003) using MplusVersion 6.1 (Muthén & Muthén, 2010). All models were two-levelmodels where the four measurement occasions (Level 1) werenested within individuals (Level 2). Two of the measurementoccasions occurred pre-furlough; two occurred post-furlough. Westarted by examining the Level 1 change in exhaustion and per-formance (Bliese & Ployhart, 2002; Pinheiro & Bates, 2000). We

then added the Level 2 predictor (recovery) to test individualdifferences in the Level 1 effects (cf. Lang & Bliese, 2009).

Results

We present the descriptive statistics, including means, standarddeviations, internal consistency estimates (Cronbach’s alpha), andcorrelations in Table 1. The internal consistency estimates fallwithin the typically accepted ranges. Moreover, the correlationswere in the expected directions (e.g., recovery experiences nega-tively correlated with exhaustion), which provide initial supportfor our hypotheses. Our control variable of tenure had little rela-tionship with the study variables, whereas organization andwhether the participant worked during the furlough did demon-strate significant relationships with the substantive variables in themodel. Consistent with the recommendations of Becker (2005), theresults presented below do not include tenure as a control since itadded no value to the understanding of the relationships.

Despite weak correlations between organization membershipand the other variables in the study, we examined the Level 1intraclass correlations (ICC1) values for the organization level ofanalysis (.05 for exhaustion, .09 for performance, and .07 fororganizational citizenship). Since low ICC values temper concernsabout non-independence and accordant concerns about statisticalpower (Bliese & Hanges, 2004), we did not model organization-level effects in our study but did control for them in the models. Onthe other hand, the ICC1 for the person level of analysis wasreasonably high (.52 for exhaustion, .47 for self-rated in-roleperformance, and .36 for organizational citizenship), suggestingsignificant variability between persons and justifying examinationof the Level 2 predictors (Bliese, Wesensten, & Balkin, 2006).

Table 1Means, Standard Deviations, Correlations, and Internal Consistency Estimates for Study Variables

Variable M SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

1. ORG1 0.43 0.50 —2. ORG2 0.25 0.44 �.20 —3. ORG3 0.19 0.39 �.27 �.33 —4. ORG4 0.13 0.27 �.29 �.25 �.35 —5. Tenure 6.46 5.09 .03 .06 .02 �.03 —6. Worked 0.30 0.20 �.02 �.01 �.04 .06 �.12 —7. EX 1 2.44 1.01 �.16 �.69 �.04 .15 �.07 �.14 .888. EX 2 2.51 0.95 �.14 �.37 .01 .21 �.05 �.13 .24 .909. EX 3 3.48 0.85 �.15 �.41 .04 .17 �.01 �.07 .20 .19 .90

10. EX 4 3.65 0.92 �.12 �.44 .07 .16 �.10 �.13 .19 .22 .31 .9311. In-Role 1 4.16 1.09 .04 .02 .00 �.03 .17 .25 �.18 �.25 �.16 �.20 .8712. In-Role 2 3.83 1.19 .01 .05 �.02 �.01 .21 .17 �.16 �.25 �.17 �.19 .31 .8213. In-Role 3 3.13 0.98 .07 .03 �.01 .00 .23 .16 �.23 �.31 �.32 �.27 .27 .23 .8414. In-Role 4 2.96 1.21 .02 .04 �.03 �.01 .18 .24 �.25 �.22 �.20 �.34 .28 .21 .37 .8015. OCB 1 3.81 1.33 �.01 .00 .02 .01 .24 .23 �.21 �.19 �.19 �.16 .32 .19 .21 .16 .8916. OCB 2 3.49 1.15 �.03 .02 .02 �.01 .30 .18 �.17 �.19 �.21 �.24 .18 .17 .23 .24 .24 .8417. OCB 3 2.69 1.10 .00 .02 .04 �.02 .25 .14 �.32 �.28 �.35 �.22 .26 .15 .20 .21 .19 .20 .8018. OCB 4 2.64 1.22 .03 .01 .05 �.01 .17 .21 �.30 �.20 �.26 �.33 .24 .13 .32 .24 .17 .19 .35 .7919. Recovery 3 2.63 1.18 .02 .06 .08 �.11 �.34 .09 �.24 �.16 �.19 �.20 .10 .09 .09 .16 .10 .13 .15 .21 .85

Note. N � 180. Correlations with the worked and ORG variables are Spearman correlations; all others are Pearson correlations. The number next to eachvariable label indicates time when it was measured (e.g., EX 1 � exhaustion at Time 1). Correlations over .14 are p � .05; correlations over .18 are p �.01. Values on the diagonal are internal consistency estimates (Cronbach’s alpha). ORG � organization participant worked for (subscript indicates potentialorganization, 0 � did not work for, 1 � worked for); Worked � worked during the furlough (coded 1 � worked during furlough, 0 � did not work duringfurlough); EX � emotional exhaustion; In-Role � in-role performance; OCB � organizational citizenship behavior.

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496 HALBESLEBEN, WHEELER, AND PAUSTIAN-UNDERDAHL

Level 1 Analysis

We modeled the effects of discontinuous change in a mannerdescribed by Singer and Willett (2003). Specifically, we capturethe elements of time and discontinuous change with two parame-ters. First, we included a DAY predictor that was coded as the daythat the survey was completed (Day 1 of the survey was coded as0). We coded for day rather than measurement occasion since thespace between the measurement equations was not equivalent.1

This also allowed us to specifically model potential differences inthe actual day the survey was completed, since occasionally par-ticipants completed it a day later than intended. Second, we in-cluded a transition predictor (TRANS) to represent the shift inexhaustion or performance as a result of the furlough (coded 0, 0,1, 1). In Table 2, we outline the data structure for one of theparticipants in the study as an illustration. The Level 1 model isrepresented in Equation 1, including both discontinuous changeparameters and the controls for organization (ORG) and whetherthe participant worked during the furlough (WORKED). Partici-pant is indicated by i and time is indicated by j, following Singerand Willett (2003).

EXij � �0i � �1i�ORGij� � �2i�WORKEDij� � �3i�DAYij�� �4i�TRNANSij� � �ij (1)

The results of the multilevel modeling are in Table 3. Prior todiscussing the hypothesis tests, we note a number of findings.First, for all three outcomes, working during the furlough had asignificant effect. Working during the furlough was associatedwith greater exhaustion and higher perceived performance butlower self-perceived organizational citizenship behavior. Thehigher perceived performance is opposite of the generally expecteddirection for the effect of the furlough but may reflect a perceptionof higher performance based on the idea that the participantengaged in performance during the furlough. Also, for both in-roleperformance and organizational citizenship behavior, the organi-zation an employee worked for had no effect. This was not the casefor exhaustion, where there were significant organizational effectsfor which we subsequently controlled.

In Hypothesis 1, we predicted that exhaustion would signifi-cantly increase following a furlough. As indicated in Table 3, thetransition parameter was significant (.88, p � .01), suggesting ashift in the mean level of exhaustion; the positive direction sug-gests that the shift is an increase in exhaustion following thefurlough and supports Hypothesis 1.2

In Hypothesis 2, we predicted that performance (self-rated in-role and self-rated organizational citizenship behavior) would de-crease following the furlough. When considering self-rated in-roleperformance, the transition parameter was significant (�.22, p �.01), suggesting a significant negative shift in self-rated in-roleperformance. Similarly, we found that the transition parameter forself-rated organizational citizenship behavior was significant(�.17, p � .05), indicating that there was also significant negativeshift in self-rated organizational citizenship behavior. These find-ings support Hypothesis 2.

Prior to adding the Level 2 predictor, we checked if the Level 1change (both day and the transition) varied between individuals bycontrasting models and using log-likelihood ratio tests (Bliese &Ployhart, 2002; Pinheiro & Bates, 2000). We found that for the

day-level effects (DAY above), there was significant random vari-ation for models of exhaustion (�diff

2 (2) � 22.79, p � .05), in-roleperformance (�diff

2 (2) � 257.69, p � .01) and organizationalcitizenship behavior (�diff

2 (2) � 15.72, p � .05). For the transitioneffects (TRANS above), there was significant random variation formodels of exhaustion (�diff

2 (2) � 31.18, p � .05), in-role perfor-mance (�diff

2 (2) � 261.38, p � .01) and organizational citizenshipbehavior (�diff

2 (2) � 230.90, p � .01).

Level 2 Analysis

To examine Hypotheses 3 regarding the impact that recoveryhas on the change patterns of the participants, we added Time 3recovery as a Level 2 predictor in the model. Our analysis strategyallowed us to examine the extent to which recovery impacted thechange parameter. Thus in addition to the variations on Equation1, we specified three additional Level 2 equations to account forthe effects of recovery (RECOV):

�0i � �00 � �01�RECOVi���0i (2)

�3i � �30 � �31�RECOVi� (3)

�4i � �40 � �41 �RECOVi� (4)

Equation 2 specifies that recovery will estimate the mean levelsin exhaustion or performance in participants across time. Equation3 specifies that recovery will estimate the slopes of exhaustion orperformance over the course of all four data collections in thestudy. Equation 4 specifies that recovery will predict individualdifferences in the shifts in exhaustion or performance after thefurlough.

We found that recovery had a significant main effect for allthree outcomes, suggesting engaging in recovery behaviors wasassociated with lower exhaustion, higher self-rated in-role perfor-mance, and higher self-rated organizational citizenship behavior(see Table 3). Testing Hypothesis 3, we found that the cross-levelinteractions between recovery and the transition parameter weresignificant in all three cases. For exhaustion, the parameter sug-gested that recovery behaviors reduced the level of shift in ex-haustion (�.19, p � .05). In other words, those who engaged inmore recovery behaviors tended to have less dramatic shifts inexhaustion. The same was true for self-rated in-role performance(�.076, p � .01) and self-rated organizational citizenship behavior(�.18, p � .01). To illustrate these effects, we graphed the bestlinear unbiased predictors (BLUPs) derived from the regression

1 We recognized that coding by day assumes something of a continuousprocess that may not fully reflect the situation. In our case, the timesbetween Data Collections 1 and 2 and 3 and 4 were not the same as the timebetween Data Collections 2 and 3. To address this, we recoded the datausing 0, 1, 2, and 3 to represent each data collection period rather than theday. We found that the results and interpretations were not substantivelydifferent, likely because the day effects were relatively small in the model.Those findings are available from Jonathon R. B. Halbesleben.

2 We acknowledge that interpreting main effects in the presence ofinteraction effects, as we have done in referring to Table 3, can be difficult.We confirmed the main effects findings by testing main-effects onlymodels that did not estimate the interaction terms. The results of the modelswere consistent with what we reported here, and the results demonstratedsupport for Hypotheses 1 and 2. For brevity, those results are not reportedhere and are available from the authors.

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497IMPACT OF FURLOUGHS

terms, for those high (�1 SD) and low (�1 SD) in recovery (seeFigures 1–3). These findings were consistent with Hypothesis 3.

Discussion

We sought to examine the impact furloughs have on change inemployee strain and performance since furloughs represent aunique transition from a threat to resources to a loss of resources.Consistent with the hypotheses, we found mean levels of emo-tional exhaustion increased following a furlough and self-ratedperformance decreased following the furlough. Finally, we foundemployees who reported higher levels of positive recovery expe-riences during the furlough experienced less of an increase inemotional exhaustion and less of a decrease in self-rated perfor-mance and organizational citizenship behavior.

Implications for Theory and Practice

Overall, our study makes several important extensions to the cur-rent literature. First, our research provides an important extension ofthe strain literature regarding the transition from threat of resourceloss to actual resource loss. Hobfoll (1998, 2001) proposed that boththreat to and actual loss of resources uniquely impact individuals’strain experiences. When a stressor shifts from a threat to an actualloss, we found that shifts in emotional exhaustion and performanceoccur. This psychological “shifting gears” is consistent with physio-logical research on stress (see Sikora, Beaty, & Forward, 2004). Theinitial stressor poses a threat to resources, which is then compoundedby the actual loss of resources.

Our research further extends the literature by examining how thethreat-to-loss transition impacts resource investment strategies.

Table 2Sample Data Structure for One Participant

Participant ORG1 ORG2 ORG3 WORKED EX PERF OCB RECOV DAY TRANS

1 1 0 0 0 2.53 4.00 3.76 2.96 0 01 1 0 0 0 2.62 3.95 3.62 2.96 7 01 1 0 0 0 3.29 3.15 2.89 2.96 11 11 1 0 0 0 3.48 2.99 2.81 2.96 18 1

Note. ORG � organization participant worked for (subscript indicates potential organization, 0 � did not work for, 1 � worked for); WORKED �Worked during furlough (0 � did not work, 1 � worked); EX � Exhaustion; PERF � in-role performance; OCB � organizational citizenship behavior;RECOV � recovery; DAY � parameter for day that survey was completed (Day 1 is coded as 0, furlough happened on Day 10); TRANS � parameterfor transition from before to after furlough.

Table 3Discontinuous Mixed-Effects Growth Models Predicting Change in Exhaustion, In-Role Performance, Organizational CitizenshipBehavior as a Function of Recovery

Exhaustion In-role performance OCB

Variable Coef. SE Std. coef. Coef. SE Std. coef. Coef. SE Std. coef.

Fixed effectsIntercept 3.33�� .19 3.91�� .10 3.43�� .27ORG1 vs. ORG4 �.50�� .048 �.44 .012 .034 .05 .083 .125 .018ORG2 vs. ORG4 �.64�� .051 �.80 .013 .029 .07 .082 .059 .02ORG3 vs. ORG4 �.34�� .030 �.15 .018 .027 .12 .083 .051 .02WORKED .22�� .032 .18 .15 .016 .98 �.27�� .067 �.15DAY .00 .010 .00 �.037�� .001 �.47 �.018�� .001 �.36TRANS .88�� .14 1.24 �.22� .10 �.73 �.17� .11 �.27RECOV �.19�� .049 �.95 .042� .025 .35 .11� .067 .45DAY � RECOV �.01�� .003 �.02 .002 .002 �.02 �.001 .001 �.01TRANS � RECOV �.19� .040 �.41 .076�� .029 .22 .18�� .029 .23

Variance SD Variance SD Variance SD

Random effectsIntercept 6.57 2.56 1.84 1.35 13.12 3.62DAY .00 .00 .00 .00 .025�� .00TRANS .013�� .01 .00 .00 .038�� .004Residual .012�� .01 .005�� .07 .025 .004

Fit indices�2 Log likelihood �480.18 �926.14 �667.54AIC �452.18 �898.14 �639.54

Note. N � 180, k � 720. WORKED � Worked during furlough (0 � did not work, 1 � worked); EX � Exhaustion; PERF � in-role performance;OCB � organizational citizenship behavior; RECOV � Recovery; TRANS � parameter for transition from before to after furlough; Coef. � coefficient;Std coef. � standard coefficient; AIC � Akaike information criterion.� p � .05. �� p � .01.

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498 HALBESLEBEN, WHEELER, AND PAUSTIAN-UNDERDAHL

Individuals respond to this rapid shift in resource depletion byconserving the resources they would otherwise invest into jobperformance. The inclusion of another investment possibility, pos-itive recovery experiences, accentuates these findings. Amid rap-idly dwindling resources, individuals can still choose among in-vestment strategies to protect or replenish resources. Employeeschoosing to invest in positive recovery experiences mitigate theloss of resources (see Figure 1).

Our study highlights the need for clearer theorizing regardingthe furlough context; specifically our research highlights broaderimplications for theories in two related research streams: othertransitions from threat to loss and time away from work. Forexample, as many organizations (including government) are down-sizing and moving operations to other locations (e.g., offshoring),the threat employees experience will likely increase. Examiningthe manner in which the transition from threat to loss impactsemployees (especially in the context of survivors of turnover ordownsizing, e.g., Datta, Guthrie, Basuil, & Pundey, 2010; Maertz,Wiley, LeRouge, & Campion, 2010) and triggers acceleration ofresource losses will be particularly valuable in such contexts inorder to maintain performance and employee well-being. Ourstudy offers a framework for examining this process.

One might also consider furloughs as time away from work,commonly known in the literature as a respite. Respite researchershave examined contexts such as vacations (Etzion, 2003; Fritz &Sonnentag, 2006; Westman & Eden, 1997), weekends (Fritz &Sonnentag, 2006), business trips (Westman & Etzion, 2002), sab-

baticals (Davidson et al., 2010), evenings following working days(Sonnentag, Binneweis, & Mojza, 2008; Sonnentag & Natter,2004), military service (Etzion et al., 1998), and even lunch breaks(Krajewski, Sauerland, & Wieland, 2011). The studies consistentlyreport that time away from work alleviates immediate stressors ofwork and allows employees to recover spent resources. Thus, timeaway from work decreases work-related strain and improves em-ployee well-being (Sonnentag, 2005). However, our findings sug-gest that furloughs are an exception. While it is possible thatsomeone might gain resources (e.g., if they focused on the recov-ery activities), in general we found that furloughs are instanceswhere time off leads to fewer resources. This may suggest a needfor researchers to carefully consider the costs and benefits of timeaway from work. Indeed, we suggest that researchers exploreunemployment through this lens as it may represent a time awayfrom work that is unwelcome but could have either costs orbenefits depending on the manner in which that time is spent.

In the study, we asked about whether participants had worked ona furlough day either from home or in the office (and had alsoindicated that they were on furlough) and controlled for theirresponse in the analysis. The high number of such responseshighlights a concern for employees faced with furloughs—thatwhile they could take the time off in theory, in practice theirrequired work must still be completed. This has been an issue insimilar studies. For example, Cropley and Millward Purvis (2003)reported that they would have had to drop 94% of their data hadthey screened out participants who had done some amount of workduring non-work time. We wonder if employees believe that whilethey have the day off, they are still expected to keep up with workdemands. While our data are preliminary in this regard, it appearsthat furloughs have a dual negative effect on resources: Employeeslose compensation but may feel pressure to invest remainingresources to perform job tasks. Future research into the motivationbehind working during furloughs would be especially valuable asorganizations seek to manage the particularly negative impact offurloughs among this group.

This study offers initial evidence that organizations, at leastgovernment agencies, should recognize the potential negative im-pact furloughs can have on employees and, in particular, thetransition from knowledge of the furlough (threat) to actual im-plementation of the furlough (loss). To the extent that emotionalexhaustion is associated with outcomes such as turnover andabsenteeism (Halbesleben & Buckley, 2004; Lee & Ashforth,

Low Recovery

High Recovery

Emot

iona

l Exh

aust

ion

Figure 1. Best linear unbiased predictors (BLUP) for emotional exhaus-tion by levels of recovery.

High Recovery

Low Recovery

In-R

ole

Perf

orm

ance

Figure 2. Best linear unbiased predictors (BLUP) for in-role performanceby levels of recovery.

High Recovery

Low Recovery

Org

aniz

atio

nal C

itize

nshi

p B

ehav

ior

Figure 3. Best linear unbiased predictors (BLUP) for organizationalcitizenship behavior by levels of recovery.

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499IMPACT OF FURLOUGHS

1996), the increase in emotional exhaustion following a furloughcould lead to larger scale problems. However, given that it isdifficult to capture financial productivity in a government setting,we did not specifically ask the participating agencies to calculateemployee productivity pre- and post-furlough. Future research,perhaps in settings where productivity can be more directly cap-tured, could calculate the indirect financial costs of lost produc-tivity resulting from increased employee exhaustion and decreasedperformance in response to furlough events.

A potential solution to mitigate exhaustion might be for orga-nizations to offer optional opportunities for resource gain duringthe furlough. As one example of this, some universities facingfurloughs organized volunteer opportunities during the furloughday. While the day was still unpaid, it offered an opportunity foremployees to invest their resources in volunteer work and engagein recovery experiences (Mojza, Lorenz, Sonnentag, & Binnewies,2010; Mojza, Sonnentag, & Bornemann, 2011). Such group out-ings might allow employees to develop positive social supportfrom their coworkers, which is particularly helpful in mitigatingemotional exhaustion (see Halbesleben, 2006). Future studies mayalso examine the role that intrinsic motivation plays in employees’ability to mitigate exhaustion by seeing time away as a recoveryopportunity (ten Brummelhuis, ter Hoeven, Bakker, & Peper,2011).

Limitations and Directions for Future Research

While an important first step in examining the threat-to-lossevent and the context of furloughs, we recognize some limitationsto our study. First, we did not have a “true” control group ofemployees that did not experience furloughs to compare our find-ings with. To have such a control group, we would have needed tosample government employees from other states or private-sectorequivalents, which would have confounded their work contextwith their membership in the control group (in other words, wemay not have had comparable groups to study). Instead, we ex-amined exhaustion and performance over the next weekend assomething of an “internal” control and examined the unique ex-perience of those worked during the furlough within the sameagencies; however, we recognize that the ideal would have in-volved a true control group.

Similarly, we cannot draw causal conclusions from the studybecause we did not randomly assign furloughs. Our findings arelimited to four agencies within one state; the generalizability ofthese findings is unknown. Each state has implemented theirfurloughs in different ways, both in the number/extent of thefurlough days and in how they are implemented. For example,some employees are allowed to determine when they are takingfurlough time. In such cases, the furlough days act more likeunpaid vacation, and one might expect that the negative impactwould be potentially diminished. Further, our study focused on thetransition between threat and loss. Since states may have differedin how and when they announced their furlough program, theexperience of threat and transition to loss may differ.

We acknowledge that our response rates were rather low. Apotential contributor to the low response was the context of thefurloughs. The decision to implement furloughs in the state wasseen as a politically charged decision and, as such, there may havebeen fear of repercussion for participating in the study, despite

being an entirely anonymous data collection with the agencyidentities also held in confidence. The commitment of a four-survey study likely also impacted response; with that in mind ourresponse rates are not different from other studies in the respite andrecovery literature (cf. Fritz & Sonnentag, 2005). Response ratesare not necessarily a marker of non-response bias (Rogelberg &Stanton, 2007). Other evidence, for example the finding that therewere no differences between Time 1-only respondents and full-study respondents, suggest that non-response may not have been abiasing factor in the study.

Due to practical limitations of studying such a politically sen-sitive topic, we were limited in how many pre- and post-furloughsurveys we could ask the participants to complete and had to keepthem entirely anonymous. This meant, in part, that we were re-quired to rely on self-report performance, which we acknowledgewas not ideal. Additionally, future research might utilize additionalmeasurement periods in order to examine the cumulative effect ofrepeated furloughs. While we predicted a shift in both exhaustionand performance as a result of a furlough, we did not specify whatwould happen following that transition. This was in part becausetheory is not definitive in how one might respond following thetransition. On the one hand, we might expect that the heightenedfuture threat that has resulted from the furlough would mean theexhaustion would be sustained. On the other hand, the time fol-lowing the furlough may allow the employee to recover resources(e.g., time with friends at work) in order to allow them to moveback to their previous resource levels.

Given the context of the study and the surprise nature of thefurlough announcement, we were unable to measure the variablesprior to the start of the threat. Future research that could examinechanges in strain and performance as the threat occurs and thenagain as the loss occurs would be particularly valuable in testingtheories regarding the roles of stress and strain and would offerpractical guidance on how best to implement announcements andimplementation of loss situations (e.g., layoffs). This also raisesthe issue of how perception of the threat may have changed overtime. As we noted in describing the context, the threat remainedhigh and the loss uncertain right up to the furlough day; however,we acknowledge that the short timeframe prior to the furlough mayhave masked some of the threat effect. That said, the increase inexhaustion following the furlough suggests that the loss is mean-ingfully experienced and not simply a situation where individualswere relieved that the threat was over, even if the outcome wasbad.

Additionally, our measure of performance included self-reported in-role performance and OCBs. While research has shownthat self-reported and other-reported OCBs are highly related(Vandenberg et al., 2004), meta-analytic data shows that in-roleperformance can be inflated in self-reports compared to boss-ratings (Heidemeier & Moser, 2009). Thus, while we were able toshow that employees believed their in-role performance and theirOCBs decreased following the furlough, it is unclear how mana-gerial perceptions of employees’ performance may have shifted asa result of the furlough event and any recovery experiences. Futureresearch should examine this issue further; it was not possible todo so in this study due to the need to keep surveys anonymous inlight of the political nature of the study.

The organizations dictated the timing of the measurements(Fridays pre-furlough and Tuesdays post-furlough). We recognize

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500 HALBESLEBEN, WHEELER, AND PAUSTIAN-UNDERDAHL

that this is problematic, as we cannot rule out the possibility thatthe changes in levels of exhaustion and performance were due tothe days they were collected, because employees might be happieror more optimistic on Fridays and thus may generally report morepositive results. Studies of time away from work suggest thatweekends and other breaks should actually increase resources(Fritz & Sonnentag, 2006; Sonnentag, 2003). Thus, in the contextof emotional exhaustion, rather than a “blue Monday” effect, wemight expect a “spent Friday” effect. Further, the “blue Monday”effect is related to mood, so its direct impact on performancewould likely be minimal. However, we acknowledge that wecannot rule out the confounding effects of days of week with thetiming of measurements.

Finally, we did not measure the actual resources gained and lostas a result of furloughs, and instead took the common approach ofassessing exhaustion as an indicator of resource loss (Janssen etal., 2010; Lam et al., 2010). Resources depend largely on theecology within which they are experienced and thus are highlyindividualized (Hobfoll, 2001, 2011). While directly measuringresources may have increased the explanatory power of our model,it would have also introduced the measurement concern of whichresources to measure such that we could capture the relevantresources for this sample. We took the general approach of mea-suring the outcome of resource loss, though we recognize thelimitations inherent in such an approach.

Conclusion

Resource threat and loss play significant roles in the strainexperience; however, their relative roles have not been previouslyexamined. We found that the threat-to-loss transitions representedby furloughs were associated with higher mean levels of strain. Wefurther found that the transition from threat to loss shifts resourceinvestment strategies of employees away from performance andtoward recovery, an extension of the previous literature on re-source investment in the face of strain (P. B. Baltes, 1997; M. M.Baltes & Baltes, 1990; Halbesleben & Bowler, 2007). Whileadditional replication is needed in other settings to confirm ourfindings, these initial results suggest furloughs must be carefullyimplemented in order to avoid negative outcomes for employeesand longer-term problems for organizations.

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Received September 8, 2011Revision received February 5, 2013

Accepted February 11, 2013 �

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503IMPACT OF FURLOUGHS