Link to Paper

Living Apart, Advancing Together: Noncohabiting Couples
and the Gender Gap on the Tenure Track
Marta Murray-Close∗
March 20, 2015
Abstract
A large empirical literature finds that family migration decisions tend to
benefit husbands at the expense of wives and that this gender asymmetry
is most pronounced among couples with traditional gender-role preferences.
Against this backdrop, a smaller qualitative literature focused on dual-career
couples finds that living apart from a spouse or partner, as a means of avoiding
the tradeoff between spouses’ careers, is especially beneficial to women. This
paper uses data from an original survey of new PhD economists to provide
the first quantitative evidence of gender asymmetry in the causes and consequences of noncohabitation. I find that living apart from a spouse or partner
predicts tenure-track employment more strongly for women than men in the
first year after graduate school and that PhDs with nontraditional gender-role
preferences are more likely than other PhDs to live apart.
1
Introduction
For men with tenure-track aspirations, marriage is conducive to professional success;
for women, it may be an obstacle. Observers of academic labor markets have speculated that marriage harms the careers of PhD women because it constrains them
to live in areas where their partners can find suitable employment. In keeping with
this observation, a small qualitative literature has found that the couples who live
apart view career growth for women as a key benefit of the arrangement. This paper
uses data from an original survey of new PhD economists to assess the contribution
of noncohabitation to gender equality on the tenure track.
Studies of work-family conflict in academia has found that the conflict is more
severe for women than for men (Mason and Goulden, 2002, 2004; Wolfinger et al.,
2008, 2009; Mason et al., 2009; Goulden et al., 2011). While children are the most
important source of work-family conflict, marriage is also a source. Marriage disadvantages women, relative to men, in the competition for tenure-track jobs (Wolfinger
∗
Department of Economics, University of Massachuetts Amherst, [email protected].
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et al., 2008), and it increases the probability that women leave the labor force (Wolfinger et al., 2009). Marriage also reduces the probability that women achieve tenure
relative to the probability that men do (Ginther and Kahn, 2004).
The negative impact of marriage on the career advancement of PhD women may
reflect their special vulnerability to dual-career location problems (Kulis and Sicotte,
2002). Given the cultural norm of hypergamy, or women marrying up, women with
PhDs are more likely than men with PhDs to have partners whose credentials and
career commitment equal or exceed their own (Shauman and Xie, 1996; Astin and
Milem, 1997; Schiebinger et al., 2008; McFall and Murray-Close, 2011). The greater
tendency of PhD women to have partners with PhDs is especially pronounced in
fields where women are underrepresented (Astin and Milem, 1997; Schiebinger et al.,
2008). In addition, PhD women may be less likely than PhD men to prioritize their
own careers over the careers of their partners, even when they are similarly situated
professionally (Schiebinger et al., 2008).
More broadly, previous research on the location choices of couples has produced
substantial evidence of gender asymmetry in the determinants and consequences of
family migration. When choosing a shared location, heterosexual couples appear to
prioritize the employment opportunities of men over those of women (Shihadeh, 1991;
Bielby and Bielby, 1992; Compton and Pollak, 2007; McKinnish, 2008; Shauman,
2010), and moves undertaken by couples tend to harm the employment prospects of
women (Spitze, 1984; Shihadeh, 1991; Boyle et al., 2001; Cooke, 2001; Boyle et al.,
2003; Cooke, 2003; Shauman and Noonan, 2007; McKinnish, 2008; Cooke et al., 2009).
The tendency of heterosexual couples to prioritize the opportunities of men is evident
even in the select group of academic and half-academic couples (Schiebinger et al.,
2008).
The standard economic model of family migration suggests that husband-centered
decision making reflects the superior labor-market position of men (Mincer, 1978).
Specifically, this model assumes that couples maximize their joint utility and that it
is therefore rational for them to accept economic losses to one partner – usually the
wife – when there are offsetting gains to the other. Recently, several studies have
challenged this assumption, pointing to evidence that gender ideology mediates the
responses of couples to the economic opportunities of husbands and wives. These
studies report that husband-centered decision making is stronger among couples who
endorse gendered family roles (Bielby and Bielby, 1992; Cooke, 2008) and those with
less egalitarian divisions of household chores (Jurges, 2006).
In light of this findings, one might expect PhD women to fare better professionally
when they live apart from their partners. One might also expect gender traditionalism to deter couples from living apart. In fact, there is some empirical evidence to
support the first expectation: the sole quantitative study that combined a measure
of living arrangements with a measure of career outcomes for academic couples found
that geographic distance from a spouse or partner increased research productivity
for women but decreased it for men (Bellas, 1997). With respect to the second ex2
pectation, the authors of some qualitative studies of noncohabitation attribute its
rise in the late 1970s to evolving gender roles and a growing commitment to equality
between husbands and wives within marriage (Gerstel and Gross, 1982).
This paper uses a unique non-convenience sample of PhDs identifying couples
who live apart to answer the following questions about noncohabitation: First, does
noncohabitation mitigate the gender gap in tenure-track employment in the first year
after graduate school? Second, does gender-nontraditionalism encourage living apart,
net of other personal and professional characteristics? I find that the answer to both
question is yes. Living apart is associated with a statistically and substantively significant increase in the probability of tenure-track employment for women but little to
no increase in the probability if tenure-track employment for men. Moreover, PhDs
who believe that traditional family roles benefit children and those with a traditional
division of household chores are statistically less likely to live apart. These results
suggest that, among PhD and half-PhD couples, nontraditional gender ideology may
facilitate nontraditional living arrangements that support women’s career advancement.
2
Quantitative data on couples who live apart
This paper uses data from the Job Seekers Project, a longitudinal data collection
project tracking the personal and professional trajectories of recent entrants to US
junior PhD job market in economics. The Job Seekers Project combines information
from publicly accessible academic and professional websites with data from a series of
web-based surveys to create a detailed dataset for studying the work-family tradeoffs
facing early-career PhDs. This paper represents the experiences of PhDs who entered
the economics job market as new job candidates during the 2008-09 and 2009-10
academic years.
2.1
Overview of the economics job market
The economics job market is highly structured and fairly transparent. Employers
who wish to hire new PhDs during a given academic year post most of their job advertisements to a website maintained by the American Economic Association (AEA)
between September and December of that year. During those same months, new
job candidates post their names, contact information, and CVs to publicly accessible job-candidate websites maintained by their graduate programs, and they submit
application packets in response to relevant job advertisements. Most first-round job
interviews take place at the Annual Meeting of the AEA in early January. Campus
visits begin almost immediately after the conference and are largely complete by the
end of March. Most job candidates know the outcome of their job search by the
end of April. The Job Seekers Project exploited these features of the economics job
market to compile a list sample of new job candidates in 2008-09 and 2009-10, to
3
obtain detailed information about the academic and professional backgrounds of the
job candidates, and to field web-based surveys near the beginning and after the end
of their first stint on the job market.
2.2
Sampling frame: Postings on job-candidate websites
Graduate programs launching new job candidates to the economics job market maintain publicly accessible job-candidate websites on which the job candidates post their
names, contact information, and CVs. The programs update these websites each
fall, near the beginning of the job-market season. To facilitate access to information about new job candidates, the National Bureau of Economic Research (NBER)
posts to their own website (http://www.nber.org/candidates) a list of links to the
job-candidate websites of the graduate programs.
In December 2008, the Job Seekers Project visited the website of the NBER and
followed each link to a job-candidate website of a graduate program located in the
United States or Canada. In December 2009, we expanded the sampling frame to
include graduate programs located in Europe. At each job-candidate website, we
recorded the names and contact information of all listed job candidates. The public
posting of job-candidate information by graduate programs is nearly universal in
economics. Consequently, we believe the Job Seekers sample includes nearly all of
the new job candidates who expected to participate in first-round job interviews at
the Annual Meeting of the AEA in January 2009 and 2010.
2.3
Publicly posted data
We used information from the job-candidate websites to code several demographic
and academic characteristics of the PhDs in our sample. We coded the PhDs as
male or female using their first names and, when available, the pictures they had
posted of themselves on the websites. To determine the gender most commonly
associated with each first name, we used an online resource called Baby Name Guesser
(http://www.gpeters.com/names/baby-names.php). Our coding of gender based
on this publicly available information matches the self-reported gender of 99 percent
of the PhDs who completed one or both surveys.
We coded PhDs as having earned their undergraduate degrees in the United States,
in Asia, or elsewhere using the educational histories on their CVs. We also used
information from the CVs to create indicator variables for having experience as a
teaching assistant and having published a journal article. We coded PhDs as having
published a journal article if their CV listed a publication in one of the 442 journals
included in the 2006 Keele University rankings of economics journals. To characterize
the graduate programs from which the PhDs had entered the job market, we created
indicator variables for entering from a program in economics, rather than a related
field such as business or public policy, and for entering from a program in the United
States. Finally, we used the 2009 US News and World Report rankings of graduate
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programs in economics to classify the programs from which the PhDs had entered the
job market as ranked 1-10, ranked 11-30, ranked 31-50, or unranked.
2.4
Web-based surveys
We obtained information about the job-market outcomes, personal circumstances, and
gender-role preferences of the PhDs in our sample using two web-based surveys. The
first survey, a pre-market survey, was fielded in late December, just before the Annual
Meeting of the AEA. At the time of the pre-market survey, most job candidates knew
whether and with whom they would have first-round job interviews at the conference,
but they did not know what would happen in the later stages of their job search. The
second survey, a post-market survey, was fielded after the job-market season had
ended. The timing of the post-market survey varied slightly across cohorts. We
fielded it to the cohort who entered the job market during the 2008-09 academic year
in November 2009 and to the cohort who entered during the 2009-10 academic year
in August 2010. At the time of the post-market survey, most job candidates knew
whether and where they would be working the following year.
2.5
Respondent selectivity
The sampling procedures described above produced a sample of 1876 PhDs who
entered the U.S. job market in economics in 2008 or 2009. We obtained CVs for
97 percent of those PhDs, pre-market survey responses for 48 percent, post-market
survey responses for 33 percent, and both pre- and post-market survey responses for
25 percent. These response rates are comparable to the response rates of typical
web-based surveys (Cook et al., 2000). Nevertheless, given that a majority of eligible
PhDs did not complete the surveys, the potential for nonresponse bias is a concern.
To assess the representativeness of the survey respondents with respect to the
population of PhDs entering the job market, I compare the groups using the demographic and academic variables coded from the publicly posted data. These variables
are available for a large majority of the population, including PhDs who did not
respond to either survey. The first two columns of Table 1 present means of key
demographic and academic variables for two samples of PhDs: a “CV sample” comprising the 91 percent of PhDs whose job-market CVs and other publicly posted
job-market materials allowed us to code all of the variables in the table, and the
subset of the CV sample who responded to the post-market survey.
On most of the measures in Table 1, the PhDs who responded to the post-market
survey are comparable to the full CV sample; the distributions of gender, field of PhD
program, location of PhD program, ranking of PhD program, publishing experience,
and teaching experience do not differ substantially between the two samples. In
contrast, PhDs who earned their undergraduate degrees in the United States are
overrepresented in the sample of post-market respondents, and those who earned their
undergraduate degrees in Asia are underrepresented. This response pattern mirrors
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Table 1: Respondent selectivity: Variable means from publicly-posted job-market
materials and post-market survey data.
Female
PhD in economics
PhD from US
PhD ranking: 1-10
PhD ranking: 11-30
PhD ranking: 31-50
PhD ranking: Unranked
Publication
TA experience
Undergrad location: US
Undergrad location: Asia
Undergrad location: Other
N
PhDs with
complete public
postings
Post-market
respondents,
unweighted
Post-market
respondents,
weighted
0.312
0.908
0.898
0.277
0.250
0.183
0.290
0.129
0.950
0.358
0.330
0.312
1702
0.300
0.923
0.923
0.298
0.265
0.195
0.242
0.120
0.949
0.491
0.185
0.324
574
0.313
0.918
0.908
0.290
0.240
0.196
0.273
0.106
0.953
0.375
0.319
0.306
574
Notes: The first column presents variable means for the sample of PhDs whose publicly posted jobmarket materials allowed us to assign values for all of the variables in the table. This public-posting
sample covers 91 percent of the PhDs in the 2008-09 and 2009-10 job-market cohorts. The second
column presents variable means for the subset of PhDs in the public-posting sample who completed
the post-market survey. The third column presents variable means for the subset of PhDs in the
public-posting sample who completed the post-market survey, weighted to correct for nonresponse
bias.
the pattern in previous surveys of the economics job market, which have obtained
higher response rates from United States citizens (Siegfried and Stock, 1999, 2004).
To correct for differential nonresponse based on the observable characteristic of
PhDs, I use variables from the publicly posted data to construct nonresponse weights
for survey respondents. Specifically, the nonresponse weight for each respondent is
the inverse of the response rate in the respondent’s propensity cell, where propensity
cells are deciles in the distribution of predicted response probabilities from a logistic
regression. To minimize any negative impact of nonresponse weighting on the precision of my estimates, I selected for the logistic regression variables that were related
not just to the probability of response but also to the values of key survey outcomes
in our analyses (Little and Vartivarian, 2005). These variables included gender, year
of undergraduate degree, location of undergraduate institution, location of PhD program, ranking of PhD program, research fields, possession of advanced degrees from
outside the PhD program, job-market cohort, and availability of job-market CV.
The third column of Table 1 presents weighted means of the same demographic
and academic variables as the first two columns. Notably, the weighted proportions
6
of PhDs from the United States and Asia are very close to the proportions in the full
CV sample. In the remainder of the paper, all estimates of population parameters
have been adjusted for survey nonresponse.
2.6
Variables from survey data
The variables of primary interest in this paper are derived from responses to the preand post-market surveys. The structure and wording of the survey questions used
to construct some of these variables changed between 2008-09 and 2009-10. In the
presentation that follows, I describe the construction of the variables using questions
from the 2009-10 surveys. When the questions from the 2008-09 surveys are different,
I present those questions in the data appendix.
2.6.1
Tenure-track status
I classify PhDs as on the tenure track if they reported at the time of the post-market
survey that the outcome of their job search was a tenure-track position at a college
or university.
2.6.2
Relationship status
I classify PhDs as partnered if they were in a relationship in November of the academic
year they entered the job market, were in a relationship with the same person at the
time of the post-market survey, and expected at that time to remain in the relationship
through March of the following academic year. I classify them as single if they were
not in a relationship in November of the academic year they entered the job market.
These classification rules group PhDs who entered a relationship after the jobmarket season began with single PhDs on the grounds that those in very new relationships were unlikely to make high-stakes decisions about where to live and work
jointly with their partner. The classification rules exclude from both the partnered
and single groups PhDs who were in a relationship when the job-market season began
but broke up with their partner by the time of the post-market survey or expected
to break up by March of the following academic year. I exclude these PhDs from
the single group because they may have entered the job market in longstanding relationships and, consequently, may have made high-stakes decisions jointly with their
partner. I exclude them from the partnered group because, as explained below, I cannot classify their post-market living arrangement as either living with their partner
or living apart.
While the precise threshhold dates used to classify PhDs as partnered or single are
somewhat arbitrary, these choices affect only a small minority of PhDs because most
reported stable personal circumstances. Of PhDs who were in a relationship at the
time of the post-market survey, 95 percent had been in a relationship with the same
person in November of the academic year they entered the job market. Of those who
7
were in a relationship in November of the academic year they entered the job market,
95 percent were in a relationship with the same person at the time of the post-market
survey, and 94 percent expected at that time to remain in the relationship through
March of the following academic year.
2.6.3
Post-market living arrangement
To determine whether partnered PhDs expected to live with their partner during
their first year at their new job, the post-market survey asked the following:
On a scale from 0 to 100 where “0” means “absolutely no chance” and
“100” means “absolutely sure to happen,” what do you think is the percent
chance that you and your husband/wife/significant other will be in the
following situations next March?
Still in your relationship and living in the same area (i.e., sharing your
primary residence or living close enough to each other that you could see
each other after work on weeknights).
Still in your relationship and living in different areas.
No longer in your relationship.
PhDs provided their subjective probability of experiencing each outcome. I classify
PhDs as expecting to live with their partner if they believed that living in the same
area was the most likely outcome of their relationship; I classify them as expecting to
live apart if they believed that living in different areas was the most likely outcome.
I do not classify the post-market living arrangements of PhDs who believed that
breaking up was the most likely outcome of their relationship.
2.6.4
Family circumstances
I classify PhDs as parents if they had at least one child by the time of the post-market
survey. I classify partnered PhDs as married if they were married to their partner by
the time of the post-market survey.
The pre- and post-market surveys asked partnered PhDs what level of education
their partner had completed and, if the partner was in school, what level of education
he or she was pursuing. Response options were a high school degree or less, an
associate degree, a bachelor’s degree, a master’s degree, a professional degree, or a
PhD. Because few PhDs had partners with less than a bachelor’s degree, I group
partners in the bottom three categories together. Similarly, because few PhDs had
partners with a professional degree, I group partners with professional and master’s
degrees together.
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2.6.5
Gender-role preferences
I employ two measures of gender-role preferences: attitudes toward the traditional
breadwinner-caretaker model of the family and, for members of cohabiting differentsex couples, the proportion of household chores performed by the woman. As discussed above, previous research using similar attitudinal and behavioral measures has
found that the migration behavior of couples identified as traditional is less responsive
than the behavior of couples identified as egalitarian to the employment opportunities
of women (Bielby and Bielby, 1992; Jurges, 2006; Cooke, 2008).
To assess attitudes toward the traditional family, the pre-market survey asked the
following:
In many heterosexual couples, women take the main responsibility for the
care of the home and children, while men take the main responsibility for
supporting the family financially. Do you think this arrangement benefits
or hurts men, women and children?
For each class of family members (first men, then women, then children), PhDs selected one of the following response options: “benefits,” “hurts,” or “neither benefits
nor hurts.” I use responses to this question to construct three measures of gender-role
preferences. Specifically, I identify PhDs as traditional with respect to each class of
family members if they said the traditional family benefits that class.
To determine how household chores were allocated between cohabiting differentsex partners, the pre-market survey asked the following:
We are interested in the time you spend doing regular work around the
house – for example, cooking, grocery shopping, and doing little repairs.
In a typical week, what percentage of this work do you do, and what
percentage does you husband/wife/significant other do?
I define the division of household chores as nontraditional if the woman did less than
half of the chores.
A potential concern for studies estimating the effect of gender-role preferences on
family migration decisions is the possibility that the preferences are endogenous to
the decisions. Jurges (2006) uses a behavioral measure of gender-role preferences, the
division of household chores on Sunday, in part because he worries that attitudinal
measures are endogenous: couples may report traditional or egalitarian attitudes to
rationalize traditional or egalitarian migration decisions and the resulting employment
outcomes for husbands and wives. But behavioral measures may also be endogenous
if couples adjust their division of household chores when they migrate. While the
measures of gender-role preferences from the Job Seekers Project cannot eliminate
all concern about endogeneity, they have a unique advantage in this regard: the data
I use to construct the measures were gathered at the beginning of the job-market
season, before PhDs knew what job offers they would receive and before most could
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form strong expectations about whether they would live together or apart from their
partner the following year.
2.7
Estimation samples
This paper uses data from 516 PhDs who entered the job market during the 2008-09
and 2009-10 academic years and for whom we obtained all of the publicly posted
and survey data required to construct the variables used in the analyses below. I
summarize the tenure-track status of all of these PhDs. I then estimate a series of
regression models for the group of primary interest: partnered PhDs. The estimation
sample for the first two regression models, which examine the interaction between
gender and noncohabitation in predicting tenure-track employment, includes all 368
partnered PhDs. The estimation samples for the third and fourth regression models,
which examine the association between gender-role preferences and noncohabitation,
are smaller because the survey data used to construct the measures of gender-role
preferences are available for only a subset of partnered PhDs. In particular, attitudes
toward the traditional family are available for the 138 PhDs who entered the job market during the 2008-09 academic year. Information about the division of household
chores is available for the 235 PhDs who lived with a different-sex partner at the time
of the pre-market survey.
3
Does noncohabitation benefit women more than men?
Descriptive results from the Job Seekers Project are consistent with the idea that
personal relationships are more compatible with tenure-track employment for men
than for women. Table 2 shows the proportion of single and partnered men and
women who secured tenure-track jobs as a result of their first job search. Among
single PhDs, whose choices on the job market were not constrained by concern for a
partner, women were as likely as men to secure tenure-track jobs: 48 percent of single
men and 49 percent of single women were on the tenure track at the time of the postmarket survey. Among partnered PhDs, on the other hand, a gender gap emerges.
While both partnered men and partnered women were more likely than their single
counterparts to secure tenure-track jobs, women were 11 percentage points less likely
than men to do so: 62 percent of partnered men but just 51 percent of partnered
women were on the tenure track.
The results in Table 2 are also consistent with the idea that geographic concerns
contribute to the gender gap in tenure-track employment among partnered PhDs.
Among PhDs who expected to live with their partner in the year after the job market,
and whose job choices were therefore subject to co-location constraints, women were
13 percentage points less likely than men to secure tenure-track jobs: 61 percent of
men but just 47 percent of women were on the tenure track. In contrast, among PhDs
who expected to live apart from their partner, women were 7 percentage points more
10
Table 2: Proportion securing tenure-track job.
Men
Women
Proportion
N
Proportion
N
0.476
0.621
0.606
0.688
90
267
221
46
0.489
0.512
0.473
0.756
58
101
87
14
Single
Partnered
Expected to live together
Expected to live apart
Notes: Columns 2 and 4 show the total number of PhDs in each gender-partnership category.
Proportions are adjusted for survey nonresponse.
likely than men to secure tenure-track jobs: 76 percent of women but just 67 percent
of men were on the tenure track.
Taken together, the results in Table 2 are suggestive evidence that cohabiting
relationships harm the tenure-track prospects of women relative to men and that,
against this backdrop, noncohabitation benefits women more than men. The gender
comparisons in the table, however, do not account for personal and professional characteristics that may differ between men and women and may condition the association
between living arrangements and the probability of tenure-track employment. To see
why this omission may bias our conclusions about the role of gender in family decision
making, suppose that women are less competitive than men as job candidates and
must therefore be more flexible about where they live to secure tenure-track jobs. Or
suppose that women are more likely than men to have partners with PhDs and are
therefore more likely to face binding co-location constraints if they cohabit. Under
these circumstances, noncohabitation would predict tenure-track employment more
strongly for women than for men, but the result would be due to gender differences in
the economic benefits of noncohabitation rather than to gender bias in family decision
making.
Table 3 shows means of the academic background variables from the publicly
posted data and the family-related variables from the survey data, separately for
men and women in the full sample of partnered PhDs. With regard to academic
credentials, women appear less competitive than men in some respects and more
competitive in others. On the one hand, women were less likely than men to have
graduated from a PhD program ranked in the top 30 and more likely to have graduated
from a program ranked in the bottom 20. On the other hand, they were more likely
to have entered the job market with a publication and to have gained experience as
a teaching assistant during graduate school. Turning to family circumstances, there
are two reasons to believe that women would gain more from noncohabitation than
men even if family location decisions treated them equally. First, women were indeed
more likely than men to have partners with PhDs, which would tend to increase their
relative benefits from noncohabitation. Second, women were less likely than men to
11
Table 3: Means of academic and family variables.
PhD in economics
PhD from US
PhD ranking: 1-10
PhD ranking: 11-30
PhD ranking: 31-50
PhD ranking: Unranked
Publication
TA experience
Undergrad location: US
Undergrad location: Asia
Undergrad location: Other
Married to partner
Parent
Partner education: Bachelor’s or less
Partner education: Master’s or professional
Partner education: PhD
N
Men
Women
0.896
0.919
0.344
0.259
0.146
0.251
0.111
0.945
0.447
0.206
0.347
0.746
0.324
0.241
0.439
0.320
267
0.929
0.908
0.279
0.222
0.242
0.256
0.144
0.985
0.391
0.372
0.236
0.714
0.202
0.189
0.258
0.553
101
Notes: Sample is partnered PhDs. Means are adjusted for survey nonresponse.
have children, which would tend to reduce their relative costs.
To determine whether noncohabitation predicts tenure-track employment more
strongly for women than for men, net of gender differences in academic credentials, I
estimate a logit model,
ln [Pr(T Ti = 1)/Pr(T Ti = 0)] = α0 + α1 N oncohabi + α2 F emalei
+ α3 F emalei × N oncohabi + Academici α4 ,
(1)
where T Ti is an indicator for tenure-track employment, N oncohabi is an indicator for
expecting to live apart from a partner in the year after the job market, and F emalei
is an indicator for female gender. The vector Academici contains the academic
background variables from the publicly posted data, as summarized in Table 3: field,
location, and ranking of PhD program; publishing and teaching experience at the
time of job-market entry; and location of undergraduate institution. To control for
possible changes in response patterns due to changes in the structure and wording of
survey questions over time, Academici also contains an indicator for entering the
job market in 2009-10 rather than 2008-09.
To determine whether gender differences in family circumstances account for any
difference in the relationship between noncohabitation and tenure-track employment
that remains after accounting for gender differences academic credentials, I estimate
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a second logit model,
ln [Pr(T Ti = 1)/Pr(T Ti = 0)] = α0 + α1 N oncohabi + α2 F emalei
+ α3 F emalei × N oncohabi + Academici α4 + F amilyi α5
+ F emalei × F amilyi α5
(2)
that adds the family-related variables from the survey data. Specifically, the vector
F amilyi contains an indicator for being married, an indicator for having children,
and indicators for having a partner with a master’s or professional degree and having a
partner with a PhD. The omitted category for partner education is a bachelor’s degree
or less. To allow for the possibility that family circumstances affect the probability of
tenure-track employment differently for women than for men, the second logit model
also includes an interaction of each family-related variable with gender.
I estimate models (1) and (2) on the full sample of partnered PhDs. I then use
the resulting coefficients to estimate the average marginal effects of gender, noncohabitation, and family circumstances on the probability of tenure-track employment,
separately for partnered men and partnered women. Table 4 shows these average
marginal effects.
The first and second columns of Table 4 show the average marginal effects from
model (1), for men and women respectively. Comparing the results in these columns
with the descriptive results in Table 2, we find that controlling for academic credentials has little impact on the relationship between gender, noncohabitation, and
tenure-track employment. The raw proportions in Table 2 suggest that noncohabitation increases the probability of securing a tenure-track job by 28 percentage points
for women and 8 percentage points for men. The estimates from model (1) suggest
that the comparable effects, net of gender differences in academic credentials, are 25
percentage points for women and 6 percentage points for men.
The third and fourth columns of Table 4 show the average marginal effects from
model (2). With respect to the relationship between gender, noncohabitation, and
tenure-track employment, the results in these columns are almost identical to the
results in the first and second columns. Net of gender differences in both academic
credentials and family circumstances, noncohabitation increases the probability of
tenure-track employment by 24 percentage points for women and 5 percentage points
for men. Taken together, the results in Table 4 indicate that neither gender differences
in publicly posted academic credentials nor gender differences in measured family
circumstances explain why noncohabitation predicts tenure-track employment more
strongly for women than for men.
While family circumstances do not appear to mediate the relationship between
noncohabitation and tenure-track employment for partnered men and women, the
estimates from models (1) and (2) suggest that personal relationships have a direct
effect on the probability of tenure-track employment and that this effect differs by
gender. Consistent with previous findings that marriage is improves the professional
standing of PhD men relative to PhD women, the results in third and fourth columns
13
Table 4: Average marginal effects on probability of tenure-track employment.
Variable
Expected to live apart
Female
Academic
Men
Women
Men
Women
0.055
(0.079)
−0.087
(0.062)
0.254∗
(0.154)
−0.097
(0.062)
368
267
368
101
0.051
(0.087)
−0.096
(0.067)
0.137∗
(0.078)
−0.060
(0.074)
0.151∗
(0.079)
0.258∗∗
(0.085)
368
267
0.242
(0.158)
−0.127∗∗
(0.061)
−0.023
(0.121)
0.058
(0.144)
−0.297∗∗
(0.147)
−0.160
(0.123)
368
101
Married to partner
Parent
Partner education: Master’s or
professional
Partner education: PhD
N for coefficients
N for marginal effects
Academic + family
Notes: Standard errors in parentheses. ∗ p < 0.10, ∗∗ p < 0.05. Logit models (1) and (2) were
estimated on the sample of partnered PhDs. Average marginal effects were estimated on the
subsamples of men and women using coefficients from these models. The dependent variable in both
models is an indicator for tenure-track employment. The omitted category for partner education is
“bachelor’s or less.” Both models includes controls for field, location, and ranking of PhD program;
publishing and teaching experience at the time of job-market entry; location of undergraduate
institution; and year of job-market entry. Both also include an interaction of expected living
arrangement with gender. The second model includes an interaction of each family-related variable
with gender. Estimates are weighted to adjust for survey nonresponse.
14
of Table 4 indicate that married men are 14 percentage points more likely than men
with unmarried partners to secure a tenure-track job. Married women on the other
hand, are no more likely than women with unmarried partners to secure a tenure-track
job.
A similar pattern is evident in the relationship between partner education and
tenure-track employment: having a partner with a graduate degree increases the
probability of tenure-track employment for men but decreases it for women. Compared with men whose partners have a bachelor’s degree or less, men whose partners
have a master’s or professional degree are 15 percentage points more likely to secure a
tenure-track job, and men whose partners have a PhD are 26 percentage points more
likely to do. In contrast, compared with women whose partners have a bachelor’s
degree or less, women whose partners have a master’s or professional degree are 30
percentage points less likely to secure a tenure-track job, and women whose partners
have a PhD are 16 percentage points less likely to do so.
4
Does gender-nontraditionalism predict noncohabitation?
Given that the consequences of noncohabitation differ by gender, do the causes also
differ? In particular, are PhDs who express nontraditional gender-role preferences –
preferences that may lead them to place greater weight on women’s career opportunities – more likely than other PhDs to live apart from their partner? Table 5 shows the
proportion of PhDs who expected to live apart from their partner in the year after the
job market. The first column of the table shows the proportion who expected to live
apart among PhDs who reported a traditional division of household chores, with the
woman performing half or more of the chores, and who viewed the traditional family
as beneficial to its members. The third column shows the proportion who expected
to live apart among PhDs who reported a nontraditional division of household chores
and who did not view the traditional family as beneficial.
The results in Table 5 are largely consistent with the idea that nontraditional
gender-role preferences facilitate noncohabitation. Among PhDs who adopted a traditional division of household chores, 9 percent expected to live apart from their
partner in the year after the job market; among those who said the traditional family
benefits women, 4 percent expected to live apart; and among those who said the traditional family benefits children, 2 percent expected to live apart. The comparable
figures for PhDs who were nontraditional by these measures were much higher, at 17
percent, 20 percent, and 23 percent. The sole measure by which traditional PhDs
were more likely than nontraditional PhDs to expect to live apart was viewing the
traditional family as beneficial to men. Among PhDs who endorsed this view, 23
percent expected to live apart from their partner in the year after the job market;
among those who did not, just 15 percent expected to live apart.
While the results in Table 5 show a positive relationship between nontraditional
gender-role preferences and expectations of noncohabitation, the meaning of this re15
Table 5: Proportion expecting to live apart from partner.
Measure of gender-role preferences
Traditional
Proportion
Woman did less than half of chores
Said traditional family benefits men
Said traditional family benefits women
Said traditional family benefits children
0.093
0.231
0.038
0.019
N
187
50
16
29
Nontraditional
Proportion
0.165
0.149
0.200
0.231
N
48
86
120
107
Notes: Sample for the first measure is partnered PhDs who lived with a different-sex partner at the
time of the pre-market survey. Sample for the remaining three measures is partnered PhDs who
entered the job market during the 2008-09 academic year. “Nontraditional” refers to members of
couples in which the woman did less than half of the household chores and to PhDs who did not
view the traditional breadwinner-caretaker family as beneficial to its members. Columns 2 and 4
show the total number of PhDs categorized as traditional and nontraditional by each measure.
lationship is not clear. In particular, we cannot determine from the raw proportions
in the table whether preferences have a direct effect on expectations or whether they
appear to have an effect because they are correlated with other characteristics of
PhDs or their partners that have an effect. To determine whether the relationship
between nontraditional gender-role preferences and expectations of noncohabitation
persists after accounting for measurable differences between PhDs in academic credentials and family circumstances, I estimate two additional logit models. The first
model,
ln [Pr(N oncohabi = 1)/Pr(N oncohabi = 0)] = α0 + α1 N ontradChoresi
+ α2 F emalei + Academici α3 + F amilyi α4 ,
(3)
regresses N oncohabi , the indicator for expecting to live apart, on N ontradChoresi ,
an indicator that equals one for members of different-sex cohabiting couples in which
the woman did less than half of the household chores and zero for members of such
coupes in which the woman did half or more of the chores. The second model,
ln [Pr(N oncohabi = 1)/Pr(N oncohabi = 0)] = α0 + N ontradAtti α1
+ α2 F emalei + Academici α3 + F amilyi α4 ,
(4)
regresses N oncohabi on N ontradAtti , a vector of three indicators identifying PhDs
who viewed the traditional family as beneficial to men, women, and children. Both
models include an indicator for female gender and, with two exceptions, controls
for the academic background variables and family-related variable from model (2).
The exceptions are as follows: Model (3) excludes teaching experience because it
perfectly predicts living apart. Model (4) excludes year of job-market entry because
the estimation sample is limited to PhDs who entered the job market during the
2008-09 academic year.
16
Table 6: Average marginal effects on probability of expecting to live apart.
Measure of gender-role preferences
Marginal
effect
Woman did less than half of chores
0.118∗
(0.068)
Said traditional family benefits men
Said traditional family benefits women
Said traditional family benefits children
N
235
∗
Marginal
effect
0.112
(0.076)
0.035
(0.131)
−0.226∗∗
(0.047)
136
∗∗
Notes: Standard errors in parentheses. p < 0.10, p < 0.05. Logit models (3) and (4) were
estimated on the samples described in the notes to Table 5. The dependent variable in both models
is an indicator for expecting to live apart in the year after the job market. The omitted category
for partner education is “bachelor’s or less.” Both models includes controls for field, location,
and ranking of PhD program; publishing experience; gender; marital status, parental status; and
partner education. The first model includes a control for year of job-market entry. The second
model includes a control for teaching experience at the time of job-market entry. Estimates are
weighted to adjust for survey nonresponse.
Table 6 shows average marginal effects of the measures of gender-role preferences
in models (3) and (4). With respect to two of the four measures, the results in Table
6 tell the same story as the descriptive results in Table 5: members of couples with a
nontraditional division of household chores are 12 percentage points more likely than
their traditional peers to expect to live apart from their partner in the year after
the job market, and PhDs who believe the traditional family benefits children are 23
percentage points less likely to expect to live apart. With respect to the remaining
two measures, however, the results in Table 6 are not clearly consistent with the
notion that gender-nontraditionalism facilitates noncohabitation: PhDs who believe
the traditional family benefits men and those who believe it benefits women are not
statistically less likely than other PhDs to expect to live apart from their partner,
and the point estimates suggest that they may be more likely.
5
Conclusions
Previous studies of the PhD workforce have found that marriage and committed
personal relationships harm the professional standing of women relative to that of
men. In light of substantial evidence that couples in the general population prioritize the careers of husbands over those of wives when deciding where to live, and
given suggestive evidence that PhD and half-PhD couples do the same, observers of
academic labor markets have speculated that dual-career location problems interfere
17
with women’s ability to secure and maintain tenure-track employment. This paper
uses data from an original survey of new PhD economists to examine a nontraditional
solution to dual-career location problems: living apart. Consistent with the idea that
living apart places women who would otherwise have subordinated their own careers
on equal footing with their male partners, we find that the gender gap in tenure-track
employment is substantially reduced among couples who live apart.
The second finding of this paper is that gender-nontraditional PhDs are more
likely than nontraditional PhDs to live apart from their partner in the year after the
job market. Although the relationship between gender-non traditionalism and living
apart holds even for PhDs whose partners also have PhDs, we cannot rule out the possibility that nontraditionalism is correlated with unobserved economic characteristics
that increase the benefits of living apart. Consequently, we cannot be sure that gender traditionalism exerts and independent causal effect on living arrangements. While
it is plausible that couples with nontraditional attitudes feel more comfortable than
other couples adopting nontraditional lifestyles, it is also plausible that couples with
nontraditional lifestyles adopt nontraditional attitudes as a form of self-justification.
18
6
Data appendix
This appendix presents the structure and wording of questions from the 2008-09 preand post-market surveys when these differ from the structure and wording of the
corresponding questions from the 2009-10 surveys.
Post-market living arrangement
The 2008-09 post-market survey asked separately about the expected duration of the
relationship and expected living arrangements. With regard to the duration of the
relationship, the survey asked the following:
What do you think is the percent chance you will be in your current
relationship in one year?
With regard to expected living arrangements, it asked the following:
Will your husband/wife/significant other be living in the same area as you
next March – that is, will he/she be living close enough to you that you
could see him/her after work on a weeknight?
Respose options were “yes,” “no,” and “unsure.” If PhDs said they were unsure
where their partner would be living, the survey asked the following:
What do you think is the percent chance that your husband/wife/significant
other will be living in the same area as you next March?
For PhDs who entered the job market during the 2008-09 academic year, we compute
the probability of living together as the probability of remaining in the relationship
multiplied by the probability of living in the same area. Similarly, we compute the
probability of living apart as the probability of remaining in the relationship multiplied by the probability of living in a different area.
Parental status
The 2008-09 pre-market survey did not ask if PhDs had children; instead, the survey
asked if any children lived in their household.
19
References
Astin, Helen S and Jeffrey F Milem (1997) “The Status of Academic Couples in {U.S.}
Institutions,” in Marianne A Ferber and Jane W Loeb eds. Academic Couples:
Problems and Promises, Chicago: University of Illinois Press.
Bellas, Marcia L (1997) “The Scholarly Productivity of Academic Couples,” in Marianne A Ferber and Jane W Loeb eds. Academic Couples: Problems and Promises,
Chicago: University of Illinois Press.
Bielby, William T and Denise D Bielby (1992) “I Will Follow Him: Family Ties,
Gender-Role Beliefs, and Reluctance to Relocate for a Better Job,” American Journal of Sociology, Vol. 97, No. 5, pp. 1241–1267.
Boyle, P J, Thomas J Cooke, Keith Halfacree, and Darren Smith (2001) “A CrossNational Comparison of the Impact of Family Migration on Women’s Employment
Status,” Demography, Vol. 38, No. 2, pp. 201–213.
Boyle, Paul, Thomas J Cooke, Keith Halfacree, and Darren Smith (2003) “The Effect of Long-Distance Family Migration and Motherhood on Partnered Women’s
Labour-Market Activity Rates in {G}reat {B}ritain and the {USA},” Environment
and Planning A, Vol. 35, pp. 2097–2114.
Compton, Janice and Robert A Pollak (2007) “Why Are Power Couples Increasingly
Concentrated in Large Metropolitan Areas?,” Journal of Labor Economics, Vol. 25,
No. 3, pp. 475–512.
Cook, Colleen, Fred Heath, and Russel L Thompson (2000) “A Meta-Analysis of
Response Rates in Web- or Internet-Based Surveys,” Educational and Psychological
Measurement, Vol. 60, No. 6, pp. 821–836.
Cooke, Thomas J (2001) “Trailing Wife or Trailing Mother? {T}he Effect of Parental
Status on the Relationship Between Family Migration and the Labor-Market Participation of Married Women,” Environment and Planning A, Vol. 33, No. 3, pp.
419–430.
(2003) “Family Migration and the Relative Earnings of Husbands and
Wives,” Annals of the Association of American Geographers, Vol. 93, No. 2, pp.
338–349.
(2008) “Gender Role Beliefs and Family Migration,” Population, Space and
Place, Vol. 14, No. 3, pp. 163–175.
Cooke, Thomas J, Paul Boyle, Kenneth Couch, and Peteke Feijten (2009) “A Longitudinal Analysis of Family Migration and the Gender Gap in Earnings in the
20
{U}nited {S}tates and {G}reat {B}ritain,” Demography, Vol. 46, No. 1, pp. 147–
167.
Gerstel, Naomi and Harriet Engel Gross (1982) “Commuter Marriages,” Marriage &
Family Review, Vol. 5, No. 2, pp. 71–93.
Ginther, Donna K and Shulamit Kahn (2004) “Women in Economics: Moving up or
Falling off the Academic Career Ladder?,” Journal of Economic Perspectives, Vol.
18, No. 3, pp. 193–214.
Goulden, Marc, Mary Ann Mason, and Karie Frasch (2011) “Keeping Women in the
Science Pipeline,” The {ANNALS} of the {A}merican {A}cademy of {P}olitical
and {S}ocial {S}cience, Vol. 638, No. 1, pp. 141–162.
Jurges, Hendrik (2006) “Gender Ideology, Division of Housework, and the Geographic
Mobility of Families,” Review of Economics of the Household, Vol. 4, pp. 299–323.
Kulis, Stephen and Diane Sicotte (2002) “Women Scientists in Academia: Geographically Constrained to Big Cities, College Clusters, or the Coasts?,” Research in
Higher Education, Vol. 43, No. 1, pp. 1–30.
Little, Roderick J A and Sonya Vartivarian (2005) “Does Weighting for Nonresponse
Increase the Variance of Survey Means?,” Survey Methodology, Vol. 31, No. 2, pp.
161–168.
Mason, Mary Ann and Marc Goulden (2002) “Do Babies Matter? The Effect of Family Formation on the Lifelong Careers of Academic Men and Woman,” Academe,
Vol. 88, No. 6, pp. 21–27.
(2004) “Marriage and Baby Blues: Redefining Gender Equity in the
Academy,” The {ANNALS} of the American Academy of Political and Social Science, Vol. 596, No. 1, pp. 86–103.
Mason, Mary Ann, Marc Goulden, and Karie Frasch (2009) “Why Graduate Students
Reject the Fast Track,” Academe, Vol. 95, No. 1, pp. 11–16.
McFall, Brooke Helppie and Marta Murray-Close (2011) “Moving out to Move Up.”
McKinnish, Terra (2008) “Spousal Mobility and Earnings,” Demography, Vol. 45, No.
4, pp. 829–849.
Mincer, Jacob (1978) “Family Migration Decisions,” Journal of Political Economy,
Vol. 86, No. 5, pp. 749–773.
Schiebinger, Londa, Andrea Davies Henderson, and Shannon K Gilmartin (2008)
“Dual-Career Academic Couples: What Universities Need to Know.”
21
Shauman, Kimberlee A (2010) “Gender Asymmetry in Family Migration: Occupational Inequality or Interspousal Comparative Advantage?,” Journal of Marriage
and Family, Vol. 72, No. 2, pp. 375–392.
Shauman, Kimberlee A and Mary C Noonan (2007) “Family Migration and Labor
Force Outcomes: Sex Differences in Occupational Context,” Social Forces, Vol. 85,
No. 4, pp. 1735–1764.
Shauman, Kimberlee A and Yu Xie (1996) “Geographic Mobility of Scientists: Sex
Differences and Family Constraints,” Demography, Vol. 33, No. 4, pp. 455–468.
Shihadeh, Edward S (1991) “The Prevalence of Husband-Centered Migration: Employment Consequences for Married Mothers,” Journal of Marriage and the Family,
Vol. 53, No. 2, pp. 432–444.
Siegfried, John J and Wendy A Stock (1999) “The Labor Market for New Ph.D.
Economists,” Journal of Economic Perspectives, Vol. 13, No. 3, pp. 115–134.
(2004) “The Market for New Ph.D. Economists in 2002,” American Economic Review, Vol. 94, No. 2, pp. 272–285.
Spitze, Glenna (1984) “The Effect of Family Migration on Wives’ Employment: How
Long Does It Last?,” Social Science Quarterly, Vol. 65, No. 1, pp. 21–36.
Wolfinger, Nicholas H, Mary Ann Mason, and Marc Goulden (2009) “Stay in the
Game: Gender, Family Formation and Alternative Trajectories in the Academic
Life Course,” Social Forces, Vol. 87, No. 3, pp. 1591–1621.
Wolfinger, Nicholas H, Mary Ann Mason, and Goulden. Marc (2008) “Problems in the
Pipeline: Gender, Marriage, and Fertility in the Ivory Tower,” Journal of Higher
Education, Vol. 79, No. 4, pp. 388–405.
22