1
Another Look at Wealth and Marital Relationships:
The Effects of House Prices on Divorce Rates
Abobaker Mused
Hunter College1
Spring 2009
Abstract
The divorce rate in the United States has increased dramatically since the 1960’s.
Much research has attempted to explain this epidemic. This paper analyzed the effects of
house price changes on divorce rates. The study is based on Gary Becker’s theory (1977)
that a drastic change in wealth increases the probability of marital dissolution. Data from
the Current Population Survey and from the Office of Federal Housing Enterprise
Oversight were used. Based on the MSA level, a fixed effects model was used to estimate
the effects over a period of one year, three year, and five year changes in the house price
index. The results supported Becker’s theory, in which positive and negative changes in
house prices significantly affected the divorce rate for homeowners. As house values
increased, we observed increased rates of separation. Moreover, if house prices
decreased, divorce rates decreased. It is predicted that as house prices decrease, married
couples are more financially dependent of each other, and they cannot afford to separate
because it is more difficult for them to access equity from their house.
1
I would like to thank Professor Purvi Sevak for her outstanding effort and help. I am in debt to her for the
valuable time she spent with me and for her availability. I would also like to thank Professor Jennifer
Tennant for her readiness and enthusiasm. Her comments and support are greatly appreciated.
2
I. Introduction
Many aspects in life can affect a couple’s marital union and can lead to a divorce.
In the United States, the divorce rate has increased in the past three decades. The divorce
rate rose from 2.2 per thousand people in 1960 to 3.6 in 2005 (Wolfers, 2006). Schoen
and Standish (2001) found that approximately 44% of marriages in the U.S. end in a
formal divorce, but does not provide details as to why. This study takes a closer look at
some factors that could have caused the divorce rates to increase between 1985 and 2008.
The largest asset that an average American has is their house. Any change in the
value of their house has major and critical affects on their wealth and financial wellbeing. Research has shown that one of the major reasons people get divorced is because
of financial problems (Poortman, 2005). Therefore it is important to study the changes in
house prices to observe any correlation there might be with the divorce rates.
House prices have been continuously changing throughout the past two decades.
Graph 1 shows that average house prices in the U.S. began to grow at an increasing rate
from the beginning of the 1990’s up until 2006, when the housing bubble burst. Major
financial institutions faltered over sub-prime mortgages that were defaulting in drastic
numbers. Now, many houses are foreclosed and the economy is in the greatest recession
since the Great Depression.
This study looks at the effects of house price changes and other macroeconomic
factors such as the unemployment rate on the divorce rate. Data were used from the
Office of Federal Housing Enterprise Oversight and from the Current Population Survey.
The analysis estimates a fixed effects model of divorce rates, by sex and age, at the
3
Metropolitan Statistical Area level to get an overview of the general economy and sample
population. Results indicated that positive changes in housing prices significantly
increased the divorce rate for people who owned a home. Furthermore, the results
indicated a significant decrease in divorce rates when there was a negative change in
house prices for homeowners.
II. Literature Review & Theoretical Framework
Becker, Landes, and Michael (1977) proposed a basic framework from which
they derived their theoretical analysis on marital dissolution. A married couple would
separate when their utility expected from remaining married falls below their utility
expected from getting divorced. The utility function is based on their wealth and the
bundle of traits they desire from their mate.
A couple may get married based on what their wealth is at the time and what they
expect their wealth to be in the future. But what happens if their wealth increases
unexpectedly? Becker believed that the couple would be less financially dependent and
the bundle of traits each mate desired would change, and so therefore they will seek to
divorce in order to maximize their utility. If the two mates had known that their wealth
would have increased drastically, they might have chosen different spouses at the time.
Some individuals might remarry after the dissolution to a new mate that has the
new bundle of characteristics they desire based on their increased wealth. This will only
occur if their expected wealth of remarrying will be greater than if they remained single.
(1)
W
mf
–W
m
4
Equation 1depicts Becker’s theory, where W
better match and W
than W
m,
m
mf
is the expected wealth from a
is the expected wealth of remaining single. If W
then the individual will get remarried. However, if W
mf
mf
is greater
is less than W
m,
then the individual will remain separate.
For instance, person A marries a woman with a bundle of characteristics based on
the wealth he has at the time and the wealth he expects to have. If his wealth increases
unexpectedly (by more than what he had anticipated), he will reevaluate his desired set of
traits in a woman and get divorced in order to remarry so that he may maximize his
utility. But this will only happen if his expected wealth will be greater when he remarries.
Becker proposed a theory in 1974 concerning why people get married. In his
article, A Theory of Marriage, he stated that single people marry only if their combined
married-wealth exceeds that of their combined-single wealth. In 1976, Becker further
extended his theory to include that a couple dissolves their marriage if and only if their
combined-single wealth exceeds their combined-married wealth.
In 1977, Becker along with Landes and Michael, described that “full wealth” is
not only money wealth as many consider it, but it also accounts for “the productivity of
nonmarket time.” People constantly try to maximize their full wealth, whether it is
through the means of getting married or getting separated. They also acknowledged that a
person might find it in their best interest to get married knowing that they will divorce,
and then remarry2.
The authors went into further detail to explain the many possibilities that will lead
to a marital dissolution. They incorporated search costs, and further categorized it as
2
Such cases that are common today are those who do not have a legal status in the U.S.; therefore they seek
to get married to a spouse that can provide them with paperwork to become residents, then they divorce
them and remarry a spouse of the optimal traits they desire.
5
extensive and intensive searches. As search costs increased, the less time a person will
spend searching, therefore increasing the probability of marrying a mate that does not
have the optimal bundle of traits they desire. This increases the probability of dissolution.
The bundle of traits included income, physical attractiveness, age, intelligence, social
background, religion, race, and others3. The greater the discrepancy between the traits of
the two mates, the higher is the probability of dissolution.
Other factors that affected the probability of dissolution were “specific capital” or
“marital capital” (an example is children). Depending on whether they choose to invest in
that capital or not, the probability of dissolution would either increase or decrease.
Furthermore, the older a person is when he/she gets married, the lower the probability of
a dissolution4. Similarly, as the duration of the marriage is longer, the lower the
probability there will be a dissolution of that marriage.
Others, such as Lehrer, argued that the majority of divorces are because of
uncertain and unfavorable outcomes. Her article, The Economics of Divorce (2003),
expanded on Becker’s theory. She concluded that people who divorce after there was a
change in their wealth most likely had a weak marriage from the beginning. Lehrer
believed that the change in wealth is not the cause of the divorce, but it is just the
catalyst.
In the New York Times, Kenneth Mueller, a psychotherapist, stated that some of
his clients were real estate executives whose wealth increased dramatically because of the
industry and now sought to divorce and remarry because their first wives were “not what
3
Education level had an ambiguous effect on divorce because as women entered the labor force, less time
was spent on specializing in the married life (such as childbearing), thus reducing gains from marriage.
4
Becker noted that the probability of dissolution begins to rise with age at marriage for relatively older
people
6
[they] really wanted” (Aug., 2007). The article reported that at least 50% of those who
visited the psychologists they interviewed “brought up real estate as a relationship issue.”
Nancy Chemtob, a Manhattan divorce lawyer, mentioned in the article that “the equity
that there is in real estate is one of the impetuses why there are so many divorces.” She
also said that since real estate went up drastically, men are willing to give up parts of
their estate to get divorced because “half of a lot is still a lot.”
Stephanie Coontz mentioned in her book, Marriage, a History: From Obedience
to Intimacy, or How Love Conquered Marriage, that during the 1920’s, wealth was being
rapidly created and divorce rates spiked (2006). She compared the past decade, where
divorce rates were soaring as the economy was expanding, like the 1920’s. She
concluded that people who accumulate wealth rapidly believe that they do not have to
abide by society’s conventional rules.
This research paper tries to test Becker’s theoretical framework by observing
increases and decreases in the values of houses. The hypothesis is that if house prices
increase (or decrease), then so does the wealth of the individuals who own the houses.
This would lead to a change in their preference of the bundle of traits they desire from
their spouse. Even though data is not available to see whether an individual remarries, we
can observe if the divorce rate among an age-sex group in a given MSA increases due to
changes in the house prices.
III. Data
A. Explanation of the HPI data:
7
For the analysis, this paper uses data from the Office of Federal Housing Enterprise
Oversight (OFHEO). Every quarter, it publishes a house price index (HPI) for each
Metropolitan Statistical Area (MSA) in the United States. The OFHEO bases its estimate
of the house prices by using a modified version of the weighted-repeat sales (WRS)
methodology proposed by Case and Shiller (1989). The HPI for each MSA is calculated
using repeated observations of housing values for individual single-family residential
properties on which at least two mortgages were originated and subsequently purchased
by either Federal Home Loan Mortgage Corporation (Freddie Mac) or the Federal
National Mortgage Association (Fannie Mae) since January 1975 (Calhoun, 1996). Since
the repeat sales methodology is used on the same physical property, it helps to control for
quality differences in the sample used for statistical estimation. Therefore, the HPI is also
known as a “constant quality” house price index.
B. Explanation of IPUMS-CPS data:
Data from the Current Population Survey (CPS) was also used for the analysis.
IPUMS-CPS is an integrated microdata set, in which it provides information about
individual persons and households. The CPS, along with the U.S. Census Bureau and the
Bureau of Labor Statistics (BLS), jointly conduct surveys of U.S. households every
month. It was established during the wake of the Great Depression to measure
unemployment, therefore labor force and demographic questions are asked. The
advantage of this data set is that it has the unemployment rates used by the BLS, which
are the rates that are most commonly referred to.
C. Merged HPI-Household Data
8
While the CPS has data on divorce and unemployment for every household in the
sample, it does not have data on house prices. Therefore, the two data sets were merged
using the variables MSA and year as the unique identifiers. Twenty four years of data are
in the file, from 1985 to 2008. The sample was limited to those who were above the age
of 20, since divorce is the subject of the analysis. The sample observation size was
95,762 people throughout the U.S., for which summary statistics are reported in Table 1.
Because the CPS is not a panel data set, it is not possible to observe when a
household gets divorced. As a result, the data was aggregated and the unit of analysis was
the divorce rate among an age-sex group in an MSA (e.g. women ages 40-44 in New
York). Control variables are also calculated among the age-sex group in the MSA. These
include the unemployment rate, immigrant status, and education level. Family Income
was also controlled for since it is a crucial aspect of a married couple’s life as mentioned
before, and can have serious effects on their marital status. Another control variable used
in the analysis was the average number of children in households, since it can
significantly affect their decision to continue or dissolve their marital relationship.
Finally, when testing to see the effects of house price changes on divorce rates, it is
critical that we control for the percent of households that are homeowners and observe
whether the effect on divorce rates is greater in areas with higher home ownership.
D. Variable definition
The percentage ‘change’ in the house price index (HPI) from year to year was used to
measure increases or decreases in house prices. Table 2 gives a detailed summary
statistics for one year, three year, and five year changes in HPI. Those who responded to
the CPS survey as married, whether or not the spouse was present, were categorized as
9
“married” in the analysis. Those who responded as divorced or separated were
categorized as “divorced.” Race was categorized as Black, Hispanic, Asian, or White;
White was used as the reference group. Sex was classified as male or female; male was
used as the reference group. Age was separated into nine groups. The first five age
groups were in five year age bands; 20-24, 25-29, 30-34, 35-39, 40-44. The rest were in
10-year age bands except for the last group, which included all those who were 75 and
above; 45-54, 55-64, 65-74, and 75 or greater.
As stated in the last section, the analysis was done at the MSA level since it not a
panel data set that is following particular individuals. For instance, it is not known if an
individual had gotten a divorce and remarried. Although by calculating the average
amounts of the other variables for each age and sex group at every MSA, we can observe
the data in a macro level which is more manageable and feasible, such as the divorce rate.
Furthermore, a category variable was generated to group each age band to a
corresponding sex, which constituted 18 categories (two genders by nine age groups).
This enabled us to get a better analysis of the specific characteristics to a subsequent
divorce rate in any given MSA. Such an example would be: the divorce rate for men
between the ages of 45 to 54 (category 6) in Akron, Ohio during the year 1995 was
29.16%.
Individuals were coded as first generation, second generation, or non-immigrants
First generation immigrants are individuals who were foreign born. Second generation
immigrants are individuals who were born in the U.S. but at least one of their parents
were foreign born. Non-immigrants were individuals whose parents were both U.S. born,
and they were used as the reference group. The education variable was also categorized
10
to classify those who did not complete high school, high school graduates, attended some
college, and college graduates.
IV. Methodology
A fixed effects model was used to estimate the coefficients on the dependant
variables. This model requires a panel data set and it is useful because it allows us to
control for unobserved time-invariant effects. This greatly reduces any omitted variable
bias. As stated before, the CPS is not a panel data set, but the dataset I created from the
CPS (of age-sex-MSA divorce rates over time) is a panel data set. Below, Equation 2
shows a basic fixed effects model.
yit = xitβ + αi + uit
(2)
In equation 2, yit is the dependent variable observed for category i at time t, xit is a
vector of regressors, β is the vector of coefficients, αi is the individual category effect
(note that it is does not contain the subscript t because it is time constant) which contains
all MSA-age-sex characteristics – observed and unobserved, that are fixed across time
(like some cultural norms for example), and uit is the error term which is also known as
the idiosyncratic error term because it is time-varying.
For the analysis, age, sex, and MSA were controlled for as fixed effects. The
following regression was estimated to examine the effects of changes in house prices on
divorce rates.
13
(3)
Yijt = β0 + β1ΔHPI ijt + β2Homeownijt + β3Unempijt + ∑ βnXijt +
n=4
2008
∑ γt Yeart + αij +uijt
t=1985
11
Equation 3 is the basic model where i represents the category (age and sex group), j is the
MSA, and t is time in years. ‘Y’ is denoted for the divorce rate. ‘ΔHPI’ is the change in
HPI. The model is separately estimated for changes in HPI over one year, three year, and
five year spans. ‘Homeown’ denotes the percentage of individuals who own a home.
‘Unemp’ is the unemployment rate among an age-sex group i, in MSA j. ‘X’ is denoted
for the rest of the control variables mentioned before. ‘Year’ is a series of dummy
variables that captures time fixed effects, 1985 being the reference year. ‘αij’ represents
all factors affecting the divorce rate that do not change over time, which includes
unobserved effects. This model estimates the effects of house price changes on divorce
rates.
Equation 4 represents the second type of model.
14
(4)
Yijt = β0 + β1ΔHPI ijt + β2Homeownijt + β3ΔHPIijtHomeownijt + ∑ βnXijt +
n=4
2008
∑ γtYeart + αij + uijt
t=1985
This model differs from the previous one (equation 2) in that it incorporates an interacted
variable noted as ‘HPI Homeown’. This allows for a better analysis of divorce rates
because it identifies whether there are greater effects of ‘change in house prices’ in
groups that have higher home ownership rates.
14
(5)
Yijt = β0 + β1ΔPosHPI ijt + β2ΔNegHPI ijt + β3Homeownijt + ∑ βnXijt +
n=4
2008
∑ γtYeart + αij +uijt
t=1985
Equation 5 is the third type of model used for the analysis. This equation differs
from the original model (equation 2) in that ‘change in house prices’ is further broken
down into two parts. ‘ΔPosHPI5Year’ represents any positive changes in house prices
12
over five years. ‘ΔNegHPI5Year’ represents any negative changes in house prices over 5
years. This allowed us to observe if there were any differences between increases or
decreases in house prices on divorce rates.
(6)
Yijt = β0 + β1ΔPosHPI ijt + β2ΔNegHPI ijt + β3Homeownijt + β4ΔPosHPIHomeownijt +
16
2008
β5ΔNegHPIHomeownijt + ∑ βnXijt + ∑ γtYeart + αij +uijt
n=6
t=1985
Equation 6 is a version that combines the models in equations 4 and 5. It observes
whether there are differential effects of positive and negative changes in HPI and whether
those effects are greater among groups with higher home ownership. This is the most
precise and detailed model in the analysis.
V. Results
A. Equation 2
This model measured the effects for one year changes in house prices, three year
changes in house prices, and five year changes in house prices. The number of
observations decreased as the calculation for the year span of change in HPI increased
since observations in the lag years cannot be accounted for. Results for this model can be
found in Table 3.
The results from Column 1 indicated that as the average HPI increased by 10
percentage points from one year to the other, the divorce rate increased by 0.2 percentage
points5. This means that the average divorce rate would increase from 15.03 % to
15.23%. The results also indicated that the divorce rate decreases by 0.9 percentage
5
This was estimated at a 10% significance level (weak significance). All other estimates that are mentioned
are to be considered very significant (at the 1% level), unless otherwise noted.
13
points from one year to another as the average amount of home ownership increases by
10 percentage points. At the mean, this results in a 14.13% divorce rate, approximately
one percentage point decrease from the average divorce rate. Moreover, the analysis
showed that a one percentage point increase in the unemployment rate from one year to
another would increase the divorce rate by 0.034 percentage points to an average divorce
rate of 15.06%6. Interestingly, the coefficient on unemployment correlates lower income
to higher divorce, while the coefficient on HPI correlates higher wealth to higher divorce.
In addition, all education levels (relative to individuals who did not complete
H.S.) showed very significant positive effects with the divorce rate. As the average
family income increased, the divorce rate significantly decreased. Also, among groups
with higher average number of children, the divorce rate was significantly lower.
Moreover, the divorce rate was significantly lower in groups with a higher percent of first
generation immigrants (relative to U.S. natives, non-immigrants). However, the analysis
indicated that as the average amount of second generation immigrants increased, the
divorce rate increased7. Hispanics and Asians showed no significant effect as their
percentage amounts increased in the U.S. relative to the White population. Although, as
the Black population increased (relevant to White), the divorce rates significantly
increased.
All of the variables mentioned in the previous paragraph, including the
unemployment rate, have the same effects and very similar magnitudes in all models.
One exception which is worthy to note is second generation immigrants. The significance
level increased to the 5% level in the third and fourth models, as opposing to no
6
A one percentage point increase was used as opposed to using a 10 percentage point increase because it is
a more realistic figure.
7
Estimated at 10% significance level.
14
significance at all in the previous models. The magnitude of the effect also increased
from 0.0114 in Model 1 (five year lag) to 0.0172 in Model 4. This could indicate that as
immigrants settle in the U.S., they accustom to the societies norms and loose some of
their background heritage, in which divorce is frowned upon in most foreign cultures.
Comparing 3 year changes and 5 year changes in HPI
While the change in HPI was significant for one year, three year, and five year
changes, the magnitude decreases as the time span lengthens. One might expect the
magnitude to increase with the time span because couples may require some time to
divorce. However, the results suggest the opposite, signifying that households respond
quickly to changes in house prices.
B. Model 2
As mentioned before, the second model is a modification of the first model in that
it incorporates an interacted variable between the change in HPI and average rate of
homeownership. Table 4 reports the results for this model. No significant effect on
divorce rates was found for the change in HPI for either of the three years spans, except
for a weak significance in the one year change (at the 10% level). It showed a 0.47
percentage point increase in the divorce rate when the change in HPI increased by 10
percentage points from one year to another.
The magnitude of homeownership was approximately the same for all three years
spans, and the effects were very significant. There was a 0.9 percentage point decrease in
the divorce rate when there was a 10 percentage point increase in the average amount of
homeownership.
15
The interacted variable showed no significant effect in the first two estimations
(one and three year changes). In the five year estimation, it showed a significant effect.
The interpretation of the interaction term in the 5-year model is as follows: the effects of
increases in HPI are greater in groups with higher home ownership. The estimated
magnitude implies that for every 10% increase in the rate of homeownership in an area, a
given 10% increase in HPI further increased the divorce rate by 0.27 percentage points.
This is evidence that increases in house prices increase divorce rates among home
owners.
C. Model 3
In this model, the variable ‘change in HPI’ was further broken down to specify
whether the change in HPI was a positive or negative change. Results for the five year
estimation are available in Table 5. No significant effect was found for a positive change
in HPI over a five year span. A significant effect was observed for a negative change in
HPI. A 10 percentage point decrease in the average HPI decreased the divorce rate by
0.71 percentage point.
D. Model 4
The fourth model extended the previous model by incorporating two interaction
variables. The interaction variables have the same application as the second model,
except that homeownership is interacted with the two newly specified variables
mentioned in model three, positive and negative changes in HPI. Table 6 presents the
estimations for the five year estimation of this model.
16
A 10 percentage point positive change in the average HPI over five years
decreased the divorce rate by 0.17 percentage points on average. No significant effect for
a negative change in HPI was observed.
The key coefficient to test the hypothesis is the interaction terms. In areas with
10% greater homeownership, a 10 percentage point positive change in the HPI increased
the divorce rate by 0.32 percentage points. Furthermore, a negative change in the average
HPI decreased the divorce rate by 2.42 percentage points. This is a large amount which
would decrease the divorce rate to a total of 12.61% from the average divorce rate
(15.03%).
VI. Conclusion
This study used houses as a measure of wealth for individuals, since it is the
largest asset an average American owns. Any change in a house’s value significantly
affects a person’s total wealth. Based on Becker’s theory (1977), a drastic change in
wealth may lead to a higher probability of a marital dissolution. The results in this study
supported his theory. Divorce rates significantly increased as house prices changed in the
U.S. between 1985 and 2008. The analysis further showed that the implications of house
price changes were more pertaining to areas where homeownership also increased. Both
increases and decreases in house prices affected the divorce rate.
As the price of a married couple’s house increases drastically, so does their
wealth. While the underlying bond of many marriages is based on love and commitment,
it is true that an increase in wealth can cause marital instability. For instance, a spouse
would have probably chosen a different mate than the one they are married to, had they
17
known that their wealth will increase drastically. Therefore the spouse might want to
divorce in order to remarry another mate that has a better bundle of traits that they desire.
They might also feel less financially dependent from each other, and might want to
divorce so that they may cash in on the newly generated wealth.
The effect of house price changes on the divorce rate was more noticeable when
there were negative changes in house prices. This was due to the fact that negative
changes were prevalent between 2006 and 2008, which is during the time the financial
crisis unfolded. Many homes were foreclosed and the unemployment rate increased
drastically.
While prior research indicated that financial distress for married couples can lead
to a higher probability of dissolution, the evidence in this paper suggests the opposite. It
could be the case that there might be many married couples who own a home that would
like to divorce, but they might not be able to afford it. They would either have to sell their
home and divide the money from the sale, or one spouse keeps the home and pays out the
other spouse. When the housing market is booming, it is easier to sell ones home and split
the money (inclusive of the generated profit from the increase of the house value). In
times like now, some couples might remain together until their house values appreciate.
The results also indicated in all models that as more people buy homes, the
divorce rate decreases. Often times, people buy houses when they get married so that they
may house their spouse and family. Because buying a house might be the result of getting
married, it only makes sense that the divorce rate will decrease as more people buy
houses.
18
One can conclude from the results that the divorce rate is affected by
macroeconomic factors. As the economy is in an expansion, when the real estate industry
is booming and house prices are at their peak, divorce rates increase. Similarly, when
there is a recession and house prices drop significantly, divorce rates decrease.
Other variables, such as the inflation rate, could be incorporated to the models to
further examine the effects of the economy on divorce rates. Moreover, this study can be
further expanded to include a model that estimates the effects of unexpected changes in
house prices on the divorce rates. This will give more insight to the topic, and allow for a
better analysis of Becker’s theory.
19
References
Becker, Gary S. “A Theory of Marriage.” In Economics of the Family, edited by T.W.
Schultz. Chicago: Univ. Chicago Press, 1974
Becker, Gary S.; Elizabeth Landes, and Robert Michael. “Economics of Marital
Instability.” NBER Working Paper no. 153, October 1976
Becker, Gary S.; Elizabeth Landes, and Robert Michael. 1977. “An Economic
Analysis of Marital Instability.” Journal of Political Economy 85(6):
1141-1187.
Calhoun, Charles A.; “OFHEO House Price Indexes: HPI Technical Description.” Office
of Federal Housing Enterprise Oversight. Washington D.C. March, 2006
Case, Karl E.; and Robert J. Shiller. “The Efficiency of the Market for SingleFamily Homes.” The American Economic Review, Vol. 79, No. 1 (Mar.,
1989), pp. 125-137
Coontz, Stephanie. Marriage, a History: From Obedience to Intimacy, or How
Love Conquered Marriage. New York, NY: Penguin Group (USA) Inc.,
2006.
Haughney, Christine. “Buy Low, Divorce High.” (2007, 08, 12). The New York
Times.
Lehrer, Evelyn. 2003. “The Economics of Divorce.” Pp. 55-74 in Shoshana
Grossbard-Shechtman (ed.) Marriage and the Economy: Theory and
Evidence from Industrialized Societies. Cambridge: Cambridge
University Press.
Poortman, Anne-Rigt. “How Work Affects Divorce.” Journal of Family Issues, Vol. 26,
No. 2, (2005) pp.168-195
Schoen , Robert; and Nicola Standish. "The Retrenchment of Marriage: Results from
Marital Status Life Tables for the United States, 1995." Population and
Development Review. Vol. 27, No. 3 (Sep., 2001), pp. 553-563
Wolfers, Justin. “Did Unilateral Divorce Laws Raise Divorce Rates? A Reconciliation
and New Results.” The American Economic Review. Vol. 96, No. 5 (Dec., 2006),
pp.1802-1820
20
Table 1
Summary Statistics
Variable
Age 20-24
Mean
0.1104509
Std. Dev.
0.3134526
Min
0
Max
1
Age 25-29
0.1110253
0.314165
0
1
Age 30-34
0.1118084
0.3151323
0
1
Age 35-39
0.1116518
0.3149392
0
1
Age 40-44
0.1117667
0.3150808
0
1
Age 45-54
0.1126125
0.3161202
0
1
Age 55-64
0.1120277
0.3154022
0
1
Age 65-74
0.1109939
0.3141263
0
1
Age ≥ 75
0.1076627
0.3099556
0
1
Divorce rate
0.1503065
0.1846513
0
1
Unemployment (%)
0.0343542
0.082142
0
1
Less than H.S. Educ
0.1780526
0.2067658
0
1
H.S. Graduate
0.3543063
0.2198753
0
1
Some College Educ
0.2520935
0.2046151
0
1
College Graduate
0.2155476
0.1953619
0
1
Homeowner
0.7091417
0.2447502
0
1
Average # of Children
0.8090493
0.696713
0
7
st
0.433567
0.4385979
0
1
nd
2 Gen Immigrant
0.0285606
0.0847418
0
1
White (%)
0.7521295
0.2499628
0
1
Black (%)
0.0935364
0.1505646
0
1
Asian (%)
0.0148796
0.059953
0
1
Hispanic (%)
0.1237395
0.2065745
0
1
Family Income
47667.72
24690.25
0
575284
Family Income (ln)
10.3938
0.5451473
0
13.26262
∆ HPI 1 year
0.0467318
0.0618812
-0.4230315
0.4352548
∆ HPI 3 year
0.1632118
0.1648394
-0.474343
0.9405308
∆ HPI 5 year
0.2879731
0.2543976
-0.3407872
1.511004
Pos ∆ in HPI (5 yr)
0.2707405
0.2498736
0
1.511004
Neg ∆ in HPI (5 yr)
-0.00484
.0238565
-0.3407872
0
1 Gen Immigrant
N=95762
21
22
Table 2
Detailed Summary Statistics- For the change in HPI over 1 year, 3 year, and 5 year
lags.
Δ HPI
25th Percentile
50th Percentile
75th Percentile
Mean
1 year
.0190047
.0409507
.0648222
.0467318
3 year
.0769863
.1312852
.203329
.1632118
5 year
.1461466
.2339843
.3573709
.2879731
23
Graph 1
24
Table 3 – Estimates for Equation 3
Fixed Effects Regression- Estimation of Change in HPI on Divorce rates
VARIABLES
ΔHPI
Homeowner
Unemployment (%)
H.S. Graduate (%)
Some College Educ (%)
College Graduate (%)
Family Income (ln)
Average # of Children
1st Gen Immigrant (%)
2nd Gen Immigrant (%)
Hispanic (%)
Asian (%)
Black (%)
Constant
(1 year ΔHPI )
Divorce rate
(3 year ΔHPI)
Divorce rate
(5 year ΔHPI)
Divorce rate
0.0201*
(0.0106)
-0.0898***
(0.00327)
0.0341***
(0.00740)
0.0340***
(0.00398)
0.0486***
(0.00443)
0.0181***
(0.00475)
-0.0785***
(0.00162)
-0.0511***
(0.00145)
-0.0642***
(0.00528)
0.0134*
(0.00777)
0.00440
(0.00526)
-0.0136
(0.0117)
0.0420***
(0.00495)
1.056***
(0.0173)
0.0124***
(0.00400)
-0.0873***
(0.00331)
0.0319***
(0.00753)
0.0354***
(0.00405)
0.0506***
(0.00449)
0.0200***
(0.00482)
-0.0793***
(0.00164)
-0.0513***
(0.00147)
-0.0641***
(0.00529)
0.0116
(0.00779)
0.00356
(0.00532)
-0.0107
(0.0118)
0.0450***
(0.00501)
1.059***
(0.0175)
0.00865***
(0.00268)
-0.0863***
(0.00337)
0.0322***
(0.00771)
0.0370***
(0.00415)
0.0523***
(0.00458)
0.0214***
(0.00492)
-0.0788***
(0.00167)
-0.0508***
(0.00150)
-0.0634***
(0.00533)
0.0114
(0.00780)
0.00586
(0.00540)
-0.00772
(0.0118)
0.0480***
(0.00511)
1.052***
(0.0178)
93386
0.076
5086
91302
0.076
5069
88393
0.075
5068
Observations
R-squared
Number of fe_category
Note for all tables:
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Regressions also include year dummies
Mean Percent Divorced: 15.03%
25
Table 4 - Estimates for Equation 4
Fixed Effects Regression- Estimations of Change in HPI for Homeowners on
Divorce rates
VARIABLES
ΔHPI
Homeowner
∆HPI*Homeowner
Unemployment (%)
H.S. Graduate (%)
Some College Educ (%)
College Graduate (%)
Family Income (ln)
Average # of Children
1st Gen Immigrant (%)
2nd Gen Immigrant (%)
Hispanic (%)
Asian (%)
Black (%)
Constant
Observations
R-squared
Number of fe_category
(1 year ΔHPI)
Divorce rate
(3 year ΔHPI) (5 year ΔHPI)
Divorce rate
Divorce rate
0.0471*
(0.0274)
-0.0880***
(0.00370)
-0.0399
(0.0374)
0.0342***
(0.00740)
0.0340***
(0.00398)
0.0485***
(0.00443)
0.0181***
(0.00475)
-0.0785***
(0.00162)
-0.0512***
(0.00145)
-0.0642***
(0.00528)
0.0134*
(0.00777)
0.00437
(0.00526)
-0.0135
(0.0117)
0.0420***
(0.00495)
1.054***
(0.0174)
0.00838
(0.0105)
-0.0882***
(0.00401)
0.00593
(0.0144)
0.0319***
(0.00753)
0.0354***
(0.00405)
0.0506***
(0.00449)
0.0199***
(0.00482)
-0.0793***
(0.00164)
-0.0513***
(0.00147)
-0.0641***
(0.00529)
0.0116
(0.00779)
0.00360
(0.00532)
-0.0108
(0.0118)
0.0450***
(0.00501)
1.060***
(0.0176)
-0.00935
(0.00704)
-0.0937***
(0.00431)
0.0267***
(0.00966)
0.0320***
(0.00771)
0.0369***
(0.00415)
0.0521***
(0.00458)
0.0212***
(0.00492)
-0.0788***
(0.00167)
-0.0508***
(0.00150)
-0.0635***
(0.00533)
0.0117
(0.00780)
0.00624
(0.00540)
-0.00845
(0.0118)
0.0478***
(0.00511)
1.057***
(0.0179)
93386
0.076
5086
91302
0.076
5069
88393
0.075
5068
26
Table 5 - Estimates for Equation 5
Fixed Effects Regression- Estimations of Positive and Negative Changes in HPI on
Divorce rates
VARIABLES
Pos Δ in HPI
Neg ∆ in HPI
Homeowner
Unemployment (%)
H.S. Graduate (%)
Some College Educ (%)
College Graduate (%)
Family Income (ln)
Average # of Children
1st Gen Immigrant (%)
2nd Gen Immigrant (%)
Hispanic (%)
Asian (%)
Black (%)
Constant
Observations
Number of fe_category
R-squared
(5 year
ΔHPI)
Divorce rate
0.00463
(0.00287)
-0.0712***
(0.0262)
-0.0881***
(0.00322)
0.0365***
(0.00725)
0.0320***
(0.00390)
0.0456***
(0.00436)
0.0159***
(0.00469)
-0.0787***
(0.00160)
-0.0514***
(0.00143)
-0.0628***
(0.00529)
0.0165**
(0.00780)
0.00357
(0.00521)
-0.0120
(0.0117)
0.0427***
(0.00489)
1.058***
(0.0172)
95728
5105
0.076
27
Table 6 - Estimates for Equation 6
Fixed Effects Regression- Estimations of Positive and Negative Changes in HPI for
Homeowners on Divorce rates
VARIABLES
Pos Δ in HPI
Neg ∆ in HPI
Homeowner
Pos ∆HPI* Homeowner
Neg ∆HPI*Homeowner
Unemployment (%)
H.S. Graduate (%)
Some College Educ (%)
College Graduate (%)
Family Income (ln)
Average # of Children
1st Gen Immigrant (%)
2nd Gen Immigrant (%)
Hispanic (%)
Asian (%)
Black (%)
Constant
Observations
Number of fe_category
R-squared
(5 year
ΔHPI)
Divorce rate
-0.0172**
(0.00734)
0.0890
(0.0739)
-0.0951***
(0.00417)
0.0322***
(0.00993)
-0.242**
(0.103)
0.0360***
(0.00725)
0.0319***
(0.00390)
0.0454***
(0.00436)
0.0157***
(0.00469)
-0.0787***
(0.00160)
-0.0514***
(0.00143)
-0.0627***
(0.00528)
0.0172**
(0.00780)
0.00404
(0.00521)
-0.0129
(0.0117)
0.0426***
(0.00489)
1.062***
(0.0173)
95728
5105
0.076
28