Casual and FMLA Absence 1
RUNNING HEAD: Casual and FMLA Absence
Blame It on the Family: Effects of an Intervention on Casual and FMLA Absence
Michael D. Johnson, Michigan State University; Adam Stetzer, Deborah Ladd, Nucleus
Solutions; & Frederick P. Morgeson, Michigan State University
Address correspondence to:
Michael D. Johnson
N475 Business College Complex
Michigan State University
East Lansing, MI 48823
Casual and FMLA Absence 2
Since it enactment in the U.S. in 1993, the Family Medical Leave Act (FMLA) has allowed
employees to take non-penalized time off work for either personal medical problems or medical
problems of close family members. This field study tracked both casual and FMLA employee
absences in two manufacturing plants of the same company over a 24-month period. Both plants
implemented stricter absence control policies at different times during the period of absence
tracking. Hierarchical linear modeling showed that the policy changes had the intended effect of
reducing casual absence, but also the unintended effect of increasing FMLA absence. Employee
perceptions of both the strength and the fairness of the absence policies provided incremental
predictive validity of absence over and above the objective policy changes. These perceptions
interacted to predict FMLA absence, such that employees who perceived the policies to be both
strong and unfair had the highest instances of FMLA absence.
Casual and FMLA Absence 3
Blame It on the Family: Effects of an Intervention on Casual and FMLA Absence
Absenteeism as a form of work withdrawal is a pernicious and costly problem for many
organizations. According to the Bureau of Labor Statistics, the absence rate for 2004 in the
private sector was 3.1% (for the public sector, it was 3.9%; Bureau of Labor Statistics, 2005).
For a private company of 25,000 employees, this translates into 200,000 lost workdays per year.
With an average salary of $40,000 and a benefits load factor of 1.35, this absence rate would
mean estimated annual direct costs of $43,200,000 for that company (Nucleus Solutions, 2005).
This figure does not include such indirect costs as the lost revenue opportunity, decreased
customer satisfaction, and lower employee morale that may accompany employee absence.
Because of the potential costs associated with unscheduled employee absence, many
organizations have engaged in interventions designed to reduce absence. For example,
Martocchio, Harrison, and Berkson’s (2000) meta-analysis showed that interventions designed to
reduce lower back pain had a small but significant negative effect on missed work. Similarly,
Baltes, Briggs, Huff, Wright, and Newman’s (1999) meta-analysis found that interventions
allowing flextime had a large negative effect on absence. Single studies have also shown other
interventions significantly reducing employee absence, including providing feedback on absence
rates program (Gaudine & Saks, 2001), implementing a recognition (Markham, Scott, & McKee,
2002), and providing group sessions to reduce burnout (van Dierendonck, Schaufeli, & Buunk,
Surprisingly, relatively few studies have examined the direct effect of absence policies on
employee absence. A few early studies on absence focused on the implementation of absence
control policies, and in general, showed that stricter policies reduced absence (Baum, 1978;
Baum & Youngblood, 1975; Morgan & Herman, 1976; see also Dalton & Mesch, 1991, for a
somewhat more recent investigation). The lack of recent research, however, on absence policies
Casual and FMLA Absence 4
is unfortunate for at least two reasons. First, the absence criterion has changed since these early
studies. Over the past three decades, absence research has refined the criterion to reflect only
voluntary absence—rather than absence in general—in order to better assess the effects of
various antecedents on absence that can presumably be controlled. Thus, it is unclear whether the
findings from the early studies will show similar results under the refined criterion; if the refined
criterion better reflects controllable absence, the earlier studies likely underestimate the effects
of absence control policies. Second, all of the absence policy studies occurred prior to the advent
of the Family Medical Leave Act (FMLA). The FMLA, enacted in the U.S. in 1993, allows
employees to take non-penalized time off work for either personal medical problems or medical
problems of close family members. At the time of the early studies, it was suggested that
absences due to medical issues were too unreliable to use as an absence criterion (Huse &
Taylor, 1962). In keeping with this recommendation, most absence research has ignored medical
absence, and no study has examined the effect of any antecedent on FMLA absence in particular.
We propose, however, that FMLA absence has a voluntary component, and policy interventions
designed to reduce absence may have the unintended effect of actually increasing absence
attributed to the FMLA. Thus, this study examined the effects of an absence policy change that
was implemented in two locations of the same company at different points in time on both casual
and FMLA employee absence.
Furthermore, we suggest that absence policies have differential effects on different
employees due to their perceptions of the policy. That is, employees’ subjective perceptions of
the policy may differ, and these perceptions may impact not only the degree to which they take
time off, but also in how they attribute their absence: as a casual absence or as an FMLA
absence. Thus, we introduce the concept of policy strength, which captures employee
perceptions of the “teeth” behind the policy, and also examine policy fairness, which captures
Casual and FMLA Absence 5
employee justice perceptions of the policy. We suggest that these perceptions will have effects
on employee absence over and above the objective effects of the policy implementation itself.
Antecedents of Absence
Absenteeism has been a difficult topic of study for organizational research, in part
because there are so many causes of absence. Some have focused on contextual factors as
predictors of absence. For example, Eby, Freeman, Rush, and Lance’s (1999) meta-analysis
found that many job characteristics were significant predictors of absence, including skill variety,
autonomy, and feedback. Individual studies have also shown absence to be predicted by other
contextual factors such as task identity (Rentsch & Steel, 1998), flextime (Baltes et al., 1999),
job complexity (Fried, Melamed, & Ben-David, 2002), job demands (Bakker, Demerouti, de
Boer, & Schaufeli, 2003), job scope (Rentsch & Steel, 1998), work monotony (Melamed,
Benavi, Luz, & Green, 1995), compensation and benefits (Anderson, Coffey, & Byerly, 2002;
Goldberg & Waldman, 2000), promotions (Lam & Schaubroeck, 2000), and workplace safety
(Hemingway & Smith, 1999).
Other research has examined the effect of individual difference variables or work-related
attitudes on absence. Demographic variables have shown relatively consistent effects in the
literature, such that women were absent more than men (Hui & Lee, 2000; Markham & McKee,
1995; Sagie, 1998; Shaw & Gupta, 2001; Steel & Rentsch, 1995; but see Iverson & Deery, 2001;
and Mason & Griffin, 2003; for exceptions), and younger workers were absent more than older
ones (Erickson, Nichols, & Ritter, 2000; Gellatly, 1995; Hardy, Woods, & Wall, 2003; Hui &
Lee, 2000; Perry, Kulik, & Zhou, 1999; Rentsch & Steel, 1998; Sagie, 1998; Shaw & Gupta,
2001; but see Judge, Martocchio, & Thoresen, 1997, for an exception). Additionally, Roth,
Huffcut, and Bobko’s (2003) meta-analysis found racial group predicted absence, with blacks
absent more than whites. Other individual differences found to affect employee absence include
Casual and FMLA Absence 6
trait affect (Iverson & Deery, 2001; Iverson, Olekalns, & Erwin, 1998; Pelled & Xin, 1999), trait
cynicism (Johnson & O'Leary-Kelly, 2003), self-esteem (Duffy, Shaw, & Stark, 2000; Hui &
Lee, 2000), and various health factors, including alcohol use (Iverson & Deery, 2001), anxiety
and depression (Hardy et al., 2003; Melamed et al., 1995), minor illness (Harvey & Nicholson,
1999), and lower back pain (Martocchio et al., 2000).
With so many potential predictors of employee absence, it is perhaps not surprising that
even when consistent findings are discovered across numerous studies, the effect sizes are
typically quite small, with bivariate correlations between .10 and .20. Thus, some research has
attempted to close the gap between predictors and absence behavior by examining attitudes as
more proximal antecedents of absence. In this vein, relatively consistent negative effects on
absence have been found for organizational commitment (De Boer, Bakker, Syroit, & Schaufeli,
2002; Eby et al., 1999; Gellatly, 1995; Johnson & O'Leary-Kelly, 2003; Lam, Schaubroeck, &
Aryee, 2002; Sagie, Zaidman, Amichai-Hamburger, Te'eni, & Schwartz, 2002; Somers, 1995),
justice perceptions (Colquitt, Noe, & Jackson, 2002; De Boer et al., 2002; Gellatly, 1995; van
Dierendonck et al., 1998), and motivation (Eby et al., 1999). The general result of examining
attitudes as predictors of absence, however, has been roughly equivalent to the examination of
contextual factors and individual differences: bivariate correlations in the .10s and .20s.
The most examined attitude in relation to absence has been job satisfaction. At least two
meta-analyses have been conducted to estimate the true correlation between these two constructs.
The result? Scott and Taylor (1985) found a corrected correlation of -.146 between job
satisfaction and absence, and Farrell and Stamm (1988) found that contextual variables
predicated absence better than job satisfaction did. Thus, no predictor of absence—be it
contextual, demographic, dispositional, or attitudinal—has shown consistent large or even
moderate effects on absence.
Casual and FMLA Absence 7
The Absence Criterion
In an attempt to address this problem of overall low predictive validity, many researchers
have focused on refining our conceptualization of the absence construct, either through counting
absence “spells”—which may account for more than one day—rather than total days absent, or
through being selective about which types of absence they include in their measure. Both of
these methods have been cited by several as being a way to better capture voluntary absence,
while removing the contaminating effects of involuntary absence from the criterion. Such a focus
on voluntary absence makes sense, as most organizational interventions designed to reduce
absence are intended to affect voluntary absence (Gaudine & Saks, 2001; Markham et al., 2002).
Presumably, this is because involuntary absence is not as easily affected as voluntary absence.
Steers and Rhodes (1978) suggested that this distinction between voluntary and
involuntary absence is represented by differential causes of absence. For involuntary absence, the
employee lacks the ability to attend work; for voluntary absence, the employee lacks the
motivation to attend work. Dalton and Mesch (1991) attempted to pull apart these types of
absence by asking employees to indicate the number of days they had missed work due to health
issues and the total amount of time they had missed. Under their conceptualization, unavoidable
involuntary absence was represented by the health measure, and avoidable voluntary absence
was the difference between the two measures. We suggest that this conceptualization is
incomplete for at least two reasons. First, involuntary absence may be caused by more than
simply personal health issues; other plausible reasons include childcare issues or personal
tragedies (i.e., death of a family member, being the victim of a crime, automobile accidents).
Thus, the criterion measure for involuntary absence may be deficient, and the criterion measure
for voluntary absence may be contaminated with involuntary absences. Second, it is difficult for
organizations to capture the degree to which an absence is voluntary; this is why Dalton and
Casual and FMLA Absence 8
Mesch (1991) had to rely on self-report measures. Self-report measures of absence are
problematic not only because they are unreliable, but also because they do not lend themselves
well to assessment of organizational interventions. When organizations implement interventions
to reduce absence, they can easily access organizational absence records to see if the intervention
was effective; self-report measures are much less accessible.
These difficulties with the absence criterion cast doubt on both the theoretical and applied
implications of absence research in general. We suggest a compromise position that assesses
absence in a way that is consistent with many organizational recordkeeping systems, and yet
captures some of the voluntary/involuntary distinction suggested in the literature. We
conceptualize unscheduled employee absence in three mutually exclusive categories that vary in
the degree to which they represent voluntary absence. We focus on unscheduled absence because
it is generally more costly than scheduled absence (e.g.., holidays, vacations, military leave), and
because this is in keeping with previous absence research.
Casual absence is the most voluntary of the three forms of unscheduled employee
absence. It comprises absences due to illness, non-work related injury, and other personal
reasons. Casual absence includes both full days off work as well as partial days, including both
tardiness and early departure. Although some have examined tardiness and early departure as
constructs distinct from absence (Iverson & Deery, 2001; Koslowsky, Sagie, Krausz, & Singer,
1997; Krausz, Koslowsky, & Eiser, 1998), their effects on the organization are the same as full
days of absence.
Our study tracked absences before an after an organizational policy change designed to
reduce casual absence. More information about this policy change is provided in the Method
section, but the basic thrust was that both the penalties associated with casual absence, and the
Casual and FMLA Absence 9
rewards associated with perfect attendance, increased and were more strictly enforced. Thus, our
first hypothesis is simply:
H1: Stricter absence policies lead to less casual absence.
Typically, absences related to the Family Medical Leave Act (FMLA) have been
excluded from measures of voluntary absence. Since it was signed into law by President Clinton
in 1993, the FMLA has allowed employees to take “no-fault” leave for up to twelve weeks in a
twelve month period for family-related medical problems. Because the act is federally mandated,
employers may not take any punitive action against employees taking absences under the FMLA.
There are, however, two reasons why FMLA absences might have a voluntary
component. First, because the allowable types of absences—and the reporting of the reasons for
those absences—is not entirely clear under the FMLA, employees may intentionally or
unintentionally claim absences as FMLA that are not legitimate family leave. Second, because
the act specifically states that employers may not “interfere, restrain, or deny the exercise of, or
the attempt to exercise any right” (2615[a]) under the FMLA, supervisors may be reluctant to
question the validity of an employee’s claim of absence under FMLA. Because of this ambiguity
associated with classifying absences under FMLA, any organizational attempt to reduce
voluntary absence may simply result in a shift in the reasons for absence to FMLA.
Given this distinction between casual and FMLA absence, we sought to investigate the
effect of an organizational intervention to reduce casual absence. No published study has
examined the effects of organizational interventions on FMLA absence; indeed, the only
published studies on FMLA absence have only examined the effect of demographic variables
(Gerstel & McGonagle, 1999; Lee & Sanford, 2004). These studies appeared to replicate earlier
findings by Blau (1985), who separated “excused sick family” absences from other excused
Casual and FMLA Absence 10
absences. He found that the best predictors of this kind of absence were demographic: both
marital status and the number of dependents were significantly related to excused sick family
absence. Notably, this study was conducted prior to the enactment of the FMLA and thus could
not have examined absences attributed specifically to FMLA.
As noted above, the intervention was designed to strengthen the rewards associated with
good attendance and the punishments associated with absence. We hypothesized that although
the policy change would reduce casual absence, it may actually increase FMLA absence. That is,
as employees see that their casual absences are being counted, they would be more likely to
attribute their absences to FMLA, which are less likely to be questioned by their supervisors. As
a consequence, what might occur is a simple shifting of reasons for absence, without any
reduction in the total number of absences claimed.
H2: Stricter absence policies lead to more FMLA absence.
Absence due to disability has generally been ignored in organizational absence research
(Cunningham & James, 2000). This may seem somewhat surprising, because organizational
interventions focused on workplace safety and on employee health are often specifically
designed to reduce disability absence. Disability absences, however, represent the least voluntary
of the three forms of unscheduled employee absence. They may be due to injuries received on
the job or to chronic health-related problems, and thus often prevent employees from attending
work whether or not they wish to attend. In our study, we tracked disability absences in addition
to casual and FMLA absences. Because of the involuntary nature of disability absence, we did
not expect the organizational intervention to affect disability absence one way or the other. It
does, however, provide a comparison to the other two forms of absence.
H3: Stricter absence policies are unrelated to disability absence.
Casual and FMLA Absence 11
Absence Policy Perceptions
HR policies are perceived differently by different employees
o Policies may be enforced differentially
o Individual differences may lead to different policy perceptions in the same way
that job attitudes (satisfaction, commitment, identification) are affected by
Policy strength refers to the degree to which employees perceive that the policy is
o In terms of absence, this relates to the extent to which rewards and/or
punishments are tied to absence behavior
o Individual perceptions of absence policy strength may account for variance
beyond that which is explained by objective policy change
H4: Policy strength will have a (a) negative effect on casual absence and (b) a positive
effect on FMLA absence over and above the effects of the objective policy change.
Policy fairness refers to the degree to which employees perceive that the policy is fair
o Fairness perceptions have been linked to absence behavior in terms of
distributive, procedural, and interactional justice
o Employees who perceive that policies are unfair may seek to defy the policy
by taking time off work
H5: Policy fairness will have a negative effect on both (a) casual absence and (b) FMLA
absence over and above the effects of the objective policy change.
These two perceptions may interact
o Employees who perceive that the policies are unfair may seek to restore equity
through taking unpunished absences (i.e., FMLA absences)
Casual and FMLA Absence 12
o People who have high perceptions of policy strength and low perceptions of
policy fairness would be the most likely to take FMLA absence
H6: Policy strength and fairness will interact to predict FMLA absence, such that
employees who perceive high policy strength and low policy fairness will take the most
Participants and Procedure
We recorded the absences of 1,019 employees in two plants of a large unionized
Midwestern automobile parts manufacturer over a 24-month period (January, 2003 to December,
2004). 733 employees worked in Plant A; 74% were male, and they had a mean age of 48.78
years (SD = 9.21) and a mean tenure with the organization of 21.47 years (SD = 9.83) at the
beginning of the absence data collection. 286 worked in Plant B; demographic data were not
available for this sample at this time. We administered the survey to 250 employees in Plant A
eighteen months after we began tracking absences (six month after the implementation of the
policy change), and were able to match 223 of these surveys to the employee’s absence data.
On January 4, 2004, twelve months into our recording of absences, the company initiated
an organizational intervention at Plant A to clarify and strengthen their absence policies.
Specifically, they implemented a “No Fault” absence policy that communicated new policies to
the employees regarding the rewards that can be gained for good attendance, as well as the
punitive measures associated with poor attendance. Among other provisions, employees received
written reprimands for their first two absences, in-house disciplinary layoffs of increasing
severity for their next three absences, and would be discharged on their sixth absence.
Additionally, employees could receive a “Good Attendance Bonus” that involved preferred
parking spaces and more control over their vacation leave. Ten months later, Plant B also
Casual and FMLA Absence 13
implemented a policy intervention. The policy change was not as extensive as it was at Plant A;
the major change was in the implementation of in-house disciplinary layoffs like those
implemented at Plant A. In addition, Plant B created a job position specifically for managing
absence. This individual audits managers’ tracking reports, produces and posts absence reports,
and tracks progress toward absence goals. Although the interventions were not identical at the
two plants, they appeared to be similar enough in intent (they were both attempts at a stricter
absence control policy, which was our construct of interest) and in application (they both
involved similar punitive measures).
Because we were able to gather absence data at multiple times over two years, at two
plants that implemented similar interventions at different times, the research design used in this
study is an interrupted time series with switching replications (Cook & Campbell, 1979).
“Switching replications” means that the treatment (in our case, a policy change) occurs at
different times in more than one sample. Unlike cross-sectional designs or even pre- and post-
test designs, strong causal inferences can be made from interrupted time series designs, because
most threats to internal validity can be ruled out. The interrupted time series with switching
designs is particularly powerful because not only does it control for virtually all threats to
internal validity, it also enhances external and construct validity, because an effect can be
demonstrated with two samples in two settings at two different moments in history.
Absence. Absence data were gathered through company records. We operationalized
absence as total unscheduled absence, which included both full and partial days off (we did not
track scheduled absences, such as holidays and vacation leave). The absence category (casual,
FMLA, or disability) was determined by the employee’s supervisor. The company provided us
Casual and FMLA Absence 14
with aggregated absence data within each category by month. This provided us with 24,456
observations of absence (24 months for each of 1,019 employees).
Policy perceptions. Policy strength and policy fairness were both measured with items
developed for this study. Policy strength was measured with seven items that captured employee
perceptions of the rewards and punishments associated with absence. The items were: “I am
appreciated for good attendance,” “Formal attendance reward programs reward or recognize
employees for good attendance,” “Management makes it a priority to recognize employees for
good attendance,” “Attendance rewards motivate me to come to work,” “Employees who are
frequently absent receive appropriate discipline,” “Employees with poor attendance fail to
succeed at this company,” and “There are serious consequences for poor attendance.” Policy
fairness was measured with four items that captured employee perceptions of how fair they
thought the absence policy was. The items were: “Attendance policies are fair and reasonable,”
“Employees are provided with sufficient time off to attend to personal matters,” “Attendance
policies are NOT sensitive to employee personal needs and obligations,” and “Limitations
regarding when employees can take time off are unfairly restrictive.” These last two items were
reverse coded. Responses were provided on a five-point scale ranging from 1 = Strongly
Disagree to 5 = Strongly Agree.
We conducted an exploratory factor analysis with varimax rotation on these ten items.
Kaiser’s criterion indicated three factors with eigenvalues over one, but examination of the scree
plot and the variance explained supported a two-factor solution. Therefore, we forced two
factors, and the items loaded primarily on their expected factors, with the first factor (policy
fairness) having an eigenvalue of 3.11 and accounting for 28.28% of the variance, and the second
factor having an eigenvalue of 2.15 and accounting for 19.52% of the variance (by contrast, the
Casual and FMLA Absence 15
third original factor had an eigenvalue of 1.08 and only accounted for 9.80% of the variance).
The resulting scales had coefficient alphas of .77 for policy fairness and .71 for policy strength.
Table 1 provides the means, standard deviations, and intercorrelations of the variables
included in the study for Plant A. In this matrix, the absence data were aggregated to pre- and
post-intervention. Because we did not have demographic or survey data for Plant B, we do not
provide a correlation matrix for this plant, but the correlations between the absence variables are
largely similar to Plant A. Inspection of this table reveals that the absence categories tended to be
positively correlated; that is, if an employee took time off work for one type of absence, they
were likely to have taken time off for another type of absence as well. Additionally, the table
shows similar results for the demographic variables as has been found in previous studies:
women were absent more than men in all absence categories, and younger workers tended to be
absent more than older workers.
Hypotheses 1-3 proposed that the policy change would reduce casual absence, increase
FMLA absence, and have no effect on disability absence, respectively. Because we had repeated
measures of absence nested within individuals, we tested these hypotheses with hierarchical
linear modeling (HLM). In these tests, we first specified a null model to determine how much
absence varied within and across individuals. Then we specified a model with a dummy variable
for whether the month fell within the time period prior to the policy change, or the time period
after the policy change. As noted above, this was different for the two plants, with Plant B’s
policy change occurring ten months after Plant A’s policy change.
The null model for casual absence showed variance components of .46 for between-
persons variance and 2.12 for within-person variance, indicating that 82.2% of the variance in
casual absence was attributable to within-person variance. In the model with the dummy variable
Casual and FMLA Absence 16
for policy change, the policy change was significant (γ10 = -.49, t1018 = -16.63, p < .01). The
within-person variance component dropped to 1.93, indicating that the policy change accounted
for 8.7% of the within-person variance. Thus, the policy change significantly reduced casual
absence in both plants, and Hypothesis 1 was supported.
The null model for FMLA absence showed variance components of .28 for between-
persons variance and .82 for within-person variance, indicating that 74.3% of the variance in
casual absence was within-person variance. In the model with the dummy variable for policy
change, the policy change was significant (γ10 = .16, t1018 = 6.91, p < .01). The within-person
variance component dropped to .73, indicating that the policy change accounted for 10.7% of the
within-person variance. Thus, the policy change significantly increased FMLA absence in both
plants, and Hypothesis 2 was supported.
The null model for disability absence showed variance components of .60 for between-
persons variance and 2.28 for within-person variance, indicating that 79.1% of the variance in
casual absence was within-person variance. In the model with the dummy variable for policy
change, the policy change was significant (γ10 = -.14, t1018 = -4.95, p < .01). The within-person
variance component dropped to .73, indicating that the policy change accounted for 2.8% of the
within-person variance. Thus, although we hypothesized no change, it appears that the policy
change also significantly decreased disability absence in both plants, and Hypothesis 3 was not
supported. It should be noted, however, that the power of the test was quite high and the effect of
this change was small relative to the changes in the other forms of absence.
In real terms, the mean number of days of casual absence per employee per month
dropped from .77 (SD = 1.88) to .23 days (SD = .95). For FMLA absence, the mean number of
days absent per employee per month increased from .09 (SD = .77) to .30 (SD = 2.09). For
disability absence, the mean number of days of casual absence per employee per month dropped
Casual and FMLA Absence 17
from .25 (SD = 1.05) to .16 days (SD = 1.05). Figures 1-3 show the mean levels of employee
absence over the 24-month period for each of the three forms of absence, and indicate when the
intervention took place at each plant.
Hypothesis 4 predicted that policy strength would (a) reduce casual absence and (b)
increase FMLA absence over and above the variance accounted for by the objective policy
change. Hypothesis 5 predicted that policy fairness would have negative effects on both (a)
casual and (b) FMLA absence over and above the variance accounted for by the objective policy
change. To test these hypotheses, we added employee perceptions of policy strength and policy
fairness as Level 2 variables in the HLM models for casual and FMLA absence. As noted above,
we were only able to capture these perceptions for 223 employees in Plant A, and thus the power
of these tests is greatly reduced. When these two variables were added to the HLM equations,
policy strength had no effect on casual absence (γ11 = .00, t220 = -.16, ns), but it did have a
significant positive effect on FMLA absence (γ11 = .08, t220 = -2.13, p < .05). Thus, Hypothesis 4
was partially supported. Policy fairness, however, had a marginally significant negative effect on
casual absence (γ12 = -.04, t220 = -1.78, p = .08) and a significant negative effect on FMLA
absence (γ12 = -.06, t220 = -2.11, p < .05). Thus, Hypothesis 5 was partially supported (with the
non-supported portion of the hypothesis being marginally significant).
Hypothesis 6 predicted that policy strength and policy fairness would interact in
predicting FMLA absence, such that those with high perceptions of policy strength and low
perceptions of policy fairness would be the most likely to take FMLA absences. To test this, we
added the interaction term for strength and fairness as a final Level 2 variable in the FMLA
HLM model. The coefficient for this term was significant (γ13 = -.07, t219 = -2.02, p < .05); the
nature of this interaction was in the expected direction and is plotted in Figure 4. This figure
Casual and FMLA Absence 18
shows that the average number of FMLA absences taken each month were approximately the
same for all employees except those who were high in policy strength and low in policy fairness.
Effects of the intervention
o The intervention had the expected effect of reducing casual absence.
o The intervention also had the unintended (but hypothesized) effect of increasing
Balloon metaphor: Although strict absence policies may deflate the total
amount of absence, applying additional pressure to the balloon may cause it to
pop out in places not covered by the policies.
FMLA absence increased markedly in Plant B two months prior to the policy
change. This may have been due to the anticipation of the change or to
spillover from Plant A.
o The intervention also had the unintended and unhypothesized effect of reducing
disability absence. This may have been due to some confusion over whether
disability absence would be punished under the new policies. The effect was
small, however, and may simply be due to sampling error.
Effects of employee perceptions
o Employee perceptions of policy strength only affected FMLA absence over and
above the effect of the intervention.
o Employee perceptions of policy fairness affected both casual and FMLA absence
o The interactive effect of these two perceptions indicated which employees were
the most likely to have taken FMLA absence. Employees may game the system to
restore equity when they perceive the system to be both strict and unfair.
Casual and FMLA Absence 19
o The absence criterion may have become unduly restricted by previous research
focusing on voluntary absence. Researchers may be overestimating the effects of
contextual variables on absence if they only measure casual absence.
o Employee perceptions of policies can account for variance in absence behavior
over and above the variance accounted for by the objective policy itself.
o Policy changes can be effective in reducing casual absences. This may be a more
cost-effective solution to high absence rates than more labor- or resource-
o FMLA absences are no less costly than casual absences. Organizations must
monitor their FMLA absence rates to see if they are being influenced by
o Organizations should monitor their employees’ perceptions of the policies as well,
because these may have additional effects on absence rates.
Directions for future research
Casual and FMLA Absence 20
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Casual and FMLA Absence 26
Table 1. Plant A means, standard deviations, and intercorrelations.
Mean SD 1 2 3 4 5 6 7 8 9 10
1. Casual absence (pre) 7.68 13.50
2. Casual absence (post) 2.51 5.00 .49**
3. FMLA absence (pre) 1.42 8.25 .16** .26**
4. FMLA absence (post) 2.64 7.99 .53** .31** .44**
5. Disability absence (pre) 1.21 4.30 .18** .15** .05 .16**
6. Disability absence (post) .93 2.99 .26** .24** .03 .23** .24**
7. Policy strength 2.41 .80 .04 -.03 .09 .14* .02 -.05
8. Policy fairness 3.00 1.02 -.19** -.21** -.10 -.14 -.24** -.07 .24**
Gendera 1.74 .44 -.26** -.16** -.15** -.25** -.11** -.11** .17* .22**
10. Age 48.78 9.21 -.27** -.25** -.14** -.22** -.06 -.07 .20** .19** .05
11. Tenure 21.47 9.83 -.28** -.26** -.16** -.25** -.11** -.08* .24** .02 .11** .73**
N = 733 for all correlations except those with policy strength and policy fairness, where N = 223.
1 = female, 2 = male
* p < .05
** p < .01
Casual and FMLA Absence 27
Figure 1. Mean number of casual absence days per month.
1.6 Plant A intervention Plant B intervention
Days absent .
Plant A Plant B
Figure 2. Mean number of FMLA absence days per month.
1.2 Plant A intervention Plant B intervention
Days absent .
Plant A Plant B
Figure 3. Mean number of disability absence days per month.
Casual and FMLA Absence 28
1 Plant A intervention Plant B intervention
Days absent .
Plant A Plant B
Figure 4. Interactive effect of policy strength and policy fairness on FMLA absence.
FMLA absence per month .
Low strength High strength