Factors Associated with Having Flextime:
A Focus on Married Workers
Deanna L. Sharpe
Joan M. Hermsen
Jodi Billings
University of Missouri–Columbia
ABSTRACT: To examine flexible work scheduling of married workers a conceptual
framework was developed and tested using the 1997 Current Population Survey Work
Schedules Supplement. Odds of flextime use were higher for married males, nonHispanic whites, those with relatively higher levels of education and income, those
with a preschool aged child, residents of the Midwest or West (as compared with the
South), managers or professionals, and employees of the federal government.
KEY WORDS: flextime; married workers.
Employees today work longer hours than they did twenty years
ago. A recent survey of labor market conditions found that between
1977 and 1997, for those employed 20 hours or more per week, time
on the job for men has increased from 47.1 to 49.9 hours per week
while, for women, the increase was from 39 to 44 hours per week.
Nearly 20% of employees surveyed reported working overtime during
the week, often without extra pay or advance notice, while about one
in three employees brought work home. Over one in ten employees
also reported spending an average of 13 hours a week at a second job,
primarily to earn extra money (Bond, Galinsky, & Swanberg, 1998).
Given a 24-hour day, the more time demanded by paid employment,
the less time available for personal, family, or household production
activities.
Deanna L. Sharpe is Associate Professor in the Department of Consumer and Family
Economics, University of Missouri–Columbia, 239 Stanley Hall, Columbia, Missouri,
65211; e-mail: SharpeD噝missouri.edu.
Joan M. Hermsen is Assistant Professor in the Department of Sociology, University
of Missouri–Columbia, Columbia, Missouri, 65211; e-mail: HermsenJ噝missouri.edu.
Jodi Billings is a graduate student in the Department of Consumer and Family Economics, University of Missouri–Columbia, 239 Stanley Hall, Columbia, Missouri,
65211.
Journal of Family and Economic Issues, Vol. 23(1), Spring 2002 䉷 2002 Human Sciences Press, Inc.
51
52
Journal of Family and Economic Issues
For married couple families, the “time squeeze” created by increased work demands can be especially tight. Ability to delegate
nonwork responsibilities can be limited if both spouses are employed.
Currently, 78% of married employees are in a dual-earner household,
up from 66% in 1977. Employed fathers with children under age 18
work almost three hours more per week than do other men (50.9 and
48 hours per week, respectively), while contributing about an hour
more per week to household tasks than did their counterparts in 1977
(Bond, Galinsky, & Swanberg, 1998). Employed mothers are typically
the primary caregivers for young children and recently, of aging relatives as well (Hochschild, 1997; Maharaj, 1998). In the press of work
and family demands, personal time for both married fathers and
mothers has declined about an hour per workday between 1977 and
1997 (Bond, Galinsky, & Swanberg, 1998).
Flexible work scheduling (flextime) can help workers alleviate
schedule conflict between work and nonwork responsibilities. Flextime allows an employee to choose an arrival and departure time
around a core “on the job” time set by the employer while still working a certain number of hours per day and workweek. Since its introduction in the U.S. in the early 1970s, flextime has become the oldest
and most widely used method of adding flexibility to a work schedule
(Kugelmass, 1995). In 1997, 27.6% of fulltime workers age 16 or older
had a flexible work schedule, up from 12.3% in 1985 (U.S. Census
Bureau, 1995; U.S. Census Bureau, 1998).
Flextime has generally received favorable reviews from both employees who appreciate arranging work schedules to facilitate meeting nonwork responsibilities and from employers who find that employees on flextime have fewer absences, lower turnover, lower stress,
and higher productivity (Christensen & Staines, 1990; Ezra & Deckman, 1996; Ralston, 1990; Winett & Neale, 1980). Still, flextime is not
without controversy. Equity in the distribution of flextime among
workers has been questioned. Use of flextime to recruit, reward, or
retain high quality employees suggests only select workers would
have work schedule flexibility, whereas use of flextime to ameliorate
work/family conflict suggests a broad spectrum of workers would have
flexible work schedules. Flextime presumes employers should accommodate the nonwork responsibilities of their employees. While some
would pressure employers to make this accommodation, others would
disagree and argue that other means of mediating work/family conflict need be found.
Existing literature on flextime sheds little light on these controver-
Deanna L. Sharpe, Joan M. Hermsen, and Jodi Billings
53
sies. Research on flextime has typically used small, localized, nonrandom samples of firms, limiting generalizability of results. Further,
this work has generally focused on evaluation of such schedules
rather than on the characteristics of workers who have a flexible
schedule (see, for example, Bohen & Viveros-Long, 1981; Ralston,
1990; Winett & Neale, 1980). The few studies that have used national
labor force data to examine the characteristics of workers who have
flextime are now dated and do not reflect recent growth in flexible
work scheduling (Mellor, 1986; Presser, 1989). Given the growth in
flextime use, the time pressures faced by married workers, and the
debates surrounding flexible work scheduling, more detailed and
more recent research on the characteristics of married workers who
have flextime schedules is needed. If significant differences exist in
the personal, family and work characteristics of those who do and do
not have flextime, the charge of discrimination in flextime benefits
becomes difficult to reject. The decision of whether or not such discrimination is justified would turn on which specific characteristics
differed. Also, if reducing schedule conflict between work and nonwork responsibilities becomes a public policy goal, knowing the characteristics of those who have flextime can guide discussion of the specific types of market or government services needed to replace
workers’ own time in completing nonwork tasks.
This study uses the 1997 Current Population Survey Work Schedules Supplement (U.S. Department of Commerce, 1997) to compare
the characteristics of married workers who do and do not have flextime and to identify the factors that significantly influence the odds
that married workers will have flextime.
Review of Literature
Studies evaluating flextime scheduling dominate the existing literature. The broad consensus of this research is that benefits of flextime appear to be synergistic (Bohen & Viverous-Long, 1981; Christensen & Staines, 1990; Ezra & Deckman, 1996; Ralston, 1990;
Winett & Neale, 1980). Employees are better able to arrange schedules to meet personal, family, and work demands. In turn, they are
able to decrease time away from the job, reduce stress, and improve
the quantity and quality of their work. These are outcomes that employers appreciate. Discontent with flextime scheduling also exists,
however. Scheduling conflicts with customers, suppliers, management
54
Journal of Family and Economic Issues
and other employees, can generate conflict and dissatisfaction for all
concerned (Baltes, Briggs, Huff, Wright & Neuman, 1999; Wood,
1998). Flextime does not necessarily resolve conflict between family
and wage work (Wharton, 1994). Further, reduced work hours and
income can be the price of obtaining flextime (Smith, 1997).
Presser (1989) and Mellor (1986) both used the Current Population
Survey, May 1985 Supplement on Work Schedules to assess the characteristics of workers who have a flexible work schedule. Presser
(1989) found that, among employed women, married women were
more likely to have flexible work schedules as compared with unmarried women; mothers of school-aged children were more likely than
mothers of preschool age children to use flexible work schedules. A
slightly higher percentage of men used flextime as compared with
women. However, a smaller proportion of men with children under
age 14 used flextime as compared with women with children under
age 14. These findings suggest that employed, married men may use
flextime more than employed, married women, but working mothers
will use flextime more than working fathers. Mellor (1986) and Ezra
and Deckman (1996) also note that men were more likely to use flexible schedules than women were.
The greater use of flextime by men is found across occupation, industry, and the public and private sector (“Flexible work schedules,”
1998). Given women’s relatively greater responsibility for childcare
and household production, this fact is somewhat surprising, but other
factors may provide an explanation. Women are typically employed by
firms that have limited ability to offer flextime either because the
firm is small or the work is not conducive to flexible schedules (Baltes
et al., 1999; Glass & Estes, 1997). There is also evidence that women
fear a request for flextime would be interpreted as a lack of work
commitment (Mulvihill, 2001).
Mellor (1986) notes that whites are more likely than either blacks
or Hispanics to use flexible work schedules. Employees aged 35 to 55
or over 64 were more likely than other age groups to use flextime
(Mellor, 1986).
In general, the literature suggests that workers with flextime
schedules tend to be married, white, male, and middle-aged, although
there is evidence that relatively more employed, married mothers
than employed, married fathers have flextime scheduling. Little else
is known about the characteristics of married workers who have flextime, however. Thus, the present study is designed to obtain a fuller
picture of the characteristics of married, employed workers who have
a flextime schedule.
Deanna L. Sharpe, Joan M. Hermsen, and Jodi Billings
55
Conceptual Framework
On the basis of existing literature, labor market characteristics,
and the realities of time demands created by personal, family, and
work life, a conceptual framework was developed to guide the research. This conceptual framework is two-tiered and illustrates the
factors likely to be most influential in use of a flextime schedule (see
Figure 1). The top tier focuses on conditions of the work and views of
the employer. It relates to factors affecting flextime availability or the
supply of flextime. The bottom tier focuses on personal and family
characteristics that lead to having flextime if it is available to the
employee, that is, the factors affecting the demand for a flextime
schedule.
Factors Affecting Flextime Availability
Realities of a given occupation determine whether or not a flextime
schedule is feasible. For example, assembly line workers must be
present on a given shift to produce a product. Flextime is not a practical option for these workers; however other types of alternative work
schedules such as compressed workweeks are possible (Christensen &
Staines, 1990; Nollen, 1982; Nollen & Martin, 1978; Petersen, 1980).
In contrast, employers who handle customer calls from a variety of
time zones may use flextime to staff extended work hours. Management or professional workers generally have greater job autonomy
than other types of workers in beginning and ending their days.
Smaller businesses may not have sufficient staffing to allow workers
discretion in work scheduling.
Legislation can dictate whether or not an employer can offer
employees a flexible schedule. Some state legislation establishes
the minimum and maximum number of hours an employee can work
within a seven-day period (Olmsted & Smith, 1994). At the federal
level, the Fair Labor Standards Act of 1938 currently precludes offering private sector employees any flexible schedule that involves working more than 40 hours per week without overtime compensation.
This restriction was amended for government workers in the Federal
Employees Flexible and Compressed Work Schedules Act of 1978.
Consequently, federal government employees should have greater access to flexible work schedules as compared with state and local government or private sector employees.
Employer’s attitude also impacts whether or not flextime will be
available to employees. If the employer is open to innovation and be-
56
Journal of Family and Economic Issues
FIGURE 1
Conceptual Framework
lieves that both company and employees would benefit from flexible
work schedules, they are likely to be offered. But, if the employer
favors a traditional work schedule; if the employer fears an employee
would take unfair advantage of fellow employees’ time; or if the employer believes the quantity or quality of goods or services produced
would decrease, flextime will not be offered even though the job is
conducive to such an arrangement.
Factors Affecting Choice of Flextime if Offered
On the basis of previous literature, it is proposed that personal,
family, and work characteristics affect the demand for a flexible work
schedule. Personal characteristics include gender, race and ethnicity,
age, education level and personal peak productivity times (bio-
Deanna L. Sharpe, Joan M. Hermsen, and Jodi Billings
57
rhythms). Extensive literature in labor economics suggests that gender, race and ethnicity, age, and education level are influential in occupational attainment (see, for example, Higginbotham & Romero,
1997; Mason, 1999; Neumark, 1999; Neumark & Stock, 1997). As previously discussed, characteristics of an occupation can dictate the feasibility of a flexible schedule. Occupations with relatively greater job
autonomy, “the power to set and execute projects” (Adler, 1993), are
also likely to offer flexible work schedules. Since job autonomy is
more likely to be held by men than women (Reskin & Padavic, 1994),
men may be more likely than women to use flextime (Ezra & Deckman, 1996; Mellor, 1986; Presser, 1989). Similarly, whites have typically had greater advantage in the labor market as compared with
any other racial group (Mason, 1999). This fact, perhaps, is why Mellor (1986) found that whites use a flexible work schedule more than
any other racial group.
Lifecycle demands and personal preferences may influence the need
for a flexible schedule. Employees in midlife may face heavy time demands for care of home and family. Older employees may use flextime
to pursue personal interests such as recreation or volunteer activities
(Mellor, 1986). Whether an employee works better in the morning or
later in the day may influence preference for a flexible work schedule
(Pierce & Newstrom, 1980).
Family characteristics of married workers consist of the presence
and age of the youngest child, number of household members, household before-tax income and gender role ideology. Time demands of
children vary by age. The nurture of very young children is time
intensive. Parents of school-aged children need to attend parentteacher conferences and other school activities and transport children to and from school or sporting activities. Having a teenager in
the home may decrease the need for work schedule flexibility since
teens can complete some household tasks and take care of themselves after school. Increased household size may also increase the
time demands for home and family. Households with higher levels of
family income can purchase market substitutes for household production, lessening the need for time away from work to accomplish
family-related tasks.
Gender role ideology refers to the norms within a family that influence responsibility for completing family and household tasks.
Hochschild (1989) found that division of labor in one’s family of origin
had a major impact on how one viewed their own household division
of labor. For example, persons reared in traditional families typically
58
Journal of Family and Economic Issues
viewed the husband as the main breadwinner and the wife as the
main household manager in their own family as well. In support of
this more traditional view of household tasks, Higgins, Duxbury, and
Lee (1994) found that young children in the home generated more
work-family conflict for mothers as opposed to fathers.
Family time demands are influenced by decisions that family members make about division of labor in the home and the commitments
family members make to various activities. Flexible work schedules
can facilitate meeting routine family schedules. An example would be
a father who begins work later in the day so that he can take his
children to school rather than having to arrange other transportation.
Employees who bear a disproportionate amount of responsibility for
home and family tasks or who have multiple and conflicting time demands may use flextime to reduce schedule conflict and stress.
For personal well-being, it is important to have time for rest, recreation, and meeting other personal needs. Flexible work schedules can
facilitate pursuit of these activities. For example, an avid gardener
may leave work at 3 p.m. to tend their garden before evening. Or,
when heavy time demands from work or family squeeze time available for personal and leisure activities, flextime can become a useful
strategy for carving out such time in a busy schedule.
The conceptual framework illustrates the reality that having a flextime schedule is the result of interplay of factors that influence both
the supply of and demand for flextime. Not every occupation is amenable to a flexible schedule. And, even in those that are, other factors
such as legislation or employer attitude can preclude altering standard work schedules. From the standpoint of the worker, personal
and family characteristics influence the need for time to complete
non-work related tasks.
Method
Data
The data are from the Current Population Survey (CPS), May 1997 Supplement on Work Schedules (U.S. Department of Commerce, 1997). The CPS is a
household sample survey focusing on the noninstitutionalized civilian population of the United States. Census Bureau staff collected the data using interviews conducted in May 1997. Data on work schedules was obtained from
each person 15 years old or older in the household that were currently em-
Deanna L. Sharpe, Joan M. Hermsen, and Jodi Billings
59
ployed. Before the data were released for public use, missing data were imputed.
Sample
The sample consisted of married women and married men who were employed full-time in the labor force and who had indicated whether or not they
had a flexible work schedule. Married individuals were selected because their
lifestyles, resources, and time demands were likely to differ from that of single individuals. Both genders were included in this analysis because the research of Levine and Pittinsky (1997) and Pleck, Staines, and Lang (1980)
indicates men and women experience equivalent levels of work-family conflict. It is important to note that, due to the data gathering techniques used in
the CPS, these men and women were not necessarily married to each other.
The self-employed and part-time workers were excluded since their work
hours and lifestyle can differ dramatically from that of a full-time wage and
salary worker. The sample size was 19,006 employed, married workers; 5,350
had flextime and 13,656 did not have flextime.
Variables
The dependent variable was dichotomous. It was coded 1 if the employed,
married worker answered “yes” to the question: “Do you have flexible work
hours that allow you to vary or make changes in the time you begin and end
work?”; 0 otherwise. Independent variables were selected to account for variations in personal, family, and employment characteristics and included: gender, age, race and ethnicity, education, number of household members, youngest child less than 6 years old, youngest child between the ages of 6 and 11,
youngest child between the ages of 12 and 17, household before-tax income
level, region of residence, percent female in occupation, occupation, and
whether or not employee is a government worker.
Gender. Gender was a dichotomous variable, 1 for female, 0 for male. On
the basis of previous research (Ezra & Deckman, 1996; Mellor, 1986; Presser,
1989), it was hypothesized that men would use flextime more than women
would.
Age. Age was a continuous variable measured in years. It was expected that
higher odds of flextime use would be associated with older ages (Mellor, 1986).
Race and ethnicity. Race and ethnicity was measured as a set of categorical
variables: non-Hispanic white (reference group), non-Hispanic black, nonHispanic other (which included Native American and Asian) and Hispanic.
Given their historical advantaged position in the labor market, non-Hispanic
whites were hypothesized to have greater odds of flextime usage compared
with other racial groups (Mellor, 1986).
Education. Education level was measured as a set of categorical variables:
less than high school (reference category), earned high school diploma, some
60
Journal of Family and Economic Issues
college, earned college degree, and earned master’s or doctoral degree. All else
equal, higher education may give workers better skill and position in negotiating flextime as a benefit. Thus, employees with a high school degree or
higher were hypothesized to have greater odds of having a flextime schedule
as compared to employees without a high school degree.
Household size. It was hypothesized that larger household size (measured
as a continuous variable) would increase family time demands and be positively associated with having a flextime schedule.
Age of the youngest child. Age of the youngest child was classified as: youngest child less than 6 years old, youngest child between the ages of 6 and 11
and youngest child between the ages of 12 and 17. Having no children under
age 18 was the reference category. Previous research suggests that having a
preschool age child is associated with a greater likelihood of using a flextime
work schedule (Higgins, Duxbury, & Lee, 1994). Thus, it was hypothesized
that odds of having a flextime schedule would be higher for married workers
with a child under age 6 as compared with married workers with no children.
Household income. The CPS does not report specific components of household income. The same categories reported in the CPS were used in this
analysis: ⬍ $10,000, $10,000–$19,999, $20,000–$29,999, $30,000–$39,999,
$40,000–$49,999, $50,000–$59,999, $60,000–$74,999, and ⱖ $75,000 (reference category). The relationship between household income and flextime is
uncertain. If the workers who have flextime also receive higher pay, higher
levels of household income would be positively associated with having flextime. Alternatively, households with higher income can purchase goods and
services to replace own household production. These households would not
need a flexible work schedule to meet family-related demands. In this event,
higher income would be negatively associated with flextime use.
Region of residence. Region was classified as: Northeast, South, West, and
Midwest. A survey of the best companies offering family friendly benefits
found relatively more firms using flextime in the Northeast, Midwest, and
West (Cowans, 1994). Regions also differ in the concentration of occupations
and industries, size of firms, and opportunities for leisure and other non-work
activity (U.S. Department of Labor, 2001). Each of these factors can affect the
availability of flextime and the desirability of its use (Cowans, 1994; Golden,
2001), therefore, it is hypothesized that the odds of flextime use are higher in
other regions as compared to the South.
Occupation. The CPS data only reports whether or not survey respondents
have flexible work schedules at time of interview; incidents of offer and refusal or of desire and denial are not ascertained. Since not all occupations are
conducive to flextime, occupation dummies were used to control for possible
differences in flextime availability. Using the standard Bureau of Labor occupational classifications, occupations reported in the CPS were grouped into
manager or professional (reference category), technical, sales, or administrative support; service; operators, fabricators, or laborers; precision production,
craft, repair. As the existing literature indicates, persons employed in mana-
Deanna L. Sharpe, Joan M. Hermsen, and Jodi Billings
61
gerial and professional occupations were expected to have higher odds of having flextime as compared with workers in other occupations.
Percent female in occupation. This variable assesses the degree to which
women are represented in a given occupation. Occupations reported by survey
respondents were assigned a number representing the percentage of women
employed in that occupation. The percentage figures corresponding to each occupation were calculated from the 1990 Census Equal Employment Opportunity
file. For example, 95% of registered nurses are female. Thus, for the registered
nurses in the sample, the variable percent female in occupation would be .95.
Female dominated occupations may offer less prestige, fewer opportunities
for workplace authority, and less access to flextime than male dominated occupations (Reskin & Padavic, 1994). Consequently, gender composition of the
occupation can reflect the supply of flextime. To the degree that flextime is an
“extra” benefit or reward provided by employers, it is not likely that the occupations women hold will offer such benefits as feminized occupations typically
receive fewer extrinsic rewards.
Type of employer. Three categorical variables were used to indicate type of
employer: federal government employee (reference category); state or local
government employee; private sector employee. The Federal Employees Flexible and Compressed Work Schedules Act of 1978 gave federal government
employees greater access to flexible work schedules as compared with other
government or private sector employees. Ezra and Deckman (1996) found
that a large percentage of federal workers use flexible work schedules. Given
this finding and the existence of Federal legislation supporting the use alternative work schedules, federal public sector workers were expected to be more
likely to have flextime than other workers.
Statistical Method
Descriptive statistics. Chi-square and t-test of mean differences were completed for employed, married workers who did and did not have flextime to
indicate statistically significant mean differences at the .01 level.
Multivariate analysis. The dependent variable in this study, have flextime,
is dichotomous. Since ordinary least squares regression is not appropriate in
this situation, logistic regression was used (Aldrich & Nelson, 1984). Analysis
was completed using SAS software. Statistical tests did not reveal problems
with multicollinearity (Menard, 1995). The population weight provided in the
CPS, adjusted to avoid inflating the chance of achieving statistical significance, was used in the logistic regression (Thomas & Heck, 2000).
Empirical Models
Three models were used to better understand the relationship between having flexible work schedules and personal, family and work characteristics.
The first model considered personal characteristics alone while the second
62
Journal of Family and Economic Issues
and third model added family and work characteristics, respectively. This approach was used to learn which relationships persisted as additional characteristics were added to the model. Due to data limitations, not all variables in
the conceptual framework could be included in the empirical models. In general, the relationship between having flextime and select characteristics of
married workers can be described as:
Flextime Probability Usage ⳱ 1 / (1 Ⳮ eⳮz)
(1)
where z is a linear combination, z ⳱ bo Ⳮ 兺bixi Ⳮ ε; bo is a constant; bi is a
vector of parameter coefficients; xi is the vector of independent variables previously described; and ε is a random error term.
In this form, the dependent variable can be thought of as the probability
that an employed married man or woman will have flextime. To facilitate
computation, the model may be rewritten:
Log(probability of having flextime/probability of not having
flextime) ⳱ bo Ⳮ 兺bixi Ⳮ ε
(2)
Using this format, the dependent variable is the log odds where the odds are
defined as the ratio of the probability that an event will occur to the probability that it will not occur. The bis are interpreted as measuring change in
the log odds given a one-unit change in the independent variable. Since odds
are easier to interpret than log odds, the antilog of equation (2) could be
taken, yielding:
probability of flextime use
⳱ eb0Ⳮb1x1... ⳱ eb0 eb1x1 . . .
probability of not using flextime
(3)
In this form, e raised to the power bi is the factor by which the odds change
when the ith independent variable increases by one unit (Demaris, 1992). An
odds ratio that is greater than, equal to, or less than 1.00 indicates a higher,
equal, or lower probability of having flextime, respectively.
Findings and Discussion
Descriptive Analysis
Comparison of the mean characteristics of employed, married workers who do and do not have flextime revealed that a significantly
larger proportion of those who have flextime were male, non-Hispanic
and had a college or professional degree, a preschool aged child,
higher income levels, residence in the Midwest or West, managerial,
professional, technical, sales, or administrative occupations, and employment in the private sector (see Table 1).
Deanna L. Sharpe, Joan M. Hermsen, and Jodi Billings
63
TABLE 1
Chi-Square and t-Test of Differences in Characteristics of Married Workers
Who Do and Do Not Have Flextime
Variable
Female
Age
Race
Non-Hispanic White
Non-Hispanic Black
Non-Hispanic Other
Hispanic
Education
Less than High School
High School
Some College
College
Master’s or Ph.D.
Number of Household Members
No Child
Age of Youngest Child ⬍ 6
Age of Youngest Child 6–11
Age of Youngest Child 12–17
Income
⬍ $10,000
$10,000–$19,999
$20,000–$29,999
$30,000–$39,999
$40,000–$49,999
$50,000–$59,999
$60,000–$74,999
⬎ $75,000
Region of Residence
Northeast
Midwest
West
South
Occupation
Management and professional
Technical, sales, administrative
Service
Operators, fabricators, laborers
Precision production, craft, repair
Percent Female in Occupation
Type of employer
Federal Government
State or Local Government
Private Sector
*p ⬍ .01.
Have flextime
(N ⳱ 5350)
Do not have flextime
(N ⳱ 13,656)
.37
40.79
.43*
40.96
.85
.05
.04
.06
.79*
.07*
.04*
.10*
.05
.28
.27
.25
.15
3.40
.48
.24
.16
.12
.11*
.36*
.27
.17*
.09*
3.50*
.49
.22*
.15
.14*
.01
.04
.09
.12
.14
.15
.16
.33
.02*
.06*
.13*
.16*
.15
.15
.14*
.20*
.19
.27
.25
.29
.20*
.24*
.23*
.33*
.40
.35
.07
.09
.08
.41
.26*
.29*
.10*
.19*
.15*
.43*
.04
.12
.83
.05*
.19*
.77*
64
Journal of Family and Economic Issues
Multivariate Analysis
Logistic regression results are reported in Table 2. Results of a chisquare test1 used to evaluate the fit of the models indicated that considering family characteristics along with personal characteristics
(model 2) was superior to considering personal characteristics alone
(model 1). Likewise, considering work characteristics along with personal and family characteristics (model 3) was superior to considering
just personal and family characteristics (model 2).
The effect of age ceased to be significant when work characteristics
were added to the empirical model. Other than this one exception, the
sign and significance level of the parameters generally persisted
across the three equations. Therefore, discussion of results will focus
on model 3, which included personal, family, and work characteristics.
Married women had 21% lower odds of having flextime as compared with married men, confirming the hypothesis that married men
have a higher probability of having flextime than married women
do. This result is consistent with the findings of Mellor (1986) who examined a broad spectrum of employees using the 1985 CPS Work
Schedules Supplement, and Ezra and Deckman (1996) who surveyed
federal employees. This continued support for gender difference in
flextime use is intriguing. Given the favorable shift in employer attitude toward flextime schedules, the increased availability of flextime
schedules for both genders, and the greater involvement of married
men in household tasks, it would have been no surprise to find that in
the late 1990’s gender difference had become statistically insignificant. That it has not suggests that while the demand for flextime
scheduling among women may still be high given their involvement
in home and family tasks, for various reasons, their access to the supply of such schedules is still limited.
Higher levels of education were associated with increasingly
greater odds of having a flexible schedule, as expected. The number of
household members was a significant factor in having flextime. But,
contrary to expectations, larger household size was associated with
lower rather than higher odds. Perhaps additional family members
not only care for themselves (for example, an older teenager or an
elderly parent) but also complete household tasks, reducing the need
for workers to have a flexible schedule.
Non-Hispanic blacks and Hispanics were less likely to work flextime than non-Hispanic whites (18% and 27%, respectively), support-
Deanna L. Sharpe, Joan M. Hermsen, and Jodi Billings
65
TABLE 2
Logistic Regression Results (n ⳱ 19,006 weighted)
Model 1
Parameter
estimate
Personal Characteristics
Respondent Age
Respondent Female
Respondent Education
High school
Some college
College
Graduate degree
Respondent Race/
ethnicity
Nonhispanic black
Nonhispanic other
Hispanic
Family Characteristics
Household income
⬍ $10,000
$10,000–$19,999
$20,000–$29,999
$30,000–$39,999
$40,000–$49,999
$50,000–$59,999
$60,000–$69,999
Household size
Model 2
Odds
ratio
ⳮ0.003** 0.997
(0.002)a
ⳮ0.266*** 0.767
(0.034)
0.429***
(0.073)
0.755***
(0.073)
1.176***
(0.075)
1.267***
(0.812)
1.536
2.128
3.240
3.550
ⳮ0.411*** 0.663
(0.066)
0.058
0.663
(0.078)
ⳮ0.389*** 0.678
(0.063)
Parameter
estimate
Model 3
Odds
ratio
Parameter
estimate
Odds
ratio
ⳮ0.006** 0.994
(0.002)
ⳮ0.290*** 0.748
(0.034)
ⳮ0.002
(0.002)
ⳮ0.237**
(0.044)
0.998
0.336***
(0.074)
0.584***
(0.076)
0.915***
(0.080)
0.953***
(0.087)
1.401
1.794
2.497
2.592
0.279***
(0.077)
0.420***
(0.793)
0.604***
(0.086)
0.716***
(0.096)
0.789
1.322
1.522
1.829
2.047
ⳮ0.353*** 0.703
(0.067)
0.114
1.120
(0.079)
ⳮ0.313*** 0.731
(0.065)
ⳮ0.204** 0.816
(0.070)
0.025
1.026
(0.083)
ⳮ0.319*** 0.727
(0.068)
ⳮ0.490*** 0.612
(0.138)
ⳮ0.587*** 0.556
(0.089)
ⳮ0.573*** 0.564
(0.067)
ⳮ0.5465*** 0.579
(0.060)
ⳮ0.3720*** 0.689
(0.057)
ⳮ0.373*** 0.689
(0.055)
ⳮ0.297*** 0.743
(0.053)
ⳮ0.049** 0.952
(0.015)
ⳮ0.322*
(0.141)
ⳮ0.462***
(0.092)
ⳮ0.390***
(0.069)
ⳮ0.379***
(0.063)
ⳮ0.236***
(0.059)
ⳮ0.269***
(0.057)
ⳮ0.222***
(0.055)
ⳮ0.044**
(0.016)
0.725
0.630
0.677
0.684
0.790
0.764
0.801
0.957
66
Journal of Family and Economic Issues
TABLE 2 (Continued )
Model 1
Parameter
estimate
Model 2
Odds
ratio
Age of youngest
child ⬍ 6
Age of youngest
child 6–11
Age of youngest
child 12–17
Parameter
estimate
Odds
ratio
Parameter
estimate
Odds
ratio
0.116*
(0.049)
0.046
(0.054)
ⳮ0.090
(0.055)
1.123
0.110*
(0.051)
0.065
(0.055)
ⳮ0.040
(0.057)
1.12
Work Characteristics
Region of residence
Northeast
Midwest
West
Percent female in
occupation
Occupation
Technical, sales,
administrative
Service
Operators, fabricators, laborers
Precision production, craft,
repair
Class of worker
State and local
government
Private
Intercept
ⳮ2 log likelihood
Model 3
1.047
0.914
1.067
0.961
ⳮ0.151**
(0.050)
0.135**
(0.045)
0.162**
(0.048)
ⳮ0.890***
(0.082)
0.859
ⳮ0.026
(0.046)
ⳮ0.448***
(0.074)
ⳮ1.218***
(0.068)
ⳮ0.337***
(0.072)
0.975
1.144
1.176
0.998
0.639
0.296
0.263
ⳮ0.709*** 0.492
(0.096)
0.163
1.176
(0.085)
ⳮ0.934***
(0.077)
21998.388
a
Standard errors reported in parenthesis.
*p ⬍ .05; **p ⬍ .01; ***p ⬍ .001.
ⳮ0.2996**
(0.1107)
21856.407
0.156
(0.148)
20909.387
Deanna L. Sharpe, Joan M. Hermsen, and Jodi Billings
67
ing the hypothesis regarding race and ethnicity. Whether this difference arises from sorting into jobs where flextime is not available or
discrimination cannot be ascertained in this study.
As expected, higher levels of household income were associated
with greater odds of having flextime. Married workers with income
levels below $75,000 had between 37% and 20% lower odds of having
flextime, depending on the specific income level considered. This result suggests that higher income earners are using flextime and they
may not be purchasing market services that replace their own time in
personal or family tasks.
Having a preschool aged child increased odds of having flextime by
12% as compared with having no children under age 17 at home. Respondents whose youngest child was school aged were not significantly different in having flextime from those without children. This
result suggests that employed, married workers may use flextime to
facilitate care of very young children. The fact that no significant results were found for children of other ages implies that time schedules of older children may be more amenable to an employed parent’s
work schedule. Also, older children are able to complete some household tasks. Both of these factors may reduce the need for parents to
have a flexible work schedule.
Previous studies of flextime have not considered the impact of region of residence on odds of flextime use. Compared to married workers living in the Southern region, Northeastern residents were 14%
less likely to have a flexible schedule. Residents of the Midwest
and West were 14% and 18% more likely to have flextime than were
Southern residents, respectively. These results supported expectations only for the Midwestern and Western regions, but not for the
Northeastern region.
The percentage of female workers in an occupation was negatively
related to the odds of having flexible work schedule. The odds that a
technical, service, or administrative worker would have a flexible
schedule were not significantly different from that of managerial or
professional workers. Service workers had 36% lower odds of having
a flexible schedule as compared with management or professional
workers. The odds against having a flexible schedule when employed
as an operator, fabricator, laborer or in precision production, craft or
repair were even greater at 70% and 74%, respectively. This finding is
also consistent with previous research (Christensen & Staines, 1990;
Nollen, 1982; Petersen, 1980).
State and local government workers had 51% lower odds of having
68
Journal of Family and Economic Issues
flextime than federal employees, supporting the hypothesis that employment by the federal government is more likely to give access to
flexible schedules. Apparently, federal government employees benefited from passage of the Federal Employees Flexible and Compressed
Work Schedules Act of 1978. No significant difference in having flextime was found between federal government and private sector workers, however, suggesting that market place incentives may have
worked as well as federal legislation in creating ways to increase flexibility of worker schedules.
Support of the Conceptual Framework
Findings from the models used in this study supported the parts of
the conceptual framework that could be tested with the CPS data.
Regarding the factors affecting flextime availability, there was evidence that type of occupation was significantly related to having flextime. Married workers employed in service or as operators, fabricators, laborers or as precision production, craft or repair workers had
lower odds of having flextime as compared with workers employed in
management or professional occupations. Some support was also
found for the impact of flextime legislation on having flextime. Although there was no significant difference between employment by
the federal government or private sector in flextime use, state and
local government employees were significantly less likely to have flextime. The CPS offered no insight into employer attitudes toward flextime.
Regarding factors affecting choice of flextime if offered, the statistical significance of several personal and family characteristics lends
credibility to the flextime model. Personal characteristics of being
male, being non-Hispanic white, and having more than a high school
education were associated with increased odds of having flextime.
Family characteristics of having the youngest child under age 6,
household income of $75,000 per year or more were associated with
greater odds of having flextime. The CPS offered no data regarding the relationship between personal peak performance times (biorhythms) or gender role ideology in the family and choice of flextime.
Neither did it provide any insight into the degree of stress workfamily schedule conflicts generated nor the specific uses that an employee might have for a flextime schedule. Examination of these factors remains for future research.
Deanna L. Sharpe, Joan M. Hermsen, and Jodi Billings
69
Summary and Implications
Previous research found that males, whites (as compared with
blacks or Hispanics), and those aged 34–55 or over 64 were most
likely to have flexible work schedules (Mellor, 1986; Presser, 1989).
Similar results were found for gender and race and ethnicity in this
research, however, age was not a significant factor in having flextime
when work-related characteristics were controlled. Extending previous research, this study also found that odds of having flextime
were greater for those with higher levels of education and household
income, those with a preschool aged child, residents of the Midwest or
West (as compared with the South), managers or professionals and
employees of the federal government (as compared with state and local government). Odds of having flextime were lower for those employed in female dominated occupations or in occupations associated
with relatively less job autonomy.
Results of this study support the argument that workers who have
flextime differ significantly from those who do not. The finding that
odds of having flextime are greater for those with higher levels of
education and income and for those with occupations associated with
relatively more job autonomy suggests employers use flextime to recruit, retain, and reward high quality employees rather than to help
all employees reduce work/family schedule conflict. Consequently,
employers may not be providing flextime to all employees who could
benefit from its use. Findings from this research suggest that females, non-Hispanic blacks or Hispanics, those with lower incomes,
and those employed in female dominated occupations and in occupations that typically offered less job autonomy might be relatively disadvantaged in access to flextime. Although odds of having flextime
are lower for married workers with these characteristics, it is not apparent that their need for flextime to reduce work/family schedule
conflict would be less than that of their counterparts.
The CPS data does not distinguish between those who have flextime but choose not to use it and those who do not have access to
flextime. Study results could change if removing barriers to flextime
led to an increase in those having a flexible work schedule. For example, if given equal access to flextime married women had significantly
greater odds of having flextime than married men then the finding in
this study that the odds of having flextime are relatively greater for
married men would simply be an artifact of existing barriers to flextime scheduling. Including occupation and type of employer in the
70
Journal of Family and Economic Issues
analysis does not completely control for access to flextime schedules.
Further research with different data would be needed to disentangle
such effects2.
In recent years, increases in the labor force participation of women,
dual-earner households and family annual work hours have exacerbated conflict between work and family schedules. The means to resolve this conflict is debated. Some believe employers should free
worker time to complete their own care giving and other household
production tasks. Others would rather substitute market goods and
services or government programs for worker’s own time in completing
non-work tasks. The policy implications of each view differ. For example, parental leave for newborn care would be advocated by the former view while infant day care would be supported by the latter view
(Glass & Estes, 1997). While the results of this study do not resolve
this debate, they do underscore the link between care giving and having flextime since significantly greater odds of having a flexible work
schedule were associated with having a preschool aged child as compared to no children even after controlling for other personal, family,
and work characteristics. Thus, it would seem reasonable that market
or government substitutes for flextime should focus on care giving
issues, especially for the portion of married workers who may face
barriers to flexible work scheduling.
Notes
Specifically, the test is (ⳮ2 log likelihood of reduced model) ⳮ (ⳮ2 log
likelihood of full model) ⳱ chi-square value with p-q degrees of freedom
where p ⳱ number of parameters in the full model and q ⳱ number of
parameters in the reduced model (Long, 1997, p. 95). In this study, the
difference between model 1 and model 2 yields a chi-square of 21998.388 ⳮ
21856.407 ⳱ 141.981 which is greater than the critical value of 19.68 for
a chi-square with 11 degrees of freedom at the 0.05 level of significance.
The difference between model 2 and model 3 yields a chi-square of
21856.407 ⳮ 20909.387 ⳱ 947.02 which is greater than the critical value
of 18.31 for a chi-square with 10 degrees of freedom at the 0.05 level of
significance.
2. We are indebted to an anonymous reviewer for making this point clear.
1.
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