The Determinants of Divorce Rate
A Cross Sectional Study of Fifty-two Nations
Submitted to
Dr. Jacqueline Khorassani
ECON 421 Empirical Analysis
by
Di Yang
May 1, 2008
1
Abstract
Utilizing a cross-sectional data set of 52 countries and the ordinary least squares
procedure, this paper analyzes the causes of divorce. The findings of this study suggest that the
percentage of the population in urban areas, the percentage of children in the population, and the
ratio of male to female population are three significant determinants of divorce.
I. Introduction
Despite the fact that some cultures and religious beliefs oppose divorce, there is no
denying that divorce is a common phenomenon in the world. For years, economists have
conducted research on the determinants of divorce. Becker et al. (1977), for example, developed
an economic theory of marital instability that established a framework for analyzing the
determinants of divorce. White (1990) has summarized the literature through the 1980s. More
recently, Nakonezny et al. (1995) and Ruth (2005) examined the effect of factors such as no-fault
divorce law on divorce rate.
This paper utilizes data from 52 countries to analyze the relationship between divorce
rates and several socio-economic variables1. In line with previous research, this study employs
ordinary least squares (OLS) method to estimate a regression equation that resembles the
approach used in previous studies. In addition, there are a few independent variables in the
empirical model of this study, such as the purchasing power parity per capita real GDP and two
variables representing religious affiliation, that are missing from previous studies.
The rest of this paper is organized as follows. A brief review of the literature is covered
in Section II, followed by the empirical model and a description of the data in Sections III and IV,
respectively. Section V conducts a multicollinearity test. Section VI tests for heteroskedasticity.
The data set on divorce rates is obtained from the United Nations. Unfortunately, the UN’s data set on divorce
rates in the last five years covers only 52 nations.
1
2
The results of the regression model are reported in Section VII. Concluding remarks are
included in the last section of this paper.
II. Literature Review
Many studies employ the OLS method to learn about the relationship between divorce
rates and a range of independent variables. Some important independent variables include
female labor force participation rate, socioeconomic development index, male to female
population ratio, and the no-fault divorce laws. Table 1 summarizes the characteristics of a
selected number of papers related to my research.
The first three papers listed in Table 1 use cross-sectional data of different countries and
do not consider the impact of no-fault divorce law. This group generally studies the relationship
between divorce rates, female labor force participation, male to female population ratio, and
socioeconomic development. Semyonov’s work (1980) is different from mine in that he uses
female labor force participation rate as the dependent variable, and the divorce rate as an
independent variable. However, to the extent that it examines the correlation between these two
variables, it relates to my work. Semyonov (1980) does not discuss the significance of the
independent variables. South and Trent (1988 & 1989) find that the ratio of male to female
population, development index, female labor force participation rate, and female average age at
the time of marriage significantly affect the divorce rate. The development index used by South
and Trent (1988 & 1989) consists of four development indicators: gross national product (GNP,
in log form), the infant mortality rate, life expectancy, and percentage of the population that is
urban.
3
Table 1: Review of Selected Papers on the Determinants of Divorce Rates
Title (year)
The Social Context of
Women’s Labor Force
Participation: A
Comparative Analysis
(1980)
Sex Ratio and
Women’s Roles: A
Cross-National
Analysis (1988)
Structural
Determinants of the
Divorce Rate: A
Cross-Societal
Analysis (1989)
The Effect of No-Fault
Divorce Law on the
Divorce Rate Across
the 50 States and Its
Relation to Income,
Education, and
Religiosity (1995)
The Determinants of
Divorce Rates (2005)
Author(s)
Method
and data
Dependent
variable(s)
Independent variables
Moshe
Semyonov
OLS; a
sample of 61
countries
female labor
force
participation rate
divorce rate; income inequality; fertility rate;
industrialization (measured by energy
consumption per capita)
Scott J.
South;
Katherine
Trent
Katherine
Trent; Scott
J. South
OLS; a
sample of
117
countries
OLS; a
sample of 66
countries
divorce rate (per
1000 population)
number of males per 100 females at ages 1549*; development index*; a dummy variable
measured data quality
divorce rate (per
1000 population)
development index*; female labor force
participation rate*; number of males per 100
females at ages 15-49*; percentage Catholic;
female average age at marriage*
Paul A.
Nakonezny;
Robert D.
Shull;
Joseph Lee
Rodgers
t-test, OLS,
and analysis
of
covariance;
50 states in
the U.S.
divorce rate (per
1000 population)
median family income*; percentage Roman
Catholics, Southern Baptists, and United
Methodists, respectively; percentage of
population age 25+ completing 4 or more
years of college
Rachel N.
Ruth
OLS; 50
states in the
U.S.
divorce rate (per
1000 population)
no-fault divorce law*; median family
income*; female participation rate (age 16+);
percentage of metropolitan population;
percentage of population in poverty;
percentage of population age 25+ with a
bachelor’s or higher degree*; combined
percentage of Jewish and Christian adherents*
* denotes significant independent variables
The last two papers listed in Table 1 focus on the causes of divorce in the U.S.
Nakonezny et al. (1995) find that median family income is a variable that significantly and
positively affects the divorce rate. On the other hand, Ruth’s (2005) study finds that no-fault
divorce law, median family income, education, and religious affiliation significantly affect the
divorce rate. Ruth’s study is a comprehensive approach as to the determinants of divorce rate. It
is very similar to my study, with the exception that she uses a U.S. data set while I use a world
data set.
4
III. Model Specification
The method used to estimate the effects of various factors on the divorce rate is the OLS
approach. Data from 52 countries covering the period from 2003 to 2005 is used to estimate
Equation 1.
Equation 1: Divorce ratei = f (FLFi, RMFi, POCi, URBi, GPPi, EDUi, CHRi, MUSi) + error termi
The dependent variable is the divorce rate measured by the number of divorces per 1000
population. The definition and the expected signs of the coefficients of the independent
variables included in Equation 1 are shown in Table 2.
Table 2: Definition of independent variables
included in Equation 1 and the expected signs of their coefficients
Independent
Expected sign
Definition
variables
of coefficients
FLF
Percentage of women 15+ who participate in the labor
Positive
force
RMF
The ratio of 15-64 year old male population to 15-64
Negative
year old female population
POC
Percentage of population under 15
Negative
URB
The percentage of the total population in urban areas
Positive
GPP
Real purchasing power parity per capita GDP
Ambiguous
EDU
Gross enrollment rate in higher education
Ambiguous
CHR
The percentage of Christians in total population
Negative
MUS
The percentage of Muslims in total population
Negative
The female labor force participation rate (FLF) measures the percentage of the population
of 15 year old and older women that is active in the labor force. This variable is expected to
have a positive impact on the divorce rate. Women who are active in the labor force are likely to
spend less time with their families as part of their focus is on their jobs. Moreover, women who
earn money themselves are more economically independent and rely less on their husbands’
income. All these factors contribute to the rising divorce rate. This positive relationship
5
between women’s work status and divorce rate is verified by several studies. For example,
South (1985) and Trent and South (1989) find that a higher female labor force participation rate
has a significant positive correlation with the divorce rate. The variable RMF is defined as the
ratio of males, 15-64 years old, to females of the same age. According to South and Trent (1988),
the literature on the divorce rate suggests that a lower male to female ratio is an indication that
men have more alternatives, causing the divorce rate to go up. This result is also consistent with
South and Trent’s (1988 & 1989) own findings.
Cherlin (1977) studies the effect of children on family stability. He finds that when a
couple’s children are in the preschool age, divorce rates tend to be lower. The reason is that
when children are very young, they have very limited ability of taking care of themselves.
Moreover, parents of young children may have a strong sense of moral obligation to fulfill their
parental duties and not dissolve their marriages. On the other hand, when children are grown up
and less dependent on their parents, parents are more likely to terminate their marriages. Thus,
having young children should have a negative correlation with the divorce rate. In this study,
due to unavailability of data on preschool-aged children, I use the percentage of the population
under the age of 15 (POC) as a proxy for the presence of children. The expected sign of the
coefficient on POC is negative.
The next independent variable is the percentage of the total population in urban areas
(URB). Generally speaking, the higher the urban population, the higher the divorce rate.
Shelton (1987) finds a strong positive correlation between residential mobility and marital
dissolution. She argues that residential mobility can be measured by the urban density of an area;
that is, the higher the urban density, the greater the residential mobility. Given that urban areas
are characterized by large population density, I expect the sign of coefficient on URB to be
positive.
6
In past studies, researchers have used family median income as an independent variable
measuring the effect of income on the divorce rate. Using a U.S. data set, Nakonezny et al.
(1995) find that median family income has a significant positive relationship with the divorce
rate. Nevertheless, Ruth (2005) discovers that median family income is negatively related to the
divorce rate, and this correlation is significant at a 5% level. Given that both Nakonezny (1995)
and Ruth (2005) use all 50 U. S. states as their samples, I conclude that their conflicting results
are in part due to the difference in the year of their studies, early 1990s versus 2000. Nakonezny
et al. (1995) justify their findings by arguing that families who have less economic burden are
more likely to dissolve. On the other hand, Ruth (2005) argues that the lack of money results in
marital fights, and eventually divorce.
This ambiguous relationship between income and the divorce rate can be applied to a
worldwide data set as well. For instance, in a relatively undeveloped country, due to the lack of
income, some people’s married life may not be very happy, resulting in divorce. On the other
hand, a low family income may be an indication that one spouse may not be economically
independent, which may strengthen the marriage. In developed countries, a couple’s financial
needs are mostly satisfied and they don’t have to worry about the problems of starvation or
poverty. Therefore, the couple may enjoy a happy married life. However, married couples in
developed nations are more likely to be economically independent, therefore, seeking divorce.
Thus, the total effect of income on the divorce rate is unclear. The family median income
captures the income of a family as a whole, but does not adjust for purchasing power differences
among various nations. Real purchasing power parity per capita GDP (GPP), on the other hand,
adjusts for the differences in purchasing power across nations. However, it is an inferior
measure of income compared to the family median income in that it only measures the income of
an average citizen. Due to unavailability of data on family median income across all the nations,
7
real purchasing power parity per capita GDP is used in this study. The expected sign of
coefficient of this variable is ambiguous.
Educational attainment is another factor that affects the divorce rate. It is expressed by
the higher education gross enrollment rate (EDU). This rate is calculated by dividing the number
of students enrolled in higher education, regardless of age, by the population of traditional
college age individuals in each country, and then multiplying the result by 100. The expected
sign of coefficient on EDU is ambiguous. The reason is that, on the one hand, it is reasonable to
assume that more educated people are less likely to divorce. According to Heaton (1991), more
educated individuals possess better interpersonal skills, levels of maturity, and other resources
that benefit a marital relationship. In addition, more educated individuals are better equipped to
evaluate the costs and benefits of divorce. Consequently, given the high opportunity cost of
divorce, more educated individuals may decide against it. On the other hand, more educated
people might be more free-minded and emotionally independent causing them to terminate their
marriages more easily.
Religiosity plays an indispensable role in many countries. As one of the religions with
the largest number of followers, Christianity generally has a negative attitude towards divorce.
Although Christians are allowed to divorce, we expect countries with a large population of
Christians to have a lower divorce rate. The situation is very similar with Islam. Thus, there are
two variables capturing the effect of religious affiliations in Equation 1. The variable CHR is
defined as the percentage of Christians in total population. The variable MUS is defined as the
percentage of Muslims in total population. The expected sign of coefficients of both variables is
negative.
Besides the independent variables described above, other variables such as average length
of marriage, a measure of income inequality, and female average age at marriage might affect the
8
divorce rate. However, due to unavailability of data on the above variables across all the nations
under study, I am not able to include them in Equation 1.
IV. Descriptive Statistics
The data set used in this empirical research is from 2003-05, and the set consists of 52
countries in five continents (all but South America and Antarctica). The complete list of
countries under study is included in Appendix I. According to the World Bank’s classification,
fifteen nations are developing countries, accounting for 29% of observations, while the other
71% are developed countries. The real purchasing power parity per capita GDP among the
nations under study ranges from $1,356 (Tajikistan) to $60,228 (Luxembourg).
Other features of the data set are summarized in Table 3. The average divorce rate
among these 52 countries is 1.96 per 1000 population2.
Table 3: Descriptive Statistics of a Sample of 52 nations, 2003-2005
DIVR (number of divorces per
1000 population)
FLF (female labor force
participation rate)
RMF (15-64 year old male to
female population ratio)
POC (percentage of children
population)
URB (percentage of urban
population)
GPP (real PPP per capita GDP)
EDU (higher education
enrollment rate)
CHR (percentage of Christians
population)
MUS (percentage of Muslims
population)
0.24
Samoa
27.50
Jordan
0.87
Armenia
13.80
Bulgaria
22.41
Samoa
$1356
Tajikistan
7.50
Samoa
0.58
Iran
0.00
Costa Rica, Samoa
2
1.96
50.12
1.05
20.68
65.91
$18843
50.11
64.91
15.07
4.23
Russian Federation
70.90
Iceland
2.28
Qatar
40.80
Samoa
98.29
Kuwait
$60228
Luxembourg
91.87
Finland
96.40
Costa Rica
95.82
Iran
To give a basis for comparison, according to the U.S. Census Bureau, in the year 2005, the divorce rate in the U.S.
was 3.6 per 1000 population.
9
A quick glance at Table 3 reveals that Samoa holds the lowest divorce rate, percentage of
the total population in urban areas, gross enrollment rate in higher education, and percentage of
Muslims in total population. Moreover, Samoa also has the highest percentage of population
under 15. This interesting fact gives us some hints about the different factors’ expected impact
on the divorce rate. Lower divorce rate is associated with higher percentage of children
population and lower urban population, which is consistent with the expected sign of coefficients
of POC and URB.
Another notable observation is that the male to female population ratio in Qatar is the
highest in my sample (2.28). This is interesting because Qatar’s population is evenly distributed
at birth (a male to female ratio of 1.05). The reason for a higher male to female ratio of the adult
population in Qatar may be due to the fact that Qatar’s oil industry attracts a large number of
foreign male workers.
Finally, Table 3 suggests a potential problem of multicollinearity between the variable
CHR (percentage of Christians in total population) and MUS (percentage of Muslims in total
population). The maximum and minimum values of these two variables are cross matched
almost perfectly. Iran has the lowest percentage of Christians and the highest percentage of
Muslims, while Costa Rica has the highest percentage of Christians and the lowest percentage of
Muslims. This makes sense because the total population is a fixed number, meaning that a
higher population of Muslims is associated a lower population of Christians, or vice versa. The
results of a multicollinearity test among independent variables included in Equation 1 are
reported in the next section.
10
V. Multicollinearity Test
In establishing an econometric model using the ordinary least square method, we should
be aware of the possibility of multicollinearity. Perfect multicollinearity exists when two
independent variables have a perfect linear relationship, and one cannot estimate the coefficients
because it is impossible to keep one of the variables constant while evaluating the effect of the
other one. Perfect multicollinearity can be easily avoided by eliminating one of the independent
variables.
Usually, independent variables are imperfectly correlated. In this case, we need to test
the strength of the correlation between each pair of independent variables. According to
Halcoussis (2004), multicollinearity can produce increased standard errors of the estimates, and
therefore smaller t-statistics. In addition, the estimated coefficients of the independent variables
of a model may vary greatly as variables are added or subtracted from the model. Fortunately,
the existence of multicollinearity does not reduce the overall fitness of the model, nor does it
strongly affect the coefficients of the independent variables without multicollinearity problem.
Also, an imperfect multicollinearity does not generate unbiased estimates.
There are several ways to test for imperfect multicollinearity. This paper adopts the
correlation coefficients method. To figure out if two independent variables are strongly
correlated, this method suggests calculating the correlation coefficients, r, between them. The
value of r is between – 1 and + 1, the closer the absolute value of r to 1, the stronger the
correlation between a pair of independent variables. Some researchers argue that an r value
higher than 0.7 or 0.8 should be a source of concern.
The results of the multicollinearity test are shown in Table 4. Clearly, most independent
variables are not strongly correlated, with the exception of the percentage of Christians versus
Muslims in total population. However, given that the correlation coefficient between these two
11
variables is between 0.7 and 0.8, multicollinearity is not a major source of concern. Therefore, I
choose to leave the model alone.
Table 4: Correlation Matrix for Equation 1 Independent Variables
FLF
RMF
POC
URB
GPP
EDU
CHR
MUS
FLF
1.000000
RMF
POC
URB
GPP
EDU
CHR
MUS
-0.270896 -0.241716 0.062731 0.276525 0.352623 0.125225 -0.330833
1.000000 0.273375 0.194013 0.116085 -0.344004 -0.280831 0.421493
1.000000 -0.313740 -0.361526 -0.557230 -0.444078 0.550105
1.000000 0.610681 0.393794 0.146343 -0.077925
1.000000 0.357580 0.375619 -0.306351
1.000000 0.394045 -0.493475
1.000000 -0.746163
1.000000
VI. Heteroskedasticity Test
When estimating Equation 1 using the OLS method, we assume that the error terms have
a constant variance. Nonetheless, in reality, different observations may have different error term
variances. Such a problem is called heteroskedasticity. Consequences of heteroskedasticity
include lower standard error of the estimates, higher t-statistics, but unbiased estimates.
The White Test is a common method to test for heteroskedasticity. The null and
alternative hypotheses are H0: Homoskedasticity; H1: Heteroskedasticity. The test requires the
estimation of a new regression model, where the estimated residuals of Equation 1 are used as
observations on the dependent variable and the independent variables include the original,
squared, and cross products of the independent variables included in Equation 1. The test
statistic is nR2, and the degrees of freedom are equal to the number of independent variables in
the new equation. Using a Chi-squared table, if nR2 is greater than the critical value, then we
reject H0, and conclude that heteroskedasticity is a problem.
12
Following the procedure outlined above, the value of nR2 for Equation 1 is estimated to
be equal to 47.58. Furthermore, the critical chi-squared is approximately 55.76. Therefore, we
cannot reject the null hypothesis, meaning that heteroskedasticity is not a problem.
VII. Estimation Results
The estimation results of two specifications of Equation 1 are shown in Table 5. Notice
that Specification 2 is different in that it excludes the variables CHR and MUS.
According to Halcoussis (2004), the adjusted R2 is an indicator of the goodness of fit of
the model, and it measures the proportion of the dependent variable’s movement around its
mean that can be explained by the independent variables. The closer the adjusted R2 is to 1, the
better the independent variables explain the movement of the dependent variable around its mean.
From Table 5, we can see that the adjusted R2 of Specification 2 is higher than that of
Specification 1. This means that the Specification 2 is a better fit than the Specification 1.
Therefore, in what follows, I only discuss the estimation results of Specification 2 of Equation 1.
The adjusted R2 of Specification 2 is 0.376, which means 37.6% of the variation in the divorce
rate around the mean divorce rate is explained by Specification 2 of the regression model.
In part, the small degree of freedom may be responsible for the low adjusted R2. According to
Halcoussis (2004), degrees of freedom measure the amount of information that is available to
estimate the regression model. Therefore, when there is less information, the model will tend to
work less efficiently. The degrees of freedom in my model are 43, which is not very high.
13
Table 5: The estimation results of two specifications of Equation 1;
the dependent variable is the divorce rate
Constant
FLF (female labor force
participation rate)
RMF (15-64 year old male
to female population ratio)
POC (percentage of
children population)
URB (percentage of urban
population)
GPP (real PPP per capita
GDP)
EDU (higher education
enrollment rate)
CHR (percentage of
Christians population)
MUS (percentage of
Muslims population)
Adjusted R2
Specification 1
1.183381
(1.342958)
0.012756
(0.015597)
-0.604215
(0.584089)
-0.036141
(0.021793)**
0.025227
(0.008633)****
-1.55E-05
(1.20E-05)*
0.005465
(0.006413)
-0.001077
(0.005190)
-0.004027
(0.006595)
0.353603
Specification 2
1.176146
(1.166150)
0.014259
(0.014746)
-0.690230
(0.553967)*
-0.040802
(0.020094)***
0.024148
(0.008268)****
-1.43E-05
(1.10E-05)*
0.006196
(0.006175)
Positive
Negative
Negative
Positive
Ambiguous
Ambiguous
Negative
Negative
0.376165
**** denotes coefficient is statistically significant at a 1% level; *** denotes coefficient is statistically significant at
a 5% level; ** denotes coefficient is statistically significant at a 15% level; * denotes coefficient is statistically
significant at a 25% level.
Except for the independent variables with an ambiguous expected sign on thier
coefficients, the signs of the estimated coefficients on all the other variables meet my
expectations. Specifically, I find that FLF (female labor force participation rate) and URB
(urban population) have positive impacts on divorce rate. On the other hand, the independent
variables RMF (male to female ratio) and POC (population of children) are negatively related to
divorce rate.
Among all the estimated coefficients of Equation 1, only the coefficient of URB is
statistically significant at a 1% level. This means that we can say with 99% level of confidence
that the higher the percentage of population living in urban areas, the higher is the divorce rate.
The estimated coefficient of URB is 0.024, which means 1 percentage point increase in the total
14
population in urban areas is associated with about 0.024 increase in divorce per 1000 population.
The low value for this coefficient indicates that the impact of percentage of urban population on
the divorce rate is very small, although it is statistically significant at a 1% level. This result is
inconsistent with Ruth’s study (2005), which finds that the effect of percentage of population
living in metropolitan areas on divorce rate is statistically insignificant.
The independent variable POC (percentage of children in population) is statistically
significant at a 5% level. This means that we are 95% sure that the percentage of children in the
population is negatively correlated with the divorce rate. The magnitude of the coefficient on
this variable (-0.04) suggests that for every 1 point increase in the percentage of children in the
population, the divorce rate declines by 0.04 per 1000 population.
The variable GPP (real PPP per capita GDP) is statistically significant at a 25%.
Consequently, we can say with 75% degree of confidence that real PPP per capita GDP and the
divorce rate are negatively correlated. However, the estimated coefficient on this variable is
close to zero, meaning that there is almost no effect of real PPP per capita GDP on the divorce
rate.
In addition, the variable RMF (15-64 year old male to female population ratio) is
statistically significant at a 25% level. Similarly, we are 75% confident that the male to female
population ratio is negatively associated with the divorce rate. This negative relationship is
consistent with South and Trent’s (1988 & 1989) finding. Moreover, given the absolute value of
0.69 of the estimated coefficient on RMF, I conclude that for every 1 percentage point increase
in the ratio of male to female population, the divorce rate declines by 0.007 per 1000 population.
Compared to the results of previous studies, my study does not show a significant
relationship between the divorce rate and female labor force participation rate or higher
15
education enrollment rate. As discussed before, for the most part I attribute the insignificance of
these variables to the small sample size, resulting in low degrees of freedom.
Furthermore, when it comes to cross-national studies, the data sets may be inconsistent.
The reason is that the international agencies in charge of data collection may have to rely on
reports prepared by each nation internally.
VIII. Conclusions
This empirical research seeks to find the major determinants of the divorce rate, by
analyzing a cross-sectional data set of 52 countries and using the OLS method of estimation.
According to my findings, three major determinants of the divorce rate are the percentage of the
population in urban areas, the percentage of children in the population, and the male to female
population ratio. Specifically, my study suggests that a high divorce rate may be a byproduct of
the degree of urbanization. Moreover, I conclude that the greater the proportion of young people
in the population, the lower is the divorce rate in that nation. Another interesting finding is that
the nations that have higher male to female population ratio are less likely to experience divorce.
For future research, it will be interesting to analyze the effect of income inequality on the
divorce rate. Future studies may also try to include more countries in the data set to increase the
degrees of freedom.
16
Appendix I: List of 52 Nations under Study
Albania
Armenia
Australia
Austria
Azerbaijan
Belgium
Bulgaria
China
Costa Rica
Croatia
Cuba
Cyprus
Czech Republic
Denmark
Estonia
Finland
France
Georgia
Greece
Hungary
Iceland
Iran (Islamic Republic of)
Ireland
Japan
Jordan
Korea (Republic of)
Kuwait
Kyrgyzstan
Latvia
Lithuania
Luxembourg
Mauritius
Mexico
Mongolia
Netherlands
New Zealand
Norway
Poland
Portugal
Qatar
Republic of Moldova
Romania
Russian Federation
Samoa
Slovakia
Slovenia
Spain
Sweden
Switzerland
Tajikistan
The Former Yugoslav Rep. of Macedonia
Ukraine
17
Data Sources

The data on divorce rates were found in the United Nations Demographic Yearbook 2005.
<http://unstats.un.org/unsd/Demographic/Products/dyb/dyb2005.htm>.

The data on female labor force participation rate were found in International Labor
Organization, Key Indicators of the Labor Market Fifth Edition.
<http://www.ilo.org/public/english/employment/strat/kilm/index.htm>.

The data on population under 15 were found in UN Human Development Reports, “Build
your own tables.”
<http://hdrstats.undp.org/buildtables/>.

The data on urban population were found in the United Nations Statistics Division, Social
Indicators.
<http://unstats.un.org/unsd/demographic/products/socind/hum-sets.htm>.

The data on male to female population were found in CIA World Facebook 2005.
<https://www.cia.gov/library/publications/download/download-2005/index.html>.

The data on PPP real GDP were found in UN Human Development Reports, “Build your
own tables.”
<http://hdrstats.undp.org/buildtables/>.

The data on education attainment were found in UNESCO Institute for Statistics,
Education Statistics, Table 14: Tertiary Indicators.
<http://stats.uis.unesco.org/unesco/ReportFolders/ReportFolders.aspx>.

The data on religious affiliations were found in Association of Religion Data Archives.
<http://www.thearda.com/Archive/Files/Descriptions/INTL2003.asp>.
18
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Cherlin, Andrew. “The Effect of Children on Marital Dissolution.” Demography 14.3 (1977):
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