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Less Education, More Divorce: Explaining the Inverse Relationship Between Women's Education and Divorce

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Highly educated women currently have more stable marriages than less educated women in several societies, yet we know little about the reasons for this difference. In this paper, we draw on social exchange theory to hypothesize how educational differences in marital satisfaction and barriers to divorce can explain the inverse educational gradient of divorce. Discrete-time event history analyses of 1,887 first marriages from the British Household Panel Survey show that marital satisfaction does not explain the negative association between women’s education and divorce. Instead, we find that higher barriers to divorce help keep the marriages of educated women intact. We use this finding to propose a novel interpretation for the reversed educational gradient of divorce in many countries.
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STOCKHOLM UNIVERSITY
Dept of Sociology, Demography Unit / www.suda.su.se
Less Education, More Divorce:
Explaining the Inverse Relationship
Between Women’s Education and Divorce
Diederik Boertien and Juho Härkönen
Stockholm
Research Reports
in Demography
2014: 11
© Copyright is held by the author(s). SRRDs receive only limited review. Views and opinions expressed
in SRRDs are attributable to the authors and do not necessarily reflect those held at the Demography
Unit.
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Less Education, More Divorce:
Explaining the Inverse Relationship
Between Women’s Education and Divorce
Diederik Boertien
Department of Political and Social Sciences
European University Institute
Email: diederik.boertien@eui.eu
Juho Härkönen
Department of Sociology
Stockholm University
Email: juho.harkonen@sociology.su.se
Abstract: Highly educated women currently have more stable marriages than less
educated women in several societies, yet we know little about the reasons for this
difference. In this paper, we draw on social exchange theory to hypothesize how
educational differences in marital satisfaction and barriers to divorce can explain the
inverse educational gradient of divorce. Discrete-time event history analyses of 1,887
first marriages from the British Household Panel Survey show that marital satisfaction
does not explain the negative association between women’s education and divorce.
Instead, we find that higher barriers to divorce help keep the marriages of educated
women intact. We use this finding to propose a novel interpretation for the reversed
educational gradient of divorce in many countries.
2
Less Education, More Divorce: Explaining the Inverse Relationship Between
Women’s Education and Divorce
In the early sixties, William J. Goode predicted that as divorcing becomes easier, the
initially positive relationship between class and divorce wanes and eventually reverses
(Goode 1962; 1963). Half a century later, a growing body of research has documented
such a shift in the association between women’s education and divorce in several
European (Hoem 1997; Chan and Halpin 2005; De Graaf and Kalmijn 2006a; Härkönen
and Dronkers 2006; Matysiak, Styrc, and Vignoli 2013) and non-Western societies
(Park, Raymo and Creighton 2009; Raymo, Fukuda, and Iwasawa 2013). In the United
States, the inverse relationship between female education and divorce has widened over
the past decades (Martin 2006). Because divorce is associated with lower well-being
among divorcees and their children, strong educational differences in its incidence can
strengthen existing socioeconomic inequalities (McLanahan and Percheski 2008).
Yet why less educated women have elevated divorce rates is poorly understood
(Amato 2010, p. 661). The stabilizing effect of men’s education on marriages is well-
established, but common theoretical accounts lead to ambiguous predictions of the
effects of women’s education on divorce (Lyngstad and Jalovaara 2010). Many theories
predict that highly educated women are more likely to divorce, and the existing
explanations for educated women’s higher marital stability are weakly grounded in
evidence.
The objective of this study is to explain why less educated women have higher
divorce rates than better educated women. After reviewing the literature, we propose an
explanatory approach based on social exchange theory and its distinction between
marital attractions and barriers to divorce (Levinger 1976). This heuristic provides a
3
framework for discussing the role of more specific factors. We analyze event-history
data for 1,887 first marriages from the 1996-2009 waves of the British Household Panel
Survey (BHPS). Core advantages of these data are their representativeness of the British
population and annual measurements of a wide range of questions, from socioeconomic
factors to marital satisfaction and indicators of personal and social stressors. The United
Kingdom is one of the countries where educated women currently have more stable
marriages than those with less education (Chan and Halpin 2005; Cooke and Gash
2010).
1
To preview our findings, they point to economic and demographic factors that
can be interpreted as barriers to divorce as an important explanation to the higher
marital stability among educated women.
BACKGROUND
Wife’s Education and Divorce: Theory and Evidence
How does the wife’s education affect divorce? Theories of divorce generally rely on a
cost-benefit model, in which marriages are maintained as long as their benefits exceed
the costs of divorce (e.g., Brines and Joyner 1999). In this framework, education affects
divorce by altering these costs and benefits. In practice, schooling is regarded as an
economic and a non-economic resource with ambiguous effects on divorce (Becker,
Landes, and Michael 1977).
Education as human capital raises the economic benefits one can reap from the
labor market. This allows for a higher level of consumption, but increases the
opportunity costs of housework, childcare, and other, traditionally female, non-labor
1
Other relevant features of British family demography are high divorce rates, high rates
of births to teen-aged and single women, and high rates of poverty among single
mothers (Chan and Halpin, 2005; Esping-Andersen, 2007).
4
market activities. According to Becker’s economic model of the family, this weakens
the returns to a household division of tasks into market and non-market work and the
interdependency between the spouses, and increases the utility of options outside
marriage (Becker et al. 1977). Women’s human capital thus decreases their dependence
on marriages and increases their possibilities of exiting them.
This “specialization and trading model” (Oppenheimer 1997) is a standard
approach to theorizing the effects of wife’s human capital on divorce. One of its
criticisms is that it pays insufficient attention to the economic utility of her earnings.
These can counteract any benefits from specialization, and increase spouses’ mutual
dependency as a guarantor of a level of living they might otherwise not attain. The
wife’s human capital additionally provides a buffer against economic risks, such as the
husband’s unemployment or illness, which can increase marital stress and lead to a re-
evaluation of the marital bargain (White and Rodgers 2000). Empirical results of the net
effects of the wife’s (un)employment and incomes are considerably conflicting (White
and Rodgers 2000; Lyngstad and Jalovaara 2010; Özcan and Breen 2012). Some studies
have found, however, that variables such as her incomes, unemployment, and working
schedules partly mediate the negative female educational gradient of divorce (Jalovaara
2001; Raymo et al. 2013).
Another critique of the specialization and trading model has been that it
implicitly treats all marriages alike. It has been refined to consider heterogeneity in
marital quality. According to this approach, education and other economic resources
provide the means to exit low-quality marriages, but may not have any influence on
high-quality ones (Sayer and Bianchi 2000; Sayer et al. 2011; Kreager et al. 2013).
Much of the evidence supports this view (ibid.).
5
Education has also been seen to affect divorce through non-economic
pathways. Yet it is unclear what the relevant ones are. The literature includes references
to such factors as general “non-market productivity” (Becker 1974), relationship skills
(Blossfeld et al. 1995; Amato 1996; Härkönen and Dronkers 2006), attitudes (Levinger
1976), and knowledge about the legal system and the divorce process (Blossfeld et al.
1995; Hoem 1997). Education, as well as other economic resources, also holds symbolic
value as a marker of social prestige which may influence marital behavior (Edin and
Kefalas 2005). Furthermore, traits, such as intelligence (Dronkers 2002) and health
(Kreager et al. 2013) correlate with education. Lastly, her high education, particularly if
it is higher than his, can go against gendered expectations of economic provision and
status relationships within marriage (Fenstermaker 2002) and provoke behaviors
conducive to marital instability. Some of these non-economic factors promote marital
stability among the highly educated, whereas others undermine it. The few empirical
studies that have directly assessed non-economic factors lead to a similar conclusion.
Raymo and colleagues (2013) found that measures intended to capture concerns with
“losing face” explained a part of the negative educational gradient in Japan, whereas
Boertien, von Scheve and Park (2012) found that highly educated German women more
often held personality traits which de-stabilize marriages.
Finally, education can shape divorce risks by structuring life course
trajectories. Higher educationand in particular, longer time in educationpostpones
marriages (for Britain, Berrington, and Diamond 2000). Later age at predicts marital
stability (Lyngstad and Jalovaara 2010) and age at marriage can mediate a part of the
educational gradient of divorce (e.g., Härkönen and Dronkers 2006; Martin 2006). In
some countries, highly educated women marry less, which may mean that those who do
are more committed to their marriages. Bernardi and Martínez-Pastor (2011) did not
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find that such selectivity was responsible for the negative educational gradient in Spain.
The educational gradient of divorce can additionally vary by marital duration.
Newlywed couples face considerable uncertainty of themselves, their spouses, and their
common life, and new information and unexpected events can alter the utility gained
from the marriage (Becker et al. 1977). At the same time, shared investments made
during the course of the marriage increase couples’ mutual dependency (Brines and
Joyner 1999). Findings assert that negative educational gradients of divorce are the
strongest at early stages of the marriage (South and Spitze 1986; Jalovaara 2002). None
of these studies, however, provided direct measures to explain this finding.
Summing up, the effects of female education on divorce are theoretically
ambiguous and many theories predict higher divorce rates for educated women, rather
than the reverse. Additionally, many explanations of why less educated women are
currently more likely to divorce than better educated ones are weakly grounded in
evidence.
Wife’s Education, Marital Attractions, and Barriers to Divorce: Theoretical
Framework and Hypotheses
In the absence of strong guidance from theory and previous research for explaining the
inverse educational gradient of divorce for women, we construct a middle range
framework to orient the formulation of hypotheses and the choice of variables.
Specifically, we build on the distinction between marital attractions and barriers as
made by social exchange theorists (e.g., Levinger 1965; 1976). Attractions are the
relative balance of the rewards and costs of a specific marriage and include emotional
returns, social approval, and economic benefits and losses. Barriers, on the other hand,
have been defined as constraints to dissolution emanating from other sources than the
7
attractions of the marriage (Levinger 1965; 1976). In our framework, education affects
marital stability either by affecting attractions of the marriage or the barriers to divorce.
Likewise, the variables used in the empirical analysis are expected to operate through
these two. The third component of social exchange theory, alternatives to the marriage,
can often be interpreted as barriers to divorce: a lack of alternatives can be a barrier to
divorce. These alternativessuch as alternative spousescan be hard to measure (but,
see South and Lloyd 1995; Lyngstad 2011). For the most part, this also holds for this
study. Findings that re-marriage rates do not differ by socio-economic variables (Shafer
and James 2013) suggest, however, that educational differences in access to alternative
spouses are an unlikely explanation to the educational gradient of divorce. Like in most
previous studies, we focus on individual and couple level factors which can affect
attractions and barriers (cf. Brines and Joyner 1999).
This general framework can also be used to interpret the mechanisms
highlighted in different theories. For instance, Becker’s thesis about marriage-specific
capital, as well as Oppenheimer’s argument about the husbands’ economic losses from
divorcing an educated wife can be seen as emphasizing the barriers to divorce. On the
other hand, findings of the destabilizing effect of economic stress should be seen as
mediated through attractions. Goode (1962; 1963) himself based his prediction of the
changing class gradients of divorce on the weakening (societal) barriers to divorce,
which permits the higher stress (lower attractions) among lower class families to find an
expression in marital dissolution (cf., Härkönen and Dronkers 2006; Matysiak et al.
2013; Park and Raymo 2013). The interplay between attractions and barriers has been
used to address various questions in the divorce literature (e.g., Heaton and Albrecht
1991; White and Booth 1991; Previti and Amato 2003; Amato and Hohmann-Marriot
8
2007), but to our knowledge, not in recent research on the educational gradient of
divorce.
Marital satisfaction and quality are commonly-used measures of marital
attractions (White and Booth 1991) and strong predictors of divorce (Karney and
Bradbury 1995). Were these attractions responsible for the negative educational
gradient of divorce for women, we would expect marital satisfaction to explain the
gradient.
HYPOTHESIS 1:Educational differences in marital satisfaction explain why
less educated women have higher divorce rates.
An obvious candidate for why education affects marital satisfaction is economic
circumstance. According to the family stress model, economic stressors affect spouses’
emotional distress and their interactions (Conger et al. 1990), which in turn affect
marital satisfaction (Karney and Bradbury 1995; White and Rodgers 2000). Likewise,
unemployment (especially of the husband) and other forms of economic hardship have
repeatedly been shown to affect marital quality and satisfaction (Conger, Conger, and
Martin 2010; Halliday Hardie and Lucas 2010). This leads to a more specific
hypothesis, which is in line with Goode’s (1962; 1963) argument:
HYPOTHESIS 1a:Economic stressorssuch as unemployment and material
hardshipaffect marital satisfaction and explain why less educated women have
higher divorce rates.
9
The household division of labor can affect marital satisfaction. Responsibility
for housework and childcare can lower marital satisfaction, in particular if this is
perceived as unfair (e.g., Wilkie, Ferree, and Ratcliff 1998; Twenge, Campbell, and
Foster 2003). Likewise, the division of housework predicts divorce (Oláh and Gähler
2014), but contingent on the division of housework prevalent in each society (Cooke
2006). Gender norms and attitudes can in themselves affect marital satisfaction (Lye
and Biblarz 1993), although the effects of these, too, can vary by social context. To the
extent that educated wives are better able to negotiate a balanced division of housework
and childcare (Bonke and Esping-Andersen 2011) and hold gender norms conducive to
higher marital satisfaction, we can formulate the following hypothesis:
HYPOTHESIS 1b:The division of housework and gender norms affect marital
satisfaction and explain why less educated women have higher divorce rates.
Educational differences in marital satisfaction have not been extensively studied.
Previous findings suggest that the highly educated are more satisfied with their
marriages, although the differences are not large (Karney and Bradbury 1995; Conger et
al. 2010; Isen and Stevenson 2010; Halliday Hardie and Lucas 2010). This suggests that
barriers to divorce may be more important.
Barriers to divorce can hold a marriage intact despite low attractions. Barriers
can be economic or non-economic (Becker et al. 1977; White and Booth 1991) and
include “commitment” (Johnson, Caughlin and Huston 1999), moral or reputational
barriers (Raymo et al. 2013), and investments to assets and marriage-specific goods
(Briner and Joynes 1999). For our purposes, these barriers should correlate with
education to explain the observed negative gradients.
10
HYPOTHESIS 2:Educational differences in barriers to divorce explain why
less educated women have higher divorce rates.
Barriers are generally measured using objective indicators such as home
ownership and common children. Both reflect joint investments. Home ownership raises
the economic (and emotional) costs of leaving the partnership (South and Spitze 1985;
White and Booth 1991; Jalovaara 2001). Loss of the economic resources of the partner
and financial dependence provide other examples of financial barriers to divorcing.
Regarding the former, they may deter the husband of an educated wife from divorcing,
but also affect the decisions of educated wives who tend to have educated husbands.
Common children are often mentioned in subjective accounts as barriers to divorce
(Knoester and Booth 2000), yet the evidence for such an effect of children beyond their
early years is not strong (Lyngstad and Jalovaara 2010). Step-children may have
different effects, as marriages with step-children can still be “incomplete institutions”
(Cherlin 1978) to which commitment is less. Parental divorce and separation is another
potential factor. Children of divorce tend to have lower levels of education than those
from intact families. They are also more likely to perceive dissolution as a viable
solution to unsatisfactory marriages and thus hold lower barriers to divorce (Wolfinger
2005). If parental divorce lowers access to educated partners (Erola, Härkönen, and
Dronkers 2012), those with higher education will be less likely to marry someone with a
divorced background. Last, scholars have complemented indicators of barriers to
divorce with measures such as attitudes and religiosity (Amato and Hohmann-Marriott
2007).
11
What are considered to be the relevant barriers to divorce varies from one study
to the next, and some variables can both act as barriers and influence marital
satisfaction. What is important is that they exert an influence on marital dissolution net
of the attractions. We formulate the following hypotheses of different barriers to explain
why less educated women divorce more.
HYPOTHESIS 2a:Economic barriers to divorcesuch as homeownership,
other wealth, financial dependency on the spouse, and husband’s education
affect divorce independently of marital satisfaction and explain why less
educated women have higher divorce rates.
HYPOTHESIS 2b:Family demographic factorssuch as parental divorce,
the number and age of children, step-children, and pre-marital cohabitation
affect divorce independently of marital satisfaction and explain why less
educated women have higher divorce rates.
HYPOTHESIS 2c:Religiosity and gender norms affect divorce independently
of marital satisfaction and explain why less educated women have higher
divorce rates.
An alternative approach to barriers considers the heterogeneity in the effects of
marital satisfaction on divorce (cf. Schumm and Bughaighis 1985; White and Booth
1991; Amato and Hohmann-Marriott 2007). According to Levinger (1965), barriers
should be trivial for those in happy marriages but hold unhappy marriages intact.
However, a growing number of studies have found that a large number of divorces
involve seemingly unproblematic marriages with at least moderate levels of marital
happiness (Amato and Hohmann-Marriott 2007). When divorce became more
12
accessible, this share may have increased (De Graaf and Kalmijn 2006b). The reason
some such marriages dissolve may have to do with low barriers and commitment
(Amato and Hohmann-Marriott 2007). Barriers may thus have most importance at
(relatively) high levels of satisfaction, whereas those in poor quality marriages would
divorce in any case. Schumm and Bugaighis (1985) argued that such a scenario could be
expected in cultures, which value personal happiness over marital stability, whereas
Levinger’s (1965) scenario would be more likely in contexts with the opposite
valuations.
A further complication for our purposes is that education can, as discussed,
provide resources to overcome barriers and at the same time itself be associated with
barriers to divorce. For example, Kreager and colleagues (2013) found that educated
women had more stable marriages when they were not characterized by marital
violence, but were also more likely to leave violent ones. Due to the complex and
contingent interrelations between women’s education, barriers to divorce, and resources
to overcome these barriers, formulating hypotheses about interaction effects of
education and satisfaction on divorce proves difficult. We refrained from formulating
such hypotheses. Nevertheless, we tested for these interactions, as we believe that they
can offer additional insights into interpreting the findings.
DATA AND METHOD
Data
We used data from the British Household Panel Survey (BHPS), a representative
longitudinal household survey of the British population, which annually interviewed all
adult members of a sample of households. The sample was selected using a stratified
clustered design based on postal codes. All members of the selected households became
13
panel members and were followed over time, also if they left the household. After
thirteen waves of the survey, 66 % of those selected for the sample in the first wave
were still part of the study (see the quality profile of the BHPS for more information
(Lynn, 2006)).
We began our observation window from 1996, which was the first year when
respondents were asked about marital satisfaction, and it extended until 2009. We
selected all heterosexual couples who were married for the first time during the
observation period, and who provided information on divorce and education (excluding
9.1% of couples and 10.9% of person-years due to missing information). Only the first
fifteen years of the marriage were taken into account in order to avoid the educational
groups studied from becoming too selective due to divorce. Some marriages had already
begun before our observation window and excluding them from the analysis would have
restricted the number of marriages further. Therefore, we included these (left-truncated)
marriages and set the duration of their marriage accordingly for the event-history
analysis (Guo 1993). In total, we observed 1,887 couples for 9,130 person-years.
We decided against including cohabiting couples in order to connect to the
general literature on female education and divorce, which has mostly focused on
married couples. Socioeconomic resources may influence the dissolution of married and
cohabiting couples differently, and whether and in which contexts this is the case
remains unresolved (e.g., Brines and Joyner 1999; Jalovaara 2013). Furthermore, the
heterogeneity of cohabitating living arrangements poses challenges to their
measurement in common surveys (for Britain, see Murphy (2000)). Previous studies
from Spain do not suggest that selection into marriage biases the results (Bernardi and
Martinez-Pastor, 2011). We believe the same to be the case for Britain. Berrington and
14
Diamond (2000), for example, showed for the 1958 British cohort that educated people
are more likely to marry than the less educated.
Measures of Divorce, Education, and Marital Satisfaction
Our dependent variable was divorce. Couples were coded as experiencing a divorce
when they reported either a separation or a divorce, conditional on being married the
previous year. 9.9% (180 couples) of the sample experienced a divorce during the
observation period. Divorce, which refers to the wave where one of the partners moved
out, is measured at t whereas all independent variables are measured at t - 1. The
survival curves predicted that 24% of first marriages ended after 15 years. This is
similar to the estimate of a 29 % divorce probability after 15 years (of all marriages),
reported by the most recent official statistics.
2
Our main independent variable was a time-varying measure of the years of
education completed by the wife by the interview date. The continuous specification of
educational attainment fit the data better (assessed using the Akaike and Bayesian
Information Criteria) than a categorical one, which we nevertheless used in the
descriptive analyses due to its informative value. The categorical measure of
educational attainment differentiated between low (GCSE grade A-C or less; ISCED 0-
2), middle (A-levels; ISCED 3-4), and high education (tertiary degrees; NVQ-level 3;
ISCED 5-6). Our regression results were robust to the specifications.
Our first hypothesis stated that marital satisfaction explains the negative
educational gradient of divorce. We measured marital satisfaction using the question
“How satisfied are you with your spouse/partner?” with responses ranging from 1 =
2
Office for National Statistics UK, website consulted 03/12/2013,
www.statistics.gov.uk
15
not satisfied at all” to 7 = “completely satisfied”. The question was asked individually
from both partners (using a self-completion questionnaire filled out after the face-to-
face part of the interview). The weakness of this measure is that it only consists of one
question, and one could argue that marital satisfaction is not just based on satisfaction
with the partner. At the same time, it is one of the most informative measures (Funk and
Rogge 2007) and the closest single one of the concept of marital satisfaction as the
overall evaluation of the relationship (Fincham and Rogge 2010). We treated the
variable as continuous, in line with most other studies (Amato and Hohmann-Marriot
2007; Schoen et al. 2006). Robustness checks were done using alternative specifications
(i.e. a logged version and a dummy of values 6 and 7 versus the rest), but they did not
change the results. The same was the case for the inclusion of lagged measures of
marital satisfaction.
Other variables
The lack of strong guidance from theory and previous research and our own hypotheses
suggest several potential variables, which can explain the inverse educational gradient
of divorce. Our demographic variables mainly reflect barriers to divorce. Ethnicity was
a dummy variable indicating whether the respondent was white or not. We acknowledge
that “non-whites” are a heterogeneous group with different divorce rates, but small cell
sizes prevented more detailed classifications. Parental divorce measured whether either
partner came from a divorced (or separated) family. Her age at marriage was measured
continuously in years and can act as a barrier to divorce (by affecting alternatives to the
current marriage) but can also affect marital satisfaction (if it correlates with maturity
and stability). Pre-marital cohabitation was a dummy variable often regarded as
reflecting attitudes toward the marital institution, whereas step-childrenindicating
16
whether one of the partners entered the marriage with a previous childcan affect both
marital satisfaction and commitment to the marriage. The number of children was a
continuous variable and the age of the youngest child was a dummy variable, indicating
whether the youngest child was less than four years old. Finally, we included the share
of opposite-sex singles in a person’s region (based on a classification of 19 regions,
such as East Midlands, Inner London, or Merseyside) and age group (everyone up to 7
years older and up to 3 years younger for women, and the opposite for men) as a crude
measure of marriage market conditions.
We included a group of variables measuring labor market and economic
conditions. Her and his unemployment (dummies) and material deprivationmeasured
as an index of six questions, and rescaled to vary between 0 and 1, on whether or not the
household could afford “eating meat on alternate days”, “replacing furniture”, and the
likeand her unusual working hours (other schedules than “just mornings” or
“mornings and afternoons”) can be marital stressors (Conger et al. 1990), whereas
annual household income can also be a barrier to divorce. Other economic barriers to
divorce included were his education (measured continuously in years), her percentage
contribution to the total household labor income (included as a measure of her
economic dependency), home ownership (dummy),
3
and her and his interests on savings
and investments, which proxy wealth (in 2005 prices).
We included a group of variables tapping into attitudes and behaviors. Her
church attendance indicates religiosity, a barrier to divorce. Her gender norms were
measured by a standardized scale based on eight seven-point scale questions on the
respondents’ views on gender roles and other issues related to family life (such as
3
We also looked at whether the ownership was shared or not but this did not prove to be
relevant for divorce risk.
17
agreement on “a woman and her family would all be happier if she goes out to work”,
Cronbach’s α = 0.68), with higher scores reflecting more egalitarianism. These can
shape both marital satisfaction and barriers to divorce. Her share of the total time
dedicated to housework by the couple indicates gender egalitarian practices in the
marriage, and can shape marital satisfaction.
Each model also included marital duration (continuous) and calendar year
(continuous). We experimented with several specifications of marital duration, but more
complicated measures of marital duration as well as interactions with education did not
improve model fit (5-year splines for duration did not improve model fit and a quadratic
term for duration was not significant; results are available upon request). We used
calendar year instead of marriage cohort because period effects on divorce dominate
over cohort effects (Härkönen 2014).
--TABLE 1 AROUND HERE--
Missing values on all variables but education and divorce were multiply
imputed using 20 datasets (see Rubin 1987). Table 1 displays the characteristics of the
original sample used in this study before imputation. 42.2% of cases and 34.4% of
person-years have one or more values imputed on the independent variables used in the
final models of the paper. The dependent variable and all independent variables were
included in the equation used for imputation to make sure the imputations are ‘proper’
(Rubin 1987). The multiple imputations were done using the ‘mi’ commands in Stata
12. Because model fit statistics do not have a clear interpretation in multiple imputation
settings, we do not report them (StataCorp 2013).
18
Analyses
Our analytical strategy had three main steps. First, we described divorce risks (using
survival and hazard function estimates) and marital satisfaction trajectories by
educational attainment.
The second part of the analysis used discrete-time event history analysis
(Yamaguchi 1991), suitable given the annual measurements in our data, to assess how
much of the female educational gradient of divorce is mediated by our independent
variables. We estimated the models using logit regression.
4
We estimated a series of discrete-time event history models where each model
includes marital duration and calendar year (centered to year 2000). In the first step, we
tested whether the educational gradient of divorce could be explained by marital
satisfaction.
In the second step of the regression analyses, we assessed whether the
educational gradient is mediated by each of the other independent variables. Due to their
large number, we first assessed each independent variable separately. We excluded from
further consideration all variables which were not significant mediators of education on
divorce at the 5 percent significance level. We used a test based on standardizing the
logit coefficients of education on the mediating variable (taken from a model with the
mediating variable as the dependent variable) and of the mediating variable on divorce,
controlling for education (Iacobucci 2012).
In the third step of the regression analysis, we entered the remaining variables in
four blocks, depending on when in the life course and where in the expected causal
chain from education to divorce they appear. Although not clear-cut, such a
4
The main analyses were also run using Linear Probability Models (LPM) and the
results were robust (cf. Mood 2010).
19
categorization helped in deciding the order in which the variables are introduced. Some
of the variables precede education, whereas others are affected by it. They also affect
one another, and therefore entering them in the correct order is necessary in order to
draw correct conclusions.
In the third and final part of the analysis, we explored the interdependencies of
the variables more closely. Particularly, we were interested in which variables of the
final model affected divorce independently of marital satisfaction and could be
considered as barriers to divorce. To this end we estimated a path model for discrete
outcomes within a Structural Equation Model setting (e.g., Winship and Mare 1983) on
the discrete-time event history data. All variables that were relevant for both divorce
and the educational gradient at the previous stage of the analysis were included in the
model as endogenous variables (except parental divorce, which precedes education).
Exogenous variables in these models were parental divorce, duration, and calendar year.
RESULTS
Female Education, Divorce, and Marital Satisfaction Trajectories
We began our analysis with descriptive analyses of the educational gradients in divorce
and of marital satisfaction trajectories in Britain. Figures 1 and 2 show survival curves
of marriages and hazards of divorce by educational attainment. These show what
previous British studies have already found: educated women have more stable
marriages. The curves predict that after 15 years of marriage 32% of the low educated
women had divorced, compared to 13% of those with high education. The educational
gradient of divorce appears the clearest during the early years of marriage. The
differences in the survival curves were statistically significant at the 0.1 % level (both
using the Wilcoxon and log-rank tests).
20
--FIGURES 1 AND 2 AROUND HERE--
Our first hypothesis stated that marital satisfaction differences explain why low
educated women divorce more. For this to hold, there should of course be educational
differences in marital satisfaction. Figures 3 and 4 show the wives’ and husbands’
average (lowess-smoothed) marital satisfaction trajectories, respectively, by her
education. Unlike expected, neither marital satisfaction levels nor its trajectories differ
much by educational attainment. His marital satisfaction appears to be more strongly
differentiated by her education. However, the differences are small at all marital
durations and never exceed 0.2 units on the scale from 1 to 7 (or one fifth of a standard
deviation).
--FIGURES 3 AND 4 AROUND HERE--
In results not presented here (but available upon request), we ran additional
growth curve regressions of marital satisfaction trajectories to assess the robustness of
this finding. These regressions accounted for period change and attrition due to divorce
(see Lynch (2003) for a similar model for analyzing educational differences in health
across the life course). The conclusion of small educational differences in marital
satisfaction remained, and we found significant differences by education only at few
durations. Given that educational differences in divorce risk exist at all durations (see
Figure 2), this suggests that marital satisfaction plays at best only a limited role in
explaining educational differences in divorce.
21
Discrete-time Event History Analysis
We continued our analysis by estimating a series of discrete-time event history models.
The purpose was to assess how much of the association between female education and
divorce is mediated by our variables. Table 2 shows results from five models. The first
model included duration and calendar year as the only controls, and showed that an
additional year of education predicts an 11 % lower annual divorce risk.
We added her marital satisfaction in the second model. Its mediating effect was
modest in size: the odds ratio of an additional year of education changed from 0.89 to
0.91.
5
The educational gradient was further reduced, but only slightly, when controlling
for his marital satisfaction (Model 3). At same levels of her and his marital satisfaction,
wives with an additional year of education have a 9 % lower annual divorce risk. This
analysis confirmed our suspicion that educational differences in marital satisfaction do
not explain lower educated women’s higher divorce rates.
--TABLE 2 AROUND HERE--
Models 4 and 5 included interactions between education and her and his
marital satisfaction, respectively. As discussed in the theory section, we did not
formulate explicit hypotheses of these interactions given their theoretical ambivalence
and contingency, but believe the results can offer valuable insights. The interaction term
between education and her marital satisfaction was not significant. However, there was
a significant interaction effect between her education and his satisfaction. The inverse
5
The change in the educational gradient was similar when comparing the coefficients
using LPM (change from -0.0025 to -0.0022), which are better suited for comparing
coefficient sizes between models (Mood 2010).
22
association between her education and divorce is stronger when he is more satisfied
with the marriage. With average levels of his satisfaction, when he would give the score
6, an additional year of her education would reduce the risk of divorce by 11 %; if he
reported a satisfaction score of 4, an additional year of education would reduce the risk
of divorce only by 3 %.
6
A possible interpretation is that the husbands of low educated
women have fewer barriers to leave at least relatively satisfying marriages. This
interpretation contrasts the proposition that when relationships are of a high quality,
barriers are irrelevant because the benefits of the relationship prevent divorce (Levinger
1965). But it is in line with recent American findings stressing the importance of
barriers for holding relatively satisfied couples together. Overall, our findings so far
suggest that barriers to divorce are a likely explanation for low educated British
women’s higher divorce rates.
We continued our analysis by testing which other variables are significant
mediators of the inverse relationship between education and divorce. First, we tested
their importance one by one, and selected for further consideration those which were
significant mediators at the 5 % level of statistical significance (see above; Iacobucci
2012).
--TABLE 3 AROUND HERE--
Table 3 presents the results from this mediation analysis. The first column
shows the standardized effect of education on each mediating variable, the second
column the standardized effect of each mediating variable on divorce, conditional on
education, the third column shows the Z-scores of the mediating effect of the respective
6
(100 % * (1.14 * 0.96
6
1) = 11 %), and (100 % * (1.14 * 0.96
4
1) = 4 %)
respectively.
23
variable, and the fourth column shows the odds ratio of female education on divorce
when controlling for each variable. The larger the Z-score, the more significant is the
mediating effect of the variable. However, for the substantive size of the mediating
effect one should look at the odds ratios that are shown in the fourth column.
Several variables did not pass the test. Importantly, age at marriage and
egalitarian gender norms actually strengthened the negative gradient in our sample.
Several other findings are also of interest. Her and his marital satisfactions are the two
single most important predictors of divorce. But because they correlate less with
education than almost all other variables, their mediating effects remain limited. Others,
such as his education, the share of singles in the age group and region (reflecting
concentration of educated women to areas such as London), household income, home
ownership, material deprivation, his savings, his unemployment and parental divorce
correlate strongly with education. Of these, the variables which are strong predictors of
divorce also have strong mediating effects. Home ownership in particular, but also
material deprivation and household incomes are important mediators, judging both by
the Z-score (above 3) of the mediation effect and the change in the odds ratio of
education when controlling for these variables.
--TABLE 4 AROUND HERE--
Ten variables mediated the educational gradient of divorce at the 5 %
significance level. We continued the event history analysis by entering these significant
mediators block by block (Table 4). We included Model 1 (from Table 2) in the table to
enable easier comparisons to the baseline gradient.
24
In Model 6, we controlled for parental divorce and ethnicity as the two
variables that precede both educational attainment and divorce. Parental divorce
increases the divorce risk, as expected, but ethnicity no longer had a significant effect.
The odds ratio of an additional year of education changed slightly, from 0.89 to 0.90.
This indicates that the fact that less educated women are more often in marriages in
which at least the other spouse comes from a divorced home explains a small part of the
inverse educational gradient of divorce. Model 7 added his education and step-child as
variables, which are generally determined at entry into marriage, but after the
completion of her education. Having step-children de-stabilizes marriages, but highly
educated husbands stabilize them. The size of the coefficient of the latter is similar in
size to that of her education, but significant only at the 10 % level. Its significance level
is smaller than when only conditioning on her education (Table 3), indicating that the
other variables in Model 7 explain part of its effect. The coefficient of her education is
0.93 in Model 7; part of the explanation of the negative educational gradient of divorce
is thus that educated women marry educated men, and particularly, that they (or their
husbands) do not have children from previous partnerships.
In Model 8, we added homeownership and (logged) household incomes as
independent variables. These are determined by education, and home ownership often
follows after marriage. Both reduce the risk of divorce, although the coefficient for
household incomes is significant only at the 10 % level. Incomes were strongly
significant in Table 3, and it is probable that the effect of incomes operates through
home ownership, which is the more proximate predictor of divorce. The same is likely
to hold for husband’s education, which is non-significant in this model. But most
importantly, the estimate for her education was also reduced to 0.96 and became not
significant for the first time.
25
In Model 9, we also included his unemployment and material deprivation as
additional independent variables, which can be less predictable events across the marital
life course. We excluded his education, which was not significant in the previous
model. The remaining significant effect of household incomes is mediated by material
deprivation and his unemployment. His unemployment doubles the divorce risk. Not
being able to afford all six items on the index likewise doubles the divorce risk, but the
coefficient for material deprivation was significant only at the 10 % level. The estimate
of her education remained not-significant and stable.
This final model suggests that parental divorce, step-children, home ownership,
material deprivation, and his unemployment are important mediators of the educational
gradient of divorce. Ethnicity, his education and household incomes were no longer
significant predictors of divorce. Their effects on divorce were rather mediated by other
variables, which had more independent predictive power. Having a highly educated
husband, for example, can help her purchase a home, which acts as the more proximate
stabilizer of marriages. Part of the effect of his education seems also to be mediated by
parental divorce and step-children; highly educated men are less likely to come from
divorced homes, have previous children, or marry women who do. Likewise, household
incomes are likely to be lower when he is unemployedwhich predicts divorce
irrespective of economic consequencesand household incomes of course promote
home ownership and lower the risk of material hardship.
Female Education and Divorce: A Path Analysis
As the final stage of the analysis, we analyzed whether the mediating variables in our
final event history model affect divorce through marital satisfaction or independently of
it. As shown above, marital satisfaction itself has a strong effect on divorce, but it has a
26
limited mediating effect on the relationship between the wife’s education and divorce.
Nevertheless, some of the important mediating variables may still affect divorce via
marital satisfaction. Variables should only be considered as indicating barriers to
divorce when they predict divorce independently of marital satisfaction. To assess the
interdependency of these variables, and to draw appropriate conclusions of the
importance of marital attractions vis á vis barriers to divorce, we estimated a path model
on our discrete-time event history data in a Structural Equations Model setting.
--FIGURE 5 AROUND HERE--
Figure 5 displays the results from this model. The estimates shown are y-
standardized coefficients taken from a path model that explicitly modeled the dependent
variable as dichotomous. The results confirm the observations from Table 4 that the
effects of his unemployment and deprivation operate through his and her marital
satisfaction. This is in line with the family stress model (e.g., Conger et al. 1990), which
emphasizes economic stressors and their influence on marital satisfaction through the
partners’ interactions. Other variables, and most notably home ownership, had a direct
suppressing effect on divorce, independently of marital satisfaction. Home ownership is
therefore best seen as a barrier to divorce, as it has been interpreted in earlier studies
(South and Spitze 1985; White and Booth 1991; Jalovaara 2001). Our demographic
variables, namely parental divorce and step-children, operate both through satisfaction
and independently. Their direct effects are stronger, however, than the effects on
partners’ satisfaction, suggesting that they too primarily operate as (lowering the)
barriers to divorce. Finally, the analysis shows a weak positive direct effect of education
on his marital satisfaction.
27
DISCUSSION
More and more studies have paid attention to the increasingly negative female
educational gradient of divorce in a variety of countries (Hoem 1997; Chan and Halpin
2005; De Graaf and Kalmijn 2006a; Härkönen and Dronkers 2006; Park et al. 2009;
Matysiak et al. 2013). These findings are in line with William J. Goode’s (1962; 1963)
thesis of a reversal in the class gradient of divorce as the decreasing external barriers of
divorce allow the supposedly higher marital stress among the lower classes to find an
expression in divorce. Although the macro-level trends fit Goode’s argument (Härkönen
and Dronkers 2006; Matysiak et al. 2013), the micro-level foundations of this
explanation have been weak and we have known little about why less educated women
are currently more likely to divorce (Amato 2010, p. 661). This gap in our knowledge
limits the understanding of this rare example of a reversal in associations between
sociological variables (Chan and Halpin 2005) and also of a demographic development
that can strengthen existing inequalities (McLanahan and Percheski 2008).
The objective of this study was to explain why low educated women currently
divorce more. We used a middle-ground theoretical framework based on social
exchange theory to guide our analyses of discrete-time event history data on first
marriages in Britain. Our findings show that marital satisfaction plays only a limited
role in explaining the current educational gradient. Despite being a strong predictor of
divorce, educational differences in marital satisfaction are minor. This questions the
micro-foundations of Goode’s explanation as well as other theoretical arguments and ad
hoc explanations that rest on educational differences in marital quality. Economic
stressors (his unemployment and material deprivation) did contribute to explaining the
gradient by operating through marital satisfaction, but their contribution was altogether
small.
28
Barriers to divorce, or the lack of them, were more important explanations.
Particularly, not owning a home, parental divorce, and step-children increased divorce
risk and contributed to explaining why wives with less education have higher divorce
rates. Both parental divorce and step-children can shape commitment to the marriage.
Parental divorce may lower the threshold at which a marriage is dissolved by providing
an example of feasible behaviors in the face of marital challenges (Wolfinger 2005),
whereas marriages involving step-children can be “incomplete institutions” (cf. Cherlin
1978) to which one is less committed. Both are more common among less educated
women and help explain why they divorce more.
Housing is households’ most important financial asset. Recent American
research has paid increasing attention to the importance of wealth for entry into
marriage and findings suggest that wealth is important both due to its symbolic and its
use value (Edin and Kefalas 2005; Schneider 2013). Home ownership has been found to
deter divorce already in earlier research (South and Spitze 1985; White and Booth 1991;
Jalovaara 2001), and our findings highlighted its importance for understanding divorce
risk differences by education. Home ownership has in previous research been regarded
as a barrier to divorce, and was identified as such by our analyses.
This study did not attempt to explain why the educational gradients of divorce
have changed, but our findings lay the grounds for new theorizing of this shift. They
highlighted the importance of some barriers to divorce, but we do not claim that the
barriers we identified are the only important ones. Instead, we maintain that barriers to
divorce may more broadly offer insights into this change. Two general types of
hypotheses can be put forward. One possibility is that barriers to divorce have become
more strongly associated with higher education. For example, education is today
associated with less approval of divorce (Rijken and Liefbroer 2012), but this was the
29
opposite in the past, at least in the United States (Martin and Parashar 2006). However,
it is unlikely that there has been an across-the-board educational shift in the barriers to
divorce. Of the barriers highlighted in this study, home ownership in Britain has for
long been divided along class lines (Ermisch and Halpin 2004) and the association
between parental divorce and educational attainment has remained stable (Sigle-
Rushton, Hobcraft, and Kiernan 2005).
Another possibility is that the effects of barriers to divorce have changed.
Accounts of family change commonly point out how the foundations of marriage have
changed toward higher expectations for marital happiness and personal gratification
(Cherlin 1992; Coontz 2005). This can mean that barriers become less important for
holding together marriages with low satisfaction (as most of them dissolve in any case),
but more important for marriages characterized by at least moderate levels of
satisfaction (Schumm and Bughaighis 1985; Amato and Hohmann-Marriott 2007). We
interpreted our finding of the interaction effect between her education and his marital
satisfaction in this light. This argument of the changing importance barriers to divorce is
similar to the one on the increasing economic requirements for marriage (Edin and
Kefalas 2005). Both reflect change into a society in which marriage no longer has its
former normative and institutionalized status.
The educational gradient of divorce may thus have changed as a result of
changes in the effects of the barriers to divorce which are associated with educational
attainment. If the threshold to divorce at least at moderate levels of marital satisfaction
decreases, education-related barriers to divorce result in keeping educated women
increasingly together relative to their less-educated peers. Some indications for the
importance of the shift in the cultural foundations of marriage for educational
differences in divorce can be found from previous studies. These found that the
30
educational gradient tends to be more negative when divorce and other family practices
associated with the “Second Demographic Transition” are more common (but not, for
example, when divorce laws are more liberal) (Härkönen and Dronkers 2006; see also
Matysiak at el. 2013).
Here, a distinction between societal barriers to divorce (such as divorce
legislation and social sanctions) and “personal” ones (such as commitment and
investments to the marriage) can be useful. Goode’s (1962; 1963) theory focused
primarily on societal barriers, such as the relaxation of divorce laws and the stigma
associated with divorce, whereas barriers discussed in this study have been more of the
latter kind. Education can help to overcome societal barriers, as well as provide the
means to break away from disruptive marriages (Kreager et al. 2013), but at the same
time be associated with factors which make the dissolution of at least moderately
functioning marriages costlier. This dual role of education means that the interaction
effects between education and marital satisfaction can be contingent on the social
context and the importance given to marital happiness and stability.
Our analysis is of course not without its limitations, which future research
would do well to address. First, the results only speak directly to marriages in the UK,
although we expect the main conclusions to hold in other similar countries. Second, our
interpretation of barriers is admittedly a “residual” one, relying on effects independent
of marital satisfaction instead of being direct measures of what is perceived as barriers.
Third, future research could look into how female education affects who files for
divorce as an additional test of the proposed explanations. Our data allowed measuring
her and his satisfaction separately, but her education may differently shape the barriers
to her and his divorce decisions. Furthermore, questions of causality remain open.
Within the frame of this study, we cannot make definitive claims of causal effects of
31
education, nor of the other variables. For example, home ownership is endogenous, as
dysfunctional couples are less likely to purchase homes. Regarding education, our
objective was not to estimate causal effects of educational attainment on divorce, but
rather, to understand why social groups characterized by different levels of education
differ in marital stability. It can also be that changing selectivity to educational
attainment levels provides cues to the changing educational gradients of divorce.
Finally, with our data we were not able to directly test what could explain the
changes in the female educational gradients of divorce. We proposed a framework
which emphasizes the changing importance of barriers as a potential explanation.
Whether it proves useful or not is left for future research to assess. In any case, our
analysis has provided evidence which redirect the theoretical foundations for
understanding these socioeconomic differences in divorce from differences in
attractions and returns from marriage toward barriers to divorce.
ACKNOWLEDGMENTS
Previous versions of this paper have been presented at the European University
Institute, the European Divorce Research Network Conference in Helsinki, the BSPS
conference in Manchester, the ECSR/EQUALSOC conference in Stockholm,
Stockholm University, Universitat Pompeu Fabra, and Yale University. We would like
to thank the participants at these events as well as Pau Baizán, Fabrizio Bernardi, Teresa
Castro, Lynn Cooke, Jaap Dronkers, Gøsta Esping-Andersen, Michael Gähler, Michael
Grätz, Marika Jalovaara, Aat Liefbroer, Torkild Lyngstad and Elizabeth Thomson for
their valuable comments on earlier drafts of this paper. Härkönen gratefully
32
acknowledges financial support from the Swedish Council for Working Life and Social
Research (no. 2010-0831).
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FIGURES AND TABLES
FIGURE 1. Wife’s Education and Divorce: Survival Curves.
SOURCE: Authors’ calculations using the British Household Panel Survey (BHPS), 1996-2009.
NOTE: 1,887 marriages, and 9,130 person-years.
0.50 0.60 0.70 0.80 0.90 1.00
Survival Into Marriage
0 3 6 9 12 15
Duration (Years)
Lowest Education Middle Education
Highest Education
43
FIGURE 2. Wife’s Education and Divorce: Smoothed Hazard Curves.
SOURCE: Authors’ calculations using the British Household Panel Survey (BHPS), 1996-2009.
NOTE: 1,887 marriages, and 9,130 person-years.
0
.01 .02 .03
Hazard
0 3 6 9 12 15
Duration (Years)
Lowest Education Middle Education
Highest Education
44
FIGURE 3. Wife’s Education and Her Marital Satisfaction: Lowess Smoothed
Trajectories.
SOURCE: Authors’ calculations using the British Household Panel Survey (BHPS), 1996-2009.
NOTE: 1,887 marriages, and 9,130 person-years.
5.8
6
6.2 6.4 6.6 6.8
Marital Satisfaction
0 5 10 15
Duration (Years)
Lowest Education
Middle Education
Highest Education
45
FIGURE 4. Wife’s Education and the Husband’s Marital Satisfaction: Lowess
Smoothed Trajectories.
SOURCE: Authors’ calculations using the British Household Panel Survey (BHPS), 1996-2009.
NOTE: 1,887 marriages, and 9,130 person-years.
6
6.2 6.4 6.6 6.8
5.8
Marital Satisfaction
0 5 10 15
Duration (Years)
Lowest Education
Middle Education
Highest Education
47
TABLE 1. Descriptive Statistics for the Sample (Person-Years)
Mean
St. Dev.
Min.
Max.
Divorce
0.02
0
1
Duration of marriage (years)
6.35
4.53
0
15
Wife’s education in years
13.4
2.3
9
17
Her satisfaction with spouse
6.25
1.14
1
7
His satisfaction with spouse
6.34
1.00
1
7
Non-white
0.04
0
1
Her or his parents divorced
0.35
0
1
Wife’s age at marriage
28.5
7.09
16.5
77.8
Cohabited before marriage
0.59
0
1
Step-children
0.10
0
1
Number of children in household
1.28
1.10
0
6
Child under 4 years of age in household
0.37
0
1
Share of single persons in age group/region
0.25
0.12
0
1
Wife unemployed
0.02
0
1
Husband unemployed
0.03
0
1
Material deprivation (index)
0.09
0.15
0
1
Her unusual working hours
0.15
0
1
Annual household income (logged)
10.4
0.62
0
14.03
Her share of labor income
0.30
0.24
0
1
Husband’s education in years
13.6
2.3
9
17
House owned by one of the spouses
0.84
0
1
Her interest on savings
114.9
307
0
1500
His interest on savings
143.6
342.1
0
1500
Her church attendance
0.10
0
1
Gender norm scale
0.10
0.52
-2
1.63
48
Her share of housework
0.73
0.20
0
1
N
9,130 person-years (1,887 couples)
SOURCE: Authors’ calculations using the British Household Panel Survey (BHPS), 1996-2009.
NOTE: St. Dev. = Standard Deviation; Min. = Minimum Value Observed; Max. = Maximum
Value Observed.
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