Marriage & Hypogamy:
The effects of Hyper/Hypogamy on Female Marital Happiness and Divorce
Quantitative Methods in the Social Sciences
Columbia University
Masters of Arts Thesis
May 1, 2013
Anthony Rafetto

Abstract
Since the 1980s a reverse gender gap has emerged in post-secondary completion, with women obtaining Bachelor’s degrees at increasingly higher rates than men. Many suggest this creates a smaller market of marriageable men. Using IPUMS census data for female respondents, I find that as relative education levels increase for women, educational hypogamy has not increased drastically but divorce rates have increased and marriage has become less common. I also find that educational hypogamy has a negative association with marital happiness, and also may have a negative association on the likelihood of getting divorced, while educational hypergamy seems to have a positive association. The effects of age on the model were not very pronounced. Work hypogamy, meanwhile, is associated with an increased likelihood of divorce. It is likely that work hypogamy among women, as it is much rarer than educational hypogamy, is still stigmatized in some way.

Introduction
Since the early 1980s the gender gap in college graduation rates has shifted, with women consistently graduating at higher rates than men (Goldin & Katz, 2007). Scholars are unsure about what is causing this trend (Bowen, Chingos, & McPherson, 2011), and it is unclear what effects the reversal of the gender gap in educational attainment may have had on marital stability, with many studies suggesting different effects. Some research suggests that, in marriages where the wife has more education than their husband, divorce is 27% to 38% more likely (Bumpass, Castro, Martin, & Sweet, 1991; Goldstein &
Harknett, 2006; Phillips & Sweeney, 2006; Teachman, 2002). In addition to decreased marital stability, the gender gap has certainly decreased the pool of “eligible”
(homogamous) men available to highly educated women.
In this paper, I review the literature on educational and income hypogamy, and examine the effects of marital inequity on marital happiness, likelihood of cheating, and divorce rates. To do this, I use a regression on General Social Survey (Smith, Marsden,
Hout, & Kim, 2011x) data, and a lagged logistic regression on Integrated Public Use
Microdata Series (Ruggles, Alexander, Genadek, Goeken, Schroeder, & Sobek, 2010) data.
For this paper, homogamy is defined as marrying somebody who is a relative equal, whether by having the same education level or the same income or work status.
Hypergamy, meanwhile, is defined as marrying up, or marrying somebody who has more education or a higher income. Finally, hypogamy is defined as marrying down, or marrying somebody who has less education or a lower income. In other words, if a

husband has less education but makes more money than his wife, she will be considered to be married with educational hypogamy but work (or income) hypergamy.
Literature Review
Historically, men have had more education than women and have been willing to marry those with less education than them. But whether this is due to necessity or preference is hard to say because typically, people prefer to marry those similar to themselves (Kalmijn, 1998), and there are possible downsides from both genders marrying down/up. On the men’s side, husbands who make less money than their wives are more likely to be unfaithful (Munsch, 2010), and domestic violence is also more likely in these relationships (Atkinson, Greenstein, & Lang 2005; Melzer, 2002).
On the women’s side, some internet dating studies have shown that women are less interested in “marrying down” in education than men (Hitsch, Hortaçsu, & Ariely,
2010). Ultimately, marriages where females have greater education are still relatively rare (Schwartz & Han, 2013), and some experiments suggest that both sexes prefer homogamous partners, and prefer to avoid relationships in which a woman has higher status (Fisman, Iyengar, Kamenica, & Simonson
, 2006; Hitsch, Hortaçsu, & Ariely,
2010). Many scholars suggest that non-homogamous marriages are less successful purely because of tension caused by those differences. For instance, interracial relationships are more likely to face outward disapproval that can strain the union (Bratter & Eschbach,
2006, Fu, Tora, & Kendall, 2001; Root, 2001).
Attitudes toward female hypogamy may be changing though. When male college students were asked whether they would be bothered if their partners earned a higher salary, almost 60% said “it wouldn’t bother me at all” in 1990, up from just 41% in 1980

(Willinger, 1993). In addition, while lower-earning husbands may be more likely to abuse their wives, Atkinson, Greenstein, & Lang (2005) found this was only the case if the husband held traditional values. Gender differences in work and education have decreased quite rapidly, but men’s family roles appear to be much slower to change
(Goode, 1982; Hochschild, 1989).
Because schools have students with educational homogamy, it is intuitive that couples that meet in school and then get married would be educationally homogamous.
With more students in school in general, educational homogamy would, in theory, increase. It is not so straightforward though. In the U.S., most studies show a rise in educational homogamy, but the results can vary (Hou & Myles 2008, Rosenfeld 2008,
Schwartz & Mare 2005). For instance, some studies suggest that trends in educational homogamy do not appear to have any consistency (Kalmijn, 1998).
As time from graduation increases, a man will become less likely to marry with educational homogamy, while a woman becomes less likely to marry at all (Schwartz &
Han, 2013). In addition, couples that marry older are less likely to get divorced, and higher educated couples are less likely to as well (Schoen, 1975; Bramlett & Mosher,
2001). The interaction between the two variables is not entirely clear, as a higher educated couple will likely be older. Ultimately, couples in which wives are more highly educated than their husbands were historically more likely to divorce, but this association has declined significantly, and in recent studies, divorce is no more common among these couples (Schwart & Han, 2013). This may be due to a change in stigma as female hypogamy becomes more prevalent.

Economically speaking, higher-earning men are more likely to marry higherearning women than they were historically (Sweeney & Cancian, 2004). They also value a woman’s financial prospects, education, and intelligence more (
Buss, Shackelford,
Kirkpatrick, & Larsen, 2001). While this could simply be because it is more likely for women to have those characteristics, it could also be due to the tougher economic climate and relative need for those attributes in a modern partner (Oppenheimer, 1988; Sweeney,
2002). When women’s socioeconomic status increases relative to men, women are more likely to select a mate with money being less of a factor (Oppenheimer & Lew 1995,
Sweeney 2002). While trend studies show that the importance of men’s earnings for marriage has not declined (Buss et al 2001, Sweeney 2002), Fernández, Guner, and
Knowles (2005) found that when women are in good shape economically, educational homogamy is not as important. “Love” becomes more important than money.
Studies suggest that marital commitment and satisfaction are more useful for predicting divorce than is the relative economic independence of partners. This suggests that while it can prevent a woman from exiting an unsatisfactory marriage, it doesn’t have a significant detrimental impact on a satisfactory marriage (Sayer & Bianchi, 2000).
Similarly, a woman’s participation in the workforce does not have a detrimental effect on happy marriages, but can increase the risk of divorce in unhappy ones (Schoen , Astone,
Kim, Rothert, & Standish , 2002). However, time series data suggests that full-time employment for a woman is associated with greater marital stability, and changes in their employment do not affect marital quality, though unhappily married women are more likely to join the workforce (Schoen, Rogers, & Amato 2006).
Hypothesis

Many of the effects of marrying up/down on divorce rates are still unknown, so I explore satisfaction of such marriages and further explore the likelihood of divorcing. I hypothesize that there are two primary experiences acting against each other in marital satisfaction and prevalence: age and satisfaction. As age of first marriage increases, divorce rates go down. But, on the other side, I predict that a woman is less satisfied in marriage if she marries down. Ultimately, I predict that a woman is actually less likely to divorce if she marries hypogamously, but will report being less happy in their marriage.
Divorce rates tend to be much lower among more educated and older people. Practically, this is partly just a function of time: if you marry later you naturally have less chance to get divorced.
Also, if you divorce at an older age, your chance of finding a better partner is likely diminished, and your incentive to leave the person you are with becomes lower. I predict that younger people are much more likely to ultimately get divorced. Finally, I hypothesize that women will be less likely to marry hypogamously given the choice, which could influence total marriage rates over the years.
Data
Data are from two sources. I use 1960, 1970, and 1980, 1990, 2000, and 2010 data from IPUMS which is composed of microdata that marks each record as an individual person, to allow for analysis of specific persons in the context of their household rather than households being the unit of observation. IPUMS data uses household size, race, group quarters, and geography to create the representative strata.
Also, the data have person weights for each year to represent how many people in
America the particular respondent represents.

All IPUMS data comes from the Census Bureau every ten years, except 2010 data, which comes from the American Community Survey. Respondents range in age from 15 to 100 years old, and only heterosexual marriage is considered. The sample sizes range, but in each given year there are between 300,000 and just over 2 million female respondents who reported being married, and between 14,000 and nearly 450,000 that reported being divorced (the highest were both in 2000, which had a total female sample of nearly 4 million, while the lowest were all in 1960, which had a total female sample of just over 450,000).
I also use all recorded data from the General Social Survey, which has performed face-to-face interviews from 1972-2012, in order to answer the questions about attitudes toward marriage. Sample size ranges from 1372 to 1613 each year, with oversamples of black respondents in 1982 and 1987. Not every question is asked of every respondent, and some questions are left out if irrelevant. For instance, the question on marital happiness (hapmar) is only asked of currently married respondents.
Both sources serve a different purpose in completing a robust analysis. For instance, GSS data is useful for assessing opinions, actions, and feelings about marriage while IPUMS gives me a much larger sample to analyze likelihood of divorce. The GSS data, for instance, has only between 60 and 500 divorced female respondents per year, not nearly enough to perform an accurate lagged analysis from year to year.
My analysis is only on married/divorced women. For this, I drop men completely from the analysis and use spouse variables included in a respondent’s information. If there is no spousal information, the respondent is removed from the sample. For my analysis of factors contributing to divorce, I take a record of all respondents who are

married in one panel year but not in the next, and perform a lagged logistic regression to determine the effects of certain variables on the likelihood of their becoming divorced.
For this analysis, my primary variables include educational attainment, marital status, age, age at first marriage, race, and salary. I do not have an age cutoff for the data because I analyze purely based on marriage then divorce, and would like to see any impact that age might have on those rates. Educational attainment will be divided into categories of degree rather than number of years of schooling. Educational attainment and income/work disparity are used as measures for homogamy. For example, spousal educational attainment is recoded according to the same process as respondents’ educational attainment and subtracted from respondents’ educational attainment indicating a difference in degree.
Because others have analyzed educational attainment differences and their effects on marital dissolution, I also include a brief exploration of the nature of relationships that are not homogamous and the participants in these relationships, as well as the hesitation of becoming a participant in such a relationship (as measured by the relative quantity).
For instance, since there are significantly more women with bachelor’s degrees than men, this may have an impact on likelihood of getting married.
Finally, I will test the satisfaction of non-homogamous relationships through things like marital happiness and preponderance of cheating. To do so, I analyze the effects of educational and work hyper/hypogamy on two variables: marital happiness, and whether a respondent has cheated on her husband. For this analysis, I use a simple yes/no variable for non-homogamy. For instance, if a woman has more education than her husband, then they have educational hypogamy. I also include several variables such as

family income, frequency of church attendance, presence of children, and age at marriage as control variables as many studies (Glenn & Weaver, 1978; Glenn & McLanahan,
1982) suggest these might be important considerations in marital happiness.
These two datasets should be sufficient to allow me to test my hypotheses on the effects of hyper/hypogamy on marital dissolution, and to observe the trends of such behavior over an extended period of time in America.
IPUMS Figures
The above table shows the percentage of all bachelor’s degrees held by men versus women in the IPUMS data. The second line in each row records the frequency.
As can be seen from the above chart, it took until sometime between 1990 and 2000 before women held a majority of bachelor’s degrees. This is because, while women graduated from college at higher rates starting in the early 1980s, there were a lot of older men and women left in the population from when men graduated with higher rates.
When just looking at ages 26-35, the change occurs earlier, as can be seen from the chart and matching graph below:

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0
P e r e n t
1960 1970 1980
Year
1990 2000 2010
Women (26-35)
Men (26-35)
The above chart shows women aged 26-35 clearly overtaking men in bachelor’s degree attainment sometime around 1990. When divided by race, data looks pretty similar. Below is a table presenting the percentage of bachelor’s degrees held by men and women (between 26-35 years old) for each race:
White (26-35)
Men
1960
78.43
1970
73.71
1980
61.47
1990
50.57
2000
47.05
2010
43.66

Women
Black (26-35)
Men
Women
Hispanic (26-35)
Men
21.57
1960
52.29
47.71
26.29
1970
53.92
46.08
38.53
1980
47.06
52.94
49.43
1990
41.36
58.64
52.95
2000
39.43
60.57
Women
Asian (26-35
Men
Women
1960
79.61
20.39
1960
66.91
33.09
1970
73.66
26.34
1970
64.62
35.38
1980
61.08
38.92
1980
58.36
41.64
1990
49.49
50.51
1990
50.14
49.86
2000
45.16
54.84
2000
49.51
50.49
As is expected, the percentages for whites are most consistent with the overall population, while black women have since the 1980s held a higher percentage of degrees than black men. Asians seem to stay pretty level, though women still superseded men at around the same time period. The trend for each race is consistent, with women’s share of bachelor’s degrees rising and men’s falling. The following figures chart the data for each respective race:
56.34
2010
35.44
64.56
2010
40.49
59.51
2010
44.50
55.50

90
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0 r c e
P e n t
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Year
1990 2000 2010
Men
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P e r t
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1960 1970 1980
Year
1990 2000 2010
Men
Women

80 r c e
P e n t
40
30
20
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0
70
60
50
1960 1970 1980
Year
1990 2000 2010
Men
Women
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90
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70
P e r c e n t
1960
1970 1980
Year
1990 2000 2010
Men
Women
Next, I code each marriage in my sample data as being either homogamous, hypergamous, or hypogamous. Hypergamy is defined as a woman married to a man with more education while hypogamy is when a woman is married to a man with less education. Homogamy is when their two education levels are equivalent. If a couple has equivalent education, their marriage is homogamous and the observation is coded 0. If a

woman has more education than their husband, they are said to marry hypergamously, and it would be recorded by a 1. If they have less education than their husband, this would be considered educational hypogamy, and would be recorded by a -1. An average of 0 would indicate an average of complete homogamy, while an average of 1 would indicate complete hypogamy, or a woman having less education than their husband.
Average Homogamy Level 1960 1970 1980 1990 2000 2010
Females 0.0059 -0.0811 -0.1311 -0.0929 -0.0519 0.0128
As can be seen in the table above, hypogamy is only slightly more common as the years progress, and in fact reverses in 2010. This trend doesn’t entirely reflect the shrinking percent of men with higher educations and smaller market for a hyper/homogamously inclined woman, but it may shed light on the decreased frequency of marriage as we see later.

100%
90%
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70%
60%
50%
40%
30%
20%
10%
0%
1960
1970 1980
Year
1990 2000 2010
Ed Hypogamy
Ed Homogamy
Ed Hypergamy
From the above chart we can see that while educational hypergamy dropped slightly from 1960 to 1980, it rose again into 2010. Homogamy, meanwhile, rose marginally but has not changed much over time. Hypogamy rose when hypergamy dropped, but went back down following the 1980s.

The above tables representing the rates of various marriages by race show Asian and Hispanic women marrying down with much more frequency. Asian women reported marrying with educational hypogamy at nearly 50% by 2010 (and just above 50% in
1980), and Hispanic women reported marrying with educational hypogamy at about 41%.
Whites, meanwhile, married with educational hypogamy only about 32% of the time in
2010.

It is interesting to see that while educational homogamy and hypergamy are only moderately common among women, work hypergamy is extremely common, though decreasing since 1970. This is fairly consistent with common intuition as women entered the workforce in increasing numbers, but still make less than men on average. This might also suggest that educational homogamy is less important of a factor: even if a woman has more education than their husband, they still aren’t likely to make more money than them. The chart below illustrates the increasing likelihood of a woman marrying with work hypogamy, which more than quadrupled from 1960 to 2010 (7 to
29%).
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
1960
1970 1980
Year
1990 2000 2010
Work Hypogamy
Work Homogamy
Work Hypergamy

Looking at divorce rates from each panel year, 6.7% of female respondents who were married in 2000 were not married in 2010, while only 4.7% of female respondents married in 1990 were divorced in 2000, and only 3.3% of female respondents married in
1980 reported being divorced in 1990. There is an upward trend in divorce rates as women began obtaining Bachelor’s degrees at higher rates than men.
Divorce Rates Year-to-Year: 1980-90
3.3%
1990-2000
4.7%
2000-2010
6.7%
I also wanted to see whether other variables, like age at first marriage, might have an impact on degrees of homogamy. From the chart below, it does appear that older women are more likely to marry with educational hypergamy, but the effect is only marginal. 28% of 20 year olds marry with hypergamy, for instance, while up to 40% of older females marry with hypergamy (40% is the rate among 48 year olds). Homogamy, meanwhile, drops as a respondent ages. This is quite intuitive, as a young lover is likely to meet their partner during school. Below age 18, percent of educational homogamy drops into the 20s from 36% as an 18 year old.

100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52
Age at First Marriage
Ed Hypogamy
Ed Homogamy
Ed Hypergamy
So if hypogamy hasn’t increased steadily over time with the increase in females with Bachelor’s degrees, does that mean that women are getting married less frequently?
The chart below suggests that to be the case, with almost 34% of respondents in 2010 not married, and only 18% in 1960.
100%
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0%
1960
1970 1980
Year
1990 2000 2010
Never Married
Widowed
Divorced
Married

The effect is even more pronounced among 26-35 year olds, with only 8% not married in
1960 but 34% not married in 2010. Whether fear of marrying hypogamously causes it or not, marriage rates have clearly dropped quite quickly.
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Never Married
Widowed
Divorced
Married
1960 1970 1980
Year
1990 2000 2010
GSS Figures
For the analysis of female respondent’s attitudes towards and behavior in marriage, nearly all of the variables I use are recoded yes/no variables. Below is a table illustrating the rates of marrying with education and work hypogamy:

There are at least a chunk of women who marry hypogamously, but it is not nearly as drastic as might be expected. Here’s a table showing average marital happiness for women as well as the standard deviation (.56). Happiness is recorded on a 1 to 3 scale, with 1 being the most happy, and 3 being the least. The second graph shows the happiness among marriage without regard for gender. So on a 1 to 3 scale, happiness is right about 50% on average.
Below are two tables showing that there is a solid group of women who married hypogamously spread out among the happiness scale. The following two tables show that there are a greater proportion of very happy women who married without hypogamy, so a regression will be useful to determine any kind of effect this has.

GSS Methods
Hapmar is the first variable I test in regression. The reason for this is because, I hypothesize, while someone may not get divorced or cheat after they marry hypo/hypergamously, they may regret it or be slightly less happy about it. For this model, I added edhypergamy and wrkhypergamy as variables, which return 1 or 0 (1 if a woman marries up, 0 otherwise. This is not a redundant addition of hypogamy because it

handles the equal situation in the opposite manner – if both are represented by a 0 then the respondent would be considered to be married with homogamy.
Next, I use family income, church attendance, presence of children, and age at marriage as control variables as many studies (Glenn & Weaver, 1978) suggest these might be important considerations in marital happiness. Other studies suggest there is no effect, and my regression did not prove any correlation among any of those variables except the presence of children and family income. Below is my initial model, with all variables initially showing as insignificant.
After removing the variables for age of marriage and belonging to church, there still was not a significant association from educational hypergamy. The effects from educational hypogamy and work hypogamy, however, are almost identical, with respondents reporting themselves as .06 points less happy from both (on average), holding other variables constant.

Logically, work hypergamy had a similar opposite association, increasing reported happiness by .04 (on average), holding other variables constant. Age and income had marginal associations. Having children slightly decreased reported happiness
(about .03 points on average, holding other variables constant), which is consistent with expectations, though other studies don’t find very powerful effects (Glenn & Weaver,
1978). Below is the regression with all significant variables.
Next, I look at the model from a different angle: is a woman more likely to report having slept with someone besides their husband if they married down? Here is the final regression. Number of children, religious attendance, family income, and even educational hypogamy proved to not be significant for the model and were removed:

The model suggests that both work hypogamy and work hypergamy have almost the same association with cheating. This might seem counterintuitive at first, but considering that work hyper/hypogamy technically means that one spouse is working and the other isn’t, we can suppose that having one spouse at home increases (as opposed to both working or both staying at home) is associated with an increase of a respondent’s cheating by about .03 for hypogamy, and about .04 for hypergamy.
IPUMS Methods
To do the analysis on likelihood of divorce using IPUMS data, I start by identifying women who are married in each year of the data, and isolate those who were divorced in the next panel year. Because the spread between panels is a 10-year period, this should be plenty of time for a divorce to occur. Then, I identify a criterion for hypergamy, homogamy, and hypogamy. For this analysis, I use two different kinds.
First, I use work as a variable, as measured by the difference in personal income between a respondent and her spouse. If the difference is greater than $1000, then there is

considered to be hypergamy or hypogamy. This limit is also useful for getting rid of marginal responses.
I also use education, as measured by the differences in personal educational attainment between a respondent and her spouse. I exclude any response that does not report either a respondent or their spouse’s earning or education. Then, using control variables including age, race, educational attainment, income, number of children, number of times married, and age at first marriage, I run a lagged logistic analysis to determine whether there is any significant impact on the likelihood of becoming divorced. A lag model is useful for this scenario as it helps to predict a value of a dependent variable in the present panel wave of the data using the previous panel wave’s values. I use logistic regression because it is particularly useful for analysis where the dependent variable is binary. The logit varies between 0 or 1, reflecting the odds of a given occurrence – in my case a 0 or 1 that reflects whether or not a respondent is divorced/separated.
To start, I evaluated my panel data to determine the number of respondents who got divorced between one wave and the next. For instance, 6.7% of respondents who were married when these data were collected in 2000 ended up divorced or separated in
2010. For an initial regression, I tested from 2000 to 2010 with no control variables except for the educational hypergamy/hypogamy variables. The results are as follows:

Educational hypogamy and hypergamy appear to have significant explanatory power for divorce between the two periods, as both hold p-values far less than .05.
Essentially, according to the regression, a change from no hypogamy to hypogamy is associated with a change in the logit by -.11 points, while a change from no hypergamy to hypergamy has a similarly opposite association, increasing the logit by .11 points as well.
This is consistent with my hypothesis that a woman who marries below themselves, educationally speaking, is not necessarily more likely to get divorced.
It is important to remember that both sets of variables for education and work are binary – they are not relative. This means that a woman either marries with hypogamy or they don’t marry with hypogamy. These variables do not measure how far apart the husband and wife actually are on the scale. This has benefits and downsides: creating a scale implies attaching a relative value to the data – for instance, coding the difference

between a respondent with a bachelor’s degree and her husband with a master’s degree would give a net hypergamy of 1, but what if the husband had a PhD? If I were to code that as a 2, that would imply that having a PhD versus a Bachelor’s degree is twice as significant as having a Master’s degree versus a Bachelor’s degree. Instead, I use dummy variables stating a 1 or 0 if the condition is true.
Below is the regression for work homogamy. A woman is defined as being married with work hypogamy if they make more than $1000 more than their husband, based on their wages from salary, tips and the like. A woman is defined as being married with work hypergamy if their husband makes more than $1000 more than them. I chose to separate the difference by $1000 to filter out any couples that failed to report their income as well as to limit any noisy differences between marginally different incomes.
While a year or two of school could be the difference between failing to complete a bachelor’s degree or obtaining a master’s degree, a difference of a few dollars would not be as significant to a two-person, two-income household.

An opposite association can clearly be seen in work hyper/hypogamy. In other words, according to the regression, when a woman is hypogamously married in the panel year 2000, the logit of their chances of divorce by the year 2010 goes up .19, with a statistically significant p-value. Work hypergamy, while having a similarly opposite association of -.08 on the logit, is not statistically significant with a p-value just shy of
.05. This is consistent with the theory that a woman who marries “down” is more likely to get divorced, though not necessarily with my hypothesis.
The next step is to add the control variables to get a sense of how profound the association actually is, and to add in the additional years. Finally, it is important to remember that this data does not record information for previous spouses, which means I can only determine whether, based on the information provided for her spouse in one

wave, a woman is more likely to be divorced in the next. While this could carry problems for the objectivity of the information, it is an unavoidable weakness of survey data. Below is my initial logistic regression, for respondents married in 2000 but divorced in 2010.
As can be seen above, work hypogamy, educational hypogamy, educational hypergamy, and age are all significant. The income of the respondent has almost no effect, and work

hypergamy is marginally insignificant, with a p-value of .058. Below I perform the same regression but remove the respondent’s income, which raises the associations of work hypogamy.
Work hypergamy remains marginally insignificant. Work hypogamy, meanwhile, is associated with a 0.18 increase on the log of the odds of getting divorced, which implies there is almost a 20% (e^0.18) increase in a hypothetical respondent’s chances of getting divorced, all other variables being held constant. Educational hypogamy is

associated with a 0.08 decrease in the log of the odds of getting divorced (which translates to about an 8% decrease in the chances of getting divorced as well, taking e^-
0.08). Educational hypergamy, meanwhile, has a near opposite association with the log of the odds of getting divorced, increasing the chances by about 8% (e^0.08). Age has a less than 1% negative association (e^-0.007). Interestingly, this suggests that a woman who marries “down”, educationally speaking, would be less likely to get divorced, which is consistent with my hypothesis that more education doesn’t necessarily increase divorce rates. However, a woman who marries a man who makes less money than them is more likely to get divorced.
Next, I perform the same regression on the respondents who were married in 1990 but divorced in 2000. The results are below.

While the associations appear similar, they have some slight variations in degree.
Income becomes significant with a p-value less than .001, but still has only a marginal association (4.55e-06). The coefficient of the log of the odds on work hypogamy also drops from 0.18 to 0.06, which would translate to only about a 6% increase in the chances of getting divorced (e^0.06) as opposed to almost 20% (e^0.18) from the

previous model. Finally, the same regression is run on married respondents in 1980 that were divorced in 1990, with the results below.
In this sample, work hypogamy and educational hypogamy lose significance. Work and educational hypergamy, on the other hand, seem to have very similar effects to other panels. Below is the data with the two insignificant variables removed:

This model suggests that a change from no work hypergamy to work hypergamy has a -0.12 association with the log of the odds of a hypothetical married respondent in
1980 being divorced in 1990, all other variables being held constant. This would translate into a 12% (e^-0.124) decrease in the chances of getting divorced. A change from no educational hypergamy to educational hypergamy, meanwhile, would have a
0.05 positive association on the log of the odds of a hypothetical married respondent in
1980 being divorced in 1990, with all other variables held constant. This would imply an

increase in the chances of getting divorced by approximately 5% (e^0.53). The associations of age and incwage would be less than 1% on average, all else being equal.
The panels, then, are not entirely consistent. The final panel, from 2000 to 2010, has the highest sample of married respondents being divorced in the second panel (6.7%).
Limitations
One frustration with both sets of data is that it does not allow me to control for the effects of degrees that are obtained after marriage, since between the waves I am not able to know which happened first, or the effects the change could cause. Also, because for
GSS data one of the variables I’m testing is whether the wife works fulltime and her spouse doesn’t, this gives me only a very crude and slightly affected way to test the effects of hypo/hypergamy. Unfortunately, the GSS does not have a question asking how much a spouse makes, only ones asking how much the respondent makes and how much their household makes. IPUMs data, meanwhile, have information on both a respondent’s income and their spouse’s income, but not on measures of happiness or satisfaction. I considered subtracting the respondent’s income from the household income, but thought it would be too full of noise to give me an accurate sampling.
Instead the question became: is a wife less happy when her husband is not working, but she is? This is actually quite a rare phenomenon by GSS data. There are 1,142 cases of a woman marrying with work hypogamy over the course of the GSS data, while there are
22,314 cases of women marring with work hypergamy over the same time.
Because the work hyper/hypogamy variables only record if a woman worked fulltime and her husband did not, it doesn’t allow for degrees of influence and could merely represent either wealthier families or families with some sort of dysfunction

(maybe the husband doesn’t work because he can’t, or because he has to take care of a family member, or something else). However, even the black/white definition proved useful in this model, which was good to see. The education hypo/hypergamy definition was much better in this regard, as if a spouse had more years of education, then it would be considered hypergamy.
Of course, this model could be confounded. We can’t say with certainty that a woman who marries hypogamously isn’t also less likely to be happy in general. We don’t know if the women originally married hypogamously or if they became hypogamous. We also don’t know whether the husband’s lack of fulltime work is driving the unhappiness of marriage in non-hypogamous ways. There is a strong possibility that the husband might not be able to find work, which might be causing unhappiness in general, and the husband’s status could just be an extension of that.
Conclusion
While the IPUMS data suggests that work hypogamy may have a negative association with the likelihood of getting a divorce, the effect is not entirely constant across the panels. On the other side, educational hypogamy seemed to have a negative effect on the chances of getting a divorce, while educational hypergamy seemed to have a positive effect. This was consistent with my hypothesis, but the effects of age that I expected to see were not very pronounced. It is likely that work hypogamy among women, as it is much rarer than educational homogamy, is still stigmatized in some way.
Ultimately, divorce is probably too drastic an action since a respondent was probably aware of any marital differences when they initially married. It might play into

the changing rates of marriage these days, which at least superficially could help to explain the large decrease in marriage rates over the years.

Works Cited
Atkinson, M. P., Greenstein, T. N., & Lang, M. M. (2005). For women, breadwinning can be dangerous: Gendered resource theory and wife abuse.
Journal of Marriage and Family , 67 (5), 1137-1148.
Bowen, W. G., Chingos, M. M., & McPherson, M. S. (2009). Crossing the finish line:
Completing college at America's public universities . Princeton University Press.
Bramlett, M. D., & Mosher, W. D. (2001). First marriage dissolution, divorce, and remarriage. In National Center for Health Statistics .
Bratter, J. L., & Eschbach, K. (2006). ‘What about the couple?’Interracial marriage and psychological distress. Social Science Research , 35 (4), 1025-1047.
Bumpass, L. L., Sweet, J. A., & Cherlin, A. (1991). The role of cohabitation in declining rates of marriage. Journal of Marriage and the Family , 913-927.
Buss, D., M., Shackelford, T. K., Kirkpatrick, L. A., & Larsen, R. J. (2001). A half century of mate preferences: The cultural evolution of values. Journal of
Marriage and Family , 63 (2), 491-503.
Fernandez, R., Guner, N., & Knowles, J. (2005). Love and money: A theoretical and empirical analysis of household sorting and inequality. The Quarterly Journal of
Economics , 120 (1), 273-344.
Fisman, R., Iyengar, S. S., Kamenica, E., & Simonson, I. (2006). Gender differences in mate selection: Evidence from a speed dating experiment. The Quarterly Journal of Economics , 121 (2), 673-697.

Fu, X., Tora, J., & Kendall, H. (2001). Marital happiness and inter-racial marriage: A study in a multi-ethnic community in Hawaii. Journal of Comparative Family
Studies , 32 (1), 47-60.
Glenn, N. D., & McLanahan, S. (1982). Children and marital happiness: A further specification of the relationship. Journal of Marriage and the Family , 63-72.
Glenn, N. D., & Weaver, C. N. (1978). A multivariate, multisurvey study of marital happiness. Journal of Marriage and the Family , 269-282.
Goldin, C., & Katz, L. F. (2007). The race between education and technology: The evolution of US educational wage differentials, 1890 to 2005 (No. w12984).
National Bureau of Economic Research.
Goldstein, J. R., & Harknett, K. (2006). Parenting across racial and class lines:
Assortative mating patterns of new parents who are married, cohabiting, dating or no longer romantically involved. Social Forces , 85 (1), 121-143.
Goode, W. J. (1970). World revolution and family patterns (Vol. 963). New York:
Free press.
Gould, E. D., & Paserman, M. D. (2003). Waiting for Mr. Right: rising inequality and declining marriage rates. Journal of Urban Economics , 53 (2), 257-281.
Hitsch, G. J., Hortaçsu, A., & Ariely, D. (2010). Matching and sorting in online dating.
The American Economic Review , 130-163.
Hoschild, A., & Machung, A. (1989). The second shift: Working parents and the revolution at home. New York, USA: Viking .

Hou, F., & Myles, J. (2008). The changing role of education in the marriage market:
Assortative marriage in Canada and the United States since the 1970s.
Canadian
Journal of Sociology , 33 (2).
Kalmijn, M. (1998). Intermarriage and homogamy: Causes, patterns, trends.
Annual review of sociology , 395-421.
Loughran, D. S. (2002). The effect of male wage inequality on female age at first marriage. Review of Economics and Statistics , 84 (2), 237-250.
Melzer, S. A. (2002). Gender, work, and intimate violence: Men's occupational violence spillover and compensatory violence. Journal of Marriage and
Family , 64 (4), 820-832.
Oppenheimer, V. K. (1988). A theory of marriage timing. American Journal of
Sociology , 563-591. Oppenheimer V. K. (1995). American Marriage Formation in the Eighties: How Important was Women's Economic Independence? In
Gender and Family Change in Industrialized Countries, ed. KO Mason, A-M
Jensen, 105-38. New York: Oxford University Press
Phillips, J. A., & Sweeney, M. M. (2006). Can differential exposure to risk factors explain recent racial and ethnic variation in marital disruption?. Social Science
Research , 35 (2), 409-434.
Root, M. P. (2001). Love's revolution: Interracial marriage . Temple University Press.
Rosenfeld, M. J. (2008). Racial, educational and religious endogamy in the United
States: A comparative historical perspective. Social Forces , 87 (1), 1-31.

Ruggles, S., Alexander, J. T., Genadek T., Goeken R.,Schroeder, M. B., & Sobek,
M.
Integrated Public Use Microdata Series: Version 5.0 [Machine-readable database]. Minneapolis: University of Minnesota, 2010.
Sayer, L. C., & Bianchi, S. M. (2000). Women's Economic Independence and the
Probability of Divorce A Review and Reexamination. Journal of Family
Issues , 21 (7), 906-943.
Schoen, R. (1975). California divorce rates by age at first marriage and duration of first marriage. Journal of Marriage and the Family , 548-555.
Schoen, R., Astone, N. M., Kim, Y. J., Rothert, K., & Standish, N. J. (2002). Women's employment, marital happiness, and divorce. Social Forces , 81 (2), 643-662.
Schoen, R., Rogers, S. J., & Amato, P. R. (2006). Wives’ Employment and Spouses’
Marital Happiness Assessing the Direction of Influence Using Longitudinal
Couple Data. Journal of Family Issues , 27 (4), 506-528.
Schwartz, C. R., & Mare, R. D. (2005). Trends in educational assortative marriage from 1940 to 2003. Demography , 42 (4), 621-646.
Smith, T. W., Marsden, P., Hout, M., & Kim, J. General social surveys, 1972-
2010 [machine-readable data file] /Principal Investigator, Tom W. Smith; Co-
Principal Investigator, Peter V. Marsden; Co-Principal Investigator, Michael
Hout; Sponsored by National Science Foundation. --NORC ed.-- Chicago:
National Opinion Research Center [producer]; Storrs, CT: The Roper Center for
Public Opinion Research, University of Connecticut [distributor], 2011.
Sweeney, M. M. (2002). Two decades of family change: The shifting economic foundations of marriage. American Sociological Review , 132-147.

Sweeney, M. M., & Cancian, M. (2004). The changing importance of white women's economic prospects for assortative mating. Journal of Marriage and
Family , 66 (4), 1015-1028.
Teachman, J. D. (2002). Stability across cohorts in divorce risk factors.
Demography , 39 (2), 331-351.
Willinger, B. (1993). College Men's Attitudes Toward Family and Work in the 1980s.
Men, work, and family , 4 , 108.