Discussion Paper No.368

Gender Difference in the Effect of NeverMarried on SWB
Tsukasa Matsuura
Chuo University
April 2022

INSTITUTE OF ECONOMIC RESEARCH
Chuo University
Tokyo, Japan

Gender Difference in the Effect of Never Married on SWB

Tsukasa Matsuura 1
Chuo University
Abstract

This article explores the effect of never-married on certain key aspects of Subjective
Well-Being (SWB) such as life satisfaction, happiness, financial satisfaction, and healthcondition, using the European Values Survey (EVS) and the World Values Survey (WVS).
It also examines the gender difference in the effect of one’s marital status on SWB that
depends on age, country, and era. Our estimates show the following. First, the negative
effect of never-married on SWB is stronger in males than in females. Second, the genderdifference in the negative effect of never-married on SWB grows as one ages. While there
is no gender difference in the effect of never-married on SWB before 40 years of age,males feel more unsatisfied with life by never having married than females, after 60 yearsof age. Third, in countries with a GDP per capita of $30,000 and more, the negative effectof never-married on SWB is greater in males than in females aged 40 and above. In
countries with a GDP per capita between $5,000 and $30,000, the negative effect ofnever-married for males is observed only for individuals aged 60 and above. In countries
with a GDP per capita of less than $5,000, the gender difference in the negative effect ofnever-married on SWB is not observed. Fourth, after 2010, the negative effect of nevermarried in males is observed only for individuals aged 60 and above in countries with a
GDP per capita of $30,000 and above.

1

t-matsu@tamacc.chuo-u.ac.jp

11． Introduction
Populations across the world, including those of developing countries, are aging rapidly.
In 1950, France faced the highest rate of aging in the world. From the 1970s to the 1990s,
Sweden faced the highest rate of aging, as shown in Figure 1. Since 1990, the rate ofaging in Japan has risen rapidly and is at its peak at present. Not only in these countries,
but also in East Asian regions such as South Korea, China, and Taiwan, is aging
progressing rapidly due to the decline in TFR. Put plainly, aging is a global phenomenon.
In addition, the rise in the never-married rate is a phenomenon common in many
countries around the world. Figures 2-1 and Figure 2-2 show the changes in average age
at the time of one’s first marriage. Although this average declined in Ukraine during theturmoil following the collapse of the former Soviet Union, it rose for both genders inother regions. Focusing on the gender difference, the average age at the time of one’s firstmarriage is lower for females than males in all countries. While it is relatively high in
Sweden, it is lower in Ukraine and the United States.
As the average age at the time of one’s first marriage increases, the rate of nevermarried also increases. Figure 3 shows the rate of never-married among individuals aged
between 30 and 34 and 50 and 54, by gender. For males, it increases by 30% in Sweden
and by 20% in other most countries, while for females, it is lower than that of males inall countries for both ages. It also increases by 20% for both genders in Sweden and
Norway, while it falls by 10% in individuals aged between 50 and 54 years for bothgenders in South Korea. In terms of gender difference, there is a large difference between
the never-married rate in Japan and South Korea. Therefore, aging and unmarried seemto go hand in hand.
A never-married status does not always lead to single-person households. Children
conceived out of marriage are common in Western European countries, though it is rare
in developing and East Asian countries. However, most never-married older people arelikely to have single-person households in countries where nuclear families are morecommon. Under these circumstances, many of the never-married are likely to remain insingle-person households as they age. Therefore, this study explores the effect of nevermarried on SWB, focusing on the gender difference. In addition, we examine how thedifference in the effect varies by age, country, era, and the reasons for the same.
In sum, this paper studies the effect of never-married on certain key aspects of SWB
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such as life satisfaction, happiness, financial satisfaction, and subjective health statususing the European Value Survey (EVS) and the World Value Survey (WVS). This paper
also analyzes whether the effect of one’s marital status on SWB differs by gender andwhether this difference varies by age, country, and era.
This research has four features. First, it explores the gender difference in the effect ofmarital status on SWB. Second, it examines whether the difference in the effect of marital
status on SWB is between genders. Third, it focuses on the gender difference in thenegative effects of never-married on SWB by age and economic development, using the
EVS-WVS data. Fourth, we use a survey conducted over long periods, for almost 40 years,
from 1981 to 1984, which makes it possible to analyze long-term trends. In other words,
this study is a multifaceted analysis of the gender difference in the negative effect ofnever-married on SWB according to age, stages of development, and era, while making
use of data surveying the long-term SWB trends in countries.
This study has also the following policy implications. Single older people havetraditionally been related to poverty. However, some researchers have focused on thepositive aspects of the single life for older people, as Klinenberg (2012) insists. Therefore,
it is possible to discuss the effect of the household setup on individual well-being, while
analyzing the SWB of single older people, who are closely related to poverty, from theperspectives of gender, age, country, and era. Further, the increase in the never-marriedrates is closely linked to lower fertility rates in many countries. Through an analysis of
the gender difference in the impact of never-married individuals on SWB, we attempt toconsider the factors behind the rise in never-married rates worldwide.
2． Previous Studies
Previous studies exploring the effects of marital status on SWB have been accumulated.
Coombs (1991) surveys the effects of marriage on drinking, suicide, and mortality in the
1970s and 1980s and proves that marriage benefits males more than females. Post the 90s,
studies have shown that the benefits of improving SWB through marriage are greater formales than for females (Stack, 1998; Horwitz et al., 1996; Sonnenberg et al., 2000; Kohler
et al., 2005; Sonnenberg et al., 2013; Mikucka, 2016). However, some studies show thatthe effect of marriage on SWB is similar between the genders (Stack, 1990; Stack andEshleman, 1998; Williams, 2003). Thus, the gender differences in the impact of marriage
3

on the SWB are also controversial. For East Asian countries, the majority of studies showthat the benefits of marriage measured by SWB are far more applicable to males than tofemales (Kaufman and Taniguchi, 2010; Raymo, 2015; Lee et al., 2016; Fu and Noguchi,
2018; Hori and Kamo, 2018; Sato, 2020; Matsuura and Ma, 2021).
In western countries, there is an accumulation of previous studies on the effects ofmarriage and cohabitation on life satisfaction and happiness (Waite and Gallagher, 2000;
Stutzer and Frey 2006; Clark et al. 2008; Musick and Bumpass, 2012; Lee and Ono, 2012;
Verbakel, 2012; Mikucka, 2016; Perelli-Harris et al., 2019). Some previous studies
explore the effect of marriage on objective and subjective health conditions (Ross et al,
1990; Horwitz et al., 1996; Simon and Marcussen, 1999; Barrett, 2000; Murray, 2000;
Hughes and Waite, 2002; Simon, 2002; Liu and Umberson, 2008; Lindström, 2009; Chenet al., 2015; Lawrence et al., 2019).
Compared to studies on western countries, there are relatively few studies focusing onother regions. Some studies analyze the effects of marital status and household structureon the SWB for older people in East Asian countries (Brown, 2002; Chou et al., 2006;
Chen and Short, 2008; Raymo et al., 2008;

Chyi and Mao, 2012; Oshio, 2012; Zhou etal., 2018; Miao and Wu, 2019; Matsuura and Ma, 2021) and not focusing the older
populations（Lim and Raymo, 2016; Fu and Noguchi, 2016; Lee et al, 2016; Fu andNoguchi, 2018; Sato, 2019; Kim and Nam, 2021).
These previous studies delineate the following findings. Although research on therelationship between marital status and SWB, using various indicators, have been
accumulated and these studies focus on gender differences or specific age groups such as
young and old as well, few studies focus on specific countries without conducting atransnational analysis. The ones that do carry out such an analysis compare very few
countries and rarely does a transnational comparison include developing countries (Stack,
1990; Stack and Eshleman, 1998; Diener et al., 2000; Lee and Ono, 2012; Lee et al., 2016;
Mikucka, 2016; Hori and Kamo, 2019; Perelli-Harris et al., 2019; Matsuura and Ma,2021). Further, few studies focus on change in time (Liu and Umberson, 2008; Mikucka,
2016). In addition, few studies examine the gender difference of the effect of nevermarried on SWB depending on age, country, and era.
Mikucka (2016) is closely related to our research as we use the WVS-EVS to examine
4marital status on SWB. She posits that females are less likely to increase their life
satisfaction through marriage than males and further examines the differing effects ofmarriage according to countries. The differences between Mikucka and our study are as
follow: we use recent data from 2010 onwards; she does not focus on the age effect on
the gender differences of never married; we also use various kinds of SWB measures suchas happiness, financial satisfaction, and subjective health 2; further, we use propensity
scores matching for robustness check.
3． Hypothesis, Model, Data
3.1 Hypothesis
In this section, we explain the hypothesis, the model, and the data. At first, we present
hypotheses on how the gender difference of the utility and disutility from never married
depends on age and time, after having discussed utility and disutility from never married
differs according to gender.
The never-married have two advantages; (a1) they can spend their time according totheir own will and (b1) they can spend their money on themselves. However, althoughnever married, they have utility and disutility from living with their parents. These utility
and disutility depend on their age.
The never-married suffer four disadvantages; (a2) they cannot share household tasks
with their spouse and children, (b2) they cannot rely on their spouse and children
financially. Marriage can improve living standards by the economy of scale (Rogers,
1995; Joung et al., 1997), (c2) The psychological cost against the norm of marriage isdesirable and (d2) they cannot be supported by their spouse psychologically. In this regard,
Joung et al. (1997) analyze the effect of mutual support in marriage.
Such utility and disutility depend on gender, age, the stage of development of thecountries, and era. Generally, males contribute less to household chores than females.
This tendency is observed in developed countries as well. Bertland (2015) demonstrates
that the wife undertakes more household tasks than the husband, conforming to gendernorms, even if she earns more than him. It leads to the household burden being heavier

2 Mikucka (2016) use multi-level analysis considering country level’s and individual level’s error, while we use

clustered robust standard error at country level.

5for females than males. This implies that net utility from household chores for nevermarried ((a1) -(a2)) is lower for males than for females. However, it is rare for the wifeto earn more than the husband in the United States, as Berland (2015) shows, implyingthat the financial net utility from never married ((b1) -(b2)) is higher for males thanfemales. The gender difference in psychological disutility against the norm to get married(c2) depends on time and country. The disutility of never-married by (d2) for females islower than for males because females are more socially outgoing and tend to interact withthe community more. Kawachi and Berkman (2001) show that males are more likely torely for psychological support on their wives, while females are more likely to rely on
relatives, children, and friends for the same. Thus, the shock of losing a spouse is greaterin the case of males than females. These results indicate it is only financiallyadvantageous for males in terms of the negative effect of never being married on SWB,
which leads to the following hypothesis.
Hypothesis 1
Males face a greater negative effect on SWB of never being married than females.
However, the gender difference in the negative effects of never-married on financial
satisfaction is not as pronounced as in other aspects of SWB, such as life satisfaction,
happiness, and subjective health conditions.
The age-related effects of the gender difference of never-married on SWB can beconsidered as the following. As age increases, males’ disadvantage of never being
married, in terms of household tasks, is expected to increase. The reason is that as people
grow older, they must manage such tasks by themselves without their parents’ help.
Although they can rely on their parents in their youth to carry out household tasks, they
are faced with taking care of their parents as they age. After the death of the parents, theycannot be supported by them. In addition, the more profound the gender difference ofdisutility for never married by lack of interaction with others (d2), the older they are. The
reason is that males are likely to lose interaction with others as they retire or following
their parents’ deaths. Kawachi and Berkman (2001) insist that the psychological shock oflosing a spouse is greater in males than in females, because females are likely to form
more intimate bonds with their relatives and friends. The same may be applied to never6

married, which leads us to the following hypothesis.
Hypothesis 2
The gender difference in the negative effects of never-married on SWB increases withage and is more pronounced in older people.
The relation between the effect of never-married on SWB and economic development
is a point of consideration. The gender wage gap is likely to decrease with an increase in
the GDP per capita. As the economy develops and social security is enhanced, female’s
pensions in their old age also expand. Therefore, the gender difference in the net financial
utility ((b1) -(b2)) of being never married lessens, as the economy develops, leading us
to the following hypothesis.
Hypothesis 3
As the economy develops, the gender difference in the negative effects of never-marriedin old age becomes more pronounced.
In terms of time, two changes affect the relationship between never married and SWB
in recent years. First, the rise in the never-married rate reduced the feeling of being aminority within the same generation than before, leading to a decrease in thepsychological cost of never married. As Figure 3 shows, the percentage of never-married
in individuals aged between 50 to 54 is over 20% for males and over 10% for females inmany developed countries. For this reason, the middle-aged and older populations of thenever-married are not minorities. Moreover, in most countries, males are older thanfemales at the time of their first marriage. The age at the psychological cost of being nevermarried (c1) has increased. Second, the use of SNS makes it easier to interact with people
beyond their families, especially for younger people. It leads to a decrease in the disutilityof never-married (d1) for middle-aged males. In contrast, as there are few never-marriedolder people in many countries and they are less likely to interact with people beyond
family members, the gender difference of the negative impact of never-married on SWB
is more remarkable for the older people than middle-aged even now. This leads us to thefollowing hypothesis.
7Hypothesis 4
In recent years, the gender gap in the negative effects of never-married for the middleaged has narrowed, but the negative effects are still greater for males in their old age.
3.2 Model and Data
We examine four hypotheses using the following equations by the ordered probit modelwith cluster-robust standard error at the country level, controlling the correlation among
countries.

𝑌𝑌𝑖𝑖𝑖𝑖𝑖𝑖 = 𝛽𝛽10 + 𝛽𝛽11 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 + 𝛽𝛽12 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 + 𝛽𝛽13 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 × 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 + 𝛽𝛽15 𝐴𝐴𝐴𝐴𝐴𝐴 + 𝑋𝑋𝛽𝛽14 + 𝜀𝜀𝑖𝑖𝑖𝑖𝑖𝑖(1)

𝑌𝑌𝑖𝑖𝑖𝑖𝑖𝑖 = 𝛽𝛽20 + 𝛽𝛽21 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 + 𝛽𝛽22 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑒𝑒 + 𝛽𝛽23 𝐴𝐴𝐴𝐴𝐴𝐴 + 𝛽𝛽24 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 × 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 + 𝛽𝛽25 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 × 𝐴𝐴𝐴𝐴𝐴𝐴 +𝛽𝛽26 𝐴𝐴𝐴𝐴𝐴𝐴 × 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 + 𝛽𝛽27 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 × 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 × 𝐴𝐴𝐴𝐴𝐴𝐴 + 𝑋𝑋𝛽𝛽28 + 𝜀𝜀𝑖𝑖𝑖𝑖𝑖𝑖(2)

𝑌𝑌𝑖𝑖𝑖𝑖𝑖𝑖 is SWB of individual i, country c, and time t. We use life satisfaction, happiness,financial satisfaction, and subjective health status as dependent variables. We focus on
the coefficient 𝛽𝛽13 , which identifies gender difference of never-married on SWB. 𝛽𝛽13 <0 indicates that the negative effect of never-married is larger for males than for females.

Further, we examine whether gender differences in the impact of never-married on SWB
differ by age, country, and era, dividing the sample by them. The coefficient 𝛽𝛽27 identifies
whether the gender difference in the impact of never-married on SWB is different by theirage. We also divide the sample by country and era to verify whether the age effect isdifferent among countries and eras.
We use the WVS and EVS started in 1981. They comprehensively ask about subjective
well-being such as life satisfaction and happiness along with demographic variables.
These surveys are conducted once every several years and have been conducted seventimes until now.
First, it is possible to analyze long-term trends because the surveys have been
conducted seven times once every several years until now. Second, it is possible toconsider differences depending on the stage of economic development, since the survey
is conducted in many countries around the world. However, it has also a limitation that8the fixed effect model cannot remove unobserved time consistent effects, as it is not panel
data.
Table 1 shows descriptive statistics. Life satisfaction, happiness, financial satisfaction,and subjective health condition are measured at 10, 4, 10, and 5 levels for each. Thesevariables are denoted to refer to SWB positively, as the number is high. The mean valuesof all SWB’s measures are higher after 2010. The percentage of never-married is 24.1%
in all samples, which is almost the same as 25% after 2010. The average age is 42.9 years
in all samples, 44.9 years after 2010, and slightly higher after 2010. The percentage ofmen is 46.8% overall, which is almost the same as 46.4% after 2010.
4． Results
4. 1 Basic ModelIn this section, we examine the hypotheses. Table 2 shows the results of the genderdifferences in the effects of never-married on SWB. The interaction term “Male × NeverMarried” is negatively significant except in column (3). It means the negative effect ofnever-married on SWB is greater for males than females. Males are likely to feel moreunsatisfied, unhappy, and unhealthy being never married than females, though there is no
gender difference of never-married on financial satisfaction.
These results suggest that the negative impact of never-married on SWB is greater formales than for females. However, such negative effects may vary with age; the effects
and the gender difference are likely to be lesser for the younger generation than for theolder people.
We focus on the effect of age on the gender difference, dividing the sample by age.
The results are shown in Table 3. Columns (1) – (3) show the result for life satisfaction.
The coefficient of “Never Married” for life satisfaction is negatively significant for all
ages. It indicates that never married lowers life satisfaction in both genders. Theinteraction term “Male × Never Married” for life satisfaction is negatively significant forpeople aged between 40 and 60 and for those aged above 60, though it is not significant
for those aged below 40. It suggests that the negative effect of never-married grows larger
for males when they are more than 40 years old.
Columns (4) – (6) show the results for happiness. The coefficient of “Never Married”
and the interaction term “Male × Never Married” for happiness is negatively significant
9for all ages. It suggests that never-married lowers happiness and that the negative effectis larger for males than for females of all ages. Columns (7) – (9) tabulate the results forfinancial satisfaction. The coefficients of “Never Married” are negatively significant for
all ages. The interaction term “Never Married” is negatively significant for those aged
above 60 and insignificant for those aged between 40 and 60. In addition, it is positively
significant for those aged below 40. It indicates that never married lowers financial
satisfaction and the negative effect for males is greater than for females aged above 60
years and lesser for those aged below 40 years.
Columns (10) – (12) show the results for the subjective health condition. The
coefficients of “Never Married” are insignificant and “Male × Never Married” arenegatively significant for subjective health at all ages. It suggests males feel unhealthier
being never married than females. These results show that never-married affects certain
aspects of SWB and the negative effect is larger for males than females. Further, thenegative effect of never-married on SWB is profound in the case of males as they are
aged above 60 years.
Next, we examine whether the negative effect of never-married increases for males as
they get older. Table 4 shows the results. The interaction terms “Male × Never Married ×Age” are negatively significant in all columns. It suggests that the gender difference inthe negative effect for never married grow more profound with age.
Further, we examine whether the gender differences in the impact of never-marriedolder people on SWB depend on economic development, dividing the sample by the GDP
per capita. Columns (1) – (3) in Table 5 show the results for those aged between 40 andmore and less than 60 years old by the stage of economic development. The interaction
term “Male × Never Married” is negatively significant only for countries where the GDP
per capita is $30,000 or more. Columns (4) – (6) in Table 5 show the results for those
aged 60 and above by the stage of economic development. The interaction terms “Male× Never Married” are negatively significant for the countries where the GDP per capita
is between $5,000 and $30,000, and $30,000 and more, while it is not significant incountries with a GDP per capita of less than $5,000. These results indicate that indeveloping countries, there is no gender difference in the negative impact of nevermarried on SWB at any age, while the negative effect of never-married on SWB is greaterfor males than females aged 40 and up in countries with a GDP per capita of $30,000 or
10

more.
These results can be summarized as the following. First, the negative effect of nevermarried on SWB is greater for males than for females. Second, the gender difference inthe effect of never-married on SWB increases with an increase in age. Especially for thoseover 40 years old, the negative effect of never-married on SWB is more pronounced inmales than in females. Third, the negative effect of never-married on SWB is greater formales than females aged 40 and above in countries where the GDP per capita is more than
$30,000, while there is no gender difference in the negative effect on SWB at any age incountries with GDP per capita of less than $5,000.
4. 2 After 2010
In section 4.1, we examine four hypotheses using the full sample since 1981. Next, we
focus on the recent changes. The following are the three major changes that have occurredin recent years. First, the rapid economic growth of the developing countries has made it
possible for many services, which previously relied on women-centric domestic labor, tobe outsourced or mechanized. Second, the rise in the never-married rate has reduced thepressure on marriage by the increase in the number of individuals with diverse lifestyles.
Third, the Internet has activated the use of SNS, easing one’s interaction with people
beyond their families. Thus, focusing on recent data, we will examine whether the genderdifference in the effect of never-married on SWB and the age-related effect of the genderdifference has changed recently.
Table 6 shows these results. The coefficient of “Male × Never Married” in Column (1)is insignificant, which indicates that the gender difference in the negative effect of nevermarried is not observed after 2010. Column (2) - (4) show the coefficients of “Male ×
Never Married” is significant for those aged between 40 and 60, and 60 and over, but
insignificant for those aged below 40. These results show that even after 2010, thenegative effect of never married is greater for males than for females aged 40 and above.
Further, the difference between all periods and after 2010 is that after 2010, the coefficientof the interaction term changes from negative to positive for those aged below 40 years,
though it is insignificant. This result is consistent with the hypothesis presented by
Mikucka (2016) i.e., the benefits of marital measured by male life satisfaction havedeclined in recent years. However, we posit that the decline in marital benefits depends
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on age. Never married males are less satisfied with life than females aged 40 and up evenafter 2010. Column (5) shows the result of whether the gender difference in the effect ofnever-married on SWB changes with age. The interaction term “Age × Male × NeverMarried” is negatively significant. It means the same tendency is still observed even after
2010.
Finally, we divided the samples by economic development for those aged between 40
and 60 and above to verify whether the gender difference in the effect of never-married
on SWB varies with economic development. Table 7 tabulates the results by the GDP per
capita. For the middle-aged group, the gender differences of the negative effects of nevermarried on SWB are not observed at all stages of economic development, as columns (1)- (3) in Table 7 show. Columns (4) – (6) show the negative effect of never-married on
SWB as being greater for males than for females aged 60 and above only in countries
where the GDP is $30,000 or more, which indicates that the gender difference is stillobserved for the older people in developed countries after 2010.
4.3 Robustness Check
We estimate using an ordered probit model. However, the model can only examine
correlation without identifying the causal relation. Instead, for robustness check, we use
the propensity score matching to identify the causal relation, since transnational panel
data is not available.
The columns (1) - (4) of Tables 8-1 are the results for females, and columns (5) - (8)show the results of males using the full sample since 1981. The coefficients of theunmatched indicate the difference in the mean value of life satisfaction by marital status.
Focusing on all ages, columns (1) and (5) indicate that never married results in higher life
satisfaction for females and lower one for males, comparing the sample means for life
satisfaction. The coefficients of ATT are the results controlling for self-selection bias
through propensity score matching. The coefficients of never-married are significantly
negative for both genders, after controlling for self-selection bias. The negative effects ofnever-married are slightly greater in males than females.
Next, we divide the sample by age. As columns (2) – (4) and (6) – (8) show, coefficientsof ATT indicate never married decreases life satisfaction for both genders, and thenegative effect is much greater for males than females aged 60 and above. The
12

coefficients of never-married are negatively significant for both genders aged between 40
and 60 years. The negative effect is greater for males than for females, though thedifference is smaller for those aged 60 and up. For those aged below 40, the coefficientis negatively significant, though there is no gender difference in the effects. These results
indicate never married has negative effects on SWB and the gender difference in thenegative effect increases with an increase in age.
Further, we use the data set after 2010 to analyze recent trends. The coefficients ofnever married in ATT are insignificant for females and negatively significant for males,
as columns (1) and (5) of Tables 8-2 show. It means that females do not feel unsatisfied
with their life due to never married, while males feel unsatisfied even after 2010. Dividing
the sample by age, the coefficients of never married in ATT are negatively significant for
those aged between 40 and 60 years for females while these coefficients are negatively
significant at all ages for males. The gender difference is large for those aged below 40
years and 60 and above. The negative effect of never married is greater for males aged 60
and above even after 2010, though we cannot confirm that the gender difference in thenegative effects changes linearly by age.
5． Conclusion
We present the benefits and costs of never married and explore how it affects the benefits
and costs of SWB by gender. We also examine whether the negative effect of nevermarried, especially for the older people, is influenced by economic development and era.
Thus, presenting four hypotheses about the gender difference in the impact of nevermarried on SWB. Then, using the WVS-EVS data for the seven periods since 1981, we
examine the gender difference in the effects of never-married on certain key aspects of
SWB, such as life satisfaction and happiness from various perspectives such as age,economic development stage, and changes in the times.
As a result, we show the following. First, the negative effect of never-married on SWB
is greater for males than for females. Second, the gender difference in the effect of nevermarried on SWB becomes larger as they age. For those over 40 years of age, the effect oflowering SWB by never married is more pronounced in males than in females. Third, thegender difference in the negative effect of never-married on SWB for the older people isobserved in developed countries, while it is not observed in developing countries. Fourth,13since 2010, the negative effect of never married is greater for males than for females, only
for those aged above 60 years in countries with a GDP of $30,000 or more.
Our research has the following limitations. First, due to limitations of data availability,
we cannot deal with problems of endogeneity sufficiently, though we use propensity scorematching to conduct a robustness check. In other words, this study analyzes only thecorrelation between never married and SWB, while Stutzer and Frey (2006) examine thecausal relationship between marital status and SWB, using panel data. Second, we do notconsider differences in religion and culture, although we classify countries by the stageof their economic development. Future research is required to identify causal
relationships using long-term, transnational panel data including cultural differences.

Reference
Barrett, A. E. (2000). “Marital transitions and mental health,” Journal of Health and Social
Behaviour, 45, pp. 451–464.
Bertrand,M., Kamenica.E., and Pan,J. (2015). “Gender Identity and Relative Income within
Households,” The Quarterly Journal of Economics, 130(2), pp.571-614.
Brown, J.W., Liang, J., Krause, N., Akiyama, H., Sugisawa, H., and Fukaya, T. (2002).
“Transitions in living arrangements among elders in Japan: Does health make adifference? ″Journal of Gerontology: Social Sciences, 57, S.209−S.220.
Chen,F. and Short,S.E.(2008). ” Household Context and Subjective Well-Being Among theOldest Old in China,” Journal of Family Issues, 29(10), pp.1379-1403.
Chen,F., Mair,C.A., Bao,L., and Yang,Y.C.(2015).”Race/Ethnic Differentials in the Health“Consequences of Caring for Grandchildren for Grandparents,” The Journals ofGerontology, Series B: Psychological Sciences and Social Sciences, 70(5), pp.793-803.
Chou,K.L., Ho,A.H.Y., and Chi, F.(2006).”Living alone and depression in Chinese older adults,”
Aging and Mental Health, 10(6), pp.583-591.
Chyi, H. and Mao, S.(2012).”The Determinants of Happiness of China’s Elderly Population,”
Journal of Happiness Studies, 13, pp.167-185.
Clark, A. E., Diener, E., Georgellis, Y., & Lucas, R. E. (2008). “Lags and leads in life satisfaction:
A test of the baseline hypothesis,” Economic Journal, 118, F222–F243
Coombs, R.H.(1991). “Marital status and personal well-being: A literature review,” Family
Relations, 40(1), pp.97–102.
Diener, E., Gohm, C. L., Suh, E., & Oishi, S.(2000). “Similarity of the relations between marital
14

status and and subjective well-being across cultures,” Journal of Cross-Cultural
Psychology, 31, pp.419–436.
Fu, R., & Noguchi, H. (2016). “Does marriage make us healthier? Inter-country comparative
evidence from China, Japan, and Korea,” PLoS ONE.
Fu, R. and Noguchi,H.(2018). “Does the positive relationship between health and marriage reflect
protection or selection? Evidence from middle-aged and elderly Japanese,” Review ofHousehold Economics, 16, pp.1003–1016.
Hori, M., & Kamo, Y. (2018). “Gender differences in happiness: The effects of marriage, social
roles, and social support in East Asia,” Applied Research in Quality of Life, 13(4), ppp.839–
857.
Horwitz, A. V., White, H. R., & Howell-White, S. (1996). “Becoming married and mental health:A longitudinal study of a cohort of young adults,” Journal of Marriage and the Family, 58,
pp.895–907.
Hughes, M. E., & Waite, L. J. (2009). “Marital biography and health at mid-life,” Journal of
Health and Social Behavior, 50, pp.344–358.
Joung, I. M. A., Stronks, K., Van De Mheen, H., Van Poppel, F. W. A., van Der Meer, J. B. W.,& Machenbach, J. P. (1997). “The contribution of intermediary factors to marital status
differences in self-reported health,” Journal of Marriage and the Family, 59, 476-490.
Kaufman, G., & Taniguchi, H. (2010). “Marriage and happiness in Japan and the United States,”
International Journal of Sociology of the Family, 36, pp.25–48.
Kawachi,I. and Berkman,L.F. (2001). “Social Ties and Mental Health,” Journal of Urban Health,78(3), pp. 458-467.
Kim,A.J. and Nam, J.Y. (2021). “Effects of the Combination of Marital Status and Household
Type on Self-Rated Health among Korean Women,” Health Policy and Management, 31(3), pp.355-363.
Kohler, H., Behrman, J. R., & Skytthe, A. (2005). “Partner + children = happiness? The effectsof partnerships and fertility on well-being,” Population and Development Review, 31, pp.
407–445.
Klinenberg,E.(2012). Going Solo: The extraordinary rise and surprising appeal of living alone.
New York: Penguin Press.
Lawrence, E.M., Rogers, R.G., Zajacova,A., and Wadsworth,T.( 2019). “Marital Happiness,
Marital Status, Health, and Longevity,” Journal of Happiness Studies, 20(1), pp.1-23
Lee, K. S., & Ono, H. (2012). “Marriage, cohabitation, and happiness: A cross-national analysisof 27 countries,” Journal of Marriage and Family, 74, 953–972.
15

Lee, S.Y., Kim, S.J., Yoo,K.B. Lee,S.G, and Park, E.C.(2016).“Gender gap in self-rated health inSouth Korea compared with the United States,” International Journal of Clinical and
Health Psychology, 16, pp.11-20.
Lim,S. and Raymo,J.M.(2016). “Marriage and Women’s Health in Japan,” Journal of Marriage
and Family, 78, pp.780-796.
Lindström, M.(2009). “Marital status, social capital, material conditions and self-rated health: A
population-based study,” Health Policy, 93(2), pp.172-179.
Liu, H. and Umberson, D.J.(2008).“The Times They Are a Changin’: Marital Status and Health
Differentials from 1972 to 2003,”Journal of Health and Social Behavior, 49, pp. 239-253.
Matsuura,T. and Ma, X.(2021). “Living Arrangements and Subjective Well-being of the Elderlyin China and Japan,” Journal of Happiness Studies,Miao,J. and Wu, X.(2019).”Subjective wellbeing of Chinese elderly: a comparative analysis
among Hong Kong, Urban China, and Taiwan,” Aging and Society, 24, pp.1-22.
Mikucka, M. (2016).“The life satisfaction advantage of being married and gender specialization,”
Journal of Marriage and Family, 78, pp.759–779.
Musick,K. and Bumpass,L. (2012).“Reexamining the Case for Marriage: Union Formation andChanges in Well-being,” Journal of Marriage and Family, 74, pp.1-18.
Oshio,T.(2012).”Gender Differences in the Associations of Life Satisfaction with Family andSocial Relations Among the Japanese Elderly,” Journal of Cross Cultural Gerontology, 27,
pp.259-274.
Perelli-Harris,B., Hoherz.S.,Lappegård.T.,and Evans.A. (2019).“Mind the “Happiness” Gap: The
Relationship Between Cohabitation, Marriage, and Subjective Well-being in the United
Kingdom, Australia, Germany, and Norway,” Demography, 56, pp.1219-1246.
Raymo, J.M.(2015).”Living alone in Japan: Relationships with happiness and health,”
Demographic Research, 46, pp.1267- 1298.
Raymo, J.M., Kikuzawa,S., Liang,J., and Kobayashi,E.(2008).“Family structure and well-beingat older ages in Japan,” Journal of Population Research, 25(3), pp.379-400.
Rogers, R. G. (1995). “Marriage, sex, and mortality,” Journal of Marriage and the Family, 57(2),515–526.
Ross, C. E., Mirowsky, J., & Goldsteen, K. (1990). “The impact of the family on health: The
decade in review,” Journal of Marriage and the Family, 52, pp.1059–1078.
Sato,K. (2020). “Does Marriage Improve Subjective Health in Japan?” Japanese Economic
Review, 71(2), pp. 247-286
Simon, R. W. (2002). “Revisiting the relationships among gender, marital status, and mental
16

health,” American Journal of Sociology, 107, pp.1065–1096.
Simon, R. W., & Marcussen, K. (1999). “Marital transitions, marital beliefs and mental health,”
Journal of Health and Social Behaviour,40, pp.111–125.
Sonnenberg, C. M., Deeg, D. J. H., Tilburg, T.G.V., Vink, D., Stek, M. L., and Beekman, A T. F.
(2013). “Gender differences in the relation between depression and social support in later
life,” International Psychogeriatrics, 25(1), pp. 61-70.
Sonnenberg, C. M., Beekman, A. T. F., Deeg, D. J. H., and Tilburg, W. van.(2000).” Sex
differences in late-life depression,” Acta Psychiatrica Scandinavica, 101, pp.286-292
Stack,S .(1998). “Marriage, Family and Loneliness: A Cross-National Study,” Sociological
Perspectives, 41(2), pp.415-432.
Stack,S. and Eshleman,J.S.(1998). “Marital Status and Happiness: A 17-Nation Study,” Journalof Marriage and Family, 60(2), pp. 527-536.
Stutzer, A., & Frey, B. (2006). “Does marriage make people happy, or do happy people getmarried?” Journal of Socio-Economics, 35, pp.326–347.
Sun, W., Watanabe, M., Tanimoto, Y., Shibutani, T., Kono, R., Saito, M., Usuda, K.,and Kono,
K. (2007). “Factors associated with good self-rated health of nondisabled elderly living
alone in Japan: A cross-sectional study,” BMC Public Health,7, pp,297−305.
Verbakel, E. (2012). “Subjective well-being by partnership status and its dependence on thenormative climate,” European Journal of Population, 28, pp.205–232.
Waite, L. J., & Gallagher, M. (2000). The case for marriage: Why married people are happier,healthier, and better off financially. New York, NY: Broadway Books.
Williams, K. (2003). “Has the future of marriage arrived? A contemporary examination of gender,
marriage, and psychological well-being,” Journal of Health and Social Behavior, 44,
pp.470–487.
Yamada,K. and Teerawichitchainan,B.(2015).”Living Arrangements and Psychological WellBeing of the Older Adults After the Economic Transition in Vietnam,” The Journals ofGerontology, Series B: Psychological Sciences and Social Sciences, 70(6), pp.957-968.
Yeung,W.J. and Cheung.A.K.(2015).”Living alone: One-person households in Asia,”
Demographic Research, 32, pp.1099-1112.
Zhou,Z., Mao,F., Ma,J., Hao,S., Qian, Z.M., Elder, K., Turner, J.S., and Fang,Y.(2018).”A
Longitudinal Analysis of the Association Between Living Arrangements and Health Among
Older Adults in China,” Research on Aging, 40(1),pp.72-97.

17

Figure 1

Trends in the proportion of the population aged 65 and over in major countries

40
35
30
25
20
15
10
5
0

1950

1960
Japan

1970
Canada

1980

1990
US

2000

2010

France2020

Germany

2030

2040

Sweden2050
UK

Source: UN, World Population Prospects: The 2019 Revision

Figure 2-1

Trends in average first marriage age Females

36.0
34.0
32.0
30.0
28.0
26.0
24.0
22.0
20.0

198019901995200020012002200320042005200620072008200920102011201220132014201520162017
Germany

Italy

Spain

UkraineUnited Kingdom

United States

Source: UNECE Statistical Database18

SwedenFigure 2-2

Trends in average first marriage age Males

40.0
35.0
30.0
25.0
20.0

1980 1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Germany

Italy

Spain

SwedenUkraineUnited Kingdom

United States

Source: UNECE Statistical Database

Figure 3 Never-married rate of 30-34 years old and 50-54 years old in major countries
80.0
70.0
60.0
50.0
40.0
30.0
20.0
10.0
0.0

2019

2014

2015

2019

2015

2019

2011

2012

CanadaFranceJapan

NorwayKorea

SwedenUK

US30～34 (Male)

30～34 (Female)

50～54（Male)

50～54（Female)

Source: UN, Population Censuses' Datasets

Table 1 Descriptive Statistics

Obs

Mean

Std. dev.

Obs

Full

Mean

Std. dev.

After 2010

Life Satisfaction

637,783

6.825

2.367

146,770

7.049

2.214

Happiness

631,592

3.066

0.727

146,205

3.134

0.710

Financial Satisfaction

465,009

5.823

2.568

88,917

5.903

2.483

Subjective Health592,195

3.785

0.906

147,208

3.835

0.890

Never-Married

646,418

0.241

0.428

147,668

0.250

0.433

Age

639,775

42.887

16.892

147,163

44.93817.414Male

646,418

0.468

0.499

147,668

0.464

0.499

19

Table 2 Gender differences in the impact of Never-married on SWB
(1)

(2)

(3)

(4)

VARIABLES

Life

Happiness

Financial

HealthNever-Married-0.0629***-0.123***0.01070.00286(0.0124)(0.0146)(0.0133)(0.0134)-0.00748

0.00922-0.00374

0.110***(0.00867)(0.00997)(0.00934)(0.0137)-0.0307***-0.0772***

0.0111-0.0332**(0.00952)(0.0112)(0.0123)(0.0146)-0.00258***-0.00727***0.000553-0.0176***(0.000568)(0.000665)(0.000727)(0.000862)

0.259***

0.232***

0.501***

0.202***(0.0124)(0.0109)(0.0215)(0.00728)

Yes

Yes

Yes

Yes

483,227

345,259448,990

Male

Male × Never-Married

AgeIncomeEducation, Job, Country, Year486,321Observations
Clustered robust standard errors by countries in parentheses*** p<0.01, ** p<0.05, * p<0.1

20

Table 3 Gender difference in the effect of never-married on SWB: by age
(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

Life

Life

Life

Happiness

Happiness

Happiness

Financial

Financial

Financial

HealthHealthHealth

>=60

40-60

<40

>=60

40-60

<40

>=60

40-60

<40

>=60

40-60

<40

-0.0568**-0.102***-0.129***-0.132***-0.183***-0.179***-0.0690**-0.0738***-0.0524***0.0301-0.01410.00463(0.0268)(0.0159)(0.0161)(0.0354)(0.0176)(0.0183)(0.0335)(0.0171)(0.0151)(0.0286)(0.0168)(0.0142)0.0192*-0.0274***-0.0301***

0.0481***-0.0154-0.0203*0.0206-0.00257-0.0334***0.0852***0.0869***0.125***(0.0103)(0.00870)(0.0116)(0.0137)(0.0114)(0.0117)(0.0156)(0.00912)(0.0125)(0.0193)(0.0147)(0.0127)-0.125***-0.0568***-0.00232-0.155***-0.0765***-0.0454***-0.0932*

0.02600.0315**-0.102***-0.0720***-0.0331**(0.0405)(0.0193)(0.0120)(0.0406)(0.0228)(0.0127)(0.0514)(0.0226)(0.0137)(0.0315)(0.0218)(0.0136)0.00295***

0.00104-0.0126***-0.000316-0.00557***-0.0159***

0.0114***0.00342***-0.0107***-0.0146***-0.0175***-0.0186***(0.000857)(0.000688)(0.00106)(0.00107)(0.000845)(0.000950)(0.00157)(0.000977)(0.000907)(0.00142)(0.00112)(0.00109)0.226***

0.277***

0.257***

0.229***

0.247***

0.217***

0.485***

0.529***

0.496***

0.204***

0.214***

0.183***(0.0105)(0.0124)(0.0154)(0.0101)(0.0110)(0.0133)(0.0226)(0.0209)(0.0237)(0.00983)(0.00804)

Education, Job ,Country, Year

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Observations

93,663

163,830

228,828

93,114

162,937

227,176

54,662

114,521

176,076

85,429151,243

212,318

VARIABLES

Never-Married

Male

Male × Never-Married

AgeIncomeClustered robust standard errors by countries in parentheses*** p<0.01, ** p<0.05, * p<0.1

21(0.00806)

Table 4 Age effect of gender difference in the effect of never-married on SWB
(1)

(2)

(3)

(4)

VARIABLES

Life

Happiness

Financial

HealthNever-Married

0.00518-0.0542**0.187***-0.0106(0.0238)(0.0275)(0.0282)(0.0250)-0.119***-0.137***-0.0939***0.0492***(0.0243)(0.0238)(0.0253)(0.0186)

0.152***

0.0987***0.135***

0.105***(0.0279)(0.0285)(0.0285)(0.0316)-0.00273***-0.00774***0.000918-0.0178***(0.000657)(0.000740)(0.000897)(0.000981)-0.00225***-0.00246***-0.00571***0.000348(0.000523)(0.000644)(0.000724)(0.000672)

0.00227***

0.00299***0.00189***0.00123***(0.000444)(0.000454)(0.000529)(0.000460)-0.00492***-0.00419***-0.00336***-0.00395***(0.000680)(0.000734)(0.000820)(0.000808)

Yes

Yes

Yes

Yes

486,321483,227

345,259448,990

Male
Male × Never-MarriedAge
Age × Never-Married
Age × MaleAge × Male × Never-Married
Income, Education, Job, Country, Year
Observations

Clustered robust standard errors by countries in parentheses*** p<0.01, ** p<0.05, * p<0.1

22

Table 5 Gender differences in the impact of never-married on Life Satisfaction: by age and by GDP per capita(1)

VARIABLES

Never-Married
Male
Male × Never-Married
AgeIncomeEducation, Job, Country, Year
Observations

GDP>=30-0.123***(0.0231)-0.0656***(0.0169)-0.0867***(0.0293)0.00321**(0.00151)
0.293***(0.0242)Yes
40,731Clustered robust standard errors by countries in parentheses*** p<0.01, ** p<0.05, * p<0.1(2)
40=<Age<60

5=<GDP<30-0.106***(0.0305)-0.0374***(0.0117)-0.000674(0.0336)-0.000239(0.00107)
0.250***(0.0177)
Yes
60,676

(3)

(4)

GDP<5
-0.0727***(0.0255)
0.00467(0.0112)-0.0536(0.0412)0.00115(0.00102)
0.302***(0.0177)
Yes
62,423GDP>=30-0.0463(0.0458)-0.0179(0.0206)-0.181***(0.0555)
0.00256(0.00172)0.233***(0.0162)Yes
31,34023(5)Age>=60

5=<GDP<30-0.0223(0.0414)0.0401***(0.0128)-0.152**(0.0701)
0.00187(0.00124)
0.204***(0.0175)
Yes
37,405(6)

GDP<5-0.121**(0.0542)0.0450**(0.0189)
0.0691(0.0825)0.00178(0.00139)
0.303***(0.0164)
Yes
24,918

Table 6 Gender differences in the impact of never-married on SWB: after 2010
(1)
(2)
VARIABLES
full
Age>=60
Never-Married-0.0728***-0.0311(0.0161)(0.0314)
Male-0.0210**-0.00861(0.00953)(0.0145)
Male × Never-Married-0.0131-0.134***(0.0119)(0.0434)Age-0.00199***
0.00384***(0.000667)(0.00117)
Age × Never-Married(3)
40=<Age<60-0.102***(0.0223)-0.0487***(0.0115)-0.0545**(0.0274)-0.000467(0.000949)(4)
Age<40-0.121***(0.0191)-0.0314**(0.0136)0.00553(0.0154)-0.0113***(0.00115)

Yes
70,615

Yes
91,735Age × MaleAge × Male × Never-Married
Income, Education, Job, Country, Year
Observations

Clustered robust standard errors by countries in parentheses*** p<0.01, ** p<0.05, * p<0.1

Yes
206,134

Yes
43,784

24

(5)
full
-0.0360(0.0305)-0.113***(0.0288)
0.189***(0.0338)-0.00204**(0.000825)-0.00110(0.000701)
0.00185***(0.000559)-0.00565***(0.000856)
Yes
206,134

Table 7 Gender differences in the impact of never-married on SWB: by age and by GDP per capita
(1)
(2)
(3)

(4)

40=<Age<60

(5)

(6)

Age>=60

VARIABLES

GDP>=30

5=<GDP<30GDP<5GDP>=30

5=<GDP<30

GDP<5

Never-Married-0.142***-0.0995**-0.0643-0.04650.00142-0.149(0.0279)(0.0394)(0.0482)(0.0363)(0.0517)(0.108)-0.0933***-0.0308*-0.0366*-0.0565***

0.02100.0370(0.0194)(0.0178)(0.0192)(0.0198)(0.0194)(0.0341)-0.0706-0.0107-0.0629-0.175***-0.1120.126(0.0452)(0.0459)(0.0704)(0.0545)(0.0750)(0.133)0.00229-0.00302**

4.67e-05

0.00404*

0.00383**-0.000930(0.00204)(0.00134)(0.00167)(0.00231)(0.00156)(0.00233)

Yes

Yes

Yes

Yes

Yes

Yes

20,942
Observations
Clustered robust standard errors by countries in parentheses*** p<0.01, ** p<0.05, * p<0.1

30,64819,025

17,893

18,210

7,681

Male

Male × Never-Married

AgeIncome, Education, Job, Country, Year25

Table 8-1 Propensity Score Matching

Unmatched
ATT
N

full
(1)
0.090***(0.012)-0.173***(0.005)255268*** p<0.01, ** p<0.05, * p<0.1

Female>=60(2)0.169**(0.052)-0.126*(0.074)
48826

60-40
(3)
-0.025(0.031)-0.205***(0.046)
86160

Male
<40
(4)
0.009
(0.014)-0.178***(0.045)119727full(5)-0.048***(0.011)-0.196***(0.036)230902>=60(6)-0.053(0.058)-0.302***(0.080)43733

Table 8-2 Propensity Score Matching: After 2010

FemaleUnmatched
ATT
N

full
(1)
-0.001(0.017)-0.008(0.044)108977*** p<0.01, ** p<0.05, * p<0.1

>=60
(2)
0.115
(0.069)-0.091(0.096)
22514

60-40
(3)
-0.167***(0.040)-0.214***(0.060)
37519

60-40
(7)
-0.151***(0.031)-0.260***(0.045)
77304

<40
(8)
-0.023(0.014)-0.178***(0.010)109024

Male
<40
(4)
-0.047*(0.021)
0.017(0.067)
48225

26

full
(5)
-0.140***(0.016)-0.167***(0.049)
97110

>=60
(6)
-0.234**(0.073)-0.220**(0.106)
19787

60-40
(7)
-0.328***(0.040)-0.288***(0.061)
32655

<40
(8)
-0.095***(0.021)-0.172**(0.072)43489

中央大学経済研究所( INSTITUTE OF ECONOMIC RESEARCH, CHUO UNIVERSITY)
代表者 林 光洋 (Director: Mitsuhiro Hayashi)〒192-0393 東京都八王子市東中野 742-1(742-1 Higashi-nakano, Hachioji, Tokyo 192-0393 JAPAN)
TEL: 042-674-3271+81 42 674 3271
FAX: 042-674-3278+81 42 674 3278
E-mail: keizaiken-grp@g.chuo-u.ac.jp
URL: https://www.chuo-u.ac.jp/research/institutes/economic/