Journal of Applied Psychology

The Economy and Absenteeism: A Macro-Level StudyMindy K. Shoss and Lisa M. PenneyOnline First Publication, January 9, 2012. doi: 10.1037/a0026953

CITATIONShoss, M. K., & Penney, L. M. (2012, January 9). The Economy and Absenteeism: AMacro-Level Study. Journal of Applied Psychology. Advance online publication. doi:10.1037/a0026953

RESEARCH REPORT

The Economy and Absenteeism: A Macro-Level Study

Mindy K. ShossSaint Louis University

Lisa M. PenneyUniversity of Houston

Despite much speculation, little is known about the net effects of the economy on the employedworkforce. To fill this gap, we used state-level data collected by the Bureau of Labor Statistics to examinethe effects of the condition of the economy, as indicated by the unemployment rate, on incidence ratesof absence reportedly due to symptoms of illness and violent acts in the workplace for 43 states from1992 to 2009. Our results suggest that the unemployment rate is positively associated with theseindicators of absenteeism, and that these effects are delayed in time.

Keywords: economy, sickness absence, workplace violence

The most recent economic recession and associated high rates ofunemployment have led to much concern and speculation aboutthe societal impact of economic events (e.g., Catalano, 2009;Ludin & Hemmingsson, 2009; World Health Organization, 2009).For the population as a whole, it appears that rises in the unem-ployment rate are likely to be met with simultaneous or subsequentrises in rates of mental and physical illnesses and violence(Goldman-Mellor, Saxton, & Catalano, 2010). However, despitethis picture of how the state of the economy may impact society ingeneral, we know little about the net effects specifically on theemployed labor force and, in particular, on organizational phenom-ena, such as absenteeism. For example, does sickness absenteeismtend to increase or decrease with a rising unemployment rate?Likewise, does workplace violence increase with a poor economy,as the popular press and HR managers appear to believe (e.g.,Chenier, 1998; Elliott & Jarrett, 1994), such that the unemploy-ment rate is associated with absenteeism due to workplace vio-lence? The current study sheds light on these issues by using datafrom the Bureau of Labor Statistics (BLS) to examine the effectsof the economy, as indicated by the unemployment rate, on state-level indicators of sickness absence and absence resulting fromviolent acts in the workplace for 43 states across 18 years. Further,because economic events (e.g., recessions) occur over time, weexamine the temporal nature of these effects by testing modelsreflecting immediate, lagged, and cumulative effects of the unem-ployment rate on these rates of absenteeism. A macro-level exam-

ination of these issues is likely to be of value to human resources(HR) managers, particularly those in large organizations withoffices in multiple communities, because they may reveal impor-tant patterns relevant for workforce planning. As Jiang, Probst, andSinclair (in press) noted, there is a surprising dearth of research onthe effects of the economy in organizational behavior; we aim tofill this gap.

Background

At a national level, the association between economic prosperityand indicators of health (e.g., newborn mortality rate and deathfrom preventable disease) is well-established. The mechanismscited for these effects have been largely financial; for example,wealthier countries are able to provide greater access to medicalsupplies and nutrient-rich foods (Bloom & Canning, 2000; Ray,1998; Strauss & Thomas, 1998). However, the impact of economiccondition is not limited to availability of financial opportunitiesand resources. Many have argued that economic, political, andsocial systems are so highly intertwined that the state of theeconomy can and arguably does impact social forces within andthe psychology of communities (e.g., Brenner, 2005; Ray, 1998).As an example, consider the seminal study by Hovland and Sears(1940), who found that the price of cotton was associated withlynchings in the South from 1882 to 1930. In addition to merelycreating tension among those experiencing financial loss, sociol-ogists argue that the poor state of the economy caused metaphorictears in the social fabric of the community that may have previ-ously constrained this behavior (Shihadeh & Ousey, 1998).

In more recent times, Catalano and Dooley (1983) have pro-posed the economic stress hypothesis. This hypothesis states thatundesirable economic events negatively impact well-being be-cause they constitute and create “ambient” psychosocial stressors.Although Catalano and Dooley did not indicate to what level thishypothesis applies, their use of it to examine data at both macro-and micro-levels, as well as to establish macro–micro linkages(e.g., between the unemployment rate and individuals’ negativefinancial and job-related events), seems to suggest that they be-lieve that it can be applied to multiple levels (e.g., Catalano &

Mindy K. Shoss, Department of Psychology, Saint Louis University;Lisa M. Penney, Department of Psychology, University of Houston.

An earlier version of this article was presented at the annual meeting ofthe Academy of Management, Montreal, Quebec, Canada, 2010. We thankAlan Witt, Dustin Jundt, David Rapach, Vivek Ajmani, Jeffrey Krischer,and Rudy Fenwick for helpful suggestions and comments on prior versionsof this article.

Correspondence concerning this article should be addressed to Mindy K.Shoss, Department of Psychology, Saint Louis University, Saint Louis,MO 63103. E-mail: mshoss@slu.edu

Journal of Applied Psychology © 2012 American Psychological Association2012, Vol. 00, No. 00, 000–000 0021-9010/12/$12.00 DOI: 10.1037/a0026953

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Dooley, 1983; Catalano, Dooley, Novaco, Wilson, & Hough,1993; Catalano, Novaco, & McConnell, 1997; Catalano, Rook, &Dooley, 1986). This is certainly plausible. A poor economy (aswell as a good one) can create and stimulate forces that operate atmultiple levels of a society to influence those operating within it(Johns, 2006; Sinclair, Sears, Probst, & Zajack, 2010). Borrowingan analogy from physics, depending on their strength, direction,and constancy, the net combination of these forces may manifestin aggregate effects (Young & Freedman, 2011). In other words,aggregate-level studies examine what Tausig and Fenwick (1999)called “systematic risk,” which “emphasizes the idea that well-being levels change because contextual conditions change and notsimply because individuals are exposed to negative experiences”(p. 13). Brenner and Mooney (1983) made an argument similar toCatalano and Dooley’s and suggested that a poor economy wouldbe expected to produce net changes in relevant health and behav-ioral outcomes because it creates perturbations of, or stress on, aninterdependent social system. More specifically, others havepointed to impoverishment, social instability, and a decrease insocial safety nets as factors influencing the aggregate incidence ofviolence-related and health-related events during and after eco-nomic crises (Bezruchka, 2009).

Following from these ideas, researchers have examined theeffects of macro-economic events on national-, state-, andcommunity-level incidences of physical and mental health out-comes associated with stress. This research has revealed positiveassociations between the unemployment rate and rates of suicide,treatment for mental disorders, and cardiovascular disease mortal-ity, among other outcomes (Brenner, 2005; Brenner & Mooney,1983; Catalano, 1991; Goldman-Mellor et al., 2010). Research hasalso established a link between the unemployment rate and indi-cators of violence at national and community levels, sometimesreferred to as the provocation effect (Catalano et al., 1997; Cata-lano, Novaco, & McConnell, 2002; Stuckler, Basu, Suhrcke,Coutts, & McKee, 2009).

Effects on the Employed Workforce

Research investigating the effects of the economy on the em-ployed workforce has largely examined macro–micro linkages(e.g., unemployment rate associated with decreases in self-ratedhappiness; Di Tella, MacCulloch, & Oswald, 2003) and the effectsof economy-related stressors (e.g., organizational restructuring,job insecurity) on individual-level outcomes (e.g., sickness ab-sence, aggression; De Cuyper, Baillien, & De Witte, 2009; Ferrieet al., 2001). However, some macro-level work has been con-ducted. Based on the fact that the majority of the populationremains employed when unemployment is high, Tausig and Fen-wick (1999) combined the economic and job stress literatures topropose that macro-level changes in the economy have macro-level effects because they stimulate organizations to make struc-tural changes, many of which affect aspects of jobs that tend toengender stress (e.g., increased demands coupled with time andbudgetary constraints). Using aggregate data from the 1973–1977Quality of Employment Panel Study, they found that the positiverelationship between changes in the unemployment rate between1971 and 1975 and changes in distress (conceptualized as physicalsymptoms associated with distress) between 1973 and 1977 wasmediated by increases in aggregate reports of job demands.

Some aggregate work has also specifically examined absentee-ism. Despite the negative effects of a high unemployment rate onhealth outcomes, which would theoretically increase absences(Harvey & Nicholson, 1999), some have argued that the aggregateunemployment rate–absence rate relationship might be negativebecause economic downturns and the concomitant threat of jobloss create a disincentive for being absent (Audas & Goddard,2001). However, many findings in line with such an effect need tobe viewed with caution. For example, Leigh (1985) found anegative unemployment rate–aggregate absence rate relationshipusing the sample of the University of Michigan Panel Study ofIncome Dynamics; however, this study estimated regressions withonly 11 observations, raising questions about the reliability of theresults. Using pooled monthly data from 17 manufacturing plantsover a 5-year period, Markham and McKee (1991) observed aninverse relationship between the local unemployment rates andmonthly plant absence rates. They also found a relationship betweenabsences and the unemployment rate the subsequent month, whichthey claimed indicated that “employees also appeared to anticipateeconomic downturns by decreasing absenteeism in the month prior tochanges in unemployment rates” (p. 952). However, this findingcould also conceivably reflect more complex patterns, such as coun-tercyclical or cumulative effects wherein the unemployment ratemight take time to influence absences. Also using monthly data,Markham (1985) reported a negative correlation between the unem-ployment rate and monthly national absenteeism during 83 monthsfrom 1976 to 1982. However, neither Markham and McKee’s studynor Markham’s study distinguished between stated causes of absences(e.g., minor illness, major illness, accident, violence), which may bedifferentially impacted by incentives to be absent. Thus, a strength ofthe current study is that we examine absenteeism reportedly due tosickness and workplace violence separately.

Another limitation of prior research is the data used in thosestudies are considerably outdated, and the impact of the economy,as well as acceptance of absence, may have changed over time(Johns, 2006; Taylor & Burridge, 1982). In fact, there may bereason to expect that the net impact of the unemployment ratemight be greater today than the estimates derived in previousresearch have indicated given that many studies used data fromover 2 decades ago. For example, households are now moreintricately connected to the financial world (e.g., the use of market-based retirement funds), making society both more aware of andmore susceptible to changes in the economy than ever before (Davis,2009). By using data collected from the past 18 years, the presentstudy provides a closer estimate of the effects of economic conditionson absenteeism today and what we might expect in the near future.

If, in general, violence in a community increases in response toa rising unemployment rate (Catalano et al., 1997), then given thatorganizations are embedded in their communities, we would ex-pect to see a greater incidence of employee absences due toviolence. This logic receives some support from a study by Dietz,Robinson, Folger, Baron, and Schulz (2003) that found a signifi-cant relationship between violent crime rates in the communitysurrounding a manufacturing plant and the number of incidencesof violence at the plant (also Dietz & Nolan, 2001). Thus, based onprior findings in macro-level data, we expect to see a positiveconnection between state-level unemployment rate and state-levelincidence of absences due to workplace violence. However, wehave no clear prediction to make with regard to the state-level

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incidence of absences reportedly due to symptoms of illness, giventhat economy-stimulated forces on health and forces on incentivesto be absent may be conflicting. As we discuss below, the natureof these relationships may depend on time.

Temporal Effects

Providing that there is a relationship between the unemploymentrate and absenteeism, this relationship could take a number offorms. For example, on one hand, the effect could be immediate;in other words, an increase in the unemployment rate is associatedwith an increase (or decrease) in absenteeism the same year. Onthe other hand, the effect could be lagged, with an increase in theunemployment rate being associated with an increase in absentee-ism the following year or a certain number of years later. Alter-natively, the effect could be cumulative. That is, the effects of theunemployment rate in the focal year could build upon effects of theunemployment rates in previous years, reflecting additive combi-nations of lagged and immediate effects. However, cumulativeeffects may take different forms. One relatively parsimoniouspossibility is that the immediate (i.e., time “t”) and lagged effects(i.e., time “t-1” . . . “t-n”) of the unemployment rate are essentiallyidentical. If so, this would indicate that a general history ofunemployment is relevant for absence. Another possibility is thatthe effect of the current year’s unemployment rate is most impor-tant, but prior years’ unemployment rates have lingering, yetdiminished, effects. This is analogous to Frese and Zapf’s (1988)dynamic accumulation model, which affords current exposure tostressors a particularly high impact at the individual level, “be-cause the original stressor had a general weakening effect on thepsychophysical system” (Zapf, Dormann, & Frese, 1996, p. 146).An analogous process could operate at the macro-level.

Unfortunately, there has been little work systematically inves-tigating the role of time in macro-level effects of the unemploy-ment rate to guide our investigation. Researchers have not alwaysused lagged unemployment rate variables, and, if they have beenused, the number of lags has not been consistent. However, priorresearch on mortality has suggested that, although effects of theunemployment rate appear to linger considerably in the future, thepeak lag appears to be around 2 years (Brenner, 2005). Therefore,we considered unemployment rate values for the focal year as wellas for 1 and 2 years prior.

Method

Unemployment Rate Data

There are many markers of the state of the economy, but perhapsthe most salient indicator is the unemployment rate (Di Tella et al.,2003). Thus, keeping with prior research, we used the unemploy-ment rate as an indicator of economic condition. We gatheredunemployment rate data from the Current Population Survey,which is conducted by the Bureau of the Census for the BLS (BLS,2010a), and considered the definitive source of unemploymentstatistics. We used state-level rather than national-level data toachieve a larger sample size than national-level data would allow.Because absence data were only collected annually and wereavailable beginning in 1992, we used annual unemployment rateestimates from 1990 to 2009.

Absence Data

The annual Survey of Occupational Injuries and Illnesses(SOII), administered by the BLS, collects data on absences (�1full day) due to work-related1 nonfatal injuries and illnesses andestimates the incidence of these absences (BLS, 2010b). In total,the sample captured in the BLS database reflects approximately230,000 private industry organizations across the United States.The organizations are selected based on a probability sampledetermined by their industry, location, and number of employees;samples are selected to represent all private sector industries ineach state. Selected employers are required to keep records for ayear according to the SOII guidelines and to include all employees,regardless of the nature of their contract. Because the data aretaken from a sample of the population, the BLS creates summaryestimates that are weighed based on estimates of unit non-responseand sampling error. Such weights are designed to enhance thegeneralizability of the estimates. Readers are referred to the BLS’s(2008) Handbook of Methods for a more detailed description ofBLS survey methodology.

The SOII categorizes absences due to a variety of natures andcauses. Given the nature of our research questions, we examinedabsences reportedly due to symptoms of illness (i.e., absences dueto “signs, symptoms, and ill-defined conditions,” such as dizzi-ness, headache, respiratory symptoms, nausea, and cardiovascularsymptoms). We also examined absences due to “assaults andviolent acts (by persons),” which included absences due to boththreats or verbal assaults and physical acts, such as kicking andscratching, occurring in the workplace. We used state-level ab-sence data, which were available from 43 states (including Wash-ington, DC as a state) from 1992 (when the SOII was redesigned)to 2009. The SOII reports the incidence rate per 20,000,000 workerhours (10,000 workers � 50 weeks per year � 40 hr per week).Due to missing data, out of the possible 774 incidence rate esti-mates for each dependent variable (18 years � 43 states), we had716 estimates for absences due to violence and 685 estimates forabsences due to symptoms of illness.

Analytical Strategy

We estimated three models predicting each of the absencevariables successively adding the unemployment at times “t,”“t-1,” and “t-2.” Because the data were nested within states, weemployed multilevel modeling using SAS Proc Mixed (Snijders &Bosker, 1999), where we included state as a random factor. Weused maximum likelihood estimation to compare the fit betweenmodels and, therefore, avoid an overfit solution (Hastie, Tibshi-rani, & Friedman, 2009). Given that time series data are oftencategorized by autocorrelation (i.e., observations closer in timetend to be more strongly correlated than observations further in

1 According to the Occupational Health and Safety Administration’srecording criteria, an injury and illness is “work-related if an event orexposure in the work environment either caused or contributed to theresulting condition or significantly aggravated a pre-existing injury orillness” (Occupational Health and Safety Administration, 2005, p. 1).Among other criteria, a preexisting condition is considered aggravated if itresults in 1 or more days absent from work that would not have occurredotherwise.

3ECONOMY AND ABSENTEEISM

time), we considered the possibility of autocorrelated errors (Sni-jders & Bosker, 1999). Because an initial examination of the datasuggested the presence of autocorrelation, we the compared mod-els with a random intercept to models that imposed an autoregres-sive structure on the error covariance matrix (R matrix; Singer,1998). In all cases, an autoregressive structure provided better fitto the data (as indicated by Akaike information criterion [AIC] andBayesian information criterion [BIC] values, as well as likelihoodratio tests; Snijders & Bosker, 1999). Therefore, the results includean autoregressive error structure.

Time series data present unique challenges due to the fact thatlagged economic variables tend to be collinear (Kennedy, 2003).This problem is compounded in models with many lags whereinthe addition of lagged variables results in a loss of degrees offreedom. When modeling cumulative effects, economists oftendeal with these issues by specifying a distributed lag distribution.These methods impose a structure on the coefficients for laggedvariables, the most common being a polynomial; however, theselected coefficient structure is commonly determined on an adhoc basis (Kennedy, 2003). Borrowing from these approaches, weimposed two lag structures consistent with the two possible cu-mulative models mentioned above and tested them separately. Thefirst model included an unweighted composite of the focal year’sunemployment rate and the unemployment rates at times “t-1” and“t-2.” The second model constrained the effects of the unemploy-ment rate at time “t,” time “t-1,” and time “t-2” to be linearlyrelated, such that the effect of the focal year’s unemployment rate isstronger than the year prior, which is stronger than 2 years prior to thefocal year. In the econometrics literature, this specification is called anarithmetic lagged function (Griffiths, Hill, & Judge, 1993).

Results

The correlations between the two dependent variables and theunemployment rate variables are displayed in Table 1. The unem-ployment rate variables were all significantly associated with therates of absence due to sickness and violence; however, we cautionthat these correlations may be biased because they do not take intoaccount the nested structure of the data.

As noted above, we ran a series of multilevel models to examinethe existence of an unemployment rate–absenteeism relationshipand to determine the temporal nature of these effects. The resultsof these models are displayed in Table 2. We include the nullmodels for each outcome as they provide a basis for assessingmodel fit. The estimates from the null models can also be used toestimate the ICC(1), the proportion of the total variance in the

absence outcomes that may be attributed to states as the Level 2grouping variable (Bryk & Raudenbush, 1992). As �2 and �2 in thenull model, respectively, reflect between-state (Level 2) varianceand within-state (Level 1) variance, an estimate of ICC(1) can beobtained by dividing �2 by the total variance (�2 � �2) (Bliese,2000). Accordingly, 41.1% of variance in violence-related absenceand 35.1% of variance in sickness absence are attributable to state.

With regard to violence-related absence, the coefficient for theunemployment rate at time “t” was significant in Model I (� � .07,SE � .03, p � .01). However, it became non-significant when theunemployment rate at time “t-1” was also included as a predictorin Model II; instead, the coefficient for the unemployment rate attime “t-1” was significant in this model (� � .23, SE � .04, p �.01). The likelihood ratio test indicated that adding the unemploy-ment rate at time “t-1” significantly improved model fit, �2(1) �24.9, p � .01. We added the unemployment rate at time “t-2” as apredictor in Model III. The coefficient for the unemployment rateat time “t-2” was not significant, and the likelihood ratio testindicated that adding this lag did not significantly improve model fit,�2(1) � 0.1, ns. We then estimated two models with our two cumu-lative variables. Although the coefficients were significant, the AICand BIC values indicated that Model II, which indicated the presenceof a lagged effect of the unemployment rate, fit the data best.

With regard to sickness absence, the coefficient for the unem-ployment rate at time “t” was not significant in Model I (� � .02,SE � .03, ns). It remained non-significant when the unemploymentrate at time “t-1” was also included as a predictor in Model II; thecoefficient for the unemployment rate at time “t-1” was significantin this model (� � .20, SE � .06, p � .01). The likelihood ratiotest indicated that adding the unemployment rate at time “t-1”significantly improved model fit, �2(1) � 10.6, p � .01. We addedthe unemployment rate at time “t-2” as a predictor in Model III.The coefficient for the unemployment rate at time “t-2” was notsignificant, and the likelihood ratio test indicated that adding thislag did not significantly improve model fit, �2(1) � 3.60, ns. Wethen estimated two models with our two cumulative variables.Although the coefficients were significant, the AIC and BIC valuesindicated that Model II, which indicated a lagged effect of theunemployment rate, fit the data best.

Given that our predictors are Level 1 (within-state) and there-fore primarily influence �2 (Level 1 residual variance), we can usethe percentage reduction in �2 as a rough indication of effect size(Snijders & Bosker, 1999). For example, in the null model pre-dicting violence-related absence, �2 is .83. It drops to .82 when theunemployment rate at time “t” is added in Model I, indicating areduction of 1.2% of Level 1 residual variance. Adding the unem-ployment rate at “t-1” in Model II reduces �2 to .77, indicating areduction of an additional 6.1% of Level 1 residual variance.Likewise, the results for the sickness absence models show thatadding the unemployment rate at time “t-1” provides a 9.5%reduction in Level 1 residual variance.2

Discussion

Our findings, based on data at the state-level, provide potentialindications of organizational phenomena associated with the state

2 We thank the editor for his helpful advice on how to most effectivelypresent our results and for his many other contributions to the article.

Table 1Descriptive Statistics and Variable Intercorrelations

Variable M SD 1 2 3 4 5

1. Unemployment rate (t) 5.31 1.60 —2. Unemployment rate (t-1) 5.19 1.40 .78�� —3. Unemployment rate (t-2) 5.19 1.40 .53�� .87�� —4. Violence-related absence 2.13 1.18 .13�� .22�� .22�� —5. Sickness absence 1.82 1.41 .09� .21�� .23�� .25�� —

Note. Ns � 680–774.� p � .05. �� p � .01.

4 SHOSS AND PENNEY

of the economy that, in some regards, reflect public health, safety,and productivity concerns (Pearce, 2000). Before discussing them,we wish to caution readers against the tendency to commit theecological fallacy, that is, to assume that this macro-level relation-ship holds for a given individual in the population studied (cf.Robinson, 1950). As Bliese and Jex (2002) noted, “analyses basedon group means may detect relationships that have both statisticaland practical significance because the aggregation process createsgroup-level variables that are sensitive to small, systematic effectsthat might otherwise go undetected” (p. 269). As a macro-levelstudy, our study aims to provide an understanding of how eco-nomic recessions affect organizations and society more broadly.Macro-level studies regarding the economy are also importantbecause many potential interventions would occur at the macro-level (e.g., job stimulation programs), and their effectivenesswould be measured at this level as well (Bliese, 2000; Zivin,Paczkowski, & Galea, 2011). In investigating these effects, wecontribute to a growing body of research on the macro-economiccontext of the workplace. We also answer Bliese and Jex’s call forgreater use of macro-level data in the study of occupational healthpsychology and Jiang et al.’s (in press) call for more research oneconomy-related issues in the workplace.

The current study makes a number of contributions. Most no-tably, we examined not only whether patterns in the unemploy-ment rate are associated with macro-level patterns of both sicknessand workplace violence-related absenteeism but also what theseassociations might look like in terms of direction and time. Forboth absence variables, the results suggest that (a) the unemploy-ment rate is positively associated with absences, and (b) theseeffects are delayed in time. Specifically, our results suggest alagged effect for the previous year’s unemployment rate for bothabsence variables. With regard to violence-related absence, thisfinding supports prior research studies connecting the economywith incidents of violence and extends them by indicating an effecton violence that specifically affects the employed workforce. Withregard to sickness absence, this finding is consistent with Vahtera,Kivimaki, and Pentti’s (1997) finding that medically certified sickleave increased during the 3 years after downsizing in Finishorganizations and Westerlund et al.’s (2004) observation that aneconomic downturn in Sweden was associated with a subsequentincrease in long-term sickness absence.

Why did we not find a negative effect of the unemployment rateon absenteeism as others have suggested? First, incentives to beabsent are unlikely to affect violence-related absences, as employ-

Table 2Multilevel Regression Results

Variable Null model Model I Model II Model III Model IV Model V

Dependent variable: Violence-related absence

Covariance parameters�2 .58 (.14)�� .58 (.14)�� .56 (.13)�� .56 (.13)�� .57 (.14)�� .57 (.14)��

Ar(1) .19 (.04)�� .17 (.04)�� .12 (.04)�� .12 (.04)�� .14 (.04)�� .15 (.04)��

�2 .83 (.05)�� .82 (.05)�� .77 (.04)�� .77 (.04)�� .78 (.04)�� .79 (.05)��

Fixed effectsUnemployment rate (t) .07 (.03)�� .04 (.03) .03 (.04)Unemployment rate (t-1) .23 (.04)�� .21 (.08)��

Unemployment rate (t-2) .02 (.06)Cumulative Ia .07 (.01)��

Cumulative IIb .03 (.01)��

Fit indicies2 loglikelihood 1,969.8 1,962.4 1,937.5 1,937.4 1,944.2 1,950.0AIC 1,977.8 1,972.4 1,949.5 1,951.4 1,954.2 1,960.0BIC 1,984.8 1,981.2 1,960.1 1,963.7 1,963.0 1,968.8

Dependent variable: Sickness absence

Covariance parameters�2 .68 (.20)�� .67 (.20)�� .69 (.19)�� .69 (.19)�� .67 (.20)�� .67 (.20)��

Ar(1) .57 (.04)�� .57 (.04)�� .52 (.04)�� .52 (.04)�� .55 (.04)�� .56 (.04)��

�2 1.26 (0.12)�� 1.26 (0.12)�� 1.14 (0.10)�� 1.14 (0.10)�� 1.20 (0.11)�� 1.23 (0.11)��

Fixed effectsUnemployment rate (t) .02 (.03) .06 (.04) .03 (.04)Unemployment rate (t-1) .20 (.06)�� .10 (.08)Unemployment rate (t-2) .12 (.06)†

Cumulative Ia .05 (.02)��

Cumulative IIb .02 (.01)�

Fit indicies2 loglikelihood 1,916.4 1,916.0 1,905.4 1,901.8 1,908.8 1,912.5AIC 1,924.4 1,926.0 1,917.4 1,915.8 1,918.8 1,922.5BIC 1,931.4 1,934.8 1,928.0 1,928.1 1,927.7 1,931.3

Note. Standard errors are displayed in parentheses. AIC � Akaike information criterion; BIC � Bayesian information criterion.a Coefficient for an unweighted composite of the current year’s unemployment rate, unemployment rate (t-1), and unemployment rate (t-2) as predictors:(Unemploymentt � Unemploymentt-1 � Unemploymentt-2). b Coefficient for a weighted composite of the current year’s unemployment rate, unemploy-ment rate (t-1), and unemployment rate (t-2) as predictors: (3 * Unemploymentt � 2 * Unemploymentt-1 � Unemploymentt-2).† p � .10. � p � .05. �� p � .01.

5ECONOMY AND ABSENTEEISM

ees are unlikely to claim workplace violence-related absence foranother reason. Second, if an incentive-based negative effect oc-curs with regard to sickness absence, this effect may only occur inthe very short-term (i.e., months or weeks) as Markham (1985)found. Third, our data are more recent than those explored byMarkham and colleagues (Markham, 1985; Markham & McKee,1991). The socioeconomic changes that have occurred that moreclosely link households to the economy (e.g., the use of market-based retirement funds, financial institutions have greater stakes inorganizational ownership; Davis, 2009) might have increased theextent to which the state of the economy influences employeestress and, as a result, may have served to counteract any negativeassociation between the unemployment rate and sickness absence.These countervailing effects might explain the null finding for thefocal year’s unemployment rate.

Implications for Theory and Practice

The current research documents an association between theunemployment rate and absences at an aggregate level. In doingso, we add to a small but growing body of literature that, takentogether, has provided some of the underpinnings for a multilevelmodel of economic stress. The majority of this research has aimedto elaborate the links from macro-level events to micro-levelprocesses (Sinclair et al., 2010). Our findings, that macro-leveleffects manifest in aggregate absence outcomes, suggest the needfor research to complete the model and understand the mechanismsleading to these aggregate effects (i.e., develop an emergent model;Kozlowski & Klein, 2000). Following from Catalano and Dooley’s(1983) economic stress hypothesis, we believe that the processessurrounding stress are a valuable starting point for this endeavor.In particular, it may be valuable to consider the ways by whichorganizations respond to economic downturn that may give rise tothese effects. For example, Tausig and Fenwick’s (1999) findingspoint to aggregate increases in job demands as a response tochanges in the unemployment rate that, in turn, contributed toaggregate distress levels. Further, a survey of 400 employers withmore than 50 employees revealed that, in addition to downsizingand restructuring jobs, organizations commonly reduced employeebenefits and eliminated bonuses and pay increases to reduce costsduring the most recent recession (Galinsky & Bond, 2009). Orga-nizations may also be less likely to monitor safety- and health-related procedures and behaviors in times of economic downturn,as they focus their efforts on increasing production (Probst, 2004;Tausig & Fenwick, 2011).

Because it is generally thought that economy-related stressorshave a more immediate negative effect on employee well-being (orincentives to be absent), any model would need to account for thetime delay between a high unemployment rate and the aggregateincidence of absence. With regard to sickness absence, one possi-bility is that stress-inducing workplace changes lag behind a highunemployment rate (Tausig & Fenwick, 1999). It may also be thatit takes some time for stress to manifest in the level of symptomsthat would lead an employee to be absent (Frese & Zapf, 1988;Leitner & Resch, 2005), which then provides cues to other em-ployees regarding their own absence behaviors (Bamberger &Biron, 2007; Mathieu & Kohler, 1990). Thus, a “tipping point”with regard to sickness absence might occur the year following ahigh unemployment rate. These explanations are consistent with

prior research but require empirical investigation. We encouragefuture research that uses multilevel-level data to examine potentialvariables (e.g., national and state employment policies, organiza-tional characteristics, workgroup processes, individual-level per-ceptions) that may provide greater insight into the cross-level andemergent processes underlying these effects (Kozlowski & Klein,2000). Likewise, research is also needed to determine the extent towhich economy-related effects on aggregate absence rates reflect“fuzzy compilation” or “fuzzy composition” processes, that is,whether the effects are distributed among many or few (Bliese,2000; Kozlowski & Klein, 2000).

Our findings indicate that state-level unemployment rates areassociated with state-level patterns in employee absence behav-ior. This is consistent with Johns’s (2006) discussion thatcontext may shape base rates. In light of this finding, wesuggest that researchers consider the broader social context inwhich organizational events occur and encourage researchersto report the years associated with data included in their anal-yses so that meta-analytic or review studies may examinepotential effects of economic and other historic events onresearch findings.

We also examine another important aspect of context—time(Johns, 2006). In doing so, we contribute to the growing discussionon the role of time in organizational behavior (Wright & Cropan-zano, 2000). Our research suggests the existence of delayed ef-fects; however the temporal nature of economy-related effects mayvary as a function of (1) the specific stressor, (2) the measure ofstrain, (3) the time lags, and/or (4) the level of analysis. Thus, wenot only echo previous calls for longitudinal stress research(Wright, 1997; Wright & Cropanzano, 2000) but also suggest thatthese considerations may be a fruitful starting point for systematicresearch on time.

The finding of a state-level delayed effect of the economy onthese indicators of employee well-being has implications for state-level policies and the allocation of resources during economicdownturn. Along these lines, some have even argued that “poli-cymakers should consider the psychological implications of theireconomic decisions” (Zivin et al., 2011, p. 1344). Research acrossEuropean countries has suggested that expenditure to promoteemployment (e.g., public employment services) and lessen thefinancial impact of unemployment may significantly improvewell-being among employed and unemployed individuals (DiTella et al., 2003; Stuckler et al., 2009). Perhaps, similar interven-tions may be effective at the state-level. State-level interventionsmay be particularly helpful in mitigating the incidence of work-place violence during times of high unemployment, as those out-side the organization, as well as employees, may perpetrate ag-gressive acts (Dietz & Gill, 2006). These findings may also beimportant for organizations that operate in multiple states. In stateswith a high unemployment rate, managers may be faced with anincreased incidence of absenteeism. We do not suggest that orga-nizations avoid locations that have experienced chronic high un-employment but rather propose that the benefits associated withpublic- or private-sector efforts to attract companies and to en-courage the creation of new jobs may extend beyond reducing theunemployment rate to reducing sickness- and violence-relatedabsences of those already employed.

6 SHOSS AND PENNEY

Limitations

The results of this study likely reflect a very conservativeestimate of the negative aggregate effects of an economic reces-sion for at least three reasons. First, poor economic conditions maylead to increased absences due to conditions beyond those includedin this study (Kendall-Tackett, 2010). Second, our sickness ab-sence measure did not include those who experienced symptomsbut did not take time off from work (Hemp, 2004). Third, ourmeasure of violence-related absence reflects incidences resultingfrom threats of or actual physical violence that resulted in anemployee being absent for 1 or more days. Thus, this measurelikely underestimates the effects of economic recessions on em-ployee safety, as other aggressive acts, such as incivility, sabotage,and theft, would also be expected to rise because they share similartriggers (Fox & Spector, 2005) but may not necessarily result inabsence.

A limitation of our study is that our data do not allow us todifferentiate between absences due to different types of violence.Both absence measures were also limited by the extent to whichindividual organizations in the sample correctly identified andrecorded absences (Ruser, 2008). Additionally, our sickness ab-sence measure may have included those who were absent for otherreasons (e.g., to seek alternative employment options). Anotherlimitation is that our sample only includes estimates from organi-zations within the United States. We recognize that the effects ofeconomic events may be influenced by the broader sociopoliticalcontext, such as the level of employment protection and size andduration of unemployment benefits (Conerly, 2004). An interest-ing avenue for future research would be to examine the work-related impact of economic events both within and across coun-tries, especially those that differ from the United States in terms ofeconomic stability and resources. Finally, although having addi-tional historical data may have been useful from an estimationstandpoint, one may argue that it would be theoretically inappro-priate to include much older data given the many changes that haveoccurred in the nature, structure, and context of the workplace overthe last 30� years. A strength of our study is that our results arebased on the most recent data available; thus, they are likely morerepresentative of the effects of economic condition in currentsociety than estimates from prior studies.

Conclusions

In conclusion, our study sheds light on a timely and under-researched area—the effects of the economy on worker absences ata macro-level. In a time of economic downturn, such as thepresent, we believe psychologists have much to offer in terms ofunderstanding and predicting effects that are, in essence, the resultof aggregate human behavior. We encourage future research onwork-related effects of economic events and on strategies that mayhelp organizations and employees alike weather tough economictimes.

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Received April 5, 2010Revision received November 28, 2011

Accepted December 9, 2011 �

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