SAME SEX MARRIAGE LAWS AND THEIR EFFECT ON WEDDING
EXPENDITURES
Alison Marie Winter
B.A., California State University, Sacramento, 2009
THESIS
Submitted in partial satisfaction of
the requirements for the degree of
MASTER OF ARTS
in
ECONOMICS
at
CALIFORNIA STATE UNIVERSITY, SACRAMENTO
SPRING
2012
SAME SEX MARRIAGE LAWS AND THEIR EFFECT ON WEDDING EXPENDITURES
A Thesis
by
Alison Marie Winter
Approved by:
__________________________________, Committee Chair
Suzanne O’Keefe, Ph.D.
__________________________________, Second Reader
Jonathan Kaplan, Ph.D.
____________________________
Date
ii
Student: Alison Marie Winter
I certify that this student has met the requirements for format contained in the University format
manual, and that this thesis is suitable for shelving in the Library and credit is to be awarded for
the thesis.
__________________________, Graduate Coordinator
Jonathan Kaplan, Ph.D.
Department of Economics
iii
___________________
Date
Abstract
of
SAME SEX MARRIAGE LAWS AND THEIR EFFECT ON WEDDING EXPENDITURES
by
Alison Marie Winter
Same sex marriage laws have recently been a topic for debate in many states in the U.S.,
with some states legalizing same sex marriage and others banning it. This research
attempts to find the effect if any that same sex marriage and civil union/domestic
partnership laws have on state-level wedding expenditures per capita, number of
weddings per capita, and average wedding cost. This question is analyzed using OLS
regression analysis and controlling for year and state specific characteristics with year
dummy variables and state fixed effects. The results show that wedding expenditures per
capita and number of weddings per capita increase but average wedding cost decreases
for states with same sex marriage laws. The results for civil union/domestic partnership
laws are similar except it is unclear what their effect is on average wedding cost. These
findings imply that same sex marriage may be a valid topic for discussion when states are
looking to increase revenues.
_______________________, Committee Chair
Suzanne O’Keefe, Ph.D.
_______________________
Date
iv
TABLE OF CONTENTS
Page
List of Tables ........................................................................................................................... vi
List of Figures………………………………………………………………………………..vii
Chapter
1. INTRODUCTION .............................................................................................................. 1
2. LITERATURE REVIEW ................................................................................................... 5
3. THEORETICAL AND EMPIRICAL MODELS ............................................................. 13
4. DATA ............................................................................................................................... 17
5. RESULTS ......................................................................................................................... 26
6. CONCLUSION .................................................................................................................. 37
References ............................................................................................................................... 39
v
LIST OF TABLES
Tables
Page
1.
Descriptive Statistics…………………………….………………………......
2.
Descriptive Statistics for States with Same Sex Marriage Laws ……………..24
3.
Descriptive Statistics for States with Civil Union/Domestic Partnership Laws 25
4.
Wedding Expenditures Per Capita as Dependent Variable…………………….33
5.
Number of Weddings Per Capita as Dependent Variable…………..………….34
6.
Average Wedding Cost as Dependent Variable………………………………..35
7.
Natural Log of Wedding Expenditures Per Capita as Dependent Variable ...…36
vi
23
LIST OF FIGURES
Figures
Page
1.
Shock to Supply Side………………………….……………………………….16
2.
Shock to Demand for Weddings…………….………………………………. 16
vii
1
Chapter 1
INTRODUCTION
Same sex marriage is a topic that creates heated debates on a daily basis.
Questions and concerns arise over the definition of marriage, privacy issues and religious
and moral ideals. Because increasingly more people are becoming tolerant of
homosexuals, the question of marriage equality has entered the political arena. A few
states have recently adopted laws allowing same sex marriage and some states have
adopted bans on same sex marriage. As of June 2011, six states and the District of
Columbia allow same sex marriages, meaning they issue marriage licenses to same sex
couples. These states are Connecticut, Iowa, Massachusetts, New Hampshire, New York,
and Vermont. California allowed same sex marriages beginning mid-June 2008 but then
banned them with an amendment to the state constitution in November 2008. The
marriages performed before the ban are still considered legal. Massachusetts offered
same sex marriage throughout the years observed, and Connecticut, Iowa, New
Hampshire and Vermont added same sex marriage laws between the years 2005 and
2009. New York did not legalize same sex marriage until after 2009. Although some
states are debating the legality of same sex marriage, there are also states that allow civil
unions or domestic partnerships for same sex couples. These unions or partnerships
allow same sex couples to have similar or limited rights to those that married couples
have. The states that allow civil unions are Delaware, Hawaii, Illinois, New Jersey, and
2
Rhode Island1. The states that allow domestic partnerships are California2, Hawaii,
Maine, Nevada, Oregon, Washington, and Wisconsin and the District of Columbia3.
When considering the effects same sex marriage has on society, one area that may
be affected is the wedding industry. It is estimated that 22% of same sex couples in the
U.S. have formalized their partnerships since same sex unions have been legalized. Of
that 22%, around 36% of these couples have married (Badgett & Herman, 2011).
Legalizing the ability to have a wedding for same sex couples may create increased
demand for wedding vendors. This would in turn lead to higher wedding expenditures
for the state allowing same sex marriage. Civil unions and domestic partnerships may or
may not have the same type of effect. One question to consider in the cases of legalized
same sex marriage and civil unions or domestic partnerships is whether same sex couples
would have a wedding or only a civil ceremony. Since there are no studies to
demonstrate which occurs more, this would be a great study for further research. Another
important factor and one of the questions asked here would be if same sex couples
decided to have a ceremony and reception, how much would they spend, and would their
spending significantly add to wedding expenditures.
This research examines the effect same sex marriage has on wedding
expenditures, number of weddings, and average wedding cost at the state level. This is
an important topic for states to look into because it can add revenues in a time when
revenues are needed, even if it is a small portion of GSP. Wedding expenditures make up
1
Hawaii and Illinois allow both same sex and different sex couples to obtain civil unions
California allows both same sex couple and different sex couples over 65 to register domestic partnerships
3
The District of Columbia grants domestic partnerships to unmarried couples
2
3
only 0.38% of GSP on average but the average wedding cost makes up 46.4% of GSP per
capita on average. Therefore, although wedding expenditures do not make up a large
share of GSP, the cost of a wedding takes up almost half of a person’s income. If the
effect is positive, supporters of same sex marriage can use this in their argument. If the
effect is negative, groups opposed to same sex marriage can use this in their argument.
This research will add to the little research related to same sex marriages and civil unions.
Insights from this research can provide us with a better understanding of the effects that
same sex marriages have on society.
One method used to see the effects of same sex marriage laws and civil
union/domestic partnership laws on wedding expenditures is raw difference in difference
estimation. This raw difference in difference is calculated for each dependent variable.
Then, OLS regression analysis is used to estimate the relationships between same sex
marriage laws or civil union/domestic partnership laws and wedding expenditures while
controlling for other factors, such as time fixed effects and state specific characteristics.
This research finds that same sex marriage laws and civil union/domestic
partnership laws have a positive and significant effect on wedding expenditures per capita
and number of weddings per capita. It is also found that same sex marriage laws have a
negative and statistically significant effect on average wedding cost. It is unclear how
civil union/domestic partnership laws affect average wedding costs after viewing the
results. These results show that there is an effect from same sex marriage laws on
wedding expenditures and states that are debating whether to allow same sex couples to
marry should include this knowledge in the debate.
4
In the following chapter, literature relating to and helping frame this research is
discussed. Chapter three describes the economic theory and empirical models associated
with this research. This is followed by a chapter describing the data. Then, chapter five
illustrates the results obtained. Finally, the last chapter concludes with a discussion about
the implications of the findings and future research possibilities.
5
Chapter 2
LITERATURE REVIEW
Section 2.1. Introduction
Because same sex marriage is a relatively current topic of debate, not much
research has been performed to study it. There are no studies that could be found
showing the relationship between same sex marriage laws and wedding expenditures.
The literature displayed below illustrates the research that is available. The next section
shows studies relating to attitudes towards same sex marriage. The reason for these being
presented is to give a background to same sex marriage and to frame the issue present.
This section is broken down into studies that use survey data and studies that use
observational data. The final section describes studies more closely related to the
research here. These studies examine the effect of same sex marriage on multiple topics
important to society.
Section 2.2.1. Survey Studies on Attitudes
Recent research has focused on what predicts attitudes towards same sex
marriage. Schwartz (2010) looks at demographic variables to determine what predicts
attitudes toward same sex marriage, adoption by gays and lesbians, and what the
demographic differences are between the two. He uses data from the News Interest
Study from the Princeton Survey Research Associates International for the Pew Research
Center for People and the Press. The demographic variables he looks at are sex, age,
6
education, political ideology, and frequency of attendance at religious services. Schwartz
uses multiple regression analysis and ANOVA to test his model.
When using same sex marriage as the dependent variable, Schwartz finds that age,
education level, political ideology, and frequency of attendance at religious services are
statistically significant. The younger and more educated are more likely to look
favorably on same sex marriage. The less religious and more liberal a person is the more
likely they will look favorably on same sex marriage. When looking at adoption by gays
and lesbians as the dependent variable, all of the demographic variables are statistically
significant. The coefficients for sex (with 1 indicating female), education, and political
ideology are positive. The problem with all the variables but sex is that Schwartz does
not convert categorical data into dummy variables. For example, the political ideology
variable is measured by assigning a 1 to individuals who state they are conservative, a 2
to individuals who state they are moderate, and a 3 to individuals who state they are
liberal. This does not estimate the model very well because the categorical values are
viewed as continuous data when running the regression. The results would change if the
researcher changes the coding for the categories and this makes the analysis invalid.
Instead, dummy variables should have been created for all but one of the categories.
The final question Schwartz looks at is that of the difference in demographic
patterns showing attitudes between same sex marriage and adoption by gays and lesbians.
Using ANOVA, he combines the two original independent variables into a “gay rights”
dependent variable. His results show that there is a significant difference between the
two topics and that religious attendance shows different attitudes towards same sex
7
marriage and towards adoption by gays and lesbians. Although there are flaws to this
research, it provides insight into what might shape attitudes toward same sex couples.
Brewer (2003) looks at how shifting public opinions have affected the current
views on gay rights. He tests two theories on why the American public has become more
supportive of gay rights. The first theory is that changes in predispositions, such as
attitudes towards gays and lesbians, moral traditionalism, and ideology, are the cause of
changes in policy opinions. The second theory is that changes in how the public uses
these predispositions to think about gay rights are what changes support for gay rights.
Brewer uses data from three pooled cross-sectional surveys, which are the 1992, 1996,
and 2000 National Election Studies. He uses the responses to two questions to create a
seven-point index of support for gay rights which ranges from -1 (opposes gay rights) to
1 (supports gay rights). The first question is “Do you favor or oppose laws to protect
homosexuals against job discrimination?” The second question is “Do you think
homosexuals should be allowed to serve in the United States Armed Forces, or don’t you
think so?” Brewer uses both OLS regression and ordered probit to estimate his model.
His results show that both theories play a part in the changing public opinion about gay
rights.
Olson et al. (2006) analyzes the effect of religion on opinions about same sex
marriage. The data they use is from a telephone survey done by Greenberg Quinlan
Rosner Research, Inc. The dependent variables being looked at are support for same sex
marriage, support for civil unions, and support for bans on same sex marriage. The main
independent variable Olson et al. want to study is religious affiliation but they also
8
include control variables such as variables indicating moral values and political ideology.
They use a logistic regression to find that religious variables show a greater effect than
only demographic variables on attitudes towards same sex marriage. Individuals that
participate in a religion are more likely to oppose same sex unions. However, religious
variables do not play as big a role in predicting support for same sex marriage bans.
These three papers are good building blocks to understand the roots of support for same
sex marriage.
These three papers are good building blocks to understand the roots of support for
same sex marriage. They all give some insight into why same sex marriage is an
important topic for debate. The more we know about a topic the better we can
understand it and make the best decisions for society as a whole.
Section 2.2.2. Observational Data Studies on Attitudes
Research has also looked at the variables influencing bans on same sex marriage.
Soule (2004) looks into the factors giving rise to same sex marriage bans from 19732000. She obtains her data from multiple sources. The dependent variable is the
probability of a state adopting a same sex marriage ban. This data is obtained from the
National Gay and Lesbian Task Force Policy Institute. The author includes many
different independent variables, such as laws relating to homosexual relationships,
political environments, and social characteristics. By using discrete time event history
analysis, she finds that interest organizations, citizen ideology, and previous policy
environments influence the adoption of these types of bans. Although this paper adds to
the research mentioned previously to further understand the debate of same sex marriage,
9
some of the aspects of the study do not fit economic modeling very well. The data source
for Soule’s independent variable may be biased because they represent the gay and
lesbian community and the analysis method is not a familiar one in the economic research
world.
This observational study helps add knowledge to the previous three studies
looking at attitudes towards same sex marriage and other gay rights issues. When
looking at a law’s or a policy’s effect on society, it is important to know why it is
important to society in the first place. When certain characteristics of society, such as
religion and political views, shape attitudes towards an issue, studies like these can help
to add information to the debate.
Section 2.3. Studies on the effect of same sex marriage
Research more closely related to the research presented here has been done on
topics other than the effects of same sex marriage on wedding expenditures. Alm et al.
(2000) investigate the impact of same sex marriage on income taxes. Based on the fact
that some married couples face a marriage tax when paying income taxes, the authors
propose that by allowing same sex marriage, income tax revenues should increase. The
marriage tax occurs due to the structure of the current tax system. It affects couples that
have similar individual earnings. Conversely, couples with a one income household tend
to face a marriage subsidy. The structure of today’s society makes it possible for
heterosexual couples to have either one or two income households. Stay-at-home moms
and dads are still a common occurrence in heterosexual couples. Whereas, Alm et al.
(2000) find that same sex couples tend to have two income households possibly due to
10
the fact they are not allowed the same legal rights as heterosexual couples. Same sex
marriage grants same sex couples the same rights given to different sex married couples.
Marriage helps protect individuals that are not earning incomes if faced with divorce due
to alimony and child support.
Alm et al.’s (2000) research is similar to the research done here because of the
investigation into same sex marriage effects on a flow of cash into the government. The
study presented here is looking at wedding expenditures instead of tax revenue but both
concern the changing flow of money. By first estimating how many same sex couples
would marry if given the chance to do so and then looking at the income characteristics
of these couples, Alm et al. find that allowing same sex marriage would increase federal
income tax revenue by approximately $0.3 billion to $1.3 billion. They note that some
same sex couples may decide against marrying their partner because of this marriage tax
but that most couples do not ponder this when considering marriage. This study helps the
research being presented here by adding to the information available about same sex
marriage.
Langbein and Yost (2009) investigate if same sex marriage poses a negative
externality on society in the form of negative impacts on marriage, divorce, abortion
rates, the amount of children born out of wedlock, and the amount of children raised by
single women. Proponents of same sex marriage bans mainly use the argument that gay
and lesbian couples getting married break down traditional marriage through these types
of negative externalities. Most of the data used is from the U.S. Census and the
remaining data on the legal recognition and forbiddance of marriage rights is from the
11
Human Rights Campaign. The authors find through regression analysis that laws
allowing same sex marriage do not adversely affect marriage rates, divorce rates, abortion
rates, the amount of children born out of wedlock, or the amount of children raised by
single women. The coefficients are statistically significant for same sex marriage’s effect
on the marriage rate, the abortion rate, and the percent of children raised by single
women. The marriage rate is increased and the abortion rate and the percent of children
raised by single women are decreased with laws allowing same sex marriage. This study
suggests these negative externalities may not exist. This study shows how same sex
marriage can affect society, which is similar to looking at its effect on wedding
expenditures because that is also showing an effect on society.
Dee (2008) studies the effect of same sex marriage on the prevalence of sexually
transmitted diseases (STD), specifically syphilis, gonorrhea and HIV. He chose these
three because they are particularly common among homosexual males. Dee also looks at
the effect of same sex marriage on tuberculosis and malaria infection as a control. He
obtains the data for his research from the World Health Organization. The data is an
unbalanced panel of 25 nations in Europe during the years 1980 to 2003. By using a twoperiod model, Dee finds that coefficient for syphilis rate is statistically significant and
negative. The coefficients for gonorrhea and HIV rates are also negative but not
statistically significant. A decrease in infection rates for STDs that are common among
gay men could help push the passage of laws allowing same sex marriage due to the high
social and financial costs associated with treating these diseases.
12
Ash and Badgett (2004) look to see the effect on health care coverage enrollment
if same sex partners are allowed the same coverage as heterosexual married spouses.
This is similar to looking at how same sex marriage would affect health care coverage
enrollment. This would present a cost to employers but a benefit to those that may be
without health care coverage otherwise. Ash and Badgett use the Annual Social and
Economic Supplement to the Current Population Survey to acquire their data. By using a
linear probability model, they estimate the dichotomous insurance-coverage outcome.
They find that a typical employer can expect an increase in enrollment of same sex
partners of 0.1% to 0.3%. This is a relatively small increase for employers when looking
at costs imposed on them. Since federal, state, and local governments bear the burden of
covering uninsured people, they would benefit from more people enrolling in health
coverage. When looking at federal estimates, the authors find that homosexuals enrolling
in their partner’s health plan could present a significant savings for the federal
government on health care expenditures.
These last four papers are all similar in the respect that they look at the effect of
same sex marriage on another important topic. Because there is relatively no research
done on the same topic performed here, these are the closest papers to build upon. These
papers show different ways of looking at same sex marriage and its effects. One can see
through these papers that same sex marriage does affect society as a whole and they each
touch on one aspect present in society. The research presented here hopes to add to the
small amount of research present on same sex marriage.
13
Chapter 3
THEORETICAL AND EMPIRICAL MODELS
Supply and demand is one of the most basic theories in economics, yet it is the
basis for most studies observing people’s behaviors. In this research, supply and demand
is at the heart of the question at hand. One would expect the demand for weddings to
increase if the number of people allowed to marry increased. The theory of supply and
demand shows there is an equilibrium price and quantity for all goods and services sold
in the marketplace. This equilibrium price and quantity is set simultaneously by demand
and supply. The demand is determined by consumers and supply is determined by
producers. Shocks to supply or demand result new equilibria. For example, a decrease in
input prices would be a shock to supply and because it is making it cheaper to produce
the item, supply shifts outwards. This can be seen in Figure 1. This would cause a
decrease in equilibrium price and an increase in equilibrium quantity. Shocks can occur
on both the supply side and the demand side.
Laws allowing same sex marriage would be like a shock to the demand for
weddings. If a state makes it legal for more of its population to marry, one would expect
there to be more people demanding weddings. If there are suddenly more people
demanding weddings, the demand for weddings would shift outwards. This can be seen
in Figure 2. This would cause the price of weddings to increase and the quantity of
weddings to increase. Because states are able to tax spending on weddings, tax revenue
14
would increase if wedding spending increased. If the government is interested in raising
revenue, then understanding how same sex marriage laws affect expenditures would
allow policy makers to weigh legalizing same sex marriage among their options.
Although the theory leads to supporting same sex marriage due to the expected
increase in demand for weddings, there are other factors that may not lead to the
anticipated results. There may be differences between the types of weddings performed
that lead to different results than expected. As seen in California, when same sex
marriage was legalized, many homosexual couples went straight to the courthouse and
obtained marriage licenses without elaborate wedding ceremonies. If this happens more
often than not with same sex couples, we may not see a large increase in wedding
expenditures at all. In addition, if wedding venues are not used in order to have same sex
weddings, we may not observe the increases in expenditures. Another possibility is that
same sex couples that want to formally celebrate their unions have commitment
ceremonies even in the absence of legally authorized marriage or civil unions. In this
case, celebration expenditures would not change when marriage is legalized. According
to the theory of supply and demand, if same sex marriage is legalized, we should see an
increase in the number of weddings and an increase in the average cost of weddings.
This would lead to an overall increase of wedding expenditures in states that legalize
same sex marriage, relative to those that do not.
For the research done here, the regression equation is:
𝑌𝑖𝑡 = 𝛼𝑖 + 𝛽1 𝑋𝑖𝑡 + 𝛽2 𝑊𝑖𝑡 + 𝛽3 𝐷𝑡 + 𝜀𝑖𝑡
15
Where 𝑌𝑖𝑡 is the dependent variable being analyzed in time t and state i. 𝑋𝑖𝑡 is a dummy
variable for either same sex marriage laws or civil union/domestic partnership laws
depending on which one we are analyzing in time t and state i. 𝑊𝑖𝑡 is the remaining
control variables such as GSP per capita and population in time t and state i. 𝛼𝑖
represents state fixed effects and 𝐷𝑡 is the year dummy variable. 𝜀𝑖𝑡 is the error.
16
Figure 1. Shock to Supply Side
Price
Supply
P1
P2
Demand
Q1
Q2
Quantity
Figure 2. Shock to Demand for Weddings
Cost of
Wedding
Supply
P2
P1
Demand
Q1
Q2
Number of
Weddings
17
Chapter 4
DATA
For this research, the dependent variables of interest are wedding expenditures per
capita, number of weddings per capita, and statewide average wedding cost. The first
two dependent variables are in per capita terms to control for states with large
populations. The independent variables are GSP per capita, unemployment rate,
population, population density, percent of the population with Bachelor’s degrees or
higher, percent of the population ages 18 to 34, and dummy variables for same sex
marriage laws and domestic partnership laws. Population is only used when average
wedding cost is the dependent variable because population is controlled for with the other
two dependent variables. These independent variables are used because they are
expected to help explain the variation in wedding expenditures. For example, one would
expect wedding expenditures to increase if GSP per capita increases because individuals
would have more to spend on a wedding. The data includes all 50 states. The District of
Columbia was left out because it was an outlier for most of the variables being
investigated. The state level data covers the years 2005 to 2009 and are from multiple
sources. The data on wedding expenditures per capita, number of weddings per capita,
and average wedding cost are from The Wedding Report, Incorporated website4. Both
wedding expenditures per capita and average wedding cost have been converted into real
4
The Wedding Report, Incorporated website can be found at http://www.theweddingreport.com/
18
2010 dollars. The data for state laws on same sex marriage and domestic partnerships
come from the website for National Conference of State Legislatures5. These data are
recorded as discrete variables with 1’s indicating that a state has laws allowing same sex
marriage or laws allowing civil unions/domestic partnerships. The state level data on
GSP per capita and unemployment rate are from the Federal Reserve Economic
Database. GSP per capita is real and is measured in 2010 dollars. The unemployment
rate only portrays those actively looking for work. The state level data on population are
from the 2010 U.S. Census Population Estimates. Population is measured in thousands.
Population density is created by dividing the total population of the state by the total area
in square miles. The data for the percent of population with Bachelor’s degrees or higher
and the percent of population ages 18 to 34 were obtained from the American
Community Survey6.
The descriptive statistics for the entire data set are in Table 1. The overall
average wedding expenditures is $1.106 billion. This may seem like it would be a large
part of a state’s GSP but the average percent of a state’s GSP made up by wedding
expenditures is only 0.38%. The average cost of a wedding in the U.S. is $24,132.96,
with the lowest state’s average cost being $14,329.25 and the highest being $37,166.13.
In the U.S., 4.0% of the states have same sex marriage laws and 13.2% have civil union
or domestic partnership laws. The average GSP per capita in the U.S. is $53,138.41, with
a low state GSP per capita of $35,595.85 in Georgia in 2005 and a high state GSP per
5
6
The National Conference of State Legislatures website can be found at http://www.ncsl.org/
The American Community Survey can be found at http://www.census.gov/acs/www/
19
capita of $89,682.01 in Alaska in 2008. We see a huge range of GSP per capita across
the states. The average unemployment rate in the sample is 5.47%. The lowest
unemployment rate is 2.5%, which was in 2006 in Hawaii. The highest unemployment
rate is 13.4%, which was in Michigan in 2009. The high unemployment rate in Michigan
has a lot to do with how hard Detroit was hit with the recession and failing Americanmade auto sales. In this sample, 24.3% of the U.S. has a bachelor’s degree or higher.
Massachusetts has the highest educated adult population with 35.11% having Bachelor’s
degrees or higher. This sample shows that 30.3% of the U.S. population are between the
ages of 18 and 34, which is the age group most commonly seen having weddings.
Because this research is looking at same sex marriage laws and civil
union/domestic partnership laws, the data were broken down to look at the descriptive
statistics for states that have same sex marriage laws and states that have civil
union/domestic partnership laws. In states that allow same sex marriage, as seen in Table
2, the average total wedding expenditures is $1.302 billion. This is higher than the
overall U.S. average by $196 million. This definitely makes it seem as though laws
allowing same sex couples to wed increase wedding expenditures but other factors
(especially population) need to be considered through regression analysis. The average
number of weddings and average wedding cost are also higher in states allowing same
sex marriage but once again, regression analysis will include other factors to help reduce
any bias in these comparisons.
When looking at states that allow civil unions or domestic partnerships, Table 3
shows that average total wedding expenditures and average wedding cost are higher than
20
averages for both the entire sample and states allowing same sex marriage. One can also
see that the mean GSP per capita is higher in states with civil union/domestic partnership
laws than in both the entire sample and in states with same sex marriage laws. Therefore,
the increased average wedding cost could be due to factors related to income and not the
laws allowing same sex couples to obtain civil unions or domestic partnerships. The
regression analysis will control for these factors by including independent variables such
as GSP per capita and show if the relationship between same sex marriage or civil
union/domestic partnership laws have a significant impact on wedding expenditures.
Another way to determine the effect of same sex marriage laws and civil
union/domestic partnership laws on wedding expenditures is to calculate a raw difference
in difference for each dependent variable. This is done by first calculating the average
for each dependent variable in three separate groups in 2005 and in 2009. The first group
is states that adopted same sex marriage laws at some point in the time period being
observed. The second group is states that adopted civil union/domestic partnership laws
at some point during the time period being observed. The third group is all other states
which do not have same sex marriage laws during the time period being observed or
states that had same sex marriage laws during the entire observed time period. After
finding the averages for each dependent variable in each group, the percent change from
2005 to 2009 is calculated for each.
The raw difference in difference for same sex marriage laws is calculated for each
dependent variable by subtracting the percent change in the third group from the percent
change in the group adopting same sex marriage laws. The raw difference in difference
21
for wedding expenditures per capita is 0.0270, so the percent change in wedding
expenditures is 2.7% higher in states adopting same sex marriage than in states that had
no change to their laws. When wedding expenditures are broken down, we see the raw
difference in difference for average wedding cost is -0.0006 and for number of weddings
per capita is -0.5607. This is showing contradictory values. The percent change in the
average wedding cost and the percent change in the number of weddings per capita
decreases with the adoption of same sex marriage laws, but the percent change in
wedding expenditures per capita increases. This contradiction can be clarified with
regression analysis.
Similarly, the raw difference in difference for civil union/domestic partnership
laws is obtained by subtracting the percent change in the third group that has no change
from the percent change in the group adopting civil union or domestic partnership laws
for each dependent variable. The raw difference in difference for wedding expenditures
per capita is -0.0739. This shows a decrease of 7.39% in wedding expenditures per capita
in states adopting civil union/domestic partnership laws compared to states with no
change in laws. When wedding expenditures is broken down, the raw difference in
difference for average wedding cost is -0.0007 and the raw difference in difference for
number of weddings per capita is -0.1062. Both the number of weddings and the average
wedding cost decreases when states adopt civil union/domestic partnership laws, which
lead to decreased wedding expenditures. This is interesting to see that less people would
get married if civil unions and domestic partnerships are legalized. This may be in part
due to the fact that in California, for example, different sex couples over the age of 65 are
22
able to register domestic partnerships but the number of couples over 65 actually
forgoing marriage and obtaining domestic partnerships may be relatively small. Further
research would need to be done to see why people are changing their behavior in this
way.
23
Table 1. Descriptive Statistics
Mean
Total Wedding
Expenditures (in
millions)
Wedding
Expenditures Per
Capita
Number of
Weddings
Number of
Weddings Per
Capita
Average
Wedding Cost
Same Sex
Marriage Law
Civil
Union/Domestic
Partnership Law
GSP Per Capita
Unemployment
Rate
Population (in
thousands)
Population
Density
Percent of
Population w/
Bachelor’s
Degree or
Higher
Percent of
Population Ages
18-34
Observations:
250
Minimum
Maximum
1106.44
Standard
Deviation
1285.72
70.68
7270.38
199.35
166.08
67.98
1532.60
43547.06
45683.11
4280
247022
0.0082
0.0063
0.0040
0.0580
24132.96
5200.92
14329.25
37166.13
0.0400
0.1964
0
1
0.1320
0.3392
0
1
53138.41
0.0547
10042.83
0.0201
35595.85
0.0250
89682.01
0.1340
6013.34
6615.34
506.00
36887.61
161.12
200.23
1.01
996.76
0.2434
0.0468
0.1544
0.3511
0.3031
0.0268
0.1766
0.4267
24
Table 2. Descriptive Statistics for States with Same Sex Marriage Laws
Mean
Standard
Minimum
Maximum
Deviation
Total Wedding
1302.48
1647.39
87.95
5812.20
Expenditures (in
millions)
Wedding
158.27
36.20
107.15
211.06
Expenditures Per
Capita
Number of
50081.30
70171.41
4701
247022
Weddings
Number of
0.0061
0.0007
0.0055
0.0076
Wedding Per
Capita
Average
26140.54
6851.52
15224.70
35202.65
Wedding Cost
GSP per Capita
63795.34
8304.88
47610.67
76794.44
Unemployment
0.0611
0.0143
0.0440
0.0830
Rate
Population (in
7974.10
10248.09
621.44
36538.01
thousands)
Population
469.15
249.39
53.46
633.99
Density
Percent of
0.3193
0.0419
0.2301
0.3511
Population w/
Bachelor’s
Degree or
Higher
Percent of
0.2919
0.0175
0.2710
0.3340
Population Ages
18-34
Observations: 10
25
Table 3. Descriptive Statistics for States with Civil Union/Domestic Partnership Laws
Mean
Standard
Minimum
Maximum
Deviation
Total Wedding
1513.03
2184.66
102.53
7270.38
Expenditures (in
millions)
Wedding
258.17
189.35
114.37
739.73
Expenditures Per
Capita
Number of
55520.52
76799.67
4937
247022
Weddings
Number of
0.0099
0.0077
0.0053
0.0410
Wedding Per
Capita
Average
26726.13
5677.79
17712.85
37166.13
Wedding Cost
GSP per Capita
58091.39
9773.29
44051.84
76794.44
Unemployment
0.0561
0.0235
0.0250
0.1250
Rate
Population (in
8035.84
12361.90
618.80
36887.61
thousands)
Population
268.07
304.89
23.87
996.76
Density
Percent of
0.2804
0.0322
0.2003
0.3291
Population w/
Bachelor’s
Degree or
Higher
Percent of
0.2860
0.0274
0.2480
0.3352
Population Ages
18-34
Observations: 33
26
Chapter 5
RESULTS
In order to see the effect of same sex marriage on wedding expenditures, multiple
regressions are run. The first set run is OLS regressions with wedding expenditures per
capita as the dependent variable. The second set is OLS regressions with number of
weddings per capita as the dependent variable. The third set is OLS regressions with
average wedding cost as the dependent variable. In addition, a last set is run with the
natural log of wedding expenditures per capita to determine if there is a non-linear
relationship present. In this last set of regression GSP per capita is replaced by the
natural log of GSP per capita. In each set of regressions, a total of six regressions are
run. The first regression is run with only same sex marriage law, year dummies, and state
fixed effects as the independent variables. The second regression is run with civil
union/domestic partnership law, year dummies, and state fixed effects as the independent
variables. The next two are run with either same sex marriage law or civil
union/domestic partnership law along with year dummy variables and the other
explanatory variables. The last two regressions are the same as the previous two but with
state fixed effects included. State fixed effects are left out in the second two regressions
but included in the last two in order to determine if multicollinearity is present. Recall
from the data section that the other independent variables are GSP per capita (or natural
log of GSP per capita in the log-log regressions), unemployment rate, population (only
used in regressions with average wedding cost as dependent variable), population density,
27
percent of the population with Bachelor’s degrees or higher, and percent of the
population ages 18 to 34. These variables all vary over time. Including year dummy
variables and state fixed effects helps to eliminate any omitted variable bias that is
present because of differences between years or states that cannot be measured or are not
captured. For example, Hawaii may have more weddings than another state because of
its tropical climate. This is captured in the state fixed effects. An example of something
that would change over time that is not measurable would be changes in the business
cycle. These changes are captured by using year dummy variables.
When looking at the regressions run with wedding expenditures per capita as the
dependent variable, the coefficient for same sex marriage laws is only statistically
significant in Regression #1. These results can be seen in Table 4. The coefficient shows
that for states that have same sex marriage laws, wedding expenditures per capita are
expected to be $21.06 higher than in states without. This is a relatively large increase
since the average wedding expenditures per capita is almost $200. Regression #4 shows
that states that have civil union/domestic partnership laws are expected to have wedding
expenditures per capita that are $109.18 higher than in states that do not have these laws
and this coefficient is statistically significant. This is more than a 50% increase from the
average for the sample. In the absence of state fixed effects, the adjusted R-squared is
only 12.29%. When including state fixed effects in Regression #6, the statistical
significance of the coefficient is eliminated but the adjusted R-squared increases to
92.4%. The model overall does a better job of explaining the variation in wedding
expenditures when state fixed effects are included.
28
Other results worth noting in Table 4 are the coefficients for GSP per capita,
unemployment rate, and education. GSP per capita is statistically significant in all of the
regressions in which it is included. The effect is small though; with only about a $0.37 to
$0.50 increase in wedding expenditures per capita for a $100 increase in GSP per capita.
The sign for the coefficient for unemployment rate is as expected in all regressions, but it
is only statistically significant in Regression #5 and #6. A one percentage point increase
in the unemployment rate is expected to decrease wedding expenditures per capita by
about $18. This is not a large amount but makes sense. If more people are unemployed,
less people will feel comfortable spending money on a wedding. The coefficient for
education is only statistically significant when state fixed effects are not included. This
could be because education levels do not vary much over time for a state but vary across
states. When state fixed effects are included, these differences in education levels across
states are controlled for.
Table 5 shows the results from the six regressions using number of weddings per
capita as the dependent variable. Once again, the coefficient for same sex marriage is
positive and statistically significant in only Regression #1. States allowing same sex
marriage are estimated to have 7 more wedding for every 10,000 people compared to
states without same sex marriage laws. Once other explanatory variables are included
though, the statistical significance is gone. Although this happens, Regression #1 has a
very high adjusted R-squared of 97.31%. The coefficient for civil union/domestic
partnership law is positive and statistically significant in Regression #4, which is run
without state fixed effects. The model in Regression #6 has a higher R-squared than
29
Regression #4, but the coefficient for civil union/domestic partnership laws becomes
statistically insignificant. When state fixed effects are included, the adjusted R-squared is
97.66%, which is very high. This model explains most of the variation in number of
wedding per capita.
In the regressions where GSP per capita is incorporated, the coefficient is
statistically significant at the 5% level but has a very small effect. GSP per capita would
have to increase by $10,000 to see only an expected increase of 1 wedding for every 1000
people. Population density is another variable that has statistical significance in all
regressions, but it also has a very small effect on number of weddings per capita. Once
again, the coefficient for education is only statistically significant when state fixed effects
are not included and it is negative. It may be negative because if a greater proportion of
the population is attending college, they may be putting off getting married until they
have finished college and started a career. Once more, the adjusted R-squared is highest
when the explanatory variables and state fixed effects are included. Therefore, this
appears to be the best model for same sex marriage laws and civil union/domestic
partnership laws because the model explains most of the variation in number of weddings
per capita.
The results for the regressions using average wedding cost as the dependent
variable are shown in Table 6. Four of these regressions use population as an explanatory
variable in order to control for highly populated states. The coefficient for same sex
marriage is not statistically significant until we include all explanatory variables, year
dummy variables and state fixed effects. In states allowing same sex marriage, it can be
30
expected that average wedding cost is $914.09 lower than in states not allowing these
marriages. So we see from this and previous results, that although there are more
weddings in states with same sex marriage laws, the average cost is lower. The decrease
in average wedding cost may be due to the fact that same sex couples may not have a big
ceremony and reception. Instead, they may only be going down to the courthouse and
having a civil ceremony. To find out if this is true, further research needs to be done.
With states allowing civil unions/domestic partnerships, the results are unclear. In
Regression #2, the effect is negative and statistically significant, but in Regression #4, the
effect is positive and statistically significant.
Other coefficients worth noting in these regressions are the coefficients for
unemployment rate, population density, and education. The sign of the coefficient for
both unemployment rate and population density changes depending on whether or not
state fixed effects are included and the coefficients are statistically significant in both
cases. This makes it uncertain what effect these two variables have on wedding
expenditures. One would expect unemployment rate to have a negative effect on average
wedding cost but it cannot be determined from these results if that assumption is true.
The coefficient for education in Regression #3 and #4 is statistically significant and
positive. Regression #3 shows that a one percentage point increase in percent of the
population with a Bachelor’s degree or higher predicts an increase of average wedding
cost of $153.33. Regression #4 shows that a one percentage point increase in percent of
the population with a Bachelor’s degree or higher predicts an increase of average
wedding cost of $111.80. The statistical significance of these coefficients goes away
31
when state fixed effects are included. Once again, this may be due to the fact that
education levels in a state stay pretty much the same over short periods of time and the
differences in education across states is captured in the state fixed effects.
In the final set of regressions, the natural log of wedding expenditures per capita
is used as the dependent variable to see if the relationship between wedding expenditures
per capita and the explanatory variables is non-linear. Table 7 shows these results. The
independent variable GSP per capita is replaced with the natural log of GSP per capita.
The coefficient for same sex marriage laws is statistically significant when including all
regressors, year dummy variables, and state fixed effects. The results show that states
that allow same sex marriage have wedding expenditures per capita that are 5.76% higher
than in states that do not allow same sex marriage. This is close to the raw difference in
difference estimation of 2.7% and because the coefficient in Regression #5 is statistically
significant, it can be said with some certainty that same sex marriage laws positively
affect wedding expenditures per capita. Regression #4 estimates that states with civil
union/domestic partnership laws have wedding expenditures per capita that are 36.28%
higher than states without these laws. This is close to what was predicted in Regression
#4 in Table 4.
After looking at the results for the regressions run, one can see that same sex
marriage laws have a positive effect on wedding expenditures per capita and number of
weddings per capita but have a negative effect on average wedding cost. For states
allowing civil unions/domestic partnerships, we can see a positive effect on wedding
expenditures per capita and number of weddings per capita. However, it is unclear what
32
effect civil union/domestic partnership laws have on average wedding cost. These results
support the adoption of same sex marriage and civil union/domestic partnership laws if a
state wants to encourage wedding expenditures, in order to raise tax revenue.
33
Table 4. Wedding Expenditures Per Capita as Dependent Variable
Same Sex
Marriage
Law
Civil Union/
Domestic
Partnership
Law
GSP Per
Capita
Unemployment Rate
Population
Density
Percent of
Population
w/
Bachelor’s
Degree or
Higher
Percent of
Population
Ages 18-34
2005
2006
2007
2008
State Fixed
Effects
Adjusted RSquared
Regression
#1
21.06**
(9.97)
Regression
#2
Regression
#3
15.92
(20.59)
-66.07
(65.04)
Regression
#4
Regression
#5
5.61
(10.70)
109.18**
(34.87)
Regression
#6
-55.65
(49.66)
0.0040**
(0.0018)
-107.81
(815.86)
-0.0371
(0.0344)
-833.32**
(421.22)
0.0037**
(0.0018)
-245.97
(709.57)
-0.0210
(0.0334)
-1072.17**
(413.50)
0.0049**
(0.0024)
-1894.30*
(1078.75)
4.05*
(2.44)
-259.31
(946.98)
0.0050**
(0.0023)
-1754.86**
(865.13)
4.24*
(2.36)
-230.35
(955.81)
-44.76
(165.00)
284.88
(184.59)
-23.16
(177.36)
-36.48
(184.51)
100.88**
(12.97)
95.38**
(11.50)
92.41**
(10.83)
21.99*
(12.04)
Yes
95.65**
(9.78)
90.16**
(8.27)
89.82**
(8.85)
21.57*
(11.61)
Yes
94.77**
(32.19)
82.40**
(35.18)
80.03**
(38.53)
12.38
(29.92)
No
96.40**
(30.27)
80.65**
(32.88)
75.39**
(36.54)
8.21
(28.96)
No
48.91**
(23.32)
25.53
(33.58)
14.50
(37.04)
-40.29
(29.38)
Yes
51.01**
(19.49)
28.21
(28.72)
19.17
(30.09)
-35.96
(24.54)
Yes
0.9106
0.9144
0.0800
0.1229
0.9212
0.9240
*Statistically significant at the 10% level
Robust standard errors in parentheses
**Statistically significant at the 5% level
34
Table 5. Number of Weddings Per Capita as Dependent Variable
Same Sex
Marriage
Law
Civil
Union/
Domestic
Partnership Law
GSP Per
Capita
Unemploy
-ment
Rate
Population
Density
Percent of
Population
w/
Bachelor’s
Degree or
Higher
Percent of
Population
Ages 1834
2005
2006
2007
2008
State
Fixed
Effects
Adjusted
R-Squared
Regression
#1
0.0007**
(0.0003)
Regression
#2
Regression
#3
0.0008
(0.0008)
-0.0011
(0.0012)
Regression
#4
Regression
#5
0.0004
(0.0003)
0.0038**
(0.0015)
Regression
#6
-0.0009
(0.0009)
0.0000002**
(0.0000000)
0.0017
(0.0399)
0.0000001**
(0.0000000)
-0.0032
(0.0353)
0.0000001**
(0.0000000)
-0.0301
(0.0212)
0.0000001**
(0.0000000)
-0.0281
(0.0176)
0.000004**
(0.000002)
-0.0394**
(0.0159)
0.000004**
(0.000001)
-0.0476**
(0.0158)
0.0001**
(0.00005)
-0.0125
(0.0215)
0.0001**
(0.00005)
-0.0117
(0.0216)
-0.0007
(0.0063)
0.0109
(0.0075)
-0.0011
(0.0037)
-0.0011
(0.0037)
0.0012**
(0.0003)
0.0009**
(0.0002)
0.0007**
(0.0002)
0.0003
(0.0002)
Yes
0.0011**
(0.0002)
0.0008**
(0.0002)
0.0007**
(0.0002)
0.0003
(0.0002)
Yes
0.0012
(0.0013)
0.0006
(0.0015)
0.0005
(0.0016)
0.0001
(0.0013)
No
0.0012
(0.0013)
0.0006
(0.0014)
0.0003
(0.0015)
-0.00004
(0.0013)
No
0.0005
(0.0004)
-0.0001
(0.0007)
-0.0005
(0.0008)
-0.0007
(0.0006)
Yes
0.0006
(0.0004)
-0.0001
(0.0006)
-0.0005
(0.0006)
-0.0007
(0.0005)
Yes
0.9731
0.9737
0.0575
0.0938
0.9761
0.9766
*Statistically significant at the 10% level
Robust standard errors in parentheses
**Statistically significant at the 5% level
35
Table 6. Average Wedding Cost as Dependent Variable
Same Sex
Marriage
Law
Civil
Union/Dom
estic
Partnership
Law
GSP Per
Capita
Unemployment Rate
Population
(in
thousands)
Population
Density
Percent of
Population
w/
Bachelor’s
Degree or
Higher
Percent of
Population
Ages 18-34
2005
2006
2007
2008
State Fixed
Effects
Adjusted
R-Squared
Regression
#1
-839.71
(561.56)
Regression
#2
Regression
#3
-134.95
(454.17)
-631.43**
(303.44)
Regression
#4
Regression
#5
-914.09**
(429.31)
1770.56**
(413.41)
Regression
#6
-455.75
(299.20)
0.0352**
(0.0124)
15929.43**
(7830.24)
0.0973**
(0.0116)
0.0304**
(0.0116)
15096.96**
(7199.07)
0.0888**
(0.0161)
-0.0206
(0.0289)
-18443.66**
(7420.79)
0.3350
(0.4261)
-0.0229
(0.0296)
-15651.48**
(7327.04)
0.2517
(0.3921)
9.70**
(0.7864)
15333.38**
(3297.06)
9.93**
(0.8908)
11180.01**
(3402.25)
-137.37**
(41.52)
-3905.15
(9846.13)
-136.22**
(41.26)
-5047.23
(9972.42)
-2710.28
(3308.61)
2874.36
(3397.12)
2221.04
(3464.19)
834.18
(3490.98)
8804.80**
(138.38)
9081.84**
(140.16)
9411.92**
(144.22)
1996.59**
(147.17)
Yes
8817.30**
(138.63)
9094.33**
(140.40)
9449.67**
(147.46)
2013.38**
(147.53)
Yes
9540.20**
(414.61)
9912.68**
(446.33)
10156.79**
(457.88)
2497.09**
(379.29)
No
9638.85**
(380.82)
9961.08**
(409.70)
10161.78**
(418.96)
2480.99**
(352.06)
No
7682.25**
(307.95)
7965.14**
(310.56)
8422.08**
(309.41)
1330.97**
(252.25)
Yes
7780.64**
(298.40)
8088.08**
(303.16)
8573.85**
(305.29)
1433.11**
(250.23)
Yes
0.9868
0.9867
0.8949
0.9064
0.9894
0.9890
*Statistically significant at the 10% level
Robust standard errors in parentheses
**Statistically significant at the 5% level
36
Table 7. Natural Log of Wedding Expenditures Per Capita as Dependent Variable
Same Sex
Marriage
Law
Civil Union/
Domestic
Partnership
Law
Natural Log
of GSP per
Capita
Unemployment Rate
Population
Density
Percent of
Population
w/
Bachelor’s
Degree or
Higher
Percent of
Population
Ages 18-34
2005
2006
2007
2008
State Fixed
Effects
Adjusted RSquared
Regression
#1
0.0672**
(0.0299)
Regression
#2
Regression
#3
0.0406
(0.0579)
-0.0049
(0.0226)
Regression
#4
Regression
#5
0.0576**
(0.0295)
0.3628**
(0.0990)
Regression
#6
-0.0027
(0.0148)
0.5580**
(0.2219)
0.5056**
(0.2089)
0.7110**
(0.1594)
0.7270**
(0.1632)
-0.2697
(2.40)
0.00003
(0.0001)
-1.94**
(0.8689)
-0.7275
(2.02)
0.00008
(0.0001)
-2.74**
(0.8611)
0.5368
(0.6281)
0.0035**
(0.0016)
0.9562
(0.9966)
0.4657
(0.6445)
0.0037**
(0.0016)
1.03
(0.9975)
0.0150
(0.5556)
1.11*
(0.5867)
-0.3927**
(0.1469)
-0.3480**
(0.1418)
0.5144**
(0.0108)
0.5014**
(0.0105)
0.4925**
(0.0089)
0.1375**
(0.0118)
Yes
0.5101**
(0.0108)
0.4971**
(0.0106)
0.4884**
(0.0089)
0.1361**
(0.0121)
Yes
0.4990**
(0.0860)
0.4686**
(0.0952)
0.4607**
(0.1012)
0.1134
(0.0898)
No
0.5053**
(0.0784)
0.4636**
(0.0855)
0.4461**
(0.0921)
0.0998
(0.0847)
No
0.5529**
(0.0251)
0.5330**
(0.0282)
0.5086**
(0.0244)
0.1421**
(0.0185)
Yes
0.5471**
(0.0257)
0.5274**
(0.0286)
0.5025**
(0.0251)
0.1386**
(0.0195)
Yes
0.9866
0.9861
0.2710
0.3442
0.9877
0.9874
*Statistically significant at the 10% level
Robust standard errors in parentheses
**Statistically significant at the 5% level
37
Chapter 6
CONCLUSION
Same sex marriage is a hotly debated topic and in the news frequently due to
states recently passing laws allowing same sex couples the right to marry. There are
many different views as to why it should or should not be legal. Some argue they should
not marry because of religious beliefs. Others argue that it is an equality issue and
homosexual couples are facing an injustice by not being allowed to marry. Still others
see same sex marriage as a threat to heterosexual marriage and that it may breakdown the
traditional family. Another option available in some states for same sex couples is civil
unions or domestic partnerships. These allow for some of the same rights that married
couples have, such as visitation rights in the hospital and health insurance coverage.
Although some rights are protected under civil unions, most committed same sex couples
would like all the rights given to married couples.
After seeing the results presented here, we can see that same sex marriage has a
positive and meaningful effect on wedding expenditures per capita and number of
weddings per capita. It also has a negative effect on average wedding cost but this
negative effect is not enough to offset the increase in number of weddings per capita and
therefore, wedding expenditures per capita experiences an increase. In the case of civil
unions/domestic partnerships, the results show a positive statistically significant
relationship when looking at wedding expenditures per capita and number of weddings
per capita. On the other hand, the results are inconclusive with respect to the effect on
38
average wedding cost. When looking at the natural log of wedding expenditures per
capita, state fixed effects must be included with year dummy variables and other
explanatory variables to get a statistically significant coefficient for same sex marriage
laws. While with civil union/domestic partnership laws, the state fixed effects must be
left out to find a statistically significant coefficient, suggesting this result is not robust.
Not much research exists on the topic of same sex marriage, and none that could
be found has been done on the effect of same sex marriage on wedding expenditures.
This research will hopefully add considerably to what is known on the topic of same sex
marriage and lead to further research. Because there are only a few states that allow
same sex marriage and because the passage of these laws have been very recent, we may
not be able to see the full effects of laws allowing same sex marriage on wedding
expenditures in this data set. It may take a few years to a decade to see these effects.
Same sex couples may not be having big wedding ceremonies and receptions due to
society’s views on homosexuality. These couples may opt for a small civil ceremony at
the courthouse. Moreover, even if they have a dinner afterwards to celebrate, it may not
be captured in wedding expenditure data because it is not at a venue for weddings.
Another phenomenon that may be observed is there may be a flood of same sex couples
getting married in the first few years of it being legal and then the number of weddings
may taper off as time passes. As the years go by, more data can be collected to better
show the relationship between wedding expenditures and same sex marriage.
39
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