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Changing workplace and inequality in work-family policies

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Changing workplace and inequality in work-family policies

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Changing Workplace and Inequality in Work-family Policies

by

Eunjeong Paek

A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
SOCIOLOGY

August 2022

Copyright 2022 Eunjeong Paek
ii

ACKNOWLEDGEMENTS
I am grateful to those who helped me to continue my academic career and finish this
dissertation. Their support played an indispensable role in my conduct of this research and in my
life as a foreigner in the United States.
I thank Jennifer Hook for advising me on my dissertation in many ways. As my advisor
for the past seven years, she has read most of my academic works. Her steady feedback helped
me to improve my ideas and overcome challenges. She understood my frustrations and shared
my joy in my achievements. Her mentoring helped me to become an independent researcher and
to feel less vulnerable to uncertainty in my career. Working with her closely for many years told
me a lot about how worthy academic mentoring and research can be. She also generously offered
the cleaned O*Net data for Chapter 1 of my dissertation and allowed me to use her office when I
handled the restricted data for Chapter 2.
I am also grateful to Emily Smith-Greenaway for supporting my work. As a committee
member, she gave me extensive and insightful feedback on how to frame and defend my
arguments in my empirical paper and dissertation. She also helped me to improve my job market
materials and prepare presentations and interviews. Despite the difference between her research
interests and mine, my work benefited a lot from her expertise and advice.
I am also thankful to Dan Schrage for his support. He kindly agreed to serve as my
dissertation committee member when he had just started his tenure-tracked position at the
university. When I was a grader in his method classes, he did not spare any effort to answer my
questions about methodologies after class. He also gave me invaluable guidance for my
dissertation and other projects, often introducing to me sophisticated methodological tools.
iii

I also thank several other faculty members. Lynne Casper helped me to access the
restricted data and made helpful suggestions for my projects. Tim Bibilarz also gave me
insightful feedback on this dissertation. Michalle Mor Barak served as my external committee
member and gave me feedback for this dissertation from an outsider’s perspective. James Polk
patiently helped me to improve my English writing skills over the past few years.
I owe my academic career to my long-time friendship with Myungji Lee. Without her, I
would not have been determined to move abroad to pursue a Ph.D. degree when I first found
empirical research fascinating in an undergraduate class. Had she not encouraged me to continue
my academic career with her and had she not shared her intellectual ambitions with me for many
years, I would have felt much lonelier in this long journey.
I also appreciate Ariane Chen, Jennifer Candipan, Alli Coritz, Kushan Dasgupta, Minwoo
Jung, Mary Ippolito, Thomas Quinn, Woorim Moon, and Taehee Kim. Without my good
memories with them, earning a Ph.D. degree or living in a foreign country would have been
much more stressful for me.
I also acknowledge my family’s unconditional love and support. Although they hardly
understood how it is like being in academia, they firmly believed that I have the strength to
pursue the career that I had long dreamed of. My parents, Kisook Lim and Younggi Paek, sent
me support and gave me courage across the continents. They also tried hard to comfort me, often
reminding me of the truth that there are always other opportunities and that it is okay to fail or
even to give up. My brother, Kyungdal Paek, had been a good listener and cheerleader. He also
did an excellent job in keeping me informed about things in Seoul, from baseball highlights to
family events that I had missed.
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For the past few years, I have been asking myself if it is still worthwhile for me to
continue pursuing a career in academia. One important reason why I hesitate to quit this career is
that these people have long given me the strength to explore new ideas, improve my work, and
overcome cyclical frustrations and countless rejections. I thank all these people for letting me
have such an invaluable experience.

v

TABLE OF CONTENTS

Acknowledgements............................................................................................................... ii
List of Tables........................................................................................................................ iv
List of Figures....................................................................................................................... v
Abstract ................................................................................................................................ vi
Introduction .......................................................................................................................... 1
Chapter 1: Workplace Computerization and Inequality in Schedule Control...................... 3
Chapter 2: Union Coverage, Work Contexts, and Family-friendly Policies........................ 40
Chapter 3: Trends in access to schedule control in 11 European Countries, 1997-2015..... 72
Conclusion ........................................................................................................................... 95
References ........................................................................................................................... 97
Appendices........................................................................................................................... 120
Appendix A. Trends of Email Use by Occupational and Educational
Groups…………………………………………………………………………….. 110
Appendix B. Predicted Probability of Schedule Control for Email Use ……...….. 111
Appendix C. The Moderating Effect of Computerization on the Association
between Educational Attainment and Schedule Control ………………………..... 112
Appendix D. Heterogeneity in the Marginal Effect of College Degree on
Schedule Control across Levels of Email Use (CPS 1997-2004) ...……………… 113
Appendix E. Heterogeneity in the Marginal Effect of College Degree on
Schedule Control across Levels of Email Use (ATUS 2017-2018) ...……………. 114
Appendix F. Heterogeneity in the Marginal Effect of College Degree on
Schedule Control across Levels of Computerization, Adjusted for Working from
Home ……………………………………………………………………………… 115
Appendix G. The Effects of Union Coverage on Family-friendly Policies, Fixed
effect Models by Gender …………………………………………………………. 116
Appendix H. Sample Size by Country and Year ………………………………… 117
Appendix I. Detailed Decomposition of Trends in Access to Schedule Control,
Full-time Workers Only 1997-2015 ……………………………………………… 118

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LIST OF TABLES
Table 1. Means and Standard Deviations of the Variables, by Education............................. 36
Table 2. Main Effects of Educational Attainment and Computerization on Schedule
Control (CPS 1991-2004) …………………………………………..………………............ 38
Table 3. Main Effects of Educational Attainment and Computerization on Schedule
Control (ATUS 2017-2018) ................................................................................................... 39
Table 4. Effects of Union on Family-friendly Policies by Work Contexts, Summary of
Prediction…………………………………………………………………………………………... 64
Table 5. Descriptive Statistics................................................................................................ 65
Table 6. Effects of Union Coverage on Family-friendly Policies, Fixed-effect Models ...... 67
Table 7. Predictions about Change in Access to Schedule Control ….................................. 90
Table 8. Changes in Schedule Control, Educational Attainment and Gender, 1997-2015.... 92
Table 9. Decomposition of Trends in Access to Schedule Control, 1997-2015.................... 93
Table 10. Detailed Decomposition of Trends in Access to Schedule Control, 1997-2015.... 94

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LIST OF FIGURES

Figure 1. Trends of Computerization by Occupational and Educational Groups (1991-
2004)....................................................................................................................................... 31
Figure 2. Trends of Schedule Control by Occupational and Educational Groups (1991-
2018)………………………………………………………………………………………... 32
Figure 3. Predicted Probability of Schedule Control for Computerization ........................... 33
Figure 4. Heterogeneity in the Marginal Effect of College Degree on Schedule Control
across Levels of Computerization (CPS 1991-2004) ..…………………………………….. 34
Figure 5. Heterogeneity in the Marginal Effect of College Degree on Schedule Control
across Levels of Computerization (ATUS 2017-2018) ...………………………………….. 35
Figure 6. Trends of Access to Family-friendly Policies (2000-2017) ..…………................. 68
Figure 7. The Moderating Effects of Public Sector Organizations on the Relationship
between Union Coverage and Family-friendly Policies ...…………………......................... 69
Figure 8. The moderating effects of occupational gender composition on the relationship
between union coverage and family-friendly policies ...………………..…...……………... 70
Figure 9. The moderating effects of the size of establishment on the relationship between
union coverage and family-friendly policies ...……………………………...……………... 71
Figure 10. Trends in access to schedule control, 1997-2015 ...………….............................. 91

viii

ABSTRACT
Employees have unequal access to family-friendly policies, such as parental leave and
schedule control, but we know little about the role of changing labor markets in shaping access
to family-friendly policies. In this dissertation, I investigate three facets of the diverging
workplace and their impacts on inequality in access to family-friendly benefits: (1) the role of
computerization in access to schedule control (2) the effects of unions on the availability of
family-friendly policies, and (3) the uneven growth of schedule control in Europe. I apply
quantitative methods, including fixed-effect models, instrumental variable estimation, and
decomposition analysis.
First, I examine how computerization increases access to schedule control and amplifies
social class disparities in schedule control. Combining the Current Population Survey and the
American Time Use Survey with time-varying occupation-level information, I find that
computerization increases access to schedule control and occupational computerization
corresponds with growing education-based inequalities in schedule control. This suggests that
computerization has led to rising inequality in work experiences with wide-ranging implications
for the intersection of work, family, and well-being.
Second, I explore whether labor unions increase access to several family-friendly policies
and how work contexts, occupational gender composition, moderate the effect of unions on these
policies. I theorize the heterogeneity of unions’ effects on family-friendly policies to better
understand inconsistent findings about the role of unions in those policies. Combining the
National Longitudinal Survey of Youth 97 with state-level characteristics, I find that unions
increase access to paid parental leave and sick days/vacations but decrease schedule control.
Unions in female-dominated occupations tend to have greater impacts on family-friendly
ix

policies. This shows that gendered workplace structures can reshape the role of unions in the
work-family interface.
Third, I decompose trends in schedule control by educational attainment and gender
across 11 European countries. I use the International Social Survey Programme to document how
aggregate changes may be explained by changes in workers’ characteristics such as educational
attainment versus changes in the association between these characteristics and schedule control
from 1997 and to 2015. These findings can help us to better understand the implications of job
polarization and gender revolution at work for the work-family interface.
Broadly, my dissertation can improve our understanding of the mechanisms underlying
the links between labor market characteristics and the availability of family-friendly policies, the
impacts of technological advances and organized labor on the work-family interface, and the
processes of the diffusion of family-friendly policies. These findings can help inform policies
related to the work-family interface, gender, and well-being.

1

INTRODUCTION
Many workplaces have not addressed workers’ increasing challenges in the work-family
interface. As more women participate in the labor market and traditional male-breadwinning
models become unsustainable in an increasing number of households, workers, especially
employed parents, engage in a substantial portion of housework and childcare in households
(Bianchi et al. 2012; Lesthaeghe 2014). However, employers still tend to expect workers to be
able to be exclusively committed to work and largely free from other roles in family (Kelly,
Ammons, and Moen 2010; Sharone 2004). Such outdated and unrealistic expectations have led
many workers, especially mothers who have disproportionate family responsibilities, to
experience chronic difficulties with arranging work and family life and to compromise their
career (Blair-Loy 2009a; Stone 2007).
Family-friendly policies, including family leave, schedule control, and paid sick days,
play an important role in helping workers to accommodate family responsibilities in their career.
However, the policies are not available to every worker. In the United States, access to most
family-friendly policies is not given as the legally protected right but largely restricted to public-
sector or high-paid workers (Gerstel and Clawson 2015; Gornick and Meyers 2003; Kelly and
Kalev 2006). Although most European countries provide workers with more generous paid
leaves and childcare support through national policy (Gornick and Meyers 2003), some policy,
such as schedule control, still tends to be unavailable among marginalized workers, too (Lyness
et al. 2012).
In this dissertation, I bridge studies of stratification, work, family and gender to deepen
our limited understanding of the role of changing labor markets in the availability of family-
friendly policies. Combining changing occupational and state-level information with the
2

individual-level data, I investigate three facets of the changing labor market and its impact on
class and gender inequality in the work-family interface: (1) the impact of technological
advances on schedule control, (2) the implications of the decline of labor unions for family-
friendly policies, and (3) the salience of demographic shifts in the labor market for increases in
schedule control. I apply quantitative methods, including fixed-effect models and decomposition
analysis. Investigating the relationship between the changing workplace and family-friendly
supportiveness illuminates how class- and gender-based disparities in the work-family interface
are reproduced.
Since the onset of Covid 19, long-standing public debates on expanding national family
polices have been reinvigorated in the United States as the pandemic has devastatingly revealed
the vulnerability of the labor force population with little access to family-friendly policies
(Miller 2021). Many workers who did not have access to family-friendly policies, especially
marginalized workers, distressed from abruptly increased caregiving responsibilities and were
not able to keep up their career because of care-facility lockdown and health concerns
(Yavorsky, Qian, and Sargent 2021). Although my dissertation focuses on the period before the
pandemic, it can provide important implications for inequality in access to family-friendly
policies throughout the pandemic and thereafter.

3

Chapter 1. Workplace Computerization and Inequality in Schedule Control

4

Workplace Computerization and Inequality in Schedule Control
ABSTRACT
I investigate how computerization increases access to schedule control and widens the class
disparity in access. I combine time-varying measurements of occupational-level computerization
with individual-level data from the Current Population Survey (1991–2004) and the American
Time Use Survey (2018). Results confirm that computerization is positively associated with
schedule control, but this association is not robust to the inclusion of other aspects of
occupations. The positive association between educational attainment and schedule control is
greater among employees in highly computerized occupations. Computerization has led to rising
inequality in working conditions with wide-ranging implications for work, family, and well-
being.

5

Family-friendly policies are crucial for employed parents reconciling work and family
responsibilities (Doran et al. 2020; Erin L. Kelly et al. 2014; Petts and Knoester 2020; Waldfogel
1999b). In particular, studies of the work-family interface suggest that schedule control, which
allows employees to choose when to start and finish their work, is a key family-friendly practice
that helps employees arrange work and family demands (Erin L. Kelly et al. 2014; Voydanoff
2004b). Studies have shown that schedule control can reduce work-family conflict and promote
employee well-being (Badawy and Schieman 2021; Erin L. Kelly et al. 2014; Voydanoff 2004b).
Previous studies offer two key findings about the diffusion of family-friendly workplace
policies, such as schedule control. First, the availability of family-friendly workplace policies is
conditioned by organizational and broader social contexts. Studies of organizations illuminate
that organizational contexts, such as female representation in managerial positions and high-
commitment work systems, can affect the provision of family-friendly workplace policies
(Guthrie and Roth 1999; Mun and Brinton 2015; Osterman 1995). Other studies highlight the
role of broader contexts, such as legal sanctions and labor market conditions, in shaping the
diffusion of family-friendly workplace policies (Berg et al. 2004; Kelly and Dobbin 1999;
Lyness et al. 2012).
Second, family-friendly policies are unevenly distributed across social class (Gerstel and
Clawson 2015; Golden 2009; Petts, Knoester, and Li 2020; Schieman, Melissa A. Milkie, and
Glavin 2009). In particular, less educated and lower-income workers are the least likely to have
autonomy over their work schedules (Gerstel and Clawson 2015; Golden 2009; Schieman,
Melissa A. Milkie, et al. 2009)—even as they have fewer resources to offset family demands
(Cohen 1998; Gerstel and Clawson 2015)—in part because less educated and lower-income
workers tend to have lower access to highly autonomous jobs and fewer competitive advantages
6

in workplace benefits (Goldin and Katz 2009; Heckman, Stixrud, and Urzua 2006; Kelly and
Kalev 2006). Although previous studies illuminate the class division, little is known about what
work contexts can alter the class disparities in access to family-friendly policies, including
schedule control.
In this paper, I argue that computerization is a key factor that increases schedule control.
Many work-family studies hypothesize that the growth of schedule control is attributable to
increasing access to technology (Glass and Noonan 2016; Hill et al. 2008; Kossek, Lautsch, and
Eaton 2006), but few studies have documented it empirically (see Blair-Loy (2009) and Chesley,
Moen, and Shore (2003) for qualitative studies in select occupations). This gap deserves
attention in part because technological advance, especially computerization, has dramatically
affected workers across social classes (Aoyama and Castells 2002; Friedberg 2003) and can play
a crucial role in schedule control by allowing workers to work outside fixed place and time
(Blair-Loy 2009b; Ticona 2015), altering job tasks (Blair-Loy 2009b; Hunter and Lafkas 2003),
and changing organizational practice (Wellman et al. 1996).
In addition, drawing on studies of labor economics, economic sociology, and unions, I
argue that computerization can be a key work context that widens class disparities in schedule
control in several ways. First, according to theories of skill-biased technological change,
computerization can complement higher educated workers’ skills, but devalue and replace lower-
educated workers’ ones (Acemoglu 2002; Autor, Levy, and Murnane 2003). Second,
computerization can amplify management’s control over lower educated workers’ work process
(Appelbaum and Albin 1989; Brown and Korczynski 2010; Griesbach et al. 2019; Prechel 1994;
Sewell 1998; Snyder 2016). Third, computerization can foment the precarity of lower educated
workers in the employment relationship (Kristal 2019; Uzzi and Barsness 1998).
7

The primary contribution of this study is to provide theoretical explanations and
empirical evidence of the role of computerization in the uneven diffusion of schedule control. I
examine the role of computerization in schedule control by combining time-varying
occupational-level data with individual-level data. I show that computerization is positively
associated with access to schedule control and amplifies the magnitude of the disparity in
schedule control between higher and lower educated workers. The results offer new insights into
how technological advance is implicated in rising social inequality in job quality, and in turn,
work-family conflict and wellbeing.
THEORETICAL FRAMEWORK
Computerization and Schedule Control
In this study, I focus on employee-driven schedule flexibility that indicates employees’
ability to control when to start and finish their work. Although schedule control may increase
work-family conflict in some work contexts (Blair-Loy 2009b; Schieman, Melissa A. Milkie, et
al. 2009), researchers generally agree that schedule control can serve as a job resource that helps
employees to better arrange work and personal life (Erin L. Kelly et al. 2014; Voydanoff 2004b).
In contrast, employer-driven schedule flexibility, measured by the uncertainty of work schedule,
can debilitate employees’ well-being and exacerbate work-family conflict (Henly and Lambert
2014; Schneider and Harknett 2019).
Technological advances in the last three decades have dramatically changed work
conditions. In particular, computer use at work approximately doubled from the mid-1980s to
mid-1990s (from 24% in 1984 to about 51% in 1997) (Friedberg 2003). With the backdrop of the
dramatic diffusion of computers across all occupational sectors of the economy, jobs that require
8

information-processing tasks have become dominant in the post-industrial economy since the
1990s (Aoyama and Castells 2002).
Studies of work-family often consider computerization as a primary source of the rise of
schedule control (Glass and Noonan 2016; Hill et al. 2008; Kossek et al. 2006). Although we do
not have yet satisfactory empirical evidence to directly support this idea, work-family and
organizational scholars offer several good reasons to believe that computerization can increase
schedule control. First, the diffusion of personal computers and other information
communication technology can increase schedule control by extending the physical boundary of
worksite and blurring the temporal border of work (Blair-Loy 2009b; Ticona 2015). Second,
computer-based technology can lessen tasks that involve physical presence and heighten other
tasks, such as sales effort and fast response to clients and colleagues, that obscure the distinction
between work and personal life (Blair-Loy 2009b; Hunter and Lafkas 2003). Third,
computerization can simplify the administrative process of adjusting work schedules by
democratizing communication at the workplace (Wellman et al. 1996) and automatically logging
work schedule.
These possibilities have been outlined in several qualitative studies. Studies have shown
that whereas personal computer and emails can blur the boundary between work and personal
life, these technological advances can allow professional workers to have more autonomy over
their work schedule arrangements (Blair-Loy 2009b; Chesley et al. 2003). Although these studies
have illuminated the role of computerization in schedule control, the findings were limited to
selected professional occupations.
Inequality in Schedule Control
9

Work-family scholars highlight that in the United States, workers in class-advantaged
positions have greater access to family-friendly policies (Gerstel and Clawson 2015; Golden
2009; Petts et al. 2020; Schieman, Melissa A. Milkie, et al. 2009). As national family-friendly
policies are largely unavailable and legal and social support for family-friendly workplaces is
meager in the United States in comparison to other OECD countries (Gornick and Meyers 2003),
access to family-friendly workplace policies remains challenging especially for marginalized
workers (Kelly and Kalev 2006).
In this paper, I focus on educational attainment that has been commonly used as social
class in studies of stratification, skill-biased technological change, and the work-family interface
(Autor et al. 2003; Goldin and Katz 2009; Kristal 2019; Schieman, Melissa A. Milkie, et al.
2009). Highly educated employees tend to have higher levels of schedule control than less
educated and lower income employees for several reasons (Gerstel and Clawson 2015; Golden
2009). First, schooling can increase access to highly autonomous jobs because it predicts
academic abilities (i.e. reading) and non-cognitive abilities (i.e. locus of control) that are often
required by highly autonomous workplaces (Goldin and Katz 2009; Heckman et al. 2006).
Second, besides these qualification-related skills, educational attainment may help to get a highly
autonomous job because employers tend to associate high education with high productivity and
high cultural status (Collins 1979). Third, highly educated workers tend to have advantages in
having family-friendly workplace supportiveness because the provision of family-friendly
policies largely depends on employers and serves as “competitive advantages” to attract and
retain highly selected workers (Kelly 2006; Kelly and Kalev 2006).
Computerization and Inequality in Schedule Control
10

Sociologists and labor economists have highlighted that computerization plays a key role
in job polarization. Along with the increase in market competitions in the global economy and
the decline in unionization, computerization has had a substantial influence on aggravating
income disparities (Acemoglu 2002; Bresnahan, Brynjolfsson, and Hitt 2002; DiMaggio and
Bonikowski 2008; Kristal and Cohen 2017). Similarly, computerization has contributed to
polarizing job quality in a way that replaces low-skilled employees (Bauer and Bender 2004),
weakens labor’s negotiating power (Kristal 2013), and spreads temporary employment (Uzzi and
Barsness 1998). These studies suggest the heterogeneous impact of computerization on working
conditions across social classes.
Expanding these studies, I argue that computerization may amplify the gap in access to
schedule control between higher- and lower-educated workers because of the impact of
computerization on skill complementation, management control, and employment relations.
The proponents of the skill-biased technological change argue that higher-educated workers
benefit most from technological advances, especially computerization, because of the change in
the nature of job tasks (Acemoglu 2002; Autor et al. 2003). Whereas computerization can replace
craft skills with routine tasks and devalue codifiable tasks (Acemoglu 2002; Autor et al. 2003),
computerization has complemented “complex” tasks of higher-educated workers, such as
abstract and problem-solving tasks (Bresnahan et al. 2002; Fernández-Macías and Hurley 2017).
Complementing higher-educated workers’ tasks, computer-related technology may provide a
more extensive technical system that allows higher-educated workers to have greater autonomy
over how to work and when to start and finish work.
In contrast, computerization may not considerably increase schedule control for lower
educated workers because of the role of computerization in advancing the management control.
11

Labor scholars have illustrated that computerization may intensify managers’ bureaucratic and
technical control by way of excessively standardizing the work processes and limiting the control
over an advanced technology for employees in the lower echelon of an organization (Appelbaum
and Albin 1989; Bolton and Houlihan 2010; Prechel 1994; Vallas and Beck 1996). Moreover,
computer-related technology substantially reduces the cost of surveillance through algorithm-
based and remote technology. Although computerized surveillance can constrain the job
autonomy of highly educated workers as well, manual and routine tasks that can be closely
monitored in the work process may be more likely to be subject to severe surveillance
(Appelbaum and Albin 1989; Brown and Korczynski 2010; Burrell and Fourcade 2021;
Griesbach et al. 2019; Sewell 1998; Snyder 2016). These arguments suggest that computerization
may not lead to a remarkable increase in lower educated workers’ autonomy over work schedule.
Furthermore, computerization may have a minuscule effect on schedule control among
lower educated workers because it can reinforce their precarity in labor relations in two key
ways. First, studies of unions have shown that computerization can debilitate lower educated
workers’ bargaining power by substituting routine-tasked jobs that are highly unionized and,
perhaps, expanding employers’ capacity to weaken unions (Kristal 2019). As unions play a key
role in increasing the bargaining power for less paid workers and reducing the disparity in wages
and workplace benefits between highly and less paid workers (Kristal, Cohen, and Navot 2020;
Parolin 2021; Western and Rosenfeld 2011), the decline in union may weaken lower educated
workers’ control over work process. Second, organizational scholars have shown that
computerization can increase the reliance on temporary employment potentially by lowering the
skill requirement for lower-educated workers and enhancing the management control over
production process (Uzzi and Barsness 1998). As temporary employment tends to offer lower
12

pay and fewer benefits (Hipple and Stewart 1996) and is concentrated among less-educated
workers (Polivka 1996), the increase in the temporal contingency of the employment relationship
may further limit lower educated workers’ job autonomy. These arguments suggest that
computerization may increase the precarity of less-educated workers in the employment structure
and thus may constrain their autonomy over work schedule.
RESEARCH STRATEGY
To examine these arguments, I investigate the association between computerization and
schedule control, paying attention to whether the association could be spurious due to
unobserved occupational characteristics. I also examine how computerization alters the
association between educational attainment and access to schedule control. Whereas the above-
mentioned arguments offer theoretical guidance on my research questions, it is beyond the scope
of this study to claim causal relationship and tease apart each mechanism responsible for
generating heterogeneity in the relationship between computerization and schedule control
because of data limitations.
Ideally, the association between computer use at work and schedule control would be
estimated using the data that includes information both for computer use and schedule control.
As there is no nationally representative U.S. data that includes information on both, I combined
occupation-level data on computer use with individual-level data on schedule control. In all data,
I cross-walked the various versions of the occupational code into the occupational code proposed
by Meyer and Osborne (2005).
Individual-level Data and Sample
13

To investigate the relationship across the wide-spread adoption of computers, I created
two sets of nationally representative individual-level data, one for 1991-2004 using the Current
Population Survey Work Schedule Supplements and the other for 2017-2018 using the American
Time Use Survey. The CPS Work Schedule Supplements (1991, 1997, 2001, 2004) are
intermittently part of the May CPSs that provide variables regarding work arrangements. As
these CPS supplements were available only until 2003-2004, I used the ATUS Leave Module
(2017-2018) that provides variables compatible with the CPS for a more recent period. The
ATUS is part of the CPS where the respondents are interviewed after their final CPS interview.
In the ATUS Leave Module, respondents answered questions regarding the access to flexible
work arrangements. Both the CPS and the ATUS were downloaded from the IPUMS-ATUS
Extract Builder (Hofferth, Flood, and Sobek 2018).
The original sample in the CPS Work Schedule Supplements is composed of 527,366
respondents. After limiting the sample to respondents who are aged 15-64, currently employed,
and work for wage and salaries in the private or government sector and dropping respondents
whose occupations do not have any year-occupation information for computerization, the
remaining sample is 216,151 respondents.
As the dependent variable (schedule control) has the largest portion of missing values
(11%) and other variables have only less than 1% missing values except work hour status (about
7%), I chose listwise deletion over multiple imputation for missing values. Although multiple
imputation has advantages in providing imputed values for missing values of independent
variables, it is recommended to exclude the imputed values of a dependent variable in the
analysis (Hippel 2007). After dropping respondents with missing values, the final sample for
1991-2004 is 181,192 respondents.
14

The original sample of the ATUS Leave Module 2017-2018 has 19,816 respondents.
After following the same sample restriction of the CPS, I retained 9,002 respondents.
Individual-level Measures
The dependent variable is schedule control. The variable is measured with a binary
variable asking “Do you have flexible work hours that allow you to vary or make changes in the
times you begin and end work?”
Educational attainment is a key independent variable. The variable was measured with
the highest year of school or degree completed. I created a binary variable for whether a
respondent has a college degree or not. College degree includes both bachelor and associate
degrees.
I controlled a set of observed individual-level demographic and work characteristics that
could be confounded with the association between computerization and schedule control. For
demographic characteristics, I controlled age, sex (male 0, female 1), race (non-Hispanic white,
non-Hispanic Black, Hispanic, other non-Hispanic), parental status (childless 0, parent 1),
presence of preschooler (no preschooler 0, any preschoolers 1), marital status (not married 0,
married 1), and regional division (New England, Middle Atlantic, East North Central, West
North Central, South Atlantic, East South Central, West South Central, Mountain, or Pacific).
I also controlled work characteristics measured by public sector (private 0, public 1),
manufacturing sector (non-manufacturing 0, manufacturing 1), and weekly work hours (part-time
work (1-35 hours), full-time work (36-49 hours), working long hours (50 or more hours)).
Whereas the variables for hours worked in the last week at main job was collected for all
employed persons in 1997-2004 and 2017-2018, the variable for 1991 was collected only from
15

the respondents in the CPS outgoing rotation groups. As this sample restriction leads to
substantial amount of missing values in 1991, I used the usual number of hours per week at main
job for 1997-2004 and the total number of work hours over all jobs during the previous week for
1991. I did not control union membership and hourly paid job because the variables were
included only in the outgoing rotation sample (about 11% of the entire sample).
Occupation-level Data
The CPS Work Schedule Supplements was merged with the occupational-level
information drawn from other data through the occupational code. I drew occupational-level
characteristics on the CPS Education (1989) and Computer and Internet Use (1997, 2001, 2003)
for the earlier period. I used values drawn from the CPS Computer and Internet Use 1989 and
2003 to values for 1991 and 2004, respectively. Although the CPS Computer and Internet Use is
the ongoing survey, the question regarding computer use at work was not included in the survey
after 2003.
For a more recent period, I drew occupation-level computerization on the O*Net (ver
23.0, released in August 2018). The O*Net provides information on occupational characteristics
regarding work abilities and skills related to work activities and contexts. Although the O*Net
provides the data in the previous year, I did not use the O*Net for the 1990-2000s period as the
O*Net Resource Center recommends using the version collected only after 2003 for longitudinal
studies.
Occupation-level Measures
The primary independent and moderating variable is occupation-level computerization. In
this study, I focus on aggregate computerization by measuring computerization as whether a
16

computer is used at work. Using this measure, previous studies have explored the relationships
between computerization and work outcomes (Autor et al. 2003; Cheng, Chauhan, and Chintala
2019; Friedberg 2003; Weinberg 2000). Whereas this approach does not capture various types of
computerized devices, including previous personal digital assistants and current smartphones, it
allows me to explore the implications of broadly defined computerization for the work-family
interface over the multiple decades.
I measured occupational-level computerization with the percentage of computer use at
work in an occupation by using the binary variable asking “do you use a computer (at/for) your
main job?” As this measure does not fully capture different types of computerization, I used the
percentage of employees who use emails at work for 1997-2004 in the supplementary analysis. I
did not use this variable in the main analysis because it has more than 30% missing values in the
CPS 1989. Although occupational-level computerization masks within-occupation
computerization, occupation-level characteristics are widely used as a proxy for job
characteristics and workplace skill because of data limitations (e.g. Horowitz 2018; Liu and
Grusky 2013). The final sample in the merged data for 1991-2004 includes 1,452 year-
occupations and 381 occupations. The mean of the number of observations within an occupation
is about 2,257. About 86 % of the occupations have 4 year-occupation observations. The weight
was applied.
This computerization measurement may be sensitive to the number of respondents in
occupation-year level. For example, there is only one observation in the occupation “marine life
cultivation” in the CPS Computer Use 1997, and that respondent uses a computer at work. This
leads to calculating the level of computerization as 100 percent, masking whether the value
reflects a true level of computerization or is driven by the small size of denominator. To
17

investigate this small denominator issue, I compared the results after dropping 744 individuals
(165 year-occupations, 26 occupations) where the number of observations in an occupation was
less than 10, and the results remained largely similar.
For the more recent period, I used the 5-point-scaled variable drawn on the O*Net, asking
“how important is working with computers to the performance of your current job?” The O*Net
provided this variable for 304 cross-walked occupations, and 272 occupations were merged into
the ATUS Leave Module in the main analysis. I did not use a variable for complexity of
interacting with computer because the question was asked only for occupations that have larger
values than 1 for the importance of interacting with computers at work and the variable is highly
correlated to the variable of importance of interacting with computers (correlation=.92). In the
supplementary analysis, I used the frequency of electronic mail.
Method
Using the combined data (N of individuals=181,192, N of occupations=381), I estimated
occupation-fixed effect linear probability regression models for 1991-2004. Compared to the
OLS regression, occupation-fixed effect models allow us to better estimate the association
between computerization and schedule control by controlling unobserved within-occupation
characteristics. That is, while holding constant time-invariant occupational characteristics, fixed
effects models of the link between (time-varying) computerization and schedule control offers a
robust window into how—net of a host of unmeasured, time-invariant association between
occupational factors and schedule control —changes in computerization contribute to changes in
schedule control. Besides the occupation-fixed effects, I included year-fixed effects with dummy
variables for 1997, 2001, and 2004 (the reference year is 1991) to focus on within-occupation
18

changes that are not associated with the secular trend. About 11% of the variation of schedule
control is associated with within-occupation variation.
I chose fixed-effect probability regression models over fixed-effect logistic regressions
because linear probability regression models offers a more intuitive inference than logistic
regression models with regard to standard error for marginal effect (Angrist and Pischke 2009).
As I presented the primary results with a focus on marginal effects, fixed-effect linear probability
regressions are a more useful analytic strategy in this study than logistic regressions.
As there is only one wave of occupation-level computerization available for 2017-2018,
it is not possible to use occupation-fixed effect models for this period. Thus, I used only linear
probability regression models for the recent period (N of individuals=9,002, N of
occupations=272). Although these models can reveal the relationship between computerization
and schedule control in a more recent year, there are caveats to comparing the results for the
recent period with the results for the 1990-2000s as the recent-period models do not rule out the
potential effect of unobserved within-occupation characteristics on the relationship between
computerization and schedule control.
The main analysis consists of two parts—estimating 1) the association between
computerization and schedule control and 2) the interaction effect of computerization on the
relationship between educational attainment and schedule control. Each analysis was conducted
separately for the earlier and later period.
Following Hainmueller and colleagues (2019)’ recommendation, I investigated the
validity of the assumptions of the conventional linear interaction models in the second part of the
analysis: 1) the marginal effect of college degree on schedule control changes at a consistent rate
19

of computerization, and 2) there are sufficient observations of college graduates and non-college
graduates across all levels of computerization and a considerable variation of college degree at a
level of computerization. As the models do not meet these assumptions, I used the kernel
estimator to examine the non-linearity of the moderating effect of computerization on the
association between educational attainment and schedule control and avoid severe extrapolation.
RESULTS
Descriptive Findings
Before the main analysis, I present descriptive statistics by education. Table 1 shows that
college graduates have greater access to schedule control than non-college graduates in both
1991-2004 and 2017-18. College graduates experience higher levels of computerization
measured by the percentage of employees who use a computer at work and the importance of
interacting with a computer at work.
[Insert Table 1 here]
Figure 1 shows how trends in computerization—measured by the percentage of
employees who use a computer at work in an occupation— vary across occupational groups.
Figure 1 shows that in 1991, managerial and professional occupations already tended to be more
computerized than other occupations and continued to be more computerized during the 1990s
and the 2000s. High computerization, however, was not limited to occupations dominated by
college graduates. Clerical occupations, where about 68% of employees do not have a college
degree (not shown in the result), reported similar levels of computerization to managerial and
professional occupations. Although sales, service and manual occupations reported lower levels
20

of computerization than managerial, clerical, and professional occupations in all years, these
occupations experienced a substantial increase in computerization as well.
[Insert Figure 1 here]
Figure 1 also shows the trend of computerization by educational group. It shows that
college graduates experience higher level of computerization than non-college graduates in all
years. Whereas this gap became wider from the early1990s to the 2000s, both college graduates
and non-college graduates experienced a significant increase in computerization during this
period. Appendix A shows similar trends for occupation-level email use in 1997-2004.
Next, I present the uneven distribution of schedule control by occupational groups. Figure
2 shows that employees in managerial and professional occupations have greater access to
schedule control than employees in most other occupations. Although the gap in access to
schedule control across occupational groups remains salient over time, all occupations obtained
more access to schedule control in 1997-2004 than 1991. Access to schedule control is much
higher in 2017-18 in all occupations in comparison to the previous years.
[Insert Figure 2 here]
Figure 2 also shows that college graduates have more access to schedule control than
non-college graduates in all waves. Whereas this finding is consistent with the previous findings
that people in higher social class have more access to schedule control (Gerstel and Clawson
2015; Golden 2009; Schieman, Melissa A. Milkie, et al. 2009), it further reveals the persistent
gap in schedule control across occupational and educational groups. Although both college
graduates and non-college graduates had increasingly obtained access to schedule control, the
21

gap between college graduates and non-college graduates was not substantially reduced over
time.
Determinants of Schedule Control
Table 2 shows the association between educational attainment and schedule control in
1991-2004. Model 1 shows that the probability of access to schedule control for college
graduates is about 12% higher than for non-college graduates. This positive association between
college degree and schedule control remains largely unchanged after adjusting for demographics
and work characteristics. Model 7 shows that adjusting for occupation-fixed effects reduces the
magnitude of the positive association of college degree and schedule control, suggesting that
college graduates’ greater access to schedule control is partially associated with occupational
characteristics. However, the positive association of college degree and schedule control remains
significant in the occupation-fixed effect models, too.
[Insert Table 2 here]
Table 3 shows similar results for 2017-2018. In Model 2, the probability that college
graduates have schedule control is about 12 % higher than the probability that non-college
graduates have schedule control when demographic and work characteristics are held constant.
Consistent with previous studies (Gerstel and Clawson 2015; Schieman, Melissa A. Milkie, et al.
2009), these findings suggest that educational attainment increases access to schedule control.
[Insert Table 3 here]
In Table 2, Model 3 shows that occupation-level computerization is positively associated
with access to schedule control in 1991-2004. In Model 4, adding the square term of
computerization improves the model fit, suggesting the non-linearity of the positive association
22

between computerization and schedule control. In Figure 3-(a), the model without occupation-
fixed effects shows that computerization does not increase access to schedule control at the 0-
20 % range in 1991-2004, but it substantially increases access to schedule control at a higher
level of computerization.
When occupation-fixed effects are included, however, the association between
computerization and schedule control becomes non-significant. Results from occupation-level
email use are largely similar (Appendix B). That is, when within-occupation time-invariant
characteristics are taken into account, we have little evidence that computerization increases
access to schedule control. This suggests that computerization is associated with an increase in
access to schedule control, but this association is largely related to other occupational
characteristics. In the supplementary analysis (available upon request), I investigated which
occupational characteristics may explain this association. I explored whether time-invariant
occupational characteristics—autonomy, authority, and importance of analytical skills (from the
O*net)—explain the association between computerization and schedule control and found that
this association is not fully explained by these occupational characteristics.
[Insert Figure 3 here]
Table 3 shows that computerization is positively associated with schedule control in
2017-2018. In Model 3 and Model 4, computerization—measured by importance of computer
use at work—has positive associations with access to schedule control, and this positive
association is non-linear. Figure 3-(b) shows that the magnitude of the positive association
between computerization and schedule control is greater at higher levels of computerization in
this period. This finding provides additional evidence that computerization increases access to
23

schedule control. In contrast to the findings for 1991-2004, however, these findings do not rule
out the role of occupation-fixed effects in these associations.
The Impact of Computerization on the Association between Educational Attainment and
Schedule Control
Next, Figure 4 presents how computerization changes the association between college
and schedule control. This association was obtained from the occupation-fixed effect models that
estimate the multiplicative interaction effect between computerization and college degree
presented in Appendix C. In the first graph, the linear interaction model shows that the
association between college degree and schedule control is positive at all levels of
computerization but the confidence interval of this marginal effect overlaps across levels of
computerization, suggesting that computerization does not significantly moderate the association
between college degree and schedule control.
[Insert Figure 4 here]
This conventional linear interaction model requires the assumption that the association
between educational attainment and schedule control linearly changes according to
computerization. Following the guideline of Hainmueller et al (2019), I diagnosed this
assumption with three binning estimates which indicate the median of computerization within a
low, medium, high tercile bin, respectively. The three bin estimates show that whereas the low
tercile bin is significantly lower than the medium tercile bin, the middle and high tercile bins are
not significantly different. The Wald test rejects the hypothesis that the standard linear
interaction model and the 3-bin models are statistically the same. This suggests that the
assumption of the linear interaction models does not hold and the association between college
degree and schedule control changes nonlinearly with computerization.
24

To investigate the nonlinearity in the moderating role of computerization, I used the
kernel estimator that allows flexibility in the functional form of the association between college
degree and schedule control in Figure 4-(b). In this graph, whereas the association between
college degree and schedule control remains similar in the medium and high range of
computerization, the size of this association is significantly smaller in the low range than the
medium or high range. This finding suggests that computerization amplifies the positive
association between educational attainment and schedule control, but this moderating effect does
not change at a consistent rate of computerization.
The figures also show that this moderating effect is supported at all levels of
computerization. The density plots, which illustrate the distributions of college graduates and
employees without college degree, suggest enough observations across levels of computerization
and sufficient variation in computerization for this period. This provides “sufficient common
support” (Hainmueller, Mummolo, and Xu 2019) for the moderating effect of computerization
on the relationship between educational attainment and schedule control.
Next, Figure 5 shows how computerization moderates the positive association between
educational attainment and schedule control in 2017-18. In Figure 5-(a), the linear interaction
model shows that computerization can significantly amplify the association between college
degree and schedule control. However, in contrast to the results for 1991-2004, there are not
sufficient observations of college graduates in the low range of computerization in this period,
reflecting that the majority of college graduates experienced high levels of computerization at
work in this recent period. Because of high skewedness of the moderator, we do not have
common support of the moderating effect of computerization on the association between
educational attainment and schedule control in this period.
25

[Insert Figure 5 here]
As linear interaction models could result in severe interpolation and misspecification
under such distribution of computerization of college graduates (Hainmueller et al. 2019), I
investigated the kernel estimators for the association between college degree and schedule
control across levels of computerization. Figure 5-(b) shows that in the low range, the
moderating effect of importance of interacting with computers is only weak in part because a
paucity of observations of college graduates in the low range of computerization produces large
confidence intervals of the association between college graduates and schedule control. In the
midrange and high range, the association between college degree and schedule control is
significantly positive and the size of this positive association becomes greater as interacting with
computers becomes more important. This finding suggests that computerization can increase the
magnitude of the positive association between educational attainment and schedule control in the
midrange and high range of computerization, where we can observe a sufficient number of
college graduates and non-college graduates.
The supplementary analysis that measures computerization with occupation-level email
use provides similar results. In Appendix D and E, the kernel estimator shows a fluctuation in the
magnitude of the association between educational attainment and schedule control in the most
range that lacks observations of college graduates, but similar to Figure 5, the amplifying effect
of occupation-level email use in the high range that has sufficient observations of college
graduates and non-college graduates.
In addition, I examined the potential mechanism of the moderating effect of
computerization. In Appendix F, when working from home is controlled, the association between
educational attainment and access to schedule control varies significantly less across levels of
26

computerization in both periods. This suggests that the amplifying effect of computerization on
the association between educational attainment and schedule control is largely explained by
working from home. However, as the variables were not compatible across surveys and did not
distinguish working from home exclusively from bringing extra work home, these results should
be interpreted cautiously.
I also investigated whether time-invariant occupational characteristics—authority,
autonomy and abstract skills (drawn from the O*Net)—are associated with the moderating effect
of computerization. These characteristics did not explain the amplifying effect of
computerization on the relationship between educational attainment and schedule control.
(available upon request). In contrast to the hypothesized mechanisms, this suggests that we have
little evidence that the greater impact of computerization on schedule control for college
graduates is associated with these occupational characteristics. However, this analysis should be
interpreted with caution because the data did not capture changes in occupational characteristics
that may have accompanied increased computerization.
DISCUSSION
Studies of the work-family interface and organizations have documented several social
contexts associated with the diffusion of family-friendly policies and highlighted the uneven
distribution of access to these policies across social class. I expanded these studies by
investigating the role of computerization in schedule control. By bridging studies of work-
family, technology, organizations, and labor, I built theoretical explanations of how
computerization can increase schedule control and amplify the positive association between
higher education and schedule control.
27

Using time-varying measures of computerization and the recent data on occupational
characteristics, I found that occupational-level computerization predicts greater access to
schedule control. Expanding the previous qualitative evidence (Blair-Loy 2009b; Chesley et al.
2003), this finding provides new generalizable evidence of how highly computerized occupations
matter to access to schedule control. This finding complements our understanding of how
computerization matters to workers’ autonomy over work schedule, and more broadly, their
working conditions. Previous studies have illuminated the salience of computerization for work
processes by investigating class-specific aspects of computerization: whereas studies on low-
wage workers have focused on digital surveillance and algorithmic labor platforms (Appelbaum
and Albin 1989; Brown and Korczynski 2010; Griesbach et al. 2019; Sewell 1998; Snyder 2016),
studies on high-wage and professional workers have paid attention to emails, laptops and
smartphones (Blair-Loy 2009b; Chesley et al. 2003; Mazmanian, Orlikowski, and Yates 2013;
Perlow 2012). This study bridges these parallel approaches and sheds light on broader
implications of computerization for changing working conditions by showing how overall
computerization has systematically reshaped schedule control.
The positive association between computerization and schedule control, however,
becomes weak after adjusting for occupation-fixed effects. Thus, the role of computerization in
schedule control is confounded with other occupational characteristics. As shown in the
supplementary analysis, the association is not fully explained by occupational-level abstract skill,
autonomy, or authority. Unfortunately, I could not explore other potential confounders, including
time-varying work characteristics, due to data limitations. Future studies should examine other
occupational characteristics, such as importance of physical presence at the work site and
increasing interactions with global clients.
28

Another key finding is that computerization is a primary occupational context that
amplifies the magnitude of the positive association between educational attainment and schedule
control even when occupational characteristics are held constant in fixed effects models. This
finding confirms that family-friendly workplace policies are more available to workers in high
socioeconomic positions (Gerstel and Clawson 2015; Golden 2009; Schieman, Milkie, and
Glavin 2009; Petts, Knoester, and Li 2020). The finding further illuminates the implications of
computerization-driven polarization for the uneven access to workplace policies that help
workers to arrange workload, family responsibilities, and other personal demands. That is, the
impact of computerization on facilitating skill replacement, reducing job control, and
intensifying employment precarity for lower-educated workers may offset the potential positive
effect of computerization on autonomy over work schedule for those workers. Perhaps, as the
supplementary analysis suggests, this is because lower-educated workers in highly computerized
workplaces may not be able to work from home. Beyond the binary measure of schedule control,
future studies should explore various levels of schedule control to fully understand the disparities
in access to schedule control.
This study shows that lower educated workers tend to experience lower levels of
computerization and benefit least from technological advancement with regards to schedule
control. This is an important finding because less-educated workers can experience work-family
conflict in a distinct way due to both rigid and unpredictable work schedules. Recent studies
have shown that growing job insecurity, especially driven by de-unionization, can make low-
wage employees and hourly workers’ work hours more volatile (Finnigan and Hale 2018;
Labriola and Schneider 2020). As lack of schedule control can have a negative impact on
worker’s work-family conflict (Henly and Lambert 2014) and well-being (Schneider and
29

Harknett 2019) particularly among low paid workers, this study suggests the increasing need for
more extensive family-friendly policies for lower-educated workers.
Relaxing the assumption that the association between educational attainment and
schedule control changes at a constant rate of computerization helps to reveal the non-linear
amplifying effect of computerization on the educational gradient in schedule control. Results
show that this amplifying effect is most pronounced in the low and middle range for 1991-2004
and in the high range for 2017-18. However, the data structure prevents concluding whether the
difference in the most amplifying range results in the differences in the period or the
measurement. Data that contains consistent measures of computerization for an extensive period
would allow researchers to fully explore how computerization reconfigures the class advantages
in power, autonomy, and resources for the work-family interface over time.
This study focused on occupation-level direct use of a computer at work in the main job.
Although I investigated email use, I was not able to explore computerized systems for just-in-
time scheduling and fast-growing digital labor platforms, such as Uber and Instacart. Moreover,
as many people have a precarious platform job as a secondary job (e.g. Griesbach et al. 2019),
and secondary jobs were not included in the analysis, the potential negative consequence of
computerization on schedule control among low-educated workers may be underestimated. In
addition, occupation-level computerization may mask within-occupation difference in
computerization. For example, lower-educated workers may not necessarily use computers even
in highly computerized occupations. Future studies should use other measures of
computerization to fully explore the relationship between workplace technology and the work-
family interface.
30

Constantly changing labor market contexts can reshape the role of computerization in the
stratification of schedule control. For example, Covid-19 may have lasting consequences for
amplifying the effect of computerization on schedule control as the disproportionate diffusion of
online communications and telework practices during the pandemic may continue to support
flexible work arrangements for higher educated workers, but not for lower educated workers
(Casselman and Koeze 2021). In addition, the emerging citywide and statewide legislative efforts
to increase employees’ schedule predictability in the service sector, such as the New York City
and Oregon Fair Work Week Law (went into effect in 2017 and thereafter), may reconfigure the
impact of computerization on schedule control for low-paid workers potentially through more
predictable use of computerized scheduling programs (Oregon House Democrats 2017; Toure
2017). Exploring these questions will help to better situate the role of computerization in the
uneven access to schedule control. Researchers should pay greater attention to the role of
technical advances in work conditions to better understand the relationships among labor market
conditions, inequality, and well-being.

31

Figure 1. Trends of Computerization by Occupational and Educational Groups (1991-2004)
Source: The CPS Work Schedule Supplement combined with the CPS Computer and Internet Use
(N=181,192)

0
20
40
60
80
% of Computer Use in an Occupation
1991 1997 2001 2004
year
Managerial
Professional
Sales
Clerical
Service
Manual
30
40
50
60
70
% of computer use in an occupation
1991 1997 20012004
year
No college degree
College degree
32

Figure 2. Trends of Schedule Control by Occupational and Educational Groups (1991-2018)
Source: The CPS Work Schedule Supplement 1991-2014 (N=181,192) and the ATUS Leave
Module 2017-2018 (N=9,002)

0
.2
.4
.6
.8
Proportion of Schedule Control
1991
1997
2001
2004
2017-18
year
Managerial
Professional
Sales
Clerical
Service
Manual
.1
.2
.3
.4
.5
.6
Proportion of Schedule Control
1991
1997
2001
2004
2017-18
year
No college degree
College degree
33

(a) CPS 1991-2004 (b) ATUS 2017-2018
Figure 3. Predicted Probability of Schedule Control for Computerization
Note: The vertical lines indicate the 95 % confidence intervals.
Figure (a) shows the marginal effect of computerization on schedule control in Model 5 and Model 10
in Table 2. Both models include year-fixed effects, educational attainment, demographic characteristics
(age, sex, parental status, presence of preschooler, marital status, race, regional division), and work
characteristics (public sector, manufacturing sector, and work hour status).
Figure (b) shows the marginal effect of computerization on schedule control in Model 5 in Table 3. The
model includes educational attainment, demographic characteristics (age, sex, parental status, presence
of preschooler, marital status, race, regional division), and work characteristics (public sector,
manufacturing sector, and work hour status).This model does not control occupation-fixed effects
because the data only has a single wave.

With occupation-fixed effects
Without occupation-fixed effects .2
.25
.3
.35
.4
.45
P r o b a b i l i t y o f S c h e d u l e C o n t r o l
0 20 40 60 80 100
% of Computer Use in an Occupation
.4
.5
.6
.7
P r o b a b i l i t y o f S c h e d u l e C o n t r o l
1.5 2 2.5 3 3.5 4 4.5
Importance of interacting with computer use at work
34

(a) Linear interaction and the 3-bin models (b) Kernel estimator
Figure 4. Heterogeneity in the marginal effect of college degree on schedule control across
levels of computerization (CPS 1991-2004)
Note: All models include occupation and year fixed-effect, demographic (age, sex, parental status,
presence of preschooler, marital status, race, and region) and work characteristics (public sector,
manufacturing sector, and work hour status). The long-dashed and short-dashed density plots indicate
the distributions of college graduates and non-college degree across levels of computerization,
respectively.

L M H
0
.02
.04
.06
.08
.1
M a r g i n a l E f f e c t o f C o l l e g e D e g r e e o n S c h e d u l e C o n t r o l
0 20 40 60 80 100
Moderator: % of computer use in an occupation
0
.02
.04
.06
.08
.1
M a r g i n a l E f f e c t o f C o l l e g e D e g r e e o n S c h e d u l e C o n t r o l
0 20 40 60 80 100
Moderator: % of computer use in an occupation
35

(a) Linear interaction and the 3-bin models (b) Kernel estimator
Figure 5. Heterogeneity in the marginal effect of college degree on schedule control across
levels of computerization (ATUS 2017-2018)
Note: All models include demographic (age, sex, parental status, presence of preschooler, marital
status, race, and region) and work characteristics (public sector, manufacturing sector, and work hour
status). The long-dashed and short-dashed density plots indicate the distributions of college graduates
and non-college degree across levels of computerization, respectively.

L M H
-.3
-.2
-.1
0
.1
.2
.3
M a r g i n a l E f f e c t o f C o l l e g e D e g r e e o n S c h e d u l e C o n t r o l
1 2 3 4 5
Moderator: Importance of interacting with computer use at work
-.3
-.2
-.1
0
.1
.2
.3
M a r g i n a l E f f e c t o f C o l l e g e D e g r e e o n S c h e d u l e C o n t r o l
1 2 3 4 5
Moderator: Importance of interacting with computer use at work
36

Table 1. Means and Standard Deviations of the Variables, by Education

CPS (1991-2004) ATUS (2017-2018)

Non-college
graduates
College
graduates
Non-college
graduates
College
graduates
Schedule control
0.220 0.331 0.498 0.617

(0.414) (0.471) (0.500) (0.486)
% of computer use
39.191 64.692

(29.265) (24.920)

Importance of computer use at work

3.303 4.004

(0.912) (0.635)
Age
37.149 39.691 41.276 42.396

(12.689) (10.543) (13.413) (10.827)
Female
0.485 0.502 0.461 0.545

(0.500) (0.500) (0.499) (0.498)
Parent
0.476 0.500 0.391 0.508

(0.499) (0.500) (0.488) (0.500)
Having any children aged under 5 0.145 0.166 0.154 0.208

(0.352) (0.372) (0.361) (0.406)
Married 0.547 0.655 0.434 0.608

(0.498) (0.476) (0.496) (0.488)
Race (ref: white)

Black
0.102 0.065 0.157 0.106

(0.302) (0.246) (0.364) (0.308)
Hispanic
0.119 0.044 0.231 0.100

(0.324) (0.205) (0.422) (0.299)
Others
0.041 0.059 0.040 0.089

(0.199) (0.236) (0.196) (0.285)
Regional division (ref: New England)

Middle Atlantic
0.124 0.139 0.091 0.125

(0.330) (0.346) (0.288) (0.330)
East North Central
0.151 0.140 0.169 0.157

(0.358) (0.347) (0.375) (0.364)
West North Central
0.105 0.111 0.084 0.093

(0.307) (0.314) (0.277) (0.291)
South Atlantic
0.162 0.162 0.186 0.196

(0.368) (0.369) (0.389) (0.397)
East South Central
0.052 0.041 0.077 0.057

(0.223) (0.199) (0.267) (0.232)
West South Central
0.090 0.071 0.130 0.101

(0.286) (0.257) (0.336) (0.301)
Mountain
0.113 0.102 0.089 0.079

(0.317) (0.303) (0.285) (0.270)
Pacific
0.122 0.133 0.137 0.135
37

(0.327) (0.339) (0.344) (0.341)
Public
0.126 0.264 0.378 0.779

(0.332) (0.441) (1.114) (1.531)
Manufacturing
0.181 0.130 0.138 0.100

(0.385) (0.336) (0.345) (0.300)
Work hour status (ref: part-time)

Full-time work (35-49 hours)
0.677 0.659 0.665 0.698

(0.468) (0.474) (0.472) (0.459)
Working long hours (50+ hours)
0.106 0.205 0.129 0.185

(0.308) (0.403) (0.336) (0.388)
N
112746 68446 3756 5246

38

Table 2. Main Effects of Educational Attainment and Computerization on Schedule Control (CPS 1991-2004, N=181,192)

Linear probability regression
without occupation-fixed effects
Linear probability regression
with occupation-fixed effects
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
College
graduates
0.121*** 0.120***

0.064*** 0.063*** 0.062***

0.062***
(0.002) (0.002)

(0.002) (0.007) (0.006)

(0.006)
% Computer use

0.003*** -0.0001 -0.0005***

0.001 -0.001 -0.001*

(0.00004) (0.0002) (0.0002)

(0.001) (0.001) (0.001)
(% Computer Use)
2

0.00003*** 0.00003***

0.00002** 0.00002**

(0.000002) (0.000002)

(0.00001) (0.00001)
Demographic
characteristics

√ √ √ √ √ √ √ √
Work
characteristics

√ √ √ √ √ √ √ √
Constant 0.117*** 0.215*** 0.196*** 0.239*** 0.231*** 0.145*** 0.251*** 0.237*** 0.280*** 0.260***

(0.002) (0.006) (0.006) (0.006) (0.006) (0.009) (0.017) (0.029) (0.026) (0.027)
R
2
0.034 0.065 0.081 0.083 0.087 0.110 0.130 0.127 0.127 0.130
AIC 212342 206365 203206 202796 202062 197330 193434 193997 193945 193346
BIC 212393 206618 203459 203058 202335 197371 193676 194240 194198 193609
* p<0.05, ** p<0.01, *** p<0.001
Note : Dummy variables for year are included in all models.
Model 2-5 and Model 7-10 include demographics and work characteristics.
Demographic characteristics include age, sex, race, parental status, presence of preschooler, marital status, and region. Work
characteristics include public sector, manufacturing sector, and work hour status.

39

Table 3. Main Effects of Educational Attainment and Computerization on Schedule Control
(ATUS 2017-2018, N=9,002)
(1) (2) (3) (4) (5)
College graduates
0.085*** 0.118***

0.046**

(0.015) (0.015)

(0.016)
Importance of computer use at work

0.126*** -0.192** -0.196**

(0.009) (0.069) (0.069)
(Importance of computer use at work)
2

0.048*** 0.047***

(0.010) (0.010)
Demographic characteristics

√ √ √ √
Work characteristics

√ √ √ √
Constant
0.526*** 0.762*** 0.384*** 0.868*** 0.875***

(0.011) (0.044) (0.055) (0.120) (0.119)
R
2

0.007 0.047 0.079 0.083 0.085
AIC
12830 12503 12200 12157 12142
BIC
12845 12666 12364 12327 12320
* p<0.05, ** p<0.01, *** p<0.001
Note : Dummy variables for year are included in all models.
Model 2-5 include demographics and work characteristics.
Demographic characteristics include age, sex, race, parental status, presence of preschooler,
marital status, and region. Work characteristics include public sector, manufacturing sector,
and work hour status.

40

Chapter 2. Union Coverage, Work Contexts, and Family-friendly Policies
1

Eunjeong Paek
University of Southern California

1
Author Note: Correspondence should be addressed to Eunjeong Paek, Department of Sociology, University of
Southern California, Los Angeles, CA 90089-1059. Email: paeke@usc.edu. Earlier version of this work was
presented at the 2021 annual meetings of the Population Association of America and the American Sociological
Association. I thank Jennifer Hook, Dan Schrage, Barry Eidlin, Jamie McCallum, and Chris Rhomberg for helpful
comments and suggestions. This research was conducted with restricted access to Bureau of Labor Statistics (BLS)
data. The views expressed here do not necessarily reflect the views of the BLS.
41

Union Coverage, Work Contexts, and Family-friendly Policies
ABSTRACT
Unions serve as primary labor market institutions that improve employees’ working
conditions, yet existing literature offers mixed results of their influence on workers’ access to
family-friendly policies. This may be partially due to the extant literature having not considered
possible variation across work contexts. In this study, I ask whether union coverage can increase
workers’ access to family-friendly policies and how work contexts—public sector organizations,
gender composition of occupation, and the size of establishments—can alter these union effects.
Using individual-fixed effect models, I analyze the effects of the transition from a nonunion
worker to a union-represented worker on the worker’s access to various family-friendly policies.
Results show that changing from a nonunion position to a union-represented one increases
individuals’ access to paid parental leave and paid sick/vacation days but decreases access to
schedule control. The findings also show that workers in public sector organizations and female-
dominated occupations tend to experience outsized benefits of union coverage on access to paid
sick/vacations days. These finding suggests that unionization does not uniformly improve
family-friendly policies, emphasizing the need to attend to sector differences and policy-specific
effects.
Keywords: unions, family-friendly policies, public sector, occupational gender
composition, establishment size, fixed-effect models

42

Family-friendly workplace policies, such as ample parental leave, autonomy over one’s
schedule, and sufficient paid leave, help employees arrange their domestic routines and cope
with family emergencies. As dual-earner households increase and more employees experience
severe work-family conflict (Nomaguchi 2009), family-friendly policies serve an increasingly
important function in helping employees maintain their careers and protect their wellbeing
(Doran et al. 2020; Erin L. Kelly et al. 2014; Moen et al. 2016; Petts and Knoester 2020; Thomas
and Ganster 1995; Waldfogel 1998, 1999b). Although the availability of national family-friendly
policies remains much lower in the United States in comparison to other high-income countries
(Gornick and Meyers 2003), access to employer-provided family-friendly workplace policies is
gradually increasing (Van Giezen 2013; U.S. Bureau of Labor Statistics 2008, 2014).
To date, we have mixed evidence of whether labor unions increase the access of
employees to family-friendly workplace policies. Some studies have shown that unions can
facilitate the diffusion of family-friendly policies by enhancing the bargaining power of
employees, raising their awareness of the availability of the policies, and inducing more policy
support from state governments (Budd and Mumford 2004; Engeman 2021; Glass and Fujimoto
1995; Kramer 2008; Park, Lee, and Budd 2019). Yet, other studies suggest that unions may play
a limited role in increasing access to family-friendly policies because of their declining
bargaining power, masculine culture, and inconsistent interests in family-friendly policies
(Brochard and Letablier 2017; Budd and Mumford 2004; Gerstel and Clawson 2001; Glass and
Fujimoto 1995; Golden 2009; Haas and Hwang 2013; Jung 2017; Kelly 2003; Rosenfeld 2014;
Roychowdhury 2014; Wajcman 2000). Although these studies illustrate compelling and
competing mechanisms for how organized labor affects family-friendly policies, they do not
43

sufficiently consider the contexts of unions’ intents and capabilities to improve working
conditions and promote work-family policies.
In this study, I revisit the role of unions in family-friendly policies by focusing on three
work contexts—public sector employment, occupational gender composition, and the size of the
establishment—that are associated with family-friendly workplace supportiveness. Drawing on
studies of unions, organizations, and gender, I predict that these contexts may reshape the effects
of union coverage on workers’ access to family-friendly policies by conditioning union
bargaining power, employee demand for family-friendly policies, and employer motivations and
resources to respond to union power (Bramley, Wunnava, and Robinson 1989; Buchmueller,
Dinardo, and Valletta 2002; Katz 2013; Milkman 2007; Rosenfeld 2014). Using individual-fixed
effect models, I analyze the effects of the transition from a nonunion worker to a union-
represented worker on the worker’s access to various family-friendly policies.
This study can advance our prior knowledge of how organized labor matters to workers’
work-family interface in two key ways. First, I contextualize how labor unions affect workers’
access to family-friendly policies by theorizing and analyzing the significance of work contexts,
especially gendered workplace structure, for what unions do for workers. Second, I update our
empirical evidence for the relationship between unions and family-friendly policies by using
generalizable and recent data and ruling out the potential time-invariant unobserved
characteristics of union-represented workers.
THEORETICAL FRAMEWORK
Union Coverage and Workers’ Access to Work-family Policies
44

Unions serve as primary labor market institutions that improve employees’ working
conditions. Studies have shown that unions increase employees’ access to wage premiums,
health insurance, and pensions by exerting bargaining power and mobilizing political resources
(Buchmueller et al. 2002; Lin, Bondurant, and Messamore 2018; Rosenfeld 2014; Voss and
Sherman 2000). Unions can also protect workers, especially lower-wage workers, from having
unpredictable work hours (Finnigan and Hale 2018; Labriola and Schneider 2020), losing
earnings as a result of automation (Parolin 2021) and living in conditions of poverty (Brady,
Baker, and Finnigan 2013).
Some studies suggest that work-family policies may be more accessible among union-
represented workers than nonunion workers for several key reasons. First, the bargaining power
of unions may effectively pressure employers to extend workers’ benefits, including access to
family-friendly policies (Buchmueller et al. 2002; Freeman and Medoff 1984). In addition,
unions can increase employees’ awareness of the availability of existing work-family policies
(Budd and Mumford 2004; Park et al. 2019). Unions can also mobilize organized power and
resources to pressure macro-level institutions, especially state governments, to increase
employees’ access to work-family policies (Cornfield 1990; Engeman 2021; Firestein and Dones
2007; Milkman and Appelbaum 2013).
In contrast, another strand of research shows that the effect of labor unions on nonunion
members may erase the disparity in workplace compensations, including family-friendly
benefits, between union-represented workers and nonunion workers. One explanation is that
employers may match nonunion employees’ compensation to union employees’ compensation to
suppress unionization (Osterman 1995). Besides this union threat effect, unions may create
normative pressure to improve employees’ working conditions and thus have positive “spillover”
45

effects on nonunion members’ compensation (Rosenfeld and Denice 2019; Western and
Rosenfeld 2011).
Other studies provide several reasons to expect that union-represented workers may not
have distinguishable advantages in access to work-family policies in comparison to nonunion
workers. First, unions may not have a substantial impact on family-friendly workplace
supportiveness as they have lost members and power over the past decades (Eidlin 2015; Jung
2017; Rosenfeld 2014). Second, unions may not prioritize work-family policies over other forms
of compensation because the policies only benefit employed parents (Barringer and Milkovich
1998; Kelly 2003). Third, as gender scholars point out, unions may be constrained by historical
masculine culture and traditional male breadwinning expectations (Brochard and Letablier 2017;
Haas and Hwang 2013; Roychowdhury 2014; Wajcman 2000). Due to these cultural constraints,
work-family policies are often treated as women’s issues and undervalued in collective
bargaining (Brochard and Letablier 2017; Haas and Hwang 2013; Wajcman 2000).
Importantly, some studies have highlighted two aspects of heterogeneity in the role of
unions in family-friendly policies. First, the salience of unions may vary across the type of
policy. Glass and Fujimoto (1995) found that unions predict greater access to leave policies, but
not to schedule control or child-care benefit. Supporting this idea, Gerstel and Clawson (2001)
found in an interview study that union members tended to easily agree with the need for leave
policies because these policies had long received attention in the collective bargaining process
and media. In contrast, unions have more mixed responses to other work-family policies, such as
childcare support and schedule control, because some members do not prioritize these policies
but are rather concerned about the financial cost of these policies and the potential loss of job
46

autonomy. These suggest that it is important to investigate various types of work-family policies
to better understand the role of unions in work-family policies.
Second, the role of unions in work-family policies can be conditioned by work contexts.
Glass and Fujimoto (1995) found that the positive effect of unions on parental leave is weaker in
female-concentrated jobs, illuminating the salience of work context for work-family policies.
However, as this study was based on information from pregnant women in the Midwest in the
early 1900s, findings may not be generalizable to the population or applicable to contemporary
workers. In addition, as this study used cross-sectional data, it did not rule out the confounding
effect of unobserved individual characteristics in the relationship between unions and work-
family policies. For example, workers with feminist values may be more likely to prefer to work
in a unionized job to address women’s issues at the workplace and be aware of family-friendly
policies (Kirton 2005).
In this study, I build on and extend these studies of the heterogeneous implications of
union coverage for work-family policies in three ways. First, following the previous studies, I
pay careful attention to a variety of family-friendly policies. Second, I use the nationally
representative and recently collected panel data that allows me to control time-invariant
unobserved characteristics of union-represented workers. Third, as I show in the next section, I
advance the theoretical framework for how work contexts can condition union-represented
workers’ access to work-family policies.
Union-represented Workers in the Public Sector and Access to Work-family Policies
Public-sector organizations have served as “early adopters” of family-friendly policies
(Bruce and Reed 1994; Christensen, Weinshenker, and Sisk 2010). Whereas national family-
47

friendly policies have remained largely absent in the U.S. private sector, public sector
organizations have long been the target of more aggressive legislative efforts to provide family-
friendly policies (Gornick and Meyers 2003; Newman and Mathews 1999). In addition, as the
public sector tends to promote gender equity and has a higher share of female workers in
comparison to the private sector, public sector organizations may use these policies to attract and
retain female workers who assume a greater burden of housework (Cooper, Gable, and Austin
2012; Gornick and Jacobs 1998; Mandel and Semyonov 2014). Although some studies have
shown that workers in the public sector are less likely to control their work schedule than
workers in the private sector (Golden 2009), other studies have shown that public sector
organizations are more likely to provide maternity leave (Waldfogel 1999a) and child care
support (Kelly 2003) than are private sector organizations.
Building on studies of unions and employment sector, I present the competing predictions
for whether the benefits of union-represented workers’ access to work-family policy are more
pronounced in the public sector than in the private sector. On the one hand, public sector
employment can magnify union-represented workers’ benefits in work-family policies because
unions in the public sector tend to have higher bargaining power than unions in the private sector
for several reasons. First, macro-economic conditions, including increasing international
competition, tend to have less impact on bargaining relationships in the public sector in
comparison to the private sector (Katz 2013). Second, despite the gradual increase in
privatization, substitute workers for public union employees are still not largely available in
comparison to substitute workers for private union employees (Katz 2013). Third, unionization
rates are higher in the public sector than in the private sector (Rosenfeld 2014).
48

In addition, unions may have a greater impact on family-friendly policies in the public
sector because of the gender composition of workers. Although men engage in an increasing
share of housework and caregiving, women still tend to assume a larger share of unpaid work at
home and experience more severe career penalty from the burden of family responsibilities
(Bianchi et al. 2000; Budig and England 2001). As unions in the public sector tend to have a
higher proportion of female workers than unions in the private sector (Rosenfeld 2014), they
may pressure employers more actively to provide and support work-family policy that could
mitigate the gendered work-family conflict.
Alternatively, union-represented workers in the public sector may have fewer advantages
in gaining access to work-family policies than union-represented workers in the private sector
because work-family supportiveness may be already formalized in the public sector (Bruce and
Reed 1994; Christensen et al. 2010). Similarly, the disparity in health insurance between union-
represented workers and nonunion workers is less pronounced in the public sector where
employer-provided health insurance is more widely available (Keefe 2012; Rosenfeld 2014),
suggesting that the union effect can be less distinct in workplaces that provide generous benefits.
These arguments suggest that union coverage may not further make family-friendly policies
more available in the family-friendly public sector.
Union-represented Workers in Female-dominated Occupations and Access to Work-family
Policies
It is less clear whether female-dominated occupations provide greater access to work-
family policies than less female-concentrated occupations. On the one hand, some studies have
shown that compared to male-dominated jobs, female-dominated jobs are more likely to provide
family-friendly policies, including maternity leave (Davis and Kalleberg 2006; Kelly and Dobbin
49

1999), schedule control (Davis and Kalleberg 2006; Deitch and Huffman 2001; Minnotte, Cook,
and Minnotte 2010), and paid sick leave (Guthrie and Roth 1999), suggesting that employers
may accommodate female employees’ greater needs for family-friendly policies to retain
workers and improve productivity (Glass and Estes 1997; Osterman 1995). In contrast, other
studies have shown that female-dominated occupations do not necessarily provide more family-
friendly benefits, potentially because these jobs are more devalued and less paid (Glass and
Camarigg 1992; Glauber 2011; Hodges 2020; Levanon, England, and Allison 2009; Maume
1999).
The union coverage advantage in access to work-family policies can be greater in the
female-dominant workplace than in the male-dominant workplace for several reasons. At the
individual level, as I wrote previously, female workers tend to engage in a greater share of
unpaid work at home than male workers and may publicize their direct interests in combining
work and family responsibilities through union activities (Bianchi et al. 2000; Cranford 2007a).
At the union level, unions in the female-dominant workplace may play a more effective role in
fostering family-friendly workplace supportiveness than unions in the male-dominant workplace
because members’ interests in egalitarian policies, including work-family policies, may be more
convergent, and unions are less dominated by traditional masculine workplace culture that
trivializes workers’ challenges in balancing work and family responsibilities (Crocker and
Clawson 2012; Milkman 2007). In particular, at the union-leader level, unions in the female-
dominated workplace may recruit more women in leadership positions who tend to be
sympathetic with work-family issues (Cranford 2007a), and leaders may be generally more
motivated to address work-family issues that are often heightened among women in an attempt to
organize more female workers (Cranford 2007b, 2007a).
50

Alternatively, union-represented workers in female-dominated occupations may have
limited benefits in family-friendly policies because of the working conditions and the gendered
constraints in union activities. Unions in female-dominated workplaces, especially in the low-
wage service sector, may have difficulty cementing their bargaining power because their
worksites are often geographically scattered and the workers are less likely to have long-term
attachment to jobs due to their marginalized employment status (e.g. temporary employees)
(Wial 1993). Besides, female union members may find it more challenging to engage in union
activities, such as union meetings, because of their disproportionate caregiving responsibilities
(Cranford 2007b).
Union-represented Workers in Large Establishments and Access to Work-family Policies
Organizational scholars have shown that the size of establishments predicts the
availability of family-friendly workplace policies (Davis and Kalleberg 2006; Glass and
Fujimoto 1995; Glauber 2011; Kelly and Dobbin 1999; Kristal 2017; Osterman 1995). On the
one hand, large establishments may be more motivated to provide employees with access to
family-friendly policies because they are subject to greater institutional pressure. That is, large
establishments are more likely to be scrutinized by the public and regulators and to have a
professional personnel administration that facilitates the adoption of emerging family-friendly
policies (Davis and Kalleberg 2006; Glass and Estes 1997; Kelly and Dobbin 1999). In addition,
large establishments tend to have more financial and human resources to offset the cost of the
family-friendly benefits and rearrange the work process for temporarily unavailable policy users
(Osterman 1995).
I expect that union-represented workers may have less union advantage in access to
family-friendly policies in large establishments than in small establishments for two reasons.
51

First, as large establishments tend to provide nonunionized employees with sufficient
compensations to offset the “union threat effect” (Bramley 1989), nonunion workers in large
establishments may have as much access to family-friendly policies as union-represented
workers do in large establishments. Second, as family-friendly policies are widely available in
large establishments in comparison to small establishments (Davis and Kalleberg 2006; Glass
and Fujimoto 1995; Glauber 2011; Kelly and Dobbin 1999; Kristal 2017; Osterman 1995),
unions in large establishments may prioritize other agendas over work-family policy.
In sum, building on studies of work, organizations, and labor, I develop hypotheses for
how work contexts moderate the effect of union coverage on family-friendly policies. Table 4
summarizes the predictions.
[Insert Table 4 about here]
Consistent with previous studies (Budd and Mumford 2004; Finnigan and Hale 2018;
Glass and Fujimoto 1995; Labriola and Schneider 2020; Park et al. 2019), I focus on individual-
level union coverage. Importantly, other quantitative studies on unions have conceptualized
union at establishment (Buchmueller et al. 2002; Kelly 2003; Lin et al. 2018; Osterman 1995),
union-local (Wilmers 2017), state (Brady et al. 2013; Lin et al. 2018; Rosenfeld and Denice
2019; VanHeuvelen and Brady 2021), industry (Jung 2017), and industry-region levels (Parolin
2021; VanHeuvelen 2018; Western and Rosenfeld 2011). While each of these measures of union
density can provide different insights into the role of unions in family-friendly policies, my main
analysis is centered on the implications of individual-level transitions in union coverage for
workers’ access to family-friendly policies.
METHODS
52

Data and Sample
I used the National Longitudinal Survey of Youth 1997. The data was a nationally
representative sample of 8984 individuals who were ages 12 to 16 in the first survey in 1997.
The data was collected every year from 1997 to 2011 and every other year from 2013 to 2017.
The sample only contains young workers (the average age is 25 and the maximum value of age is
38), who are likely to experience severe work-family conflict because of child-rearing
responsibilities. I limited the sample to employees who were aged 20 and over and worked in the
government sector or private company. Because this restriction dropped 1997 and 1998 and left
fewer than 50 observations in 1999, I used the data collected from 2000 to 2017. As the
proportion of item missing in the sample are small (less than 5% for all variables except female
percentage in an occupation, less than 8% for female percentage in an occupation), I list-wise
deleted missing values. I restricted the sample to persons who have 2 or more person-year
observations to use fixed effect models. The final sample includes 12,029 person-year
observations and 4,105 persons.
Variables
The dependent variables are access to family-friendly workplace policies. Access to paid
parental leave and schedule control are measured with the binary variables drawn from a
question "which of the benefits on this list would it be possible for you to receive as part of your
job with this employer?" I used a continuous variable for the number of paid vacations or sick
days. As paid sick days and paid vacation days were asked in two separate variables in 1997-
2011, I constructed a variable that sums the values of the two variables to make the measures
consistent with the measure for later waves. I limited the sample to 2011 or earlier years to
investigate whether the measurement difference can change the results. The main results remain
53

largely similar. I top-coded paid sick/vacation days at 95 percentile and bottom-coded it at 1
percentile. I did not use childcare support because the prevalence of access to childcare support
remains very low across time (about 5%).
Union coverage is measured with a binary variable. This measure does not allow me to
distinguish union members from nonunion members whose jobs are covered by a union contract.
Person-year observations of workers who are covered by a union contract consist of about 10%
of the sample (Table 1), and this percentage ranges from 6 % to 12 % across time (not shown in
the result). On average, about 9 % of nonunion workers became a union-represented worker, and
about 73% of union-represented workers became a nonunion worker in each year.
The moderating variables are public sector organization, the percentage of female
workers in an occupation, and the size of establishment. Public sector organization is measured
with a binary variable for being employed by government. The percentage of female workers in
an occupation is calculated from the Current Population Survey Annual Social and Economic
Supplement. I cross-walked the occupation code into the 2002 Census Code. I merged the
calculated values into the NLSY through the cross-walked occupation code. The size of
establishment was measured with the number of employees in an establishment. I recoded the
variable into a 5-group category for 1-49, 50-99, 100-249, 250-499, and 500 or more employees.
Control variables include demographic, job, and state characteristics. Demographic
characteristics include age, educational attainment (highest grade completed), marital status (0
not married, 1 married), the presence of children under age 6 (0 no child under age 6, 1 having
any child under age 6), and living in urban area (0 no urban residence, 1 urban residence). As a
fixed-effect model does not estimate time-invariant characteristics, I did not control them
including gender and race. Job characteristics include part-time work (0 working 35 or more
54

hours per week, 1 working less than 35 hours per week), seniority, hourly wages, 6-group
occupational categories (managerial, professional, service, sales, administrative, and others),
manufacturing sector (0 non-manufacturing, 1 manufacturing), and the eligibility of the Family
and Medical Act (0 not eligible, 1 eligible). The top and bottom values of seniority and hourly
wages were replaced at 95
th
percentile and 1
st
percentile, respectively. According to the FMLA,
employees who have worked 1250 hours for the employer during the 12 months and work at a
worksite with 50 or more employees within 75 miles have the legal right to 12-week unpaid
leave (U.S. Department of Labor 2012). I constructed a binary variable for a proxy of the FMLA
eligibility that indicates an employee working 25 hours or more per week, working for the
employer for at least a year, and working at an establishment with 25 or more workers.
I created state-level data and combined it with the administrative state information of the
NLSY. State-level control variables include real GDP per capita, annual unemployment rate,
right-to-work law (0 no, 1 yes), paid parental leave legislation (0 no, 1 yes), and paid sick leave
legislation (0 no, 1 yes). The sources of real GDP per capita, annual unemployment rate, and
right-to-work law are the Bureau of Economic Analysis, the Bureau of Labor Statistics, and the
National Conference of State Legislatures, respectively. For paid parental leave and paid sick
leave legislations, I used the policy reports (National Partnership for Women & Family 2021,
2022).
Analytic strategies
The analysis is consisted of two parts. First, I estimate the main effects of switching from
a nonunion position to one with union representation on the workers’ access to family-friendly
policies. In the supplementary analysis, I stratified the models by gender. Second, I estimate the
moderating effects of the work contexts on unions’ effects on access to those policies. In all
55

models, I used fixed-effect regression models to reduce bias from unobserved individual
characteristics. In these models, the coefficients for unions indicate the effects of within-
individual transition in union coverage on access to family-friendly policies.
For binary outcomes (paid parental leave and schedule control), I used linear probability
models. For a continuous moderating variable (occupational gender composition), I used kernel
estimator to estimate the non-linear moderating effect. In all models, I included year dummy
variables to control the increase in family-friendly policies over time.
RESULTS
Table 5 shows that nonunion workers are distinguished from union-represented workers
in many ways. Union-represented workers, on average, have greater access to paid parental leave
and paid sick/vacation days but less access to schedule control than nonunion workers. Union-
represented workers are more likely to work in the public sector, less female-dominated
occupations, and large establishments. Compared to nonunion workers, union-represented
workers are less likely to be female and tend to be more educated. Union-represented workers
also tend to report higher wage, greater seniority. Union-represented workers are less likely to be
a part-time worker and more likely to be eligible for the FMLA. Union-represented workers are
less likely to live in a state with a right-to-work law and more likely to live in a state with paid
parental leave law.
[Insert Table 5 about here]
Figure 6 further shows the trends of access to family-friendly policies by union coverage
status in 2000-2017. Both union-represented workers and nonunion workers gained more access
to paid parental leave and longer paid vacation/sick leave days over time. Compared to nonunion
56

workers, union-represented workers tend to have greater access to paid parental leave and longer
paid vacation/sick leave days in most waves. The trend for schedule control is less consistent.
Whereas nonunion workers had greater access to schedule control in the 2010s than the 2000s,
union-represented workers did not witness a significant change in access to schedule control over
time. In the 2010s, nonunion workers tend to have greater access to schedule control than union-
represented workers.
[Figure 6 is about here]
Table 6 presents whether the transition from a nonunion worker to a union-represented
worker increases access to family-friendly policies. Result shows that becoming a union-
represented worker is positively associated with the probability of having access to paid parental
leave. Similarly, the transition from a nonunion worker to a union-represented worker
significantly increases the number of paid sick or vacation days. In contrast, this transition is
negatively associated with access to schedule control. These findings suggest that union
coverage can increase workers’ access to family-friendly policies, but these effects depend on the
type of family-friendly policy.
[Table 6 is about here]
In the supplementary analysis, I investigated the gender difference in the association
between union coverage and access to family-friendly policies. Female workers may have more
access to family-friendly policies, especially paid maternity leave, than male workers potentially
because they tend to have more urgent demand for the policies and be thus more aware of the
availability to the policies (Baird and Reynolds 2004; Bianchi et al. 2000). In the gender-
57

stratified models, however, I found no evidence that the effects of unions on other family-
friendly policies vary significantly across gender (Appendix G).
Next, I examined the main effects of three work contexts—public sector organization,
occupational gender composition, and the size of establishment. When adjusting for union
coverage and control variables, public sector organizations are positively associated with access
to paid sick/vacation days but negatively associated with schedule control. Female representation
in an occupation is positively associated with the number of paid sick/vacation days. Compared
to employees in small establishments, employees in larger establishments tend to have more
access to all family-friendly policies.
Next, I investigated how work contexts can moderate the effects of union coverage on
family-friendly policies. Figure 7 shows that becoming a union-represented worker increases the
number of paid sick/vacation days both in the private and private sector, and the magnitude of
this positive effect of unions is significantly greater in the public sector than in the private sector.
This finding supports the idea that public sector organizations amplify the positive effect of
unions on paid sick/vacation days.
[Figure 7 is about here]
The moderating effects of public sector organizations on the associations between union
coverage and access to other family-friendly policies are not statistically significant. Although
the positive effect of union coverage on access to paid parental leave is more pronounced in the
public sector, the difference in the magnitude of the effect between the private and public sector
is not statistically significant. Union coverage is negatively associated with access to schedule
58

control both in the private and public sector, and the gap in the magnitude of this association
between the private and public sector is not statistically significant.
In Figure 8, I examined how occupational gender composition can alter the effect of
change in union coverage on access to family-friendly policies. Result shows that the positive
effect of union coverage on paid sick/vacation days is more pronounced among employees in
female-dominated occupations. The density plots at the bottom of the graph show that there are
sufficient observations for both workers with union coverage (the red line) and workers without
union coverage (the black line) across the distribution of the percentage of female workers in
occupation. This distribution suggests that the moderating effect is supported at all range of the
percentage of female workers in an occupation without a severe extrapolation in the estimation.
In sum, this finding supports the idea that female-dominated occupations can magnify the
positive effect of union coverage on paid sick/vacation days. However, the effects of unions on
other family-friendly policies do not significantly vary across the percentage of female
employees in an occupation.
[Figure 8 is about here]
Figure 9 shows how the size of establishment can moderate the effects of union coverage
on family-friendly policies. It shows that the size of establishment does not significantly
moderate the effects of union coverage on family-friendly policies. This suggests that we have
little evidence that establishment size alters the magnitude of the positive effects of union
coverage on workers’ access to family-friendly policies.
[Figure 9 is about here]
DISCUSSION
59

In this study, I investigated whether—and in which contexts—union-represented workers
can obtain greater access to family-friendly policies than nonunion workers. Previous studies
offer competing explanations and mixed evidence of whether unions increase family-friendly
policies. I aimed to improve our limited understanding of the role of unions in family-friendly
policies by exploring various family-friendly policies and three key family-friendly work
contexts. Drawing on labor research, organizational studies, and work-family literature, I
provided a theoretical framework that contextualizes the effects of unions on workers’ access to
family-friendly policies.
I found that the transition from a nonunion worker to a union-represented worker tends to
increase workers’ access to paid parental leave and the number of paid sick/vacation days. These
findings support the idea that organized labor helps increase family-friendly supportiveness in
the workplace (Budd and Mumford 2004; Engeman 2021; Glass and Fujimoto 1995; Kramer
2008; Park et al. 2019). However, becoming a union-represented worker tends to significantly
lower access to schedule control, further revealing that the role of unions in family-friendly
policies may vary across the type of family-friendly policy. Perhaps this is partially due to the
fact that union members may be concerned about the possibility that flexible work arrangements
may reduce their job autonomy by increasing employers’ discretion in changing workers’ work
schedule (Gerstel and Clawson 2001). Whereas unions may make workers’ work schedule more
predictable (Finnigan and Hale 2018; Labriola and Schneider 2020), we do not have yet
sufficient evidence to argue that unions may increase workers’ access schedule control that helps
to combine work and family responsibilities.
Another important finding is that public sector organizations, where union-represented
workers are concentrated, can increase the magnitude of the positive effect of unions on paid
60

sick/vacation days. Consistent with the previous idea, this finding suggests that unions in the
public sector have a greater impact on workers’ benefits and public sector organizations are more
likely to be gender egalitarian (Katz 2013; Laird 2017; Rosenfeld 2014). This finding further
shows that these characteristics of public sector organizations can reshape the intersection
between organized labor and family-friendly workplace supportiveness.
Moreover, although union-represented workers tend to have less female-dominated
occupations, female-dominated occupations can amplify the positive effects of unions on paid
sick/vacation days. Confirming the qualitative evidence (Crocker and Clawson 2012; Milkman
2007), this finding provides more generalizable evidence that unions in female-dominated
occupations can provide family-friendly workplace supportiveness more effectively than unions
in male-dominated occupations. Put differently, given that female workers may have
disproportionate interests in family-friendly policies, unions’ interests in different types of
workers’ rights and benefits may not be equal but dependent on workers’ interests. Whereas
Glass and Fujimoto (1995) focused on the cross-sectional association between union
membership, female-dominated occupations, and family-friendly policies among pregnant
women in the Midwest, I showed a new aspect of how gendered workplace structure reshape the
role of unions in family-friendly policies by ruling out union-represented workers’ time-variant
unobserved characteristics among younger cohorts of workers in the nationally representative
panel data.
However, I found no evidence that the female-dominated workplace significantly
changes the effect of unions on access to other family-friendly policies. Despite female workers’
high demand for family-friendly policies, unions in female-dominated occupations may play a
limited role in increasing access to family-friendly policies, as previously explained, potentially
61

because of workers’ poor working conditions and challenges in engaging in union activities.
Future studies should explore how gendered workplace structure can condition unions’ resources
and motivations to increase family-friendly workplace supportiveness.
In this study, I aimed to improve our limited knowledge of the role of labor unions in
family-friendly policies by theorizing the salience of the bargaining power, gendered workplace
structure, and establishment-level contexts. I argued that the impact of union coverage on
workers’ access to family-friendly policies can be conditioned by work contexts, including the
bargaining power and workers’ demand for work-family policies. Whereas a few qualitative
studies illuminated some of these contexts (Crocker and Clawson 2012; Milkman 2007), this
study advanced theoretical explanations and provided generalizable evidence that helps us to
better understand mixed evidence of unions for family-friendly policies in the existing literature.
In this study, I focused on workers who experienced a change in union status. This
strategy allowed me to rule out workers’ time invariant characteristics associated with union
coverage and access to family-friendly policies, such as generalizable workers’ job preference.
However, it has two limitations. First, although the control variables, such as having a
preschooler, helped to partially adjust for workers’ potential time-varying preference to a
unionized and family-friendly job, they may not do the full adjustment. For example, workers
who experienced a hostile treatment and severe work-family conflict in the previous workplace
may tend to develop a strong preference for a unionized job over time. Second, the analysis does
not tell us how union coverage matters to workers whose union status did not change, such as
union-represented workers who stayed at a unionized job over their careers. Future studies could
address these limitations by using other analytical strategies, such as instrumental variable
methods.
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This study could not overcome several limitations associated with measurements. First,
the binary outcomes did not capture a gradient of the availability of family-friendly policies,
potentially underestimating the magnitude of the disparities in access to the policies between
union-represented and non-union workers. It is possible, for example, that the disparities in long
paid parental leave may be more pronounced than the disparities in short paid parental leave.
Second, the results did not show different mechanisms of how unions affect family-friendly
policies. The effect of unions on increasing the availability of family-friendly policies, for
example, was not teased apart from their effect on increasing awareness of existing family-
friendly policies. Third, occupational gender composition does not accurately measure the
gender composition of employees at the workplace. As male and female workers are separate not
just at occupational level but also at job level (e.g. Petersen and Morgan 1995), occupational
gender composition may not fully estimate how gendered workplace reshape the role of unions
in family-friendly policies. In sum, future studies on labor unions and family friendly policies
could benefit from better measurements.
Despite these limitations, this study improves our understanding of how unions matter to
the work-family interface by revealing the heterogeneity of the role of unions in family-friendly
policies. Previous studies have shown that the association between workers’ union coverage and
union outcomes can be conditioned by broader social contexts, such as state-level union density
and country-level industrial relation (Finnigan and Hale 2018; Hipp and Givan 2015;
VanHeuvelen and Brady 2021). This study focuses on work contexts by drawing on the insight
of organizational researchers and gender scholars for public sector employment, gender
composition of occupations, and the size of establishment. I highlight that these contexts have
important implications for how unions can foster family-friendly workplace supportiveness.
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As national work-family policies are insufficient and access to the policies at the
workplace level remains rare among low-paid workers in the United States (Gerstel and Clawson
2015; Golden 2009; Han, Ruhm, and Waldfogel 2009; Kelly and Kalev 2006; Petts, Knoester,
and Li 2020; Schieman, Milkie, and Glavin 2009), labor unions may serve as an important
alternative institution that facilitates family-friendly workplace supportiveness and potentially
offsets the class disparities in access to family-friendly policies.
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Table 4. Effects of Union on Family-friendly Policies by Work Contexts, Summary of
Prediction
Work contexts The effects of unions on family-friendly policies
Public sector employment ±
Female-dominant occupations ±
Large establishment -
Note: - indicates attenuating the effect of unions on family-friendly policies; ± indicates either
amplifying or attenuating the effect of unions on family-friendly policies

65

Table 5. Descriptive Statistics

Workers with
union coverage
Workers without
union coverage
Paid parental leave
a

.33 .20

(.47) (.40)
Schedule control
a

.28 .34

(.45) (.47)
Number of paid sick/vacation days
a

8.83 6.02

(7.98) (7.50)
Female
a

.41 .50

(.49) (.50)
Public sector
a

.30 .07

(.46) (.26)
% female in an occupation
a
46.78 50.92

(31.96) (30.36)
Size of establishment (ref: 1-49 employees)

50-99 employees
.13 .12

(.33) (.32)
100-249 employees
a

.20 .12

(.40) (.33)
250 + employees
a

.31 .15

(.46) (.36)
Demographic characteristics
Age
a

25.88 25.39

(4.48) (4.41)
Years of schooling
a

13.37 13.07

(2.57) (2.45)
Being married
.23 .22

(.42) (.41)
Urban residence
.92 .93

(.26) (.26)
Having a preschooler child
.29 .27

(.46) (.44)
Job characteristics
Part-time work
a

.18 .32

(.39) (.46)
Hourly wage
a

14.61 11.79

(7.77) (7.00)
Years of seniority
a

1.01 .94

(1.07) (.95)
FMLA eligibility
a

.18 .09

(.38) (.29)
Manufacturing sector
a

.13 .09
66

(.34) (.28)
Occupation (ref: managerial)

Professional
a

.20 .10

(.40) (.31)
Service
a

.19 .23

(.39) (.42)
Sales
a

.07 .17

(.25) (.37)
Administrative
a

.15 .17

(.35) (.38)
Others
a

.37 .25

(.48) (.43)
State characteristics
Real GDP per capita
a

51003.80 49591.27

(10764.68) (9810.02)
Unemployment rate
a

5.99 5.87

(1.80) (1.81)
Right-to-work law
a

.34 .47

(.48) (.50)
Paid parental leave law
a
.11 .07

(.32) (.26)
Paid sick leave law
a

.03 .02

(.16) (.13)
N of person-year observations
1156 10873
N of persons
932 4039
a. The difference between workers with union coverage and workers without union coverage is
statistically significant (p<.05)

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Table 6. Effects of Union Coverage on Family-friendly Policies, Fixed-effect Models

Paid parental leave Schedule control Paid sick/vacation days
Union coverage .04* -.05** .85**

(.02) (.02) (.27)
Work contexts
Public sector .03 -.07*** 1.77***

(.02) (.02) (.32)
% female in occupation -.00 .00 .01*

(.00) (.00) (.00)
Size of establishment
(Reference: 1-49 employees)

50-99 employees .05*** .02 .43

(.01) (.02) (.23)
100-249 employees .10*** .01 1.00***

(.01) (.02) (.23)
250 + employees .13*** .06*** 1.84***

(.01) (.02) (.24)
Note: N of the pooled sample is 12,029.
Paid parental leave and schedule control are binary outcomes. Paid sick/vacation days is a
continuous outcome.
All models control demographic (age, educational attainment, marital status, urban residence,
having a preschooler child), job (part-time work, hourly wage, years of seniority, industry,
occupation, FMLA eligibility) and state characteristics (real GDP per capita, unemployment
rate, right-to-work law, paid parental leave law, paid sick leave law) and include year-fixed
effects.

68

Figure 6. Trends of Access to Family-friendly Policies (2000-2017)
Source : National Longitudinal Survey of Youth 97

69

Figure 7. Moderating Effects of Public Sector Organizations on the Relationship between
Union Coverage and Family-friendly Policies (N=12,029)
Note: Paid parental leave and schedule control are binary outcomes. Paid sick/vacation days is a
continuous outcome.
All models control demographic (age, educational attainment, marital status, urban residence, having a
preschooler child), job (part-time work, hourly wage, years of seniority, industry, occupation, FMLA
eligibility) and state characteristics (real GDP per capita, unemployment rate, right-to-work law, paid
parental leave law, paid sick leave law) and include year-fixed effects.

70

Figure 8. Moderating Effects of Occupational Gender Composition on the Relationship
between Union Coverage and Family-friendly Policies (N=12,029)
Note: Paid parental leave and schedule control are binary outcomes. Paid sick/vacation days is a
continuous outcome.
The density plots indicate the distribution of percentage of female workers in an occupation for
workers with union coverage (red) and workers without union coverage (black).
All models control demographic (age, educational attainment, marital status, urban residence, having a
preschooler child), job (part-time work, hourly wage, years of seniority, industry, occupation, FMLA
eligibility) and state characteristics (real GDP per capita, unemployment rate, right-to-work law, paid
parental leave law, paid sick leave law) and include year-fixed effects.
71

Figure 9. Moderating Effects of the Size of Establishment on the Relationship between Union
Coverage and Family-friendly Policies (N=12,029)
Note: Paid parental leave and schedule control are binary outcomes. Paid sick/vacation days is a
continuous outcome.
All models control demographic (age, educational attainment, marital status, urban residence, having a
preschooler child), job (part-time work, hourly wage, years of seniority, industry, occupation, FMLA
eligibility) and state characteristics (real GDP per capita, unemployment rate, right-to-work law, paid
parental leave law, paid sick leave law) and include year-fixed effects.

72

Chapter 3. Trends in Schedule Control in 11 European Countries, 1997-2015

73

Trends in Schedule Control in 11 European Countries, 1997-2015
ABSTRACT
In this study, I examine whether more workers obtained access to schedule control from 1997 to
2015 and how trends in access to schedule control vary across 11 European countries. Building
on studies of stratification and gender, I pay attention to the role of educational attainment and
gender in explaining trends in schedule control. Findings show that as the growth of access to
schedule control is most pronounced in countries already reporting great access to schedule
control in 1997 (Denmark, Norway, Germany and Sweden), the cross-national disparity in access
to schedule control became more salient in 2015. Decomposition analyses reveal that the
increase in workers with tertiary education is a key source of the increase in countries reporting
the increase in schedule control. Compositional change in high-educated workers is mostly
driven by compositional change in high-educated male workers. This study can help us better
understand the implications of educational expansion for the diffusion of schedule control.
Keywords : schedule control, educational attainment, gender, decomposition analysis,
cross-national comparison

74

Workers who use work-family policies can manage the competing work demands and
family responsibilities more effectively than workers who do not receive the policy support.
While some policies, including parental leave and childcare support, facilitate working parents’
careers in their primary childrearing age, employee-driven schedule control helps workers across
age groups to adjust their work arrangements to accommodate their family and personal demands
(Lott and Chung 2016). Schedule control can enhance workers’ well-being and alleviate work-
life conflict that afflicts an increasing number of workers (Badawy and Schieman 2021; Erin L
Kelly et al. 2014; Moen et al. 2016; Nomaguchi 2009; Voydanoff 2004a).
Previous studies on work-family policies have illuminated how different work-family
policies have expanded in high-income countries but paid relatively scant attention to trends in
schedule control (Daly and Ferragina 2018; Ferragina and Seeleib-Kaiser 2015; Gauthier 1999;
Lewis and Campbell 2007). Although a report showed the increase in schedule control in some
countries (Hegewisch 2009), we do not much know yet how schedule control has increased over
time and this trend varies across country. It is important to further explore trends in schedule
control in part because the demographic characteristics that predict the availability of schedule
control—especially educational attainment and gender—have been shifted and working
conditions have been changed across various groups of workers in the labor markets (Acemoglu
2002; Autor, Levy, and Murnane 2002; Bar-Haim, Chauvel, and Hartung 2019; Bianchi et al.
2012; Gebel and Giesecke 2011; Hook and Paek 2020; Horowitz 2018; Mandel and Semyonov
2014; Pailhé, Solaz, and Stanfors 2021; Schofer and Meyer 2005).
In this paper, I aim to extend our understanding of changing availability of family policy
by documenting the trends in schedule control in 11 European countries and the United Sates.
Using the International Social Survey Programme 1997-2015, I explore whether access to
75

schedule control increased—or decreased—across country. Building on studies of stratification
and labor markets, I pay attention to the role of educational attainment and gender—key
changing demographic features in the labor market—in these trends. In particular, I examine
how the changes in access to schedule control was driven by the changing portions of college-
graduated workers and female workers (compositional change) and explained by the changes in
the association between tertiary education and schedule control and the association between
gender and schedule control (coefficient change). Findings can help us to better understand the
implications of changing labor market contexts for the diffusion of family-friendly policies.
BACKGROUND
Trends in Schedule Control in Europe
Most European countries have provided increasingly generous family policies (Ferragina and
Seeleib-Kaiser 2015; Gauthier 1999). In particular, the trajectories of national family policies
showed a distinct phase in the early 2000s and 2010s. Whereas maternity leave became longer in
the earlier period and such extension reached the plateau in this period, paternity leave started to
emerge from near non-existence, and spending on early child education and care continued to
grow (Daly and Ferragina 2018). As a result, the type of family policies had been more
diversified and the scope of policy support had been expanded in many European countries.
Coinciding with these trends, employee-driven flexible work arrangements have received
increasing attention. Researchers have long paid attention to the cross-national difference in the
availability of schedule control (Chung 2017; Hegewisch and Gornick 2008; Lyness et al. 2012;
Stier and Yaish 2014). Several countries, including the United Kingdom, Netherlands, and
Germany, had introduced policy interventions to increase various forms of flexible work
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arrangements, including reduced hours and increased control over daily scheduling (Hegewisch
2009). Although most of these countries have focused on the right to reduced hours for
caregivers, the right to control over daily schedule control had received increasing attention in
collective bargaining and policy debate (Hegewisch 2009; Hegewisch and Gornick 2008).
Educational Attainment and Schedule Control
One possible source that reshapes the diffusion of schedule control is educational expansion.
College education has been expanded in many European countries over the past few decades
(Schofer and Meyer 2005). Researchers have paid attention to the implication of the growth of
college graduate workers for various facets of work and family life, including income (Breen and
Salazar 2009; Parolin and Gornick 2021), labor force participation (Hook and Paek 2020), and
housework and childcare (Pailhé et al. 2021). Work-family researchers have shown that high-
educated workers tend to have advantages in controlling work schedules because high-educated
workers tend to have sufficient autonomy over their work process than low-educated workers,
and employers tend to have motivations to support the work-family issues of workers in highly
competitive positions (Gerstel and Clawson 2014; Golden 2009; Kelly and Kalev 2006;
Schieman, Melissa A Milkie, and Glavin 2009).
We also have several reasons to investigate whether the propensity of high-educated
workers to be able to control their work schedule increases. Compared low-educated workers,
high-educated workers tend to increasingly rely on information and communication technology
that allows workers to have some discretion over when and where to work (Blair-Loy 2009b). At
a broader level, advanced technology complements “complex” and less routine tasks where high-
educated workers primarily engage (Acemoglu 2002; Autor et al. 2002). By increasing the
importance of high-educated workers’ skills, technological advances, including workplace
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computerization, may enhance their autonomy over work process and scheduling practice. On
the other hand, as legal protections for temporary workers have waned since the 1990s across
countries, the cost of hiring and firing them has been reduced (Gebel and Giesecke 2011). As a
result of the deregulations, working conditions of low-educated workers who are concentrated in
temporary employment may grow more precarious and less autonomous.
Alternatively, the benefit of high-educated workers in access to schedule control may
decrease over time. As the growing number of college graduates exceeds the number of high-
paying jobs, the advantage of having a college degree in securing a high-paying job has
diminished (Bar-Haim et al. 2019; Horowitz 2018). Given the declining position of
postsecondary degree in the labor markets, high-educated workers’ access to schedule control
may grow smaller.
Gender and Schedule Control
Another major changes in the labor market that may impact the diffusion of schedule
control is the shift in the gender composition of workers. Compared to male workers, female
workers are less likely to have autonomy over their work arrangements (Lyness et al. 2012; Stier
and Yaish 2014). This is in part because female workers tend to have disadvantages in getting a
job in a highly autonomous working condition (Adler 1993). In addition, female workers are less
likely to work extremely long hours (Mutari and Figart 2001) and concentrated in part-time work
where workers can arrange their domestic responsibilities relatively easily and thus employers
are not motivated to allow workers to control over work schedule (Beham et al. 2019; Eurofound
2017; Voydanoff 2004a). Whereas female labor market participation rate declined in former
soviet countries from the late 1990s to the 2010s, women reported increasing labor market
participation rate during this period in other European countries (Hook and Paek 2020). Such
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changes in women’s tendency for labor market participation rate may lead to changes in the
share of female workers in the labor market. The increase in the share of female workers may be
translated into compositional changes toward less access to schedule control.
We have opposite predictions for the change in female workers’ tendency to have access
to schedule control. First, studies on gender and work have shown that the gender wage disparity
has been gradually closed, albeit not entirely, as discrimination against women become less
salient (Mandel and Semyonov 2014). This suggests that more female workers may be able to
engage in highly paid and autonomous jobs as much as male workers. That is, the negative
association between being a female worker and schedule control may be weakening. Second,
although men still spend much less time on housework than women, their time spent on
housework has increased (Bianchi et al. 2012; Pailhé et al. 2021), and fathers in dual-earner
couples experience increasingly severe work-family conflict (Nomaguchi 2009). As men who
have substantial challenges in arranging work and family responsibilities may demand family-
friendly workplace policies, the magnitude of the positive association between being a male
worker and schedule control may be amplified.
Educational Attainment, Gender and Schedule Control
How trends in schedule control are attributed to educational attainment may vary by
gender. First, compositional change in educational attainment may be more pronounced among
male workers. Highly-educated male workers tend to have high level of job autonomy and
authority than highly-educated female workers in part because of gendered career choice and
workplace discriminatory practice against women (Smith et al. 2008; Yaish and Stier 2009).
Male advantages in control and power at work that facilitate work processes may amplify high-
educated workers’ advantages in controlling work arrangements.
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Coefficient change associated with educational attainment may be gendered as well.
Studies have shown that although high-educated men are still committed to unpaid work as much
as their spouse, they tend to increase involvement in housework and childcare in greater
magnitude than low-educated men (Altintas and Sullivan 2017) as they are more likely to live in
a dual-earner household and adopt egalitarian attitudes and intensive parenting (Pampel 2011;
Raley, Mattingly, and Bianchi 2006; Schwartz and Mare 2005). As high-educated male workers’
changing behaviors in the work-family interface can intensify work-family conflict and amplify
their need for family-friendly work arrangements, high-educated male workers may obtain more
access to schedule control. Table 7 shows the summary of predictions.
[Table 7 is about here]
METHOD
Data
I used the data for 11 countries in Europe (Germany, Great Britain, Hungary, Norway,
Sweden, Czechia, Slovenia, Poland, France, Denmark, and Switzerland) that were included in
the International Social Survey Programme 1997 and 2015. The data provide information for
workers’ working conditions, including flexible work arrangements. I limited the sample to
employees who were aged 20-65 and working for pay at the time of the survey. I did not include
Spain because the data did not provide a variable for employment status.
I deleted observations that have any missing values. Most variables have only less than
3.2% of missing values, and as exceptions, about 6% of the variable for educational attainment
has missing values in Germany 2015 and about 9% of the variable for part-time work has
missing values in Switzerland 2015. In sum, 3.8% of the original sample was deleted. The final
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sample includes 14,708 respondents. The sample size for each country-year is included in
Appendix H.
Variables
The dependent variable is schedule control. Using a 3-group categorical variable (1 I
cannot change, 2 I can decide within certain limits, 3 I am entirely free to decide), I created a
binary variable that indicates respondents who can control their working schedule within certain
limit or entirely freely. I did not use the variable for working from home because the measures
were inconsistent.
The predictors are educational attainment and gender. For educational attainment, I
created a binary variable that indicates college graduates.
Method
I applied the Kilagawa-Blinder-Oaxada decomposition method. This method divides the
group difference in outcome into the portions attributable to group differences in observed
predictors and the portions attributable to group differences in unobserved predictors (Jann
2008). Following previous studies (Hook and Paek 2020; Pailhé et al. 2021), I treated year as
group of interest and explored schedule control by year. I used a linear probability model that
allows us to interpret the result more intuitively than a logit model (Jann 2008).
I conducted two parts of the analysis. First, I decomposed mean difference in schedule
control between 1997 and 2015 into differences in educational attainment and gender between
the two waves and differences in the coefficients. In this part, I showed each contribution of
educational attainment and gender to change in schedule control. Second, I used four groups—
low-educated male workers, low-educated female workers, high-educated male workers, and
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high-educated female workers— in the models to explore the multiplicative effect of the
predictors. In this part, I decomposed mean change in schedule control into differences in the
frequency of each group and difference in the coefficients for each group. Data was weighted.
The data does not provide household information such as the number of children.
RESULT
Descriptive Finding
Table 8 shows the various trends of schedule control between 1997 and 2015. I listed
countries from the largest rise of schedule control to the largest fall of schedule control. On
average, the change in schedule control is 2 percentage points and varies from -9 to 11
percentage points. Denmark, Norway, Germany, Slovenia, and Sweden showed the significant
increase in access to schedule control during this period. Except Slovenia, these countries
already reported relatively high access to schedule control in 1997. That is, the diffusion of
schedule control in this period is most pronounced in countries with high levels of existing
access to schedule control.
[Table 8 is about here]
Whereas Nordic countries showed the growing prevalence of schedule control, the United
Kingdom, a liberal regime country, did not show significant changes in the prevalence of
schedule control, and conservative regime countries and former soviet countries showed
heterogeneous trajectories in access to schedule control. Although Switzerland reported great
access to schedule control as much as Sweden in 1997, the access did not increase over time in
Switzerland. Similarly, France reported a similar level of access to schedule control to Norway in
2005, but the disparity between these countries became significant in 2015 as access to schedule
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control substantially declined in France during the period. Former soviet countries except
Slovenia did not show the increase in schedule control: Czechia and Poland did not show a
significant change in the prevalence of schedule control, and Hungary reported a substantial
decline in schedule control.
In Table 8, Column 4 and 5 show changes in the shares of college graduates and female
workers from 1997 to 2015. On average, the share of college graduates increased by about 10
percentage points. In 7 countries, the share of high-educated workers significantly increased
during this period. In particular, all countries that reported the increase in schedule control—
Denmark, Norway, Germany, Slovenia, and Sweden—experienced the significant expansion of
high-educated workers. In most countries, the share of female workers increased, but the
magnitude of the increase is generally small. While the share of female workers significantly
increased in Germany and Sweden, it significantly decreased in the United Kingdom.
Columns 6-9 show the changes in the share of low-educated male workers, high-educated
male workers, low-educated female workers, and high-educated female workers, respectively. In
Column 8, the share of low-educated female workers decreased in most countries. In particular,
the size of the decline is significant in Denmark, Norway and Sweden. In contrast, the share of
high-educated female workers increased in most countries. In countries showing increasing
access to schedule control, the magnitude of the increase in high-educate female workers is
significant. Male workers showed similar patterns. The share of low-educated male workers
decreased in 8 countries. Countries reporting increasing access to schedule control showed the
significant decline in low-educated workers. In most countries, the share of high-educated male
workers increased, but this increase is relatively small in comparison to the increase in the share
of high-educated female workers.
83

[Figure 10 is about here]
Figure 10 shows the disparities in trends in access to schedule control by gender and
educational attainment. In all countries except Denmark, the share of workers who reported
having access to schedule control among high-educated workers remained greater than among
low-educated workers over time. This is consistent with the idea of the class disparities in access
to schedule control (Gerstel and Clawson 2015; Golden 2009; Schieman, Melissa A Milkie, et al.
2009). In Denmark, the prevalence of schedule control among low-educated men increased from
1997 to 2015, and this increase erased the gap between high-educated women and low-educated
men.
Among high-educated workers, access to schedule control is most prevalent among men
than women. In Denmark, Germany, Slovenia, and Switzerland, whereas the share of workers
with schedule control among high-educated women did not change much or decreased from
1997 to 2015, the share among high-educated men further increased. As a result, the gender
disparity in access to schedule control among high-educated workers widened.
Low-educated women reported the lowest access to schedule control and did not post
substantial change in access in most countries. In Germany and UK, low-educated women
showed greater access to schedule control than low-educated men. In countries reporting greater
access to schedule control, the prevalence of schedule control increased among low-educated
men, although the magnitude of the increase varies across countries.
Decomposition by Educational Attainment and Gender
Table 9 shows the decomposition of trends in schedule control. The magnitude of the
interaction between compositional and coefficient change is small in all countries.
84

A substantial portion of change in schedule control is attributable to compositional
change. In 9 countries, the overall compositional change leads to more access to schedule
control. In all countries reporting the growth of access to schedule control, compositional change
significantly contributes to increase in access to` schedule control. In the United Kingdom,
compositional change leads to less access to schedule control, but the magnitude of this
contribution is small. Columns 3 and 4 show that compositional change is concentrated on
compositional change in educational attainment. In 10 countries, change in the prevalence of
high-educated workers leads to greater access to schedule control. The contribution of
compositional change in educational attainment is most pronounced in countries reporting a
significant increase in the share of high-educated workers. In contrast, compositional change in
gender attributes little to change in access to schedule control in all countries.
Column 2, 5, 6 and 7 show the pattern of coefficient change across countries. In general,
change in the association between tertiary degree and schedule control explains an only small
portion of change in access to schedule control. Coefficient change is more associated with
change in the association between gender and schedule control. In most countries, more female
workers lost access to schedule control from 1997 to 2015 in comparison to low-educated male
workers. Coefficient change is also pronounced in the constant that indicates change in schedule
control among the reference group (low-educated male workers).
How a trend in schedule control is attributed to coefficient change substantially varies
across countries. Countries reporting increasing schedule control showed positive coefficient
change that augments positive compositional change. In these countries, positive coefficient
change is largely associated with an increasing tendency of low-educated men to have schedule
control. The magnitude of positive coefficient change associated with low-educated men is
85

greater than the magnitude of negative coefficient change associated with female workers. In
contrast, countries reporting decline in schedule control showed negative or zero coefficient
change. In these countries, negative coefficient change is compounded of a declining tendency of
low-educated men to have access to schedule control and a declining tendency of female workers
to have access to schedule control. In three of these countries, negative coefficient change
surpasses positive compositional change in magnitude.
In countries reporting the increasing share of high-educated workers, the changes lead to
positive compositional changes. In Denmark, Germany, and Slovenia, the share of low-educated
male workers decreased in significant size, but low-educated male workers obtained more access
to schedule control over time. In these countries, the share of female workers increased but this
increase does not lead to compositional change. Rather, female workers were less likely to have
access to schedule control in 2015 than 1997.
To summarize, findings support the idea that in countries reporting increasing access to
schedule control, educational expansion is a key factor of the growth of schedule control. In
Switzerland and France, the significant increase in the share of high-educated workers leads to
positive compositional change, but compositional change in high-educated workers is offset by
negative coefficient change.
[Table 10 is about here]
Decomposition by Educational Attainment x Gender
In Table 10, focusing on the intersection between educational attainment and gender, I
present detailed decompositions for low-educated male workers, high-educated male workers,
low-educated female workers, and high-educated female workers. Column 1 shows that in
86

countries reporting the decline in the share of low-educated male workers, compositional change
in low-educated male workers leads to more access to schedule control. In countries
experiencing the increase in the share of low-educated male workers, compositional change in
low-educated male workers leads to less access to schedule control. Similarly, in Column 3,
countries reporting the declining share of low-educated female workers show positive
compositional change in low-educated female workers.
Compositional change in high-educated workers is more gendered. In countries reporting
the increase in the share of high-educated male workers, more high-educated male workers lead
to positive compositional change. In these countries, the share of high-educated female workers
increased in similar or greater magnitude than the share of high-educated male workers, but the
changes for high-educated female workers do not lead to compositional change. Slovenia is the
only country where the increase in the share of high-educated female workers leads to significant
positive compositional change.
Compared to compositional change, coefficient change is generally less clearly
pronounced in detailed decomposition analysis. In 8 countries, low-educated male workers were
increasingly likely to have access to schedule control, but the magnitude of this change is only
significant in Slovenia. Similarly, in 7 countries, the tendency of high-educated male workers to
have schedule control increased, but the size of this increase is largely small except in Germany
and Switzerland. In Germany, where the increase in the share of high-educated male workers
leads to positive compositional change, high-educated male workers were increasingly likely to
have schedule control. Switzerland showed little increase in the share of high-educated male
workers and small compositional change in high-educated workers but reported the increasing
tendency of high-educated male workers to have schedule control.
87

Coefficient changes associated with low-educated and high-educated female workers are
less clear, too. In 8 countries, low-educated female workers were less likely to have schedule
control in 2017 than 1997, and but the magnitude of the decline is not significant in all countries.
Similarly, in 7 countries, high-educated female workers lost access to schedule control over time,
but the size of the decline in their access to schedule control is only substantial in Slovenia. In
Slovenia, negative coefficient change associated with female workers is largely about negative
coefficient change associated with high-educated female workers.
In the supplementary analysis, I examined whether limiting the sample to full-time
workers changes the main results from detailed decomposition analysis (Appendix I). Results are
largely similar. As exceptions, in Sweden, compositional change in high-educated female
workers is more salient in the sample limited to full-time work than the full sample. In Slovenia,
positive compositional change in low-educated female workers is significant in the full-time only
sample. This suggests that the availability of part-time work may mask the contribution of the
growth of high-educated female workers (or the decline in low-educated female workers) to
change in access to schedule control in some countries. However, the findings provide little
evidence that the uneven distribution of access to part-time work across class and gender
explains the main results.
DISCUSSION
In this study, I documented trends in schedule control with a focus on educational
attainment and gender. This study provided the theoretical mechanisms of how the diffusion of
schedule control is driven by shifts in the portions of college graduate workers and female
workers in the labor market, changes in the relationships between educational attainment and
schedule control, and alterations in the associations between gender and schedule control.
88

Findings help to explain the implications of growing college enrollments and gender-egalitarian
practices at work and family for inequality in autonomy over work schedule.
I showed how the trajectories of access to schedule control vary across country. While
Denmark, Norway, Germany, Slovenia, and Sweden reported the growth of access to schedule
control, access to schedule control did not substantially increase in other countries and even
significantly decreased in France and Hungary. As most of the countries experiencing the growth
of access to schedule control already reported great access to schedule control in 1997, the cross-
national disparity in access to schedule control became wider in 2015. Given that countries with
greater access to schedule largely overlap with countries providing greater access to other
national work-family policies, such as maternity leave (Gornick and Meyers 2003), national
work-family policies may interact with the diffusion of schedule control potentially by making
workplace culture supportive of workers’ personal life and family responsibilities. This finding
enhances our knowledge of the scope of cross-national variation of family-friendly
supportiveness by providing new evidence of changing cross-national disparities in access to
schedule control.
Another finding is that educational expansion is a primary source of the growth of access
to schedule control in countries reporting increasing access to schedule control. There is little
evidence that the propensity of high-educated workers to have access to schedule control
increased. These findings resonate our previous understanding on the advantages of high-
educated workers in controlling their work schedules (Gerstel and Clawson 2015; Golden 2009;
Schieman, Melissa A Milkie, et al. 2009). This study helps us to better explain the changing
pattern of the class disparity in schedule control by showing how trends in schedule control are
89

connected to growing proportions of high-educated workers and their changing working
conditions.
Compositional change in high-educated workers is mostly driven by compositional
change in high-educated male workers. Although the share of high-educated female workers
increased across countries, this increase was not translated into the growth of schedule control in
all countries except Slovenia. This result remains largely consistent when the sample excludes
part-time workers, suggesting that the gendered distribution of part-time work does not explain
the gender difference in compositional change in educational attainment. This supports the idea
that other gendered working conditions, such as job autonomy and long work hours, may
condition access to schedule control among high-educated workers (Adler 1993; Mutari and
Figart 2001). This finding further informs us that despite high-educated workers’ advantages in
schedule control, growing female advantages in access to high education does not facilitate the
diffusion of schedule control. Unfortunately, the small sample size and limited data prevented
me from examining the mechanisms associated with the gender difference in compositional
change. Further studies are needed.
This study helps us better understand various trajectories of access to schedule control in
European countries. Whereas previous studies illuminated various predictors of workers’ access
to schedule control, I focused on the changing aspects of the two key predictors—social class
and gender—in the availability of schedule control. Researchers should look more closely at
various changing work contexts, including workplace technologies, employment regimes, and
family-friendly supportiveness, to fully explain the diffusion and changing stratification of
schedule control and, more broadly, resources to arrange work and family responsibilities.
90

Table 7. Predictions about Change in Access to Schedule Control
Prediction Compositional change Coefficient change
High-educated workers have
more access to schedule
control
The growth of workers with
tertiary degree, positive
Technological advances and
the deregulation of
temporary employment,
positive

The decline in advantages of
tertiary degree, negative

Female workers have less
access to schedule control
The growth of female
workers, negative
Decreasing workplace
discrimination against
female workers, positive

Men’s increasing demand
for family-friendly policies,
negative

91

Figure 10. Trends in Access to Schedule Control, 1997-2015
Note: Data is weighted.

0
50
100
0
50
100
0
50
100
1997 2015
1997 2015 1997 2015 1997 2015
Denmark N orw ay Germany Slovenia
Sweden Czechia Poland U K
Switzerland France H ungary
Low-educated men High-educated men
Low-educated women High-educated women
% o f w o rk e rs w ith a c ce s s to s ch e d u le co n tro l
Year
Graphs by country
92

Table 8. Changes in Schedule Control, Educational Attainment and Gender, 1997-2015

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Schedule control

Male Female

1997 2015 Change High ed Female Low ed High ed Low ed High ed
Denmark 0.57 0.68 0.11 ** 0.38 ** 0.02

-0.17 ** 0.15 ** -0.21 ** 0.23 **
Norway 0.47 0.56 0.09 ** 0.25 ** 0.04

-0.12 ** 0.08 ** -0.13 ** 0.17 **
Germany 0.46 0.55 0.08 ** 0.12 ** 0.10 ** -0.16 ** 0.06 ** 0.05

0.06 **
Slovenia 0.33 0.41 0.08 * 0.09 ** 0.03

-0.04

0.01

-0.05

0.08 **
Sweden 0.64 0.70 0.06 * 0.25 ** 0.05

-0.13 ** 0.08 ** -0.12 ** 0.17 **
Czechia 0.37 0.37 0.00

-0.01

0.00

0.02

-0.02

-0.01

0.01

Poland 0.25 0.24 -0.01

0.03

-0.03

0.02

0.01

-0.05

0.02

UK 0.40 0.39 -0.01

-0.05

-0.09 * 0.09 ** -0.01

-0.04

-0.04

Switzerland 0.63 0.60 -0.02

0.08 ** 0.06 * -0.04

-0.01

-0.03

0.09 **
France 0.45 0.38 -0.07 * 0.12 ** 0.02

-0.08 * 0.05 * -0.05

0.07 **
Hungary 0.26 0.17 -0.09 ** 0.04

0.04

-0.03

0.00

-0.01

0.04 *
Min 0.25 0.17 -0.09

-0.05

-0.09

-0.17

-0.02

-0.21

-0.04

Max 0.64 0.7 0.11

0.38

0.1

0.09

0.15

0.05

0.23

Mean 0.44 0.46 0.02 0.12 0.02 -0.06 0.04 -0.06 0.08
Note: *p<0.05, **p<0.01

93

Table 9. Decomposition of Trends in Access to Schedule Control, 1997-2015
Country
Overall decomposition Composition Coefficient Interaction
(1) (2) (3) (3) (4) (5) (6) (7) (8) (9)
Comp Coeff Interaction High ed Female High ed Female Constant High ed Female
Denmark 0.07 ** 0.05 -0.01 0.07 ** 0

-0.01

-0.07 * 0.13 * -0.01 0
Norway 0.05 ** 0.04 0 0.05 ** 0

0

0

0.04

0 0
Germany 0.03 ** 0.06 * 0 0.03 ** 0.01

0.02

-0.04 * 0.08 * 0.01 -0.01
Slovenia 0.03 * 0.06 -0.01 0.03 * 0

-0.03

-0.06 * 0.15 ** -0.01 0
Sweden 0.05 ** 0.02 -0.01 0.05 ** 0

0

-0.02

0.04

0 0
Czechia 0

0 0 0

0

-0.01

0.01

0

0 0
Poland 0.01

-0.02 0 0.01

0

-0.01

-0.02

0.01

0 0
UK -0.01

0 0 -0.01

0

0.02

0.02

-0.03

0 0
Switzerland 0.01

-0.04 0 0.01 ** 0

0.01

-0.03

-0.02

0 0
France 0.02 * -0.09 ** 0 0.02 ** 0

0

0

-0.09

0 0
Hungary 0.01

-0.09 ** -0.01 0.01

0

-0.02

-0.03

-0.05

0 0
Min -0.01

-0.09 -0.01 -0.01

0

-0.03

-0.07

-0.09

-0.01 -0.01
Max 0.07

0.06 0 0.07

0.01

0.02

0.02

0.15

0.01 0.07
Mean 0.02 0.00 0.00 0.02 0.00 0.00 -0.02 0.02 0.00 0.02
Note: *p<0.05, **p<0.01
94

Table 10. Detailed Decomposition of Trends in Access to Schedule Control, 1997-2015

Composition Coefficient

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Male Female Male Female
Constant Country Low ed High ed Low ed High ed Low ed High ed Low ed High ed
Denmark 0.013 * 0.021 ** 0.022 ** 0.01

0.034 0.005 -0.025

-0.009

0.049

Norway 0.01 ** 0.017 ** 0.017 ** -0.001

0.012 -0.011 -0.013

0.012

0.038

Germany 0.026 ** 0.009 * -0.002

0.003

-0.005 0.013 * -0.021

-0.002

0.06 *
Slovenia 0.007 0.003 0.009

0.012 * 0.045 * 0.004 0

-0.021 * 0.036

Sweden 0.015 ** 0.014 ** 0.013 ** 0.009

0.013 -0.004 -0.006

0.001

0.011

Czechia -0.002 -0.003 0.001

0.001

0 0.005 0.023

-0.006

-0.014

Poland -0.003 0.001 0.006

0.001

0.021 -0.005 0

-0.001

-0.03

UK -0.012 * -0.001 0.002

-0.001

-0.002 -0.004 -0.009

0.011

0.005

Switzerland 0.003 -0.002 0.004

0.004

-0.007 0.018 * -0.009

-0.003

-0.027

France 0.009 * 0.007 * 0.004

0.003

0.015 -0.01 -0.01

0.012

-0.095 *
Hungary 0.004 -0.001 0.001

0.003

0.026 0.001 0.003

-0.008

-0.111 *
Min -0.012 -0.003 -0.002

-0.001

-0.007 -0.011 -0.025

-0.021

-0.111

Max 0.026 0.021 0.022

0.012

0.045 0.018 0.023

0.012

0.06

Mean 0.01 0.01 0.01 0.00 0.01 0.00 -0.01 0.00 -0.01
Note: *p<0.05, **p<0.01
Interaction effects between compositional and coefficient changes were not significant and omitted in the table.

95

CONCLUSION
In my dissertation, I aimed to theorize and examine how changing working conditions
matter to workplace family-friendly supportiveness. Focusing on changes in workplace
technology, collective bargaining, and demographic compositions, I explored how inequality in
access to work-family policies is connected to broader labor market contexts.
This dissertation has several contributions to studies of stratification, work, gender, and
social policy. First, I documented trends in work-family policies. In Chapter 1, I showed trends
in access schedule control over 25 years in the United States by combining the Current
Population Survey with the American Time Use Survey. In Chapter 3, I identified cross-national
differences in changes in access to schedule control about over 20 years in 11 European
countries.
Second, my findings help us to better understand social factors that explains the diffusion
of family-friendly policies. In Chapter 1 and Chapter 2, I showed that computerized occupations
and union coverage are key to workers’ access to schedule control. In Chapter 3, I showed that
educational expansion facilitates the diffusion of schedule control.
Third, I contextualized the significance of workplace changes for work-family policies by
focusing on the variance across class, organizations, and gender. In Chapter 1, I showed that
computerization has a greater impact on schedule control among high-educated workers. In
Chapter 2, I showed that the positive effect of union coverage on paid sick days is most
pronounced in public sector organizations and female-dominated occupations. In Chapter 3, I
showed that educational expansion among male workers, not the one among female workers,
explains a significant portion of the growth in schedule control.
96

In conclusion, the availability of work-family policies is conditioned by working
conditions. While national-level legal support plays a crucial role in making family-friendly
policies widely available, the diffusion of workplace family-friendly policies is intertwined with
various shifts in broader labor market contexts, including technological advances, union
diversifications, and demographic pressure in the labor force, as well. We can better understand
how workers’ work-family interface is socially constructed when we further explore how
workplace changes shape the disparity and diffusion of family-friendly policies.

97

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110

APPENDICES

Appendix A. Trends of Email Use by Occupational and Educational Groups (1997-2004)

Source : The CPS Work Schedule Supplement combined with the CPS Computer and Internet
Use (N=131,456)

0
20
40
60
80
% of Email Use in an Occupation
1997 2001 2004
year
Managerial
Professional
Sales
Clerical
Service
Manual
20
30
40
50
60
% of Email Use in an occupation
1997 2001 2004
year
No college degree
College degree
111

Appendix B. Predicted Probability of Schedule Control for Email Use

(a) CPS 1997-2004 (b) ATUS 2017-2018
Note: The vertical lines indicate the 95 % confidence intervals.
Figure (a) shows the marginal effects of occupation-level email use on schedule control. The models
include year-fixed effects, educational attainment, demographic characteristics (age, sex, parental
status, presence of preschooler, marital status, race, regional division), and work characteristics (public
sector, manufacturing sector, and work hour status).
Figure (b) shows the marginal effect of occupation-level frequency of email use on schedule control.
The model includes educational attainment, demographic characteristics (age, sex, parental status,
presence of preschooler, marital status, race, regional division), and work characteristics (public sector,
manufacturing sector, and work hour status).

With occupation-fixed effects
Without occupation-fixed effects .2
.3
.4
.5
.6
P r o b a b i l i t y o f S c h e d u l e C o n t r o l
0 20 40 60 80 100
% of Email Use in an Occupation
.4
.5
.6
.7
.8
P r o b a b i l i t y o f S c h e d u l e C o n t r o l
1 2 3 4 5
Frequency of Email Use
112

Appendix C. The Moderating Effect of Computerization on the Association between
Educational Attainment and Schedule Control
CPS (1991-2004) ATUS (2017-2018)
(1) (2)
College graduates 0.008 -0.155**

(0.005) (0.051)
% Computer use 0.002***

(0.000)
College x % Computer use 0.097***

(0.008)
Importance of computer use at work 0.001***

(0.000)
College x Importance of computer use at work 0.054***

(0.013)
Constant 0.200*** 0.457***

(0.006) (0.039)
R
2
0.086 0.082
AIC 202340 12170
BIC 202613 12347
* p<0.05, ** p<0.01, *** p<0.001
Note : All models include demographic (age, sex, parental status, presence of preschooler, marital
status, race, and regional divisions) and work characteristics (public sector, manufacturing sector, and
work hour status). Model 1 includes occupation and year fixed effects.

113

Appendix D. Heterogeneity in the marginal effect of college degree on schedule control across
levels of email use (CPS 1997-2004)
(a) Linear interaction and the 3-bin models (b) Kernel estimator
Note: All models include occupation and year fixed-effect, demographic (age, sex, parental status,
presence of preschooler, marital status, race, and regional divisions) and work characteristics (public
sector, manufacturing sector, and work hour status). The long-dashed and short-dashed density plots
indicate the distributions of college graduates and non-college degree across levels of computerization,
respectively.

L M H
0
.02
.04
.06
.08
.1
.12
M a r g i n a l E f f e c t o f C o l l e g e D e g r e e o n S c h e d u l e C o n t r o l
0 20 40 60 80 100
Moderator: % of email use in an occupation
0
.05
.1
.15
M a r g i n a l E f f e c t o f C o l l e g e D e g r e e o n S c h e d u l e C o n t r o l
0 20 40 60 80 100
Moderator: % of email use in an occupation
114

Appendix E. Heterogeneity in the marginal effect of college degree on schedule control across
levels of email use (ATUS 2017-2018)
(a) Linear interaction and the 3-bin models (b) Kernel estimator
Note: All models include demographic (age, sex, parental status, presence of preschooler, marital
status, race, and regional divisions) and work characteristics (public sector, manufacturing sector, and
work hour status). The long-dashed and short-dashed density plots indicate the distributions of college
graduates and non-college degree across levels of computerization, respectively.

L M H
-.4
-.2
0
.2
.4
M a r g i n a l E f f e c t o f C o l l e g e D e g r e e o n S c h e d u l e C o n t r o l
1 2 3 4 5
Moderator: Frequency of email use
-.4
-.2
0
.2
.4
M a r g i n a l E f f e c t o f C o l l e g e D e g r e e o n S c h e d u l e C o n t r o l
1 2 3 4 5
Moderator: Frequency of email use
115

Appendix F. Heterogeneity in the marginal effect of college degree on schedule control across
levels of computerization, adjusted for working from home
(a) CPS 1991-2004 (b) ATUS 2017-2018
Note: All models include demographic (age, sex, parental status, presence of preschooler, marital
status, race, and regional divisions) and work characteristics (public sector, manufacturing sector, and
work hour status). The long-dashed and short-dashed density plots indicate the distributions of college
graduates and non-college degree across levels of computerization, respectively.

0
.02
.04
.06
.08
.1
M a r g i n a l E f f e c t o f C o l l e g e D e g r e e o n S c h e d u l e C o n t r o l
0 20 40 60 80 100
Moderator: % of computer use in an occupation
-.3
-.2
-.1
0
.1
.2
.3
M a r g i n a l E f f e c t o f C o l l e g e D e g r e e o n S c h e d u l e C o n t r o l
1 2 3 4 5
Moderator: Importance of interacting with computer use at work
116

Appendix G. The effects of union coverage on family-friendly policies, fixed effect models by
gender
Paid parental
leave
Schedule control Paid sick/vacation
days

Men Women Men Women Men Women
Union coverage
.03 .06* -.03 -.08** .86* .96*

(.02) (.03) (.02) (.03) (.34) (.43)
Work contexts

Public sector
.02 .04 -.06 -.08** 2.14*** 1.47***

(.03) (.02) (.03) (.03) (.54) (.41)
% female in occupation
-.00 -.00 .00 -.00 .01 .00

(.00) (.00) (.00) (.00) (.01) (.01)
Size of establishment
(Reference: 1-49 employees)

50-99 employees
.05** .06** .01 .03 .67* .56

(.02) (.02) (.02) (.03) (.31) (.32)
100-249 employees
.10*** .13*** .00 .01 .86** 1.53***

(.02) (.02) (.02) (.02) (.31) (.33)
250 + employees
.13*** .16*** .04 .06** 2.13*** 1.81***

(.02) (.02) (.02) (.02) (.32) (.34)
Note: N of men and women is 6,097 and 5,932, respectively.
All outcomes except paid sick/vacation days are binary outcomes. Paid sick/vacation days is a
continuous outcome.
All models control demographic (age, educational attainment, marital status, urban residence, having a
preschooler child), job (part-time work, hourly wage, years of seniority, industry, occupation, and
FMLA eligibility), and state characteristics (real GDP per capita, unemployment rate, right-to-work
law, paid parental leave law, paid sick leave law), and include year-fixed effects.

117

Appendix H. Sample size by country and year

1997 2015
Denmark 570 602
Norway 1355 880
Germany 801 885
Slovenia 433 402
Sweden 684 575
Czechia 418 633
Poland 381 672
UK 459 763
Switzerland 1341 644
France 642 583
Hungary 507 478

118

Appendix I. Detailed decomposition of trends in access to schedule control, full-time workers only 1997-2015

Composition Coefficient

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Male Female Male Female
Constant Country Low ed High ed Low ed High ed Low ed High ed Low ed High ed
Denmark 0.018 * 0.019 ** 0.02 ** 0.016

0.029 0.008 -0.019

-0.009

0.039

Norway 0.016 ** 0.014 ** 0.012 ** 0.004

0.01 -0.01 -0.012

0.011

0.059 *
Germany 0.023 ** 0.014 ** -0.002

0.003

-0.007 0.014 -0.015

-0.002

0.075 *
Slovenia 0.002 0.002 0.014 * 0.013 * 0.043 * 0.005 0.003

-0.023 * 0.05

Sweden 0.017 ** 0.012 ** 0.014 ** 0.015 * 0.013 -0.003 0.002

-0.004

0.004

Czechia -0.004 -0.005 0.004

0.001

0.018 -0.001 0.022

-0.004

-0.012

Poland 0 0.002 0.008

0.001

0.019 -0.01 0.004

0.005

-0.011

UK -0.011 -0.004 0

0

-0.007 -0.005 -0.005

0.013

0.014

Switzerland 0.003 -0.004 0

0.002

-0.012 0.024 * -0.01

-0.002

-0.031

France 0.013 * 0.007 0.003

0.007

0.017 -0.006 -0.003

0.001

-0.106 **
Hungary 0.003 0 0.002

0.004

0.028 0.003 -0.001

-0.009

-0.124 **
Min -0.011 -0.005 -0.002 0 -0.012 -0.01 -0.019 -0.023 -0.124
Max 0.023 0.019 0.02 0.016 0.043 0.024 0.022 0.013 0.075
Mean 0.01 0.01 0.01 0.01 0.01 0.00 0.00 0.00 0.00
Note: *p<0.05, **p<0.01
Interaction effects between compositional and coefficient changes were not significant and omitted in the table.

Abstract (if available)

Abstract Employees have unequal access to family-friendly policies, such as parental leave and schedule control, but we know little about the role of changing labor markets in shaping access to family-friendly policies. In this dissertation, I investigate three facets of the diverging workplace and their impacts on inequality in access to family-friendly benefits: (1) the role of computerization in access to schedule control (2) the effects of unions on the availability of family-friendly policies, and (3) the uneven growth of schedule control in Europe. I apply quantitative methods, including fixed-effect models, instrumental variable estimation, and decomposition analysis.
First, I examine how computerization increases access to schedule control and amplifies social class disparities in schedule control. Combining the Current Population Survey and the American Time Use Survey with time-varying occupation-level information, I find that computerization increases access to schedule control and occupational computerization corresponds with growing education-based inequalities in schedule control. This suggests that computerization has led to rising inequality in work experiences with wide-ranging implications for the intersection of work, family, and well-being.
Second, I explore whether labor unions increase access to several family-friendly policies and how work contexts, occupational gender composition, moderate the effect of unions on these policies. I theorize the heterogeneity of unions’ effects on family-friendly policies to better understand inconsistent findings about the role of unions in those policies. Combining the National Longitudinal Survey of Youth 97 with state-level characteristics, I find that unions increase access to paid parental leave and sick days/vacations but decrease schedule control. Unions in female-dominated occupations tend to have greater impacts on family-friendly policies. This shows that gendered workplace structures can reshape the role of unions in the work-family interface.
Third, I decompose trends in schedule control by educational attainment and gender across 11 European countries. I use the International Social Survey Programme to document how aggregate changes may be explained by changes in workers’ characteristics such as educational attainment versus changes in the association between these characteristics and schedule control from 1997 and to 2015. These findings can help us to better understand the implications of job polarization and gender revolution at work for the work-family interface.
Broadly, my dissertation can improve our understanding of the mechanisms underlying the links between labor market characteristics and the availability of family-friendly policies, the impacts of technological advances and organized labor on the work-family interface, and the processes of the diffusion of family-friendly policies. These findings can help inform policies related to the work-family interface, gender, and well-being.

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Asset Metadata

Creator Paek, Eunjeong (author)

Core Title Changing workplace and inequality in work-family policies

School College of Letters, Arts and Sciences

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Degree Program Sociology

Degree Conferral Date 2022-08

Publication Date 08/10/2022

Defense Date 08/09/2022

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