Precarious Work Schedules in Low-Level Jobs: · Web viewStill, we maintain that a work-family...

Precarious Work Schedules in Low-Level Jobs:

Implications for Work-Life Interferences and Stress

Julia R. Henly & Susan Lambert

University of Chicago

March 2010

Draft: Please do not quote or cite without permission of authors.

Address correspondence to either author at 969 E. 60th St. Chicago, Illinois 60637, USA [email protected], [email protected]

Note: The authors would like to thank Lauren Gaudino and Ellen Frank for excellent research assistance. This research would not have been possible without the generous support of the Russell Sage Foundation, the Ford Foundation, and the Annie E. Casey Foundation, to which the authors are very grateful.

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precarious work schedules 2


Abstract

Drawing from a survey of employees in a national apparel chain, this study considers the

precariousness of employment in the retail sector by examining two dimensions of work schedules

(schedule unpredictability and limited employee input) that are hypothesized to increase general

work-to-family conflict, interfere with workers’ ability to plan and structure nonwork time, and

heighten levels of perceived stress. We find that net of individual, family, and other work

characteristics, unpredictability and limited input into schedules are related to each of the outcomes.

Unpredictability appears to have a stronger and more significant influence across model

specifications. Overall, the findings for unpredictable schedules are robust to several model

specifications, including the inclusion of enabling factors such as supervisor support that may be

thought to ameliorate the negative impacts of precarious schedules (Voydanoff, 2004). Limitations

of the study and future research directions are discussed.


The current economic recession is increasing the vulnerability of workers at all levels of the

U.S. labor market. Unemployment increased from 4.9 to 7.2 percent in 20081, continuing its rise to

9.17 percent in February 2010. The destabilizing effects of the economic downturn, however, are

felt not only by those who lose their jobs. Those who remain employed face increasingly

precarious work conditions. For example, involuntary part-time work has reached a 30-year high

(8.8 million) and the length of the average workweek has fallen to a record low of under 34 hours2.

Although the current recession has extended the reach of precarious work to a broader

segment of workers, it did not give rise to these conditions. Indeed, job characteristics and

employer-worker relations have become increasingly precarious over the past three decades, due to

a combination of factors including macro-economic changes in the structure of the labor market

(shifts from manufacturing to service, for example), the decline in unionization, the growth of

business strategies emphasizing cost containment, and the loosening of government labor standards

(Blank, Danziger, & Schoeni, 2006; Kalleberg, 2009; Lambert, 2008). Precariousness has increased

globally, although the focus of this paper is the United States where worker protections are

particularly weak.

Growing precariousness is evidenced by increases in nonstandard employment

arrangements, such as part-time, contingent and temporary jobs, as well as in the use of scheduling

practices that allow employers to make quick adjustments to staffing levels in standard jobs (Henly

& Lambert, 2005). Notably, in good times and bad, employees at the front lines of many of today’s

firms bear the brunt of routine fluctuations in demand for services and products through their work

schedules. Hourly workers have limited control over their work schedules and increasingly

experience variable, unpredictable, and reduced work hours that can compromise their job

1 All statistics in this paragraph taken from Bureau of Labor Statistics, Current Population Survey, accessed March 13, 2010; http://www.bls.gov/news.release/empsit.nr0.htm.

2 This is the lowest average number of work hours for production and nonsupervisory workers in private firms since this statistic has been tracked, beginning in 1964.


performance and their ability to earn an adequate living (Lambert, 2008). Indeed, the scheduling

practices commonly employed across key industrial sectors in the U.S. economy contribute to a

growing precariousness of employment that has important implications for individuals, families,

and communities (Kalleberg, 2009; Lambert, 2008; Henly, Shaefer, & Waxman, 2006).

In this paper, we focus on the precariousness of work schedules for hourly non-management

employees in the retail sector. Drawing from a survey of employees in a national retail apparel

chain, we examine two dimensions of work schedules that we hypothesize to influence both

behavioral and stress-related outcomes of workers. In particular, we consider (1) the

unpredictability of employees’ work schedules, that is, the extent to which employees have limited

advance notice of their work schedules and cannot count on or anticipate getting a particular work

schedule (in terms of days, shifts, and the number of hours worked) and (2) the lack of employee

input into the time, days, and hours they work (e.g., lack of schedule control). We view these two

dimensions of work schedules as indicators of employment precariousness in everyday work and

hypothesize that net of other employment characteristics and personal and family circumstances,

both dimensions of precariousness will increase general work-to-family conflict, interfere with

workers’ ability to plan and structure their nonwork time, and heighten levels of perceived stress.

Building from Greenhaus and Beutell’s (1985) now classic distinction between strain-based and

time-based work-to-family conflict, we further hypothesize that input into scheduling may be

particularly important for reducing stress and will compensate to some degree for an unpredictable

schedule in terms of reducing workers’ stress but will not significantly reduce interferences with

nonwork activities. Our survey findings lend support to these hypotheses (with one exception) and

are robust to several model specifications, including the inclusion of enabling factors such as

supervisor support that may be thought to ameliorate the negative impacts of precarious schedules.

The exception concerns our findings for schedule input and stress. We do not find that increased


schedule input is more highly associated to stress than the other dependent variables under

investigation, nor do we find that it significantly reduces the association between schedule

unpredictability and perceived stress, as hypothesized.

BACKGROUND

The Growing Precariousness of Work

In his 2009 presidential address to the American Sociological Association, Arne Kalleberg

spoke of the growing precariousness of work and increasing insecurity of workers in the United

States and globally. By precariousness, Kalleberg refers to “employment that is uncertain,

unpredictable, and risky from the point of view of the worker” (p.2, 2009). He states:

Precarious work has far-reaching consequences that cut across many areas of concern to

sociologists. Creating insecurity for many people, it has pervasive consequences not only

for the nature of work, workplaces, and people’s work experiences, but also for many

nonwork individual (e.g., stress, education), social (e.g., family, community), and political

(e.g., stability, democratization) outcomes. It is thus important that we understand the new

workplace arrangements that generate precarious work and insecurity.

Thus, work has become more precarious over the past 30 years as employers increasingly

pass risk from the market onto workers and demand for labor flexibility – the ability of employers

to flexibly and quickly adjust the number of employees and their work hours – has increased

(Lambert, 2008). In jobs paid by the hour, maintaining a close link between employees’ work hours

and variations in labor demand is one strategy employers use in their efforts to contain labor costs.

Lambert (2008) demonstrates several ways in which labor flexibility practices operate in

workplaces—for example, through strategic use of work status categories and “just-in-time”

scheduling practices—suggesting that employer-driven variation in work hours, rather than

flexibility that benefits workers, is common practice in many workplaces, especially in lower-level


jobs. Whatever the reasons for growing employer reliance on labor flexibility (e.g., global

competition, cost over quality strategies, eroding labor standards), the practices used to achieve

labor flexibility shift risk from firms to workers, resulting in increased instability and insecurity in

hours and income (Lambert, 2008). The literature on firm-level labor markets provides strong

evidence that passing instability onto workers leads to problems with performance, such as

heightened absenteeism and turnover (Appelbaum, Bailey, Berg, & Kalleberg, 2000; Appelbaum,

Bernhardt, & Murnane, 2003; Baron & Bielby, 1980; Jacobs, 1994; Osterman, 1999).

Dimensions of Precarious Work in Low-Level Jobs

Nonstandard Employment Status and Work Schedules

Workers employed in hourly, non-management jobs are at particular risk of precarious

employment. Research demonstrates that low-level workers disproportionately hold jobs with

nonstandard status and nonstandard schedules (Golden, 2005; Presser, 2003; Henly & Lambert,

2005). Regarding employment status, the literature demonstrates that employers are relying less on

“regular, full-time” employment status categories (Herzenberg, Alic, & Wial, 1998; Tilly, 1996),

replacing regular status workers with part-time, contingent, and temporary employees and adopting

new work statuses such as “full-time flex” and “reduced compensation professional.” These new

statuses serve to restrict access to benefits and facilitate employers’ ability to apply flexible labor

practices to a wider set of workers beyond the traditional part-time/full-time status distinction

(Lambert & Waxman, 2005; Lambert, 2009). Henly & Lambert (2005) argue that employment

status is often quite ambiguous in low-level jobs, such that “one’s status on paper may not be

indicative of the number of hours worked or even whether an employee is working at all” (p.479).

In Lambert’s study of low-level jobs across four industrial sectors (financial, transportation,

hospitality, and retail), the primary distinction that is found between part-time and full-time status

hourly employees is the classification’s implication for benefit access and employment and training


programs. Neither full-time nor part-time status reliably indicated the number of hours worked, with

hour fluctuations proving routine for both full-time and part-time hourly workers. Thus, one’s

employment status may indeed signal greater or lesser job security—especially in terms of benefit

access—but it is an insufficient proxy for work precariousness. Even workers in full-time jobs may

have difficulty getting enough hours and may face nonstandard work schedules into which they

have little input and are given little advance notice.

Thus, in addition to employment status, it is important to consider the ways in which the

scheduling practices that employers use can contribute to the precariousness of employment. Much

of the literature on nonstandard work schedules concerns the timing of work (e.g., nontraditional

hours). The growth of the service industry and increasing globalization have increased demand for

nonstandard hour workers (Presser, 2003). Based on 1997 Current Population Survey (CPS) data,

Harriet Presser (2003, p.1) estimates that “two-fifths of all employed Americans work mostly at

nonstandard times”, and even more employees regularly work at least some of their hours outside of

regular daytime, weekday hours.

Nonstandard schedule work is common across occupations, but service and laborer positions

disproportionately require nonstandard hours (Presser, 2003). These occupations also heavily

employ workers for whom precarious work schedules are likely to be most detrimental; for

example, workers with limited education and skills, limited financial resources, minorities, and

single parents (Presser & Cox, 1997; Presser, 2003).

Unpredictability of Work Schedules and Limited Employee Control Over Schedules

In addition to concerns about the timing of work (e.g., nontraditional hours), other

nonstandard scheduling features may also be important to understanding precarious employment.

In particular, when someone works may be a less relevant indicator of precariousness than the

predictability of one’s work or the control a worker has over his or her work schedule.


There has been limited research attention to work schedule predictability—that is, the extent

to which workers can count on or anticipate receiving a particular work schedule from week to

week, and the length of advance notice given regarding work hours. Recent studies suggest that

unpredictable schedules in low-level hourly jobs are widespread. Lambert’s previously mentioned

study of low-level jobs across four industrial sectors found that unpredictable scheduling practices

(limited advance notice and frequent last-minute changes) were a typical employer strategy for

managing fluctuations in consumer demand, observed in all 22 workplaces studied (Lambert &

Waxman, 2005). In Henly’s companion study of low-income mothers employed in six of the retail

settings studied by Lambert, the majority of participants reported having less than one week’s

notice regarding the following week’s schedule, and changes to schedules were often made after the

schedule was posted (Henly, Shaefer, & Waxman, 2006). Moreover, two-thirds of participants

reported that posted schedules routinely varied week to week. Workers were regularly called in for

unscheduled, “last-minute” hours, sent home early, or pressed to stay later than a shift’s scheduled

end-time. Thus, schedule unpredictability – distinct from the timing of work hours – may be an

important dimension of precarious work that has implications for workers and their families.

The benefits of a predictable schedule for workers may be attenuated in situations where

schedules are not sufficiently flexible to accommodate nonwork-related responsibilities. Indeed,

schedule predictability may turn into rigidity if workers have limited input into their work schedules

or if posted schedules are not amenable to change. Thus, another key dimension of work schedules

is the control (or input) that employees have over the hours, days, and shifts they work.

Unlike the paucity of work on predictability, control over schedules has received relatively

more attention, especially by work-family scholars concerned with flexibility. It is important to

distinguish between employee-driven flexibility, which is discussed here as indicating employee

input into the number and timing of work hours, and employer-driven variation, which indicates


employer control over the scheduling process, as in the aforementioned concern over an increasing

reliance on labor flexibility practices by employers (see Lambert & Waxman, 2005; Moss,

Salzman, and Tilly, 2005; Henly, et al., 2006). Although the work-family literature does not always

distinguish these two very different sources of variation in work hours, there is little ambiguity that

it is the first – employee-driven control over schedules – that is assumed to benefit workers.

Flexible work is often heralded as a critical workplace benefit that has the capacity to

deliver workers greater control over how, where, and when they carry out their work, thereby

easing the integration of work with family (and other nonwork) roles (Kossek, Lautsch, & Eaton

2005). For example, employee input into work schedules may make it possible for a parent to

arrange work hours around a child’s school schedule or allow a worker to make a last minute

schedule change to accommodate an unanticipated medical appointment. Employee surveys

consistently demonstrate that workers desire flexible work arrangements, believe flexibility in hours

would improve their quality of life, and would even trade other forms of work opportunity for

flexibility (Golden, 2005). Yet, research based on national samples of workers indicates that

employee-driven flexibility in work hours is rare among low-level workers (Galinsky, Hughes, &

David, 1990; Golden, 2005). For example, Current Population Survey (CPS) data demonstrate that

low-skilled unmarried mothers – who may arguably have the greatest need for flexible work

arrangements – are least likely to have them (Golden, 2005). In fact, flexible work schedules are

distributed according to predictable stratification statuses: men more than women, whites more than

nonwhites, and those with higher education over lesser education benefit disproportionately from

flexible work schedules (Golden, 2005). Smaller, more focused studies of specific job sectors

demonstrate how the process used to set work schedules for low-level, hourly, nonmanagement

workers typically provides only very restricted formal avenues for employees to exercise input over


their work hours. As a result, employee input into scheduling is negotiated informally, if at all, and

is at the discretion of the manager (Henly et al., 2006).

Implications of Precarious Work on Nonwork Spheres and Employee Stress

Work-life and work-family scholars have long been interested in the various ways in which

work conditions interact with nonwork domains. Much of this literature has focused on

professional, white collar employment (and middle- and upper-class two-parent families), although

there have been notable exceptions (Lambert, 1990;1999), and in recent years there has been

increased attention to work-family linkages across a broader range of individuals and a more

diverse set of jobs (Grzywacz, Almeida, & McDonald, 2002; Perry-Jenkins, 2005; Swanberg,

2005). The growth in precarious employment underscores the importance of examining the

adequacy of our understanding of the work-family interface for today’s workers (Lambert, 1999;

Rayman & Bookman, 1999). Several recent studies of the low-wage labor market that examine the

characteristics of low-level jobs (Acs & Loprest, 2008; Kalleberg, Reskin, & Hudson, 2000), firm-

level practices (Lambert, 2009; Hacker, 2006) and work and family life among low-income and

working class families (e.g., Lein, Benjamin, McManus, & Roy, 2005; Perry-Jenkins, 2005;

Heymann, 2000) suggest that key concepts and findings from the work-family field may benefit

from refinement to better reflect common but understudied job characteristics as they are

experienced by workers with limited economic means.

Toward this end, the current paper investigates whether the two dimensions of precarious

work schedules discussed above are related to both behavioral and stress outcomes in a sample of

low-wage, hourly, non-management retail workers. In particular, we consider whether the

unpredictability of employees’ work schedules and the amount of input employees have into the

time, days, and hours they work are associated with general work-to-family conflict, and whether


these indicators of precarious work contribute to perceived stress and interfere with workers’ ability

to plan and structure nonwork time.

Our conceptual framework joins the research on firm practices in the low-wage labor market

(as discussed above), poverty research on work-family management strategies, and the work-family

literature on job demands and work-family conflict. By bringing these three literatures together,

our goal is to contribute to a fuller understanding of the consequences of precarious work demands

for women employed in low-level retail jobs.

Work-to-family conflict is conceptualized as “a form of interrole conflict in which the role

pressures from the work and family domains are mutually incompatible in some respect” (p.77,

Greenhaus & Beutell, 1985). According to Greenhaus and Beutell, the sources of work-to-family

conflict can be time-based, in which the time pressures of one role interfere with the time demands

of another role, or strain-based, in which the strain symptoms produced by one role interfere with

one’s ability to carry out another role.3 Importantly, work-family conflict is conceptualized in the

literature as bidirectional (e.g., Frone, Russell, & Cooper, 1997; Frone, Yardley, & Markel, 1997;

Greenhaus & Beutell, 1985; Kelloway, Gottlieb, & Barham, 1999; Voydanoff, 2005). Work roles

can interfere with family (work-to-family conflict) and family roles can interfere with work (family-

to-work conflict). Because the current study is specifically interested in the behavioral and stress

implications of precarious work schedules, our focus in unidirectional – from work to family –

although we certainly recognize the importance of better understanding family-to-work conflict as

well for workers in precarious jobs.4

3 Greenhaus & Beutell also identify behavior-based work-family conflict, in which the behaviors expected in one role (compassion with family) are difficult to adjust to another role (competitiveness on the job). Our models do not address behavioral flexibility across roles.

4 Although our models are based on traditional work-family frameworks derived from a role conflict perspective (e.g., Kahn, Wolfe, Quinn, Snoek, & Rosenthal, 1964), we do not assume the inevitability of conflict across work-family roles more generally. Role accumulation can have positive benefits on worker health and well-being under particular circumstances (Barnett & Hyde, 2001; Marks, 1977; Perry-Jenkins, Repetti, & Crouter, 2000; Ruderman, Ohlott, Panzer, & King, 2002; Moen & Yu, 2000; Greenhaus & Powell, 2006; Grzywacz & Bass, 2003). Moreover, we


Critical to our conceptualization is the notion of job demands as sources of time-based and

strain-based work-to-family conflict. Accordingly, job demands (in this case, demands resulting

from unpredictable schedules and limited input into work schedules) “compete for a person’s time”

(Greenhaus & Beutell, 1985), draining resources and complicating temporal work-family patterns

that would otherwise facilitate the fulfillment of obligations and desired activities in the family

domain (or another domain, in an expanded work-life conflict framework). Importantly, our

conceptualization reflects a modification of conventional notions of work-to-family conflict in that

our concern is about time-based and strain-based conflicts resulting from unpredictability and lack

of input into schedules rather than a concern with excessive job demands that overload roles and

create time pressures – as in the problem of too much work or a preoccupation with work during

nonwork times (see Major, Klein, & Ehrhart, 2002; Pleck, Staines, & Lang, 1980; Kahn et al.,

1964).

Regarding time-based sources of work-to-family conflict, the evidence is strong that

excessive time demands create work-to-family conflict. Whether the work conditions of low-level

hourly workers, who often have trouble getting enough hours (see Lambert, 2008), create time-

based work-to-family conflict has not been sufficiently studied. Thus, we consider the behavioral

consequence – in terms of interferences in nonwork domains – of unpredictable and uncontrollable

time–based work demands. Informed by theories of social time (e.g., Almeida & McDonald, 2005;

Presser, 2003), we argue that work demands that create ambiguities around the timing of work

complicate the activities central to other spheres of life. For example, unpredictable work schedules

over which employees have limited control are hypothesized to interfere with the planning of

nonwork activities such as doctor’s appointments, social outings, or arranging other family

appreciate that enabling resources such as job autonomy and boundary-expanding resources such as work supports increase the likelihood of facilitation rather than conflict between work and family roles (Voydanoff, 2004). Still, we maintain that a work-family conflict perspective is more appropriate than a role accumulation/enrichment or facilitation perspective for this study, given what is known about the limited availability of work-family supports in low-level jobs and the generally precarious job characteristics of hourly work in the retail industry.


activities, as planning is facilitated by advance knowledge of and control over one’s available time.

We assume that both unpredictability and limited schedule input will be sources of time-based

conflict, and that even when employees have input into their schedules, schedule unpredictability

may interfere with nonwork activities.

We know of no large scale studies that examine the relationship between schedule

unpredictability and time-based work-to-family conflict per s; however, other aspects of work

schedules, such as nonstandard work-timing and irregular work hours are associated with time-

based work-to-family conflict (Major, Klein, & Ehrhart, 2002; Pleck, Staines, & Lang, 1980;

Fenwick & Tausig, 2005). Moreover, there is evidence that greater control over work schedules can

reduce time-based conflict (Fenwick & Tausig, 2005; Kossek, 2005), although other research

suggests that the degree of employee schedule control must be significant for it to matter in any

meaningful way (Greenhaus & Beutell, 1985; Bohen & Viveros-Long, 1981). Relatedly, there has

been a growing concern about whether and how the temporal nature of nonstandard work schedules

influences the organization and conduct of family life (Almeida & McDonald, 2005; Presser, 2003).

Survey findings reveal significant associations between nonstandard work hours and the time

parents spend with children (Almeida & McDonald, 2005) and on specific parent-child activities

(Presser, 2003; Heymann, 2000). A number of qualitative studies also illustrate the challenges that

nonstandard work schedules pose for family routines and child care, and these studies suggest that

timing, unpredictability, and limited control over schedules matter for family functioning (Roy,

Tubbs, & Burton, 2004; Henly & Lambert, 2005; Scott et al., 2005).

A stress model underlies much of the work-family literature (see reviews in Eckenrode &

Gore, 1990; Edwards & Rothbard, 2005), and the transmission of stress between work and family

has been characterized as a manifestation of negative spillover (Pleck, 1995; Voydanoff, 2004).

Over the past quarter century, a substantial laundry list of work stressors with demonstrated


relationships to an equally impressive list of strain symptoms and stress-related outcomes has

accumulated. For example, different measures of work demands have been associated with

depression (Frone, Russell, & Barnes, 1996; Googins, 1991), fatigue (Pleck et al., 1980; Googins,

1991), irritability (Pleck et al., 1980), somatic complaints (Burke, 1988), general well-being (Grant-

Vallone & Donaldson, 2001), heavy alcohol use (Frone et al., 1996), psychological distress

(Matthews, Conger, & Wickrama, 1996) and problematic marital relationships (Matthews et al.,

1996).

Greenhaus & Beutell (1985) point out that whereas some work stressors that induce strain

are independent of time-based work demands (for example, role ambiguity, role conflict and

boundary spanning activities), “extensive time involvement in a particular role also can produce

strain symptoms,” (p. 81) suggesting that time-based and strain-based conflict can result from the

same work stressors. We extend Greenhaus and Beutell’s argument to our concern with schedule

precariousness, which we hypothesize will contribute to strain symptoms. Staines and Pleck (1983)

provide some preliminary support for this view. They find that atypical work hours and days, as

well as variable hours, relate to work-family role strain, whereas greater schedule flexibility (i.e.,

control) relates to less role strain. Fenwick & Tausig (2005) find that schedule control is related to

work-life conflict measures, as well as spillover and distress-related outcomes; and importantly,

their findings suggest that schedule control operates independently from the timing of work.

Relatedly, Campbell and Moen (1992) find that when considered in the same model, limited control

over work schedules, but not nonstandard work hours, contribute to strain. Given these results, we

expect that unpredictable schedules and lack of input into scheduling will both induce strain, but

that having greater schedule input may compensate for (and therefore, reduce) the strain of

unpredictable schedules.

Worker Vulnerability to Employment Precariousness


Certain workers may be particularly vulnerable to both precarious work conditions and

stress and work-family conflict (Kalleberg, 2009). Research on labor market inequalities

demonstrates how common markers of social status such as low education, single parenthood,

minority status, and being female increase the likelihood of employment in an occupational or

industrial sector with fewer worker protections and in jobs with lower wages, fewer benefits, and

less standard scheduling practices (Padavic & Reskin, 2002; Grzywacz, Almeida, & McDonald,

2002; Golden, 2005; Kalleberg, et al., 2000; Christensen, 1998; Gottschalk & Danziger, 2005).

Demographic variables such as education, age, race and ethnicity, marital status, and health status

also show important relationships to measures of work-family conflict and stress (Frone, Yardley,

& Markel, 1997; Grzywacz, Almeida, & McDonald, 2002; McLoyd, Toyokawa, & Kaplan, 2008;

Moen & Yu, 1999; Presser, 2003). Moreover, resources available in workers’ households, such as

other sources of income and a partner to share caregiving, may be related to workers’ experiences

of precarious employment as well as stress and work-to-family conflict.

Thus, in this paper, we include several vulnerability measures as controls in the multivariate

analyses in order to assess the extent to which unpredictability and lack of input are associated with

stress- and time-based conflict independent of other personal and household factors that may put

workers at risk for precarious employment and work-to-family conflict.

Study Hypotheses

The primary focus of the current study is on two dimensions of precariousness – schedule

unpredictability and lack of schedule input– and their association with general work-to-family

conflict, time-based conflict (interferences with planning nonwork activities), and perceived stress,

in a sample of women working in hourly retail jobs. Based on the findings reviewed above from

the work-family literature, we advance a behavioral and stress model to explain the hypothesized

relationships between these variables. Overall, we expect both unpredictability and lack of input to


be related to the outcomes. However, because we argue that input into work hours can reduce the

stress of unpredictable schedules, we expect lack of input to have a stronger relationship to the

stress outcome than the others, and we expect the association between unpredictability and stress to

be attenuated when both dimensions (unpredictability and input) are considered together in the

same model. However, we argue that input into schedules is insufficient to fully compensate for the

interferences in nonwork activities that unpredictability can cause; thus, we expect that schedule

unpredictability will have a robust relationship to the behavioral measures that tap into interferences

with nonwork activities, even after the contribution of schedule input has been considered in the

model.

Thus, we assess the following hypotheses:

Hypothesis 1: Controlling for indicators of vulnerability, the unpredictability of work

schedules is positively related to general work-to-family conflict (1a), work interferences in

nonwork activities (1b), and perceived stress (1c).

Hypothesis 2: Controlling for indicators of vulnerability, lack of input into work schedules

is positively related to general work-to-family conflict (2a), work interferences in nonwork

activities (2b), and perceived stress (2c).

Hypothesis 3: Controlling for indicators of vulnerability, the positive association between

unpredictable work schedules and work interferences (3) will remain when input is also included in

the model (because we assume that input cannot compensate for the time-based conflict that

unpredictability in scheduling can create).

Hypothesis 4: Controlling for indicators of vulnerability, the positive association between

unpredictable work schedules and perceived stress (4) will be attenuated when input is added to the

model (because we assume that input into schedules reduces the strain-based conflict that

unpredictability in scheduling can create).


We do not advance an a priori hypothesis regarding whether the coefficients for

unpredictability and control will remain significantly related to the general work-to-family conflict

measure when they are both included together in the model because the work-to-family conflict

measure is a general measure that includes dimensions of both time-based and strain-based work-to-

family conflict.

METHOD

Data

The analyses are based on two waves of data from the Retail Employee Scheduling Survey,

a telephone survey conducted in 2008 of hourly retail workers in the Midwest. Sample eligibility is

based on employment within one of 21 stores from a national retail chain participating in a

workplace-based intervention. Overall, 136 respondents participated in Wave 1 and 156

respondents participated in Wave 2 of the study. Eligibility was determined at Wave 1 based on

employment at the participating store during the recruitment phase. Eligibility was determined at

Wave 2 based on eligibility at Wave 1 and/or employment at the participating store during

recruitment at Wave 2 (as described further below). For the purposes of this study, we exclude all

managers from the sample and all respondents who were not working at the store at the time of the

Wave 2 survey. This results in an analysis sample of 123 for Wave 1 and 120 for Wave 2. Further,

for the analyses that rely on both waves of data (Table 6), we only include non-manager

respondents that were employed at the store at both waves 1 and 2 of the survey and for whom we

have information at both time periods. This restriction limits the sample to 92 for all analyses that

rely on both waves of data.

Sampling Strategy

The research team was provided the name of all employees but not their contact information

(telephone numbers and/or addresses) prior to recruitment. Because contact information for


employees in the 21 participating stores was not initially available, sample recruitment took place

through a two stage process at both Waves. In the first stage, a letter for every employee was

provided by the research team to the store manager, who distributed them to store employees with

their weekly paycheck. The manager’s role was simply to distribute the letter, not to encourage

employees to participate in the study. (Managers were also eligible for participation, but were not

included in the present analyses given they are responsible for the scheduling process and would

therefore have missing data on our independent variables.) The letter explained the purpose of the

survey and requested that employees provide the researchers with contact information by returning

a self-addressed, stamped, contact card to the researchers. Employees were not asked at this stage to

commit to participation, but rather for their willingness to provide the research team with contact

information so that they could be contacted regarding the survey. After the letter was distributed, a

follow up letter was provided. In addition to a follow-up letter, research team members visited the

21 stores in person (sometimes multiple times) in an effort to collect additional cards. Employees

were provided with a $10 gift card for supplying contact information. The first stage of recruitment

for Wave 1 yielded a response rate of 68.0% -- that is, 172 of 253 eligible store employees provided

contact information during the Wave 1 recruitment.

Of the 253 employees eligible for participation in Wave 1, 215 were eligible in Wave 2.

Individuals were only deemed ineligible for Wave 2 if we had no way to contact them because they

did not return contact information at Wave 1 and they no longer worked in one of the 21

participating stores. Individuals who had left the firm but had provided contact information at

Wave 1 were eligible for Wave 2. In addition to the 215 eligible Wave 1 employees, an additional

41 individuals became eligible in Wave 2 because they had been hired into one of the 21 stores

since recruitment took place for Wave 1. This resulted in 256 eligible Wave 2 participants.


For Wave 2, we followed a similar process to Wave 1 to collect updated contact information

(or new contact information for employees hired since Wave 1 recruitment and employees who did

not return contact information during the first recruitment period). For respondents who were no

longer employed at one of the 21 stores and for whom we had contact information, we sent a letter

requesting updated telephone contact information to their home address. All other letters were

distributed in the stores in a manner consistent with that of Wave 1 recruitment. Overall, we

received contact information from 190 of 256 eligible participants at Wave 2. Thus, we attempted to

interview 74.2% of eligible Wave 2 participants.

The University of Wisconsin Survey Center was responsible for the second stage of the

recruitment for both Waves 1 and 2, which involved calling employees who had provided contact

information, conducting informed consent, and administering the survey. All survey respondents

received a $25 check for their participation at Wave 1 and a $50 check for participation at Wave 2.

At Wave 1, the second stage of recruitment yielded a response rate of 79.1%; that is, 136 of the 172

employees who returned response cards participated in the survey. Thus, the overall response rate

for Wave 1 is 53.8% (136 respondents out of 253 eligible store employees). At Wave 2, the second

stage of recruitment yielded a response rate of 82.1% (156 respondents out of 190 who returned

response cards), resulting in an overall response rate for Wave 2 is 60.9% (156 out of 256). Eighty-

eight percent of Wave 1 respondents were successfully reinterviewed in Wave 2 (120 of 136).

For the purposes of this paper, we exclude all managers (13 in Wave 1 and 15 in Wave 2)

and we exclude any Wave 2 respondents who were no longer working at the retail establishment

(20, 1 manager and 19 non-managers). This results in a sample of 123 Wave 1 respondents and 122

Wave 2 respondents. Of the 123 Wave 1 respondents in the restricted sample, 92, or 74.8%, were

reinterviewed at Wave 2; the remaining 21 Wave 2 employees in the restricted sample had joined

the company after Wave 1.


Measures

The majority of variables were constructed from responses to self-report survey questions

(see Appendix A for specific items). Three control variables (race, age, and employment status)

come from the personnel records of the corporation that were obtained as part of the broader

workplace intervention.

Independent Variables

Two indices created for the purposes of this study are used to capture precariousness of

employment. Schedule unpredictability is a measure that assesses the amount of advance schedule

notice and the extent to which respondents can predict their schedules based on a three-item index

(alpha=.65) that captures the extent to which respondents agreed or disagreed that they can

anticipate and/or count on the number and timing of hours that they will be working week-to-week.

Based on these two measures (advance schedule notice and hours/timing predictability), we

constructed a measure of schedule unpredictability that has three possible values including Most

Predictable (more than one week notice of work schedule and a score in the top half of the median

split of the 3-item index of hours/timing predictability, coded as 1), Somewhat Predictable (one

week or less notice of work schedule and a score in the top half of the median split of the 3-item

index of hours/timing predictability, coded as 2) and Unpredictable (one week or less notice of

work schedule and in the bottom half of the median split of the 3-item index of hours/timing

predictability, coded as 3). We treated Schedule Unpredictability as a continuous variable in the

multivariate analyses reported here; although the results are unchanged when Schedule

Unpredictability is considered categorically, with Most Predictable serving as the excluded

category.


Lack of Input is a 4-item index (alpha=.82) that assesses the amount of input into the

number of hours respondents report working each week, the days they work each week, the days

they have off, and their starting and ending times5.

Supervisor Support is a 4-item emotional-support subscale developed by Hammer, Kossek,

Yragui, Bodner, & Hanson (in press) that assesses the extent to which workers agree that their

immediate supervisor is willing to talk about and is interested in their work and nonwork conflicts.

(Higher scores indicate moresupport.) This variable is used in Model 5 and Model 5b to test the

robustness of our hypotheses to alternative model specifications.

Dependent variables

General Work-to-Family Conflict is a 5-item index adapted from Netemeyer, Boles, and

McMurrian (1996) that combines both time- and strain-based items (alpha=.87). For example,

workers rated how often the demands of their work interfere with their personal or family time,

create strain that makes it difficult to fulfill personal or family responsibilities, and cause them to

adjust their personal plans. Work Interferences in Nonwork Activities is a 4-item index of questions

that asked workers whether they have “more than enough time, just enough time, or not enough

time” to schedule doctor’s appointments, plan activities with friends or family, and arrange to cook

a meal at home (alpha=.84).6 Perceived Stress is an 8-item scale adopted from the 14-item scaled

developed by Cohen, Kamarck, and Mermelstein (1983). The items asked workers to report how

5 We also constructed a measure similar to our lack of input indicator that is based on three items originally intended to be an instrumental support subscale of supervisor support. These items are "I can depend on my manager to help with scheduling conflicts", "my manager lets me come in late or leave early to accommodate my family", "when I have to miss work, my manager helps me make up the hours". In the paper, we do not report results for this measure, however the multivariate results are similar regardless of whether we use this measure or the lack of input measure as an indicator of employee input over schedules. Our preference is for the lack of input measure because it assesses schedule input directly (e.g., simply asking about whether respondents have input over weeks, days, and start and end times) rather than invoking the role of the manager in providing that input. Thus, we view the lack of input measure as a cleaner and more independent measure of input than is the manager instrumental support measure. 6 We developed this scale by “unpacking” a single question that Swanberg et al. (2008) used to assess the extent to which workers in a drug-store chain reported sufficient time to “plan personal, family, or other responsibilities.”


often in the prior month they experienced feelings of anxiety, personal control, and confidence in

their coping skills (alpha=.82).

Control variables

We include several measures of vulnerability as control variables in the multivariate

regressions. Personnel records provided information to construct Age, a continuous variable

calculated from the workers’ birthdate, Race, a dichotomous variable that differentiates workers

who are recorded as White (coded 1) in the company’s personnel records from other workers

(coded 0), and Part-time, a dichotomous variable that identifies workers who hold part-time (coded

1) rather than full-time (coded 0) positions at the retail firm. A series of additional dichotomous

variables are constructed from workers’ survey responses: High School Education identifies

workers who reported having no more than a high school degree (coded 1) from workers who

reported additional education (coded 0); Child under 18 in the home distinguishes workers with at

least one child under 18 who lives with them (coded 1) from workers without children younger than

18 in their home (coded 0); Respondent has Other Job indicates whether respondent reports a job in

addition to the target retail job (coded 1) or does not (coded 0). To capture both marital status and

employment status of partner, we constructed a trichotomous measure that includes Does Not Have

Partner (coded as 1), Has Partner Who Does Not Work or Works Part-Time (coded as 2), and Has

Partner Who Works Full-Time (coded as 3). Does Not Have a Partner is treated as the excluded

variable in the analyses. Subjective Health is a one-item question asking respondents to rate their

health as excellent, very good, good, fair, or poor. Given the distribution on the measure, it was

dichotomized to indicate excellent or very good health (coded as 0) or good, fair, or poor (coded as

1). We also include a control for whether the respondent worked for an experimental or control

store in all analyses, although we do not evaluate the intervention itself in this study. A variable that


measures the length of time between Waves 1 and 2 surveys is included in all regressions that use

both waves of data. The average time between surveys was 6 months.

Analytic Plan

First, we present descriptive statistics of the control, independent, and dependent variables

for both Waves 1 and 2. Second, using Wave 2 cross-sectional data, we examine five multiple

regression models for each of the three dependent variables. The models assess the relationship

between each dependent variable and the set of vulnerability variables, which we treat as controls

(Model 1), the unpredictable schedule variable net of control variables (Model 2, Hypothesis 1), the

lack of input variable net of control variables (Model 3, Hypothesis 2), and our two independent

variables entered simultaneously (unpredictable schedules and lack of input over schedules) with

the set of controls (Model 4). Model 4 assesses hypotheses 3 and 4 which are concerned with the

independent associations of each dimension of precarious work schedules net of the other, and

whether input into schedules compensates for the stress induced by unpredictable schedules (HO 4)

but not for the time-related conflicts (nonwork interferences) (HO 3). The fifth model includes

supervisor emotional support in the full model as a robustness check to determine if the associations

observed with the independent variables remain once level of supervisor emotional support is

considered (Model 5).7

The five models are presented together for each dependent variable on Table 3 (General

Work-to-Family Conflict), Table 4 (Interferences with Nonwork Activities), and Table 5 (Perceived

Stress). In interpreting the tables, note that the independent and dependent variables are scaled such

that positive coefficients mean that more unpredictable schedules and more limited input over

schedules are related to higher work-to-family conflict, more interferences with nonwork activities,

7 A similar robustness test was conducted with job autonomy and job challenge in the model instead of (and together with) supervisor support. Controlling for job autonomy and challenge does not appreciably change the coefficients on the independent variables of interest.


and more perceived stress. Because the respondents were drawn from 21 stores, and hence the data

are not independent, the standard errors are adjusted in all models to account for the clustering of

employees by store to obtain a robust variance estimate (Williams, 2000; Rogers, 1993).

The Wave 2 cross-sectional analyses attempt to deal with the problem of correlated error

between the independent variable and the error term by including explicit multivariate controls

measured at Wave 2 (Berk, 2004). Because unmeasured characteristics that may be correlated with

both scheduling practices and work-life outcomes are not included in the models, these multivariate

specifications are still likely to produce biased estimates of the relationships of interest (e.g., the

concern of omitted variable bias, Greene, 2000). In order to reduce this bias, we rerun the models

reported in Tables 3 through 5, taking into account prior measures of the dependent variable of

interest using Wave 1 data (see Table 6). This does not eliminate the problem of correlated error

nor does it exclude the possibility of reverse causality explaining our results. But by removing the

association of prior measures of the dependent variables on the observed Wave 2 relationships,

these regressions reduce the bias and offer a more conservative test of our hypotheses.

More specifically, in the second stage of the analyses, we rerun the five models for each

dependent variable but include in the regression the Wave 1 value of the dependent variable as a

control along with the prior control variables. We also include a control for the time that elapsed

between the Wave 1 and Wave 2 interviews, and we again adjust the standard errors to account for

nonindependence (i.e., clustering). We label these models, 1b, 2b, 3b, 4b, and 5b to differentiate

them from the core Wave 2 analyses and present the results on Table 6. To conserve space, Model

1b (the model with only control variables) is not presented on Table 6 nor are the coefficients for

the control variables for Models 2b through 5b. Table 6 presents the coefficients for schedule

unpredictability and lack of input on the Wave 2 outcomes of interest, and also shows the


relationship between the Wave 1 and Wave 2 outcomes. All models and their full set of coefficients

are available upon request of author.

RESULTS

Descriptives

Descriptive statistics for the sample of retail employees at each Wave are provided on

Tables 1 and 2. The sample is exclusively female, over three-fourths are non-hispanic white, and

only a minority are parenting a child under 18 years of age (20.3% in Wave 1; 22% in Wave 2). The

sample is relatively diverse by age (mean age is 47.7 years in Wave 1, 48.1 years in Wave 2), with

approximately one-fifth under 35 years of age and more than one-third over 55. Just over 40% in

both waves have more than a high school degree (approximately one-fifth have graduated from

college, not shown on Table 1). Sixty four percent of the sample report that their health was

excellent or very good (as opposed to good, fair, or poor) in Wave 1, whereas 56% reported

excellent or very good health at Wave 2. At both waves, about 40% of respondents are neither

married nor living with a partner, about 42% are married or living with a partner who works full

time, and the remainder are married or living with a partner who is either unemployed or working

part time. Wages for respondents are above the legal minimum, but arguably low at $9.36 per hour

at Wave 1 and $9.20 at Wave 2. The average wages of full-time employees are approximately four

dollars higher than part-time employees. Well over one-third of respondents (38.2% in Wave 1,

40% in Wave 2) hold a job at another place of employment in addition to the position they have at

the retail firm.

Table 2 reports the means and standard deviations of the two independent variables

(unpredictable work schedules and lack of input), supervisor emotional support, and the three

dependent variables for both Waves. As Table 2 indicates, the scores are quite similar across the

two Waves.


Multivariate Analyses

Table 3 reports the results of the five models for general work-to-family conflict. Model 1

indicates that respondents with more than one job report significantly more work-to-family conflict

than those with only one job and part-time employees compared to fulltime employees report

significantly less work-to-family conflict. These results remain significant across models.

Respondents who report poorer health (or health that is something other than “excellent” or “very

good”) have marginally greater work-to-family conflict in Model 1; the relationship holds when

schedule unpredictability is included in the model (Model 2), but does not remain significant across

the other models. Although age is not significant in Model 1, it becomes so when schedule

unpredictability is taken into account together with the other variables (Models 2, 4, and 5) such

that younger respondents report more work-to-family conflict. Somewhat surprisingly, respondents

with children under 18 in the household report less work-to-family conflict, although the

relationship is only significant in Models 1 and 2.

Controlling for these measures of vulnerability, we find that unpredictable schedules (Model

2) and lack of input into schedules (Model 3) are both postively related to general work-to-family

conflict. Both variables remain highly significant in Model 4 (when schedule unpredictability and

input are considered together). These findings lend support to the first two study hypotheses. Model

5 considers the possibility that supervisor emotional support acts as a resource that can reduce

general work-to-family conflict. The results lend support to this hypothesis as respondents who

report less agreement with the supervisor support items report marginally significantly more work-

to-family conflict. Moreover, taking supervisor support into account diminishes (but not to

nonsignificance) the relationships between work-to-family conflict and both schedule

unpredictability and input . Thus, these results suggest that for our general work-to-family conflict

measure, which includes aspects of both time-based and strain-based conflict, having supervisor


support somewhat ameliorates the challenges posed by unpredictable schedules and limited

schedule input, but it does not fully compensate for them.

Table 4 reports the results of the five models for interferences with nonwork activities. We

hypothesized that the time-based conflict induced by unpredictable schedules (HO1) and limited

employee control (HO2) would be positively associated with interferences with nonwork activities,

and that greater input into schedules would be insufficient to eliminate the time-based conflicts

posed by unpredictability (HO3). These hypotheses were supported.

Model 1 indicates that, with the exception of health, none of the vulnerability variables are

significantly related to the interference measure. This lack of association is constant across the

models. As with general work-to-family conflict, we find that unpredictable schedules (Model 2)

and lack of input into schedules (Model 3) are both significantly related to interferences with

nonwork activities. The coefficient for both variables dissipates somewhat in Model 4, when input

into schedules is considered together with unpredictability, although both unpredictability and input

remain significant. Thus, consistent with hypothesis three, the results suggest that although input

into schedules can reduce nonwork interferences, it cannot completely compensate for the

challenges created by unpredictable schedules that contribute to nonwork interferences. In Model

5, when supervisor emotional support is considered together with unpredictability and input, the

coefficients of both input and unpredictability are somewhat reduced. However, schedule

unpredictability remains significantly related to interferences with nonwork activities, input into

schedules is marginally significant, and the supervisor support variable itself is not significant.

Thus, these results do not provide strong evidence that supervisor support mediates the relationship

between precarious schedules and interferences with nonwork activities.

Table 5 reports the results of the five models for perceived stress. We hypothesized that

unpredictable schedules (HO1) and limited input into schedules (HO2) would pose strain-based


challenges that contribute to perceived stress. We further hypothesized that unlike time-based

conflicts, the strain-based conflict resulting from unpredictable work schedules could be

ameliorated for workers with significant input over their schedules (HO4). As elaborated below,

our first two hypotheses were confirmed. However, we do not find support for our hypothesis that

having input into schedules reduces the association between time-based conflict and perceived

stress. In fact, for this sample, schedule unpredictability is a more powerful predictor of stress than

having input into the scheduling process.

As reported on Table 5, respondents with poorer health report marginally significantly more

perceived stress, and respondents with less education report significantly lower levels of perceived

stress (Model 1). These results are consistent across the models. Although having another job is not

significant in Model 1, it becomes marginally significant in the other models such that respondents

with more than one job report more perceived stress. Respondents who are not married or

cohabiting report marginally less stress than those who are married or cohabiting to a full-time

employed partner, in all models except Model 1.

As hypothesized, having an unpredictable work schedule (Model 2) and having limited input

into work schedules (Model 3) are both related to significantly higher levels of perceived stress.

However, contrary to our hypothesis, we do not find evidence that having input into one’s schedule

reduces the association between unpredictable schedules and perceived stress. To the contrary,

although the schedule unpredictability and lack of input coefficients are both slightly reduced in

Model 4, the coefficient for schedule unpredictability remains significant, whereas the coefficient

for input is reduced to nonsignificance. Adding supervisor emotional support to the model together

with schedule unpredictability and schedule input (Model 5) does not change the size or

significance level of the coefficient for either scheduling variable. Moreover, and somewhat

surprisingly, there is no observable relationship between supervisor support and perceived stress.


Table 6 summarizes the results of the stage two analyses that replicate Models 1 – 5 (here

referred to as Models 1b – 5b), with the addition of a control variable that takes into account Wave

1 levels of the dependent variable. To conserve space on the table, we do not report the coefficients

for the vulnerability measures or other control variables. The results are presented on Table 6 as

three horizontal panels, one for each dependent variable. As with Tables 3-5, the models are

represented as columns in Table 6, although we have excluded Model 1b (the vulnerability model

without scheduling measures).

As the first row of each panel indicates, and as would be expected, the Wave 1 indicator of

the outcome is highly related to the Wave 2 indicator of the outcome such that Wave 1 general

work-to-family conflict is significantly and strongly related to Wave 2 general work-to-family

conflict, Wave 1 interferences with nonwork activities is significantly and strongly related to Wave

2 interferences with nonwork activities, and so on. Taking those relationships into account, we find

that schedule unpredictability continues to be significantly associated with each of the dependent

measures (Model 2b), and the significant association remains in each of the models even when lack

of input and supervisor support are considered together with unpredictability (Models 4b & 5b).

The scheduling input measure proves less robust. Although in the earlier models lack of input into

schedules was found to be positively related to general work-to-family conflict, interferences with

nonwork activities, and perceived stress, this relationship only holds for perceived stress when the

Wave 1 measure of these dependent variables is taken into account (top and middle panel, Models

3b, 4b, & 5b). That is, when we control for Wave 1 indicators of perceived stress, lack of input at

Wave 2 is significantly related to Wave 2 perceived stress in Model 3b. This relationship

diminishes in Model 4b and 5b, although it is still significant in 5b (when emotional support is

considered). Overall, schedule unpredictability but not schedule input proves to be quite robust


across the different model specifications and across different outcomes, suggesting its salience as a

contributor to both time-based and strain-based work-family challenges.

DISCUSSION

This study explored a behavioral and stress model in an examination of work schedule

precariousness (unpredictability and limited input into schedules) and its implications for three

outcomes related to work-family well-being. The three measures were chosen to represent a general

measure of general work-family conflict that includes both stress and time-based conflict, as well as

a behavioral measure addressing time-based conflict (interferences with nonwork activities) and a

perceived stress measure, addressing strain-based conflict. The study findings are based on a sample

of retail workers drawn from 21 Midwestern stores of a U.S. retail apparel firm. The exclusively

female sample represented diverse individual and family circumstances, although their employment

was exclusively low-wage and primarily part-time. Focusing on employees retail jobs helps

expands the work-life field by providing insight into the work-life challenges and experiences of

workers in a growth sector of the economy that is characterized by low wages and precarious

scheduling practices (Presser, 2003).

Our analyses lend support to the first three study hypotheses, but not the fourth. In

particular, we find that net of individual, family, and other work characteristics, unpredictability and

limited schedule input are related to each of the outcomes, suggesting that precarious work

schedules are an important source of time-based and strain-based work-to-family conflict. We

further find that, as hypothesized, scheduling input does not significantly reduce the association

between unpredictable schedules and interferences with nonwork activities (such as planning social

activities, a home-cooked meal with family, or making doctor’s appointments). Contrary to our

hypothesis, however, we also do not find that input into scheduling compensates for an

unpredictable schedule in terms of reducing workers’ perceived stress.


Our central findings are robust to several model specifications. In particular, the general

pattern of results holds, net of our control measures which take into account personal and family

vulnerabilities among the sample, and net of supervisor support which has been shown to affect a

host of work-family related outcomes in previous research (Frye & Breaugh, 2004; Warren &

Johnson, 1995). The results for schedule unpredictability are robust to an even more conservative

test of the hypotheses, that is, when the Wave 1 measure of the dependent variable is controlled for

in the models. Our measure of schedule input proved less robust in these two-wave models,

maintaining its significant relationship to only one of the dependent variables, perceived stress. In

results not shown, we also find that the results are replicated when other work-related factors such

as degree of job control and job autonomy are included in the regressions. Moreover, although

somewhat less stable across model specifications, we find the same pattern of results when a lagged

model is assessed; that is, when Wave 1 measures of schedule unpredictability and lack of input

(together with Wave 1 control variables) are used to predict Wave 2 outcome measures (also not

shown, but available upon request of author). The robustness of our results is especially noteworthy

given the study’s limited power due to a relatively small sample and the large number of control

variables included in the models.

These analyses represent an initial attempt to explore relationships between precarious

employment schedules and individual and family life. We are currently subjecting these models to

further scrutiny, considering other factors that may explain the relationships observed among the

cross-sectional survey data, and considering conditions that may moderate the relationships. For

example, we are examining further the mostly null effects for household composition and spousal

employment, and considering alternative methods of specifying relationship status and household

circumstances, and revising our models to reflect the possibility that particular household

arrangements may interact with schedule predictability and input. The survey includes measures on


the work schedules of partners and other adults in the household as well as including significant

information about the work schedules of respondents’ second job, for those holding more than one

job. These variables may prove important to identifying distinct household-employment

vulnerabilities that interact with precarious schedules in meaningful ways.

In addition, we are developing additional measures of employment precariousness from the

multiple data sources available to us through the broader workplace intervention on which the

current study is based. One source of data is a separate manager telephone survey that collects data

on management hiring and scheduling practices for each of the 21 stores.8 A second source of data

is actual employee schedules and payroll hour reports from each of the 21 stores. Because we have

more than nine months of weekly store schedules for each employee at each store, as well as

information indicating the actual dates that schedules were posted, we are able to calculate an

unpredictability measure that reflects a true measure of advanced scheduling notice (rather than

relying on self-report data). Moreover, because we also have company payroll reports that indicate

the exact days and hours employees worked over this same period, we are able to construct

objective measures of nonstandard schedules (e.g., nonstandard timing), variable schedules

(variation in the days and shifts worked week-to-week), and fluctuating weekly hours. These

objective measures of precarious schedules will allow us to construct analytic models that are less

subject to the endogeneity concerns of our current models that rely on the survey data.

We have attempted to explore the relationships of interest within the constraints imposed by

limited power. Although several model specifications were considered in an effort to limit the

biases inherent in correlational research designs, we are unable to control for omitted variables or

satisfactorily address reverse causality concerns. Thus, we remain cautious regarding our

8 The manager survey was administered to approximately 150 store managers employed by this retail firm. Because the employee survey on which the current study is based was restricted to the 21 Midwestern stores, further analyses with these employee survey data may benefit from linking relevant information on labor flexibility practices from the 21 corresponding manager surveys to employee survey data.


interpretation of the observed relationships, and it is important to consider our results in light of

several endogeneity concerns. For example, although our conceptual model is concerned with

pathways emanating from work to other domains, our Wave 2 cross-sectional results do not rule out

the possibility that our “outcomes” are themselves contributing to precarious work schedules. For

example, respondents who are under significant stress and whose households operate with limited

predictability in routines may experience less predictable schedules because their nonwork lives

preclude them from holding a more regular schedule. Moreover, higher levels of perceived stress

(wherever it originates) may influence the level of input into scheduling that managers offer

employees. Indeed, to the extent that managers consider employee input into schedules a worker

reward to be earned (Henly et al. 2006), employees who are under stress or otherwise not

performing up to manager standards may be given fewer opportunities to provide input into their

schedules. We respond to these alternative interpretation issues by including Wave 1 indicators of

each of the dependent variables in the second set of analyses, but of course these controls do not

eliminate the possibility that Wave 2 outcomes operate on Wave 2 predictors. In future work, we

will further exploit the longitudinal data to examine the hypothesized relationships, constructing

measures of change between Waves 1 and 2 and incorporating store-level data on changes in work

schedules over the course of the intervening period between the two survey waves. Although

longitudinal correlational data still preclude true causal effects to be observed, it is possible to

further examine lagged associations and make use of repeated measures to more carefully examine

correlated changes over time.

Overall, the results reported in this paper suggest that unpredictable schedules and schedules

that offer employees limited input into their work hours have negative consequences for individual

and family life. Precarious scheduling practices are not isolated within a few organizations but

rather reflect growing national and international trends that have profound implications for


individual and family security (Kalleberg, 2009). Thus, our models for understanding work-life and

work-family linkages must take into account the increasing precariousness of employment

conditions and the changing nature of employer-worker relationships if they are to be useful

explanatory tools.


Table 1 Sample Descriptives

Wave 1(n=123)

Wave 2(n=120)

RaceBlack (%)White (%)Latina or Hispanic (%)

12.3077.059.02

12.5080.006.67

Child under 18 in home (%) 20.3 22.00

Age: mean (sd)% under 35% over 55

47.72 (14.67)21.9537.40

48.05 (13.84)19.1735.00

More than high school education (%) 43.09 42.50

Rates health as excellent or very good (%) 64.23 55.83Family Structure

Not married or cohabitating (%)

Married or cohabiting, partner does not have a full-time job (either not working or working part-time) (%)Married or cohabiting, partner has a full-time job (%)

40.65

17.0742.28

41.67

15.8342.50

Hourly wage: mean (sd) $9.36 (2.22) $9.20 (2.18)

Additional job (%) 38.21 40.00


Table 2: Descriptives of Independent and Dependent VariablesWave 1

(n=123)

Wave 2

(n=120)

Independent variables:

Unpredictable work schedule

mean (sd)

2.33

(0.64)

2.31

(0.64)

Input over schedule

mean (sd)

2.11

(0.84)

2.22

(0.80)

Emotional support from supervisor

mean (sd)

1.66

(0.51)

1.94

(0.65)

Dependent variables:

Work-family conflict

mean (sd)

2.25

(0.79)

2.27

(0.82)

Interference with nonwork activities

mean (sd)

1.73

(0.64)

1.70

(0.63)

Perceived stress scale

mean (sd)

2.23

(0.62)

2.26

(0.66)


Table 3: Dependent Variable: General Work-to-Family Conflict, Models 1 – 5, Wave 2 Model 1 Model 2 Model 3 Model 4 Model 5

VARIABLESvulnerability (controls)

1 + unpredictability

1 + lack of input 1 + 2 + 3

1 + 2 + 3 + supervisor emotional support

Health is good, fair, or poor 0.2883 0.2827+ 0.1995 0.2186 0.2221

(0.1786) (0.1600) (0.1770) (0.1631) (0.1558)Age -0.0059 -0.0088* -0.0048 -0.0074* -0.0095**

(0.0039) (0.0035) (0.0034) (0.0032) (0.0033)Race (white vs others) 0.3332 0.2483 0.2365 0.1907 0.2405

(0.1951) (0.1888) (0.1888) (0.1881) (0.1937)Education [more than HS (0) vs no more than HS (1)] -0.1958 -0.1760 -0.2334 -0.2062 -0.1574

(0.1515) (0.1393) (0.1467) (0.1337) (0.1244)Child under 18 -0.4664** -0.3819* -0.2982 -0.2653 -0.2897+

(0.1612) (0.1549) (0.1903) (0.1746) (0.1546)Not Married or Cohabiting (vs M/C and spouse is full-time employed) -0.0239 -0.0921 -0.0700 -0.1156 -0.1266

(0.1384) (0.1475) (0.1439) (0.1466) (0.1523)Married or Cohabiting and spouse is unemployed or part-time employed -0.0216 -0.0501 0.0394 -0.0052 -0.0383

(0.1826) (0.1781) (0.1737) (0.1761) (0.1608)

Respondent has other job 0.5668** 0.6186** 0.7092** 0.7199** 0.6994**

(0.1585) (0.1640) (0.1539) (0.1572) (0.1633)

Part-time at retail job -0.4670* -0.4476* -0.5001* -0.4710* -0.4594*(0.1707) (0.1717) (0.1794) (0.1783) (0.1774)

Schedule unpredictability 0.3702** 0.3022** 0.2263*

(0.0969) (0.0965) (0.0988)Lack of input 0.3211** 0.2490* 0.1834+

(0.1051) (0.0967) (0.0972)

Supervisor emotional support 0.2378+

(0.1160)

Observations 120 119 120 119 119R-squared 0.273 0.362 0.349 0.405 0.430Beta coefficients are unstandardized; Robust standard errors in parentheses. **p<0.01, *p<0.05, +p<0.1. Note: All models adjusted for intervention condition


Table 4: Dependent Variable: Interferences with Nonwork Activities, Models 1 – 5, Wave 2 Model 1 Model 2 Model 3 Model 4 Model 5

VARIABLESvulnerability (controls)

1 + unpredictability

1 + lack of input 1 + 2 + 3


Health is good, fair, or poor 0.2735* 0.2206* 0.1951+ 0.1638+ 0.1661+

(0.1183) (0.0927) (0.1119) (0.0934) (0.0957)Age 0.0009 -0.0010 0.0020 0.0002 -0.0011

(0.0038) (0.0033) (0.0034) (0.0032) (0.0032)

Race (white vs others) 0.0627 -0.0365 -0.0225 -0.0876 -0.0557(0.1701) (0.1772) (0.1610) (0.1700) (0.1759)

Education [more than HS (0) vs no more than HS (1)] -0.0714 -0.0852 -0.1045 -0.1120 -0.0806

(0.0941) (0.0877) (0.0992) (0.0877) (0.0920)Child under 18 -0.2747 -0.2180 -0.1263 -0.1146 -0.1302

(0.1630) (0.1538) (0.1522) (0.1474) (0.1433)Not Married or Cohabiting (vs M/C and spouse is full-time employed) 0.0894 0.0298 0.0487 0.0090 0.0019

(0.1072) (0.1181) (0.1051) (0.1115) (0.1114)Married or Cohabiting and spouse is unemployed or part-time employed -0.0187 0.0519 0.0351 0.0917 0.0704

(0.1448) (0.1430) (0.1416) (0.1415) (0.1333)Respondent has other job -0.1627 -0.1191 -0.0371 -0.0294 -0.0425

(0.1315) (0.1353) (0.1176) (0.1244) (0.1249)

Part-time at retail job 0.0153 -0.0412 -0.0140 -0.0619 -0.0545(0.1575) (0.1395) (0.1463) (0.1359) (0.1407)

Schedule unpredictability 0.3420** 0.2817** 0.2330**

(0.0639) (0.0579) (0.0812)Lack of input 0.2833* 0.2208* 0.1787+

(0.1056) (0.0966) (0.1019)Supervisor emotional support 0.1527

(0.1080)

Observations 120 119 120 119 119R-squared 0.126 0.255 0.226 0.312 0.329

Beta coefficients are unstandardized; Robust standard errors in parentheses. **p<0.01, *p<0.05, +p<0.1. Note: All models adjusted for intervention condition


Table 5: Dependent Variable: Perceived Stress, Models 1 – 5, Wave 2 Model 1 Model 2 Model 3 Model 4 Model 5

VARIABLESvulnerability

(controls)1 +

unpredictability1 + lack of

input 1 + 2 + 3


Health is good, fair, or poor 0.3486+ 0.3297* 0.2930+ 0.2914+ 0.2914+

(0.1707) (0.1519) (0.1622) (0.1502) (0.1518)Age -0.0007 -0.0025 0.0001 -0.0017 -0.0017

(0.0033) (0.0032) (0.0029) (0.0029) (0.0029)Race (white vs others) 0.0882 0.0209 0.0277 -0.0136 -0.0126

(0.1755) (0.1792) (0.1593) (0.1688) (0.1724)Education [more than HS (0) vs no more than HS (1)] -0.2467* -0.2426* -0.2702* -0.2606* -0.2597*

(0.1166) (0.1122) (0.1056) (0.1011) (0.1016)Child under 18 -0.0822 -0.0284 0.0231 0.0414 0.0409

(0.1169) (0.1110) (0.1309) (0.1245) (0.1236)Not Married or Cohabiting (vs M/C and spouse is full-time employed) -0.1821 -0.2299+ -0.2110+ -0.2440+ -0.2442+

(0.1201) (0.1249) (0.1170) (0.1213) (0.1212)Married or Cohabiting and spouse is unemployed or part-time employed -0.0833 -0.0734 -0.0451 -0.0466 -0.0472

(0.1380) (0.1452) (0.1549) (0.1620) (0.1642)Respondent has other job 0.2150 0.2508+ 0.3041+ 0.3113+ 0.3109+

(0.1373) (0.1414) (0.1513) (0.1516) (0.1531)Part-time at retail job -0.1202 -0.1296 -0.1410 -0.1436 -0.1433

(0.1402) (0.1402) (0.1399) (0.1413) (0.1427)

Schedule unpredictability 0.2649** 0.2242** 0.2227*

(0.0810) (0.0771) (0.0882)Lack of input 0.2010* 0.1490 0.1477


(0.1085)

Observations 120 119 120 119 119R-squared 0.165 0.232 0.211 0.256 0.256

Beta coefficients are unstandardized; Robust standard errors in parentheses. **p<0.01, *p<0.05, +p<0.1. Note: All models adjusted for intervention condition


Table 6: Wave 2 Regressions Controlling for Wave 1 Outcome Levels, All Dependent Variables

General Work-to-Family Conflict Model 2b Model 3b Model 4b Model 5bWave 1 General work-to-family conflict 0.3949** 0.4429** 0.3884** 0.3752**

(0.0948) (0.0956) (0.0946) (0.0941)Schedule unpredictability 0.2950** 0.2803** 0.2118*

(0.0809) (0.0823) (0.0779)Lack of input 0.1263 0.0472 0.0104

(0.1162) (0.0947) (0.1092)Supervisor emotional support 0.1799+

(0.0955)

Observations 92 92 92 92R-squared 0.583 0.534 0.585 0.602

Interferences with Nonwork Activities Model 2b Model 3b Model 4b Model 5bWave 1 Interferences with Nonwork Activities 0.4143** 0.4833** 0.4323** 0.4243**

(0.0895) (0.0971) (0.0929) (0.0980)Schedule unpredictability 0.2757** 0.2855** 0.2558*

(0.0718) (0.0730) (0.0939)Lack of input 0.0319 -0.0411 -0.0540


(0.1189)

Observations 92 92 92 92R-squared 0.431 0.359 0.433 0.437

Perceived Stress Model 2b Model 3b Model 4b Model 5bWave 1 Perceived stress 0.6769** 0.7067** 0.6664** 0.6887**

(0.0826) (0.0994) (0.0933) (0.0930)Schedule unpredictability 0.2083** 0.1753* 0.2090**

(0.0720) (0.0670) (0.0732)Lack of input 0.1531* 0.1023 0.1212*

(0.0643) (0.0655) (0.0580)Supervisor emotional support -0.0965

(0.0937)

Observations 92 92 92 92R-squared 0.611 0.595 0.621 0.627Beta coefficients are unstandardized; Robust standard errors in parentheses**p<0.01, *p<0.05, +p<0.1Note: All regressions adjusted for experimental condition, time between survey waves, health status, age, race, education, kids, partner job, respondent other job, and part-time retail job. Sample includes only those employees who were working at the store at both waves of the survey.


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Appendix AIndices from Employee Survey

Schedule Unpredictability

Schedule unpredictability is constructed by combining responses to an item about the number of days of advance notice respondents receive regarding their work schedule and respondents’ scores on an index of hours/timing predictability.

The measure of advance notice asks: “Usually, how many days in advance do you know your schedule?” Answers were provided by respondents in days, and then recorded by interviewer as “0-3 days”, “4-7 days”, “8-14 days”, “15-21 days”, “more than 21 days”. These responses were collapsed into a dichotomous indicator, one week or less versus more than one week.

The measure of hours/timing predictability is based on a three-item index (alpha = 0.65) that captures the extent to which respondents agreed or disagreed that they can anticipate and/or count on the number and timing of hours that they will be working week-to-week. The respondents are asked how much they agree (strongly agree, agree, disagree, or strongly disagree?) with the following three statements:

You can easily anticipate what days and times you’ll be working week-to-week. Most weeks, you can count on getting the number of hours you want. Most weeks, you can count on working the days and shifts you want.

Based on these two measures (advance schedule notice and hours/timing predictability), a measure of schedule unpredictability was constructed that has three possible values including (1) more than one week notice of work schedule and a score in the top half of the median split of the 3-item index of hours/timing predictability, (2) one week or less notice of work schedule and a score in the top half of the median split of the 3-item index of hours/timing predictability, and (3) one week or less notice of work schedule and in the bottom half of the median split of the 3-item index of hours/timing predictability.

Lack of Scheduling Input

ALPHA = 0.82For each of the following, please tell me whether you feel you have a lot of input, some input, a little input, or no input at all.

The days you have off each week. The days you work each week. When you begin and end each work day. The total number of hours you work each week.

General Work-to-Family Conflict

ALPHA = 0.87


Would you say [this] happens all of the time, most of the time, some of the time, hardly any of the time, or none of the time?

The demands of your work interfere with you personal or family time. Your work schedule makes it difficult to fulfill your personal or family responsibilities. Things at home do not get done because of the demands of your job. Your work produces strain that makes it difficult to fulfill personal or family duties. You have to make changes to your plans due to work related duties.

Note: Adapted from Netermeyer, R.G., Boles, J.S., & McMurrian, R. (1996). Development and Validation of Work-Family Conflict and Family-Work Conflict Scales. Journal of Applied Psychology, 81(4) 400-410.

Work Interferences in Nonwork Activities

ALPHA = 0.84Given how far in advance you generally know your upcoming work schedule, please tell me whether you feel you have more than enough time, just enough time, or not enough time to organize each of the following.

Scheduling a doctor’s appointment for yourself, a child or someone else. Planning activities with your friends. Planning a family outing. Arranging to cook a meal at home.

Note: We developed this scale by “unpacking” a single question that Swanberg et al. (2008) used to assess the extent to which workers in a drug-store chain reported sufficient time to “plan personal, family, or other responsibilities.”

Perceived Stress

ALPHA = 0.82 For each question, please tell me how often you have felt or thought this way over the course of the last month (very often, fairly often, sometimes, almost never, or never).

How often have you been upset because of something that happened unexpectedly? How often have you felt that you were unable to control the important things in your life? How often have you felt nervous and stressed? How often have you felt confident about your ability to handle your personal problems? How often have you found that you could not cope with all of the things that you had to do? How often have you felt that you were on top of things? How often have you been able to control the way that you spend your time? How often have you felt difficulties were piling up so high that you could not overcome

them?

Note: Adapted from the 14-item Perceived Stress Scale: Cohen, S., Kamarck, T., & Mermelstein, R. (1983). A global measure of perceived stress. Journal of Health and Social Behavior, 24, 385-396.

Supervisor Support (emotional support subscale)

ALPHA = 0.83


With each of the following statements, would you strongly agree, agree, disagree, or strongly disagree?

My supervisor is willing to listen to my problems in juggling work and nonwork life. My supervisor takes the time to learn about my personal needs. My supervisor makes me feel comfortable talking to him/her about my conflicts between

work and nonwork. My supervisor and I can talk effectively to solve conflicts between work and nonwork issues.

Note: Hammer, L. B. Kossek, E. E., Yragui, N. L., Bodner, T. E. & Hanson, G. C. (in press). Development and Validation of a Multidimensional Measure of Family Supportive Supervisor Behaviors (FSSB). Journal of Applied Psychology.

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