ISSN (Print) 0473-453X

Discussion Paper No. 1172

ISSN (Online) 2435-0982

RELATION BETWEEN RELATIVE INCOME
AND MARRIAGE IN JAPANRyo Mikami

April 2022

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

Relation between Relative income and Marriage inJapan ∗Ryo Mikami†
April 2022

Abstract
This study investigates the relationship of relative income on males’
marital behavior using individual data taken from the Japanese Employment Status Survey. The data show that relatively low-income
males among each reference group are more likely to marry when theirincome approaches the 50th percentile in his reference group’s income
distribution. However, if males’ earnings are above the 50th percentile
around, the inﬂuence of an increase in income becomes very small. The
ﬁndings show that the mean income of a regular-employment worker
within his own reference group is an important predictor of marital status, particularly for Japanese males in the bottom of national income
distribution.

Keywords: Marriage behavior, Income distribution, Identity
JEL classiﬁcation: D91, D31, J12∗
I thank Masaru Sasaki, Fumio Ohtake for their useful comments. I would like to thank
Editage (www.editage.jp) for English language editing. Based on the Statistics Act, thisresearch utilizes the micro data from the Anonymous Data of Employment Status Survey
from National Statistics Center Statistics. It is diﬀerent from that are provided andprocessed by the Ministry of Ministry of Public Aﬀairs.
This paper has been submitted to the ”Journal of Behavioral Economics and Finance” forrevision.
†
Graduate School of Economics, Osaka University, 1-7 Machikaneyama-cho, Toyonakashi, Osaka, 560-0043, Japan. E-mail: mikamiryomyself@gmail.com.

1

1

Introduction

Many developed countries are experiencing a decrease in their fertility rates.
In Japan, a drop in the birthrate is exacerbated due to people postponing
the timing of marriage. According to previous studies (e.g., Retherford etal., 2001, Fukuda 2020), late marriage is the main cause of the current low
birthrate in Japan because the rate of illegitimate childbirth is very low.
While the average number of births is increasing slightly for older married
women, owing mainly to progress in medical technology, the total fertility
rate (TFR) is overwhelmingly decreasing as a result of the trend of latemarriages. The postponement of marriage timing means women give birth
to fewer children because they have less time in which to do so.
This problem is viewed as urgent in many developed countries and, asa result, the decision by those in low-income groups on when to marry has
attracted much attention. Many previous studies about marriage are basedon traditional economic search models (e.g., Becker and Lewis, 1973; Becker,1981). In Japan, several studies have examined theoretical hypotheses using empirical analyses, with most focusing on the marriage rate of females.
Sasaki (2017) suggests that the income inequality among Japanese males
reduces the female marriage rate by decreasing the probability of meetingan attractive partner. However, the marital behavior of males is diﬀerent tothat of females, and remains unclear.
On the other hand, behavioral models on social prescription or the identity of gender have been applied to marital behavior to provide alternative
explanations to those of standard economic models of the marriage market,
especially in the United States (e.g., Akerlof and Kranton, 2000).
The purpose of this study is to investigate males’ marital behavior using
the concept of “relative income” (e.g., Watson and McLanahan, 2011) andextensive individual data taken from Japan’s Employment Status Survey. In
Japan, prior studies have found a positive correlation between an increase inindividual income and the probability of marriage for males. Here, we ﬁndthat those who earn a high income are not necessarily more likely to marrythan those who earn a low income in Japan. Why do these high-income
males not marry, and what are the reasons for low-income males deciding tomarry? Some researchers believe that the relative income within a person’s
own social reference group is more important than the absolute income interms of marital decisions (e.g., Easterlin, 1980, Akerlof and Kranton 2000).
In this context, relative income refers to the diﬀerence between the incomeof an individual and that of others with similar social characteristics.
Our results suggest that relative income is a good predictor of an individual’s marital status, especially for males in the low-income group. In
other words, where a person’s income falls relative to the mean for regularemployment workers within the local reference group is more important than
the absolute value of income or the median of the population. This result is2

similar to those found for the United States.
This study makes two contributions to the related literature. First, thisis the ﬁrst empirical study of relative income and marriage decisions basedon behavioral models and Japanese data. Second, the ﬁndings show that theincome distribution of the reference group is a primary factor in Japanesemales’ marriage decisions. Several empirical studies suggest that ﬁnancial
status aﬀects marital status. However, to the best of our knowledge, nostudies have focused on the relationship between relative income status andmarriage in the case of Japan.
First, we create reference groups based on social status, and then sortindividuals into these groups in order to estimate the parameters for eachgroup’s income distribution. Second, we calculate the reference group median income. Then, relative income is deﬁned as an individual’s income
relative to the group median income. We use this variable to infer an individual’s ﬁnancial status relative to others within their social group. Then, inthe main analysis, we estimate the eﬀect of this variable on marital status.
This paper proceeds as follows. Section 2 discusses the current situation
with regard to marriage in Japan. In Section 3, we review the literature on
the relationship between income and childbirth and marriage behavior. In
Section 4, we develop a simple theoretical framework based on the model of
Watson and Mclanahan (2011), which is based on the theoretical frameworkof relative status of Akerlof and Kranton (2000). In Section 5, we discuss thedata, and then describe the empirical method employed here in Section 6.
Section 7 presents the empirical results, which are then discussed in Section
8. Section 9 concludes the paper.

2

Japanese marriage and fertility

In general, the minimum TFR necessary to maintain the current populationlevel is around 2.09. The Japanese TFR has been lower than this value,and lower than all other developed countries, since 1973. At the same time,
the Japanese marriage rate has been decreasing. The National Fertility
Survey shows that Japanese people have a strong concern about getting
married, and believe that the main purpose of marriage is having children. 1
Since 1970, the average ideal number of children for a household has been
greater than 2.3, which exceeds the TFR required to maintain the current
population. However, the real value of the TFR has been less than 1.9,
and is decreasing. The survey also shows that 40 percent of males believethat ﬁnding a suitable partner is the biggest obstacle to a marriage decision.
In addition, 52 percent of households responded that the ﬁnancial cost ofchildren is the reason why they cannot have the ideal number of children.
1“The National Fertility Survey” (2015) by The National Institute of Population andSocial Security Research.

3

Figure 1: Proportion of unmarried males who want to get married.
Source : The National Fertility Survey (2015)
Thus, people’s ﬁnancial situation is an important factor in marriage decisions
and after marriage.
This problem is more serious for those in the low-income group, andwill become so for those in the high-income group who are planning on
having several children in the future. In Japan, the low marriage rate among
male non-regular employees has attracted much attention. Sasaki (2017)
suggests that an increase in the number of irregular employees has reduced
the number of “marriageable men” and the marriage rate, especially for
those who are less educated.
Figure 2 depicts the distribution of Japanese males aged 35 to 39 years
who currently work relative to the number of males who have ever beenmarried, by reference group income (percentiles). The vertical line indicatesthe median income of the reference group by education level. The ﬁgureshows that, for all education groups, the share of males who have beenmarried kinks around the vertical lines, and then increases gradually to theright of the line. Because education level and income are strongly positively
correlated, the median value of college graduate income at this age is muchhigher than that of high school graduates. However, we observe a similar
trend in all education groups.
The ﬁgure also indicates a possibility that the relative position of anindividuals in their reference groups, measured by the relative income ranking, aﬀects their marriage decisions. Although individuals may earn a highincome in absolute terms, they may not feel rich, and so might hesitate to
marry if they belong to a reference group in which the other members earna higher high income.

4

Table 1: Descriptive Statistics from The National Fertility Survey (2010)
The rate of males who think that
Average the ideal number of children“Marriage fund is the biggest obstacle”
for unmarried male
(Sample Size N = 2, 155)
(Sample Size N = 3, 164)
Age20-24
Age20-24
Middle-School
52%
Elementary
2.00
High-School
61%
High-School
2.08
Some College
60%
Some College
2.08
College+41%
College+
2.11
Age25–29
Middle-School
High-SchoolSome College
College+Age30–34
Middle-School
High-SchoolSome College
College+

46%
54%
61%
51%

Age25–29
ElementaryHigh-SchoolSome College
College+

2.00
1.96
2.09
2.09

58%
54%
47%
45%

Age30–34
ElementaryHigh-SchoolSome College
College+

1.88
1.93
1.95
1.90

ElementaryHigh−School

0.06
0.06

0.05

0.05

0.04

0.04
0.00

0.25

0.50

0.75

1.00

0.00

Some College

0.25

0.50

0.75

1.00

0.25

0.50

0.75

1.00

College+

0.060

0.055

0.055

0.050

0.050

0.045
0.045

0.040
0.040
0.00

0.25

0.50

0.75

1.00

0.00

Figure 2: The share of males who have ever been married, by reference group
income percentile5This study contributes to the growing body of literature on the link
between relative income and behavior, because it is the ﬁrst to focus on therelation between relative income and marriage decisions and to analyze

3

Literature

Several previous studies examine the relation between income and marriage,
and identify a positive relation between an increase in a male’s income andthe probability of getting married. Becker (1981) provides a classic economic
model of marriage that hinges on specialization in home production. The
beneﬁt of specialization might be particularly important to those in thelower income groups. However, for the same group, if a male’s income islower than that of a female at the bottom of the distribution, the gains from
specialization decrease and marriage becomes less likely. Based on their
qualitative work, Gibson-Davis et al. (2005) suggest that couples’ ﬁnancial
status aﬀects their marriage decisions even when they have children. Edin
(2000) also points to the importance of ﬁnancial stability as a precursor to
marriage.
However, few studies have examined male marriage activity in Japan,
with most of them focusing on the income distribution of females. Thosestudies are based on the marriage search model and focuse on the opposite
gender’s income distribution. Based on data taken from the Employment
Status Survey, Sasaki (2017) suggests that the poor employment environment decreases the number of attractive partners. However, there has beena slight increase in the proportion of single males as a result of the decreasein the female regular employment rate. Sasaki (2017) further shows that anincrease in the regular employment rate of males has a positive eﬀect on themarriage probability of both genders in Japan.
Studies on the relationship between marriage and ﬁnancial norms are
not new. Easterlin (1980) suggests that a person’s own standard of living isrelated to marriage and childbearing. Similarly, Akerlof and Kranton (2000)
propose a theoretical model to identity decisions based on social characteristics. Akerlof and Kranton (2000) suggest that people’s characteristics andthe social groups to which they belong aﬀect their personal identity anddecisions.
Bertrand et al. (2015) explain a strange phenomenon observed in the
United States, where the distribution of the share of wife’s income exhibitsa sharp drop when her income exceeds that of her husband. They analyze
this discontinuity pattern using the ﬁndings of Akerlof and Kranton (2000)
and explain it in terms of a US-speciﬁc behavioral prescription of genderthat “a man should earn more than his wife.”
Watson and Mclanahan (2011) test Akerlof and Kranton’s (2000) theoretical framework using an empirical approach. They show that the ratio of6

fully employed males’ median income to own income in each local referencegroup explains a man’s marital status successfully in the case of the UnitedStates. They also show that a 10 percent higher reference group medianincome is associated while a 2 percent reduction in the rate of marriage.
Several studies examine relative income and childbirth in Japan. Matsuura (2011) suggests that an increase in income increases the number ofbirths only in those households in which the household income is lower than
the average income of their reference group (who have the same social characteristics). Matsuura (2011) also conﬁrms diﬀerences in income elasticity
according to the educational investment by each reference group. Therefore, for relatively rich individuals, an increase in income level increases
their educational investment for children they already have. However, having a greater number of children is eﬀective for those with a relatively low
income. Although prior studies have shown that income level is an important factor aﬀecting individuals’ decision-making, their relative income levelis more important and, in Japan, aﬀects decisions related to numbers ofbirths. This study investigates the eﬀect of relative income on marriage
decisions, which precede decisions on how many children to have.
This study adopts the empirical method of Watson and Mclanahan(2011) to estimate the eﬀect of relative income on marriage using Japanesedata. We investigate whether low-income males are aﬀected by the median income of their own reference groups, deﬁned for the same gender, andwhether the reference group median is a good predictor of marriage decisions. To the best of our knowledge, this is the ﬁrst study to investigate theeﬀect of relative income on marital status in Japan.

4

Model

The proposed model is based on that of Watson and Mclanahan (2011),
which is based on the theoretical framework of Akerlof and Kranton (2000).
Akerlof and Kranton (2000) suggest that there is a diﬀerence in social prescriptions by group. An individual’s identity is created in relation to reality
and the social prescriptions of the group to which he/she belongs. This, inturn, aﬀects people’s decision-making.
Watson and Mclanahan (2011) construct a model in which there are
diﬀerences in the income level thresholds that people consider necessary
for marriage, as per their social characteristics. Suppose a locality has anequal number of men and women in the marriage market. Each person isendowed with income drawn from the same distribution. Suppose further
that the desirability of men and women is represented by their income Yi .
In the matching process, we assume that men and women are matched by
the levels of their income, such that within each couple, the man and thewoman have equal levels of income. The men decide whether or not to7marry. The value of marrying is determined by background characteristics(e.g., education, living place, income), which, in turn, aﬀect the ﬁnancial
returns and personal returns to marriage.
Following the model in Akerlof and Kranton (2000), I describe “married people” as a group c in a set of social categories C with which men
and women may choose to identify. The prescriptions P describe the ideal
characteristics and behavior for each category. For example, although individuals may earn a high income in absolute terms, the gap between own
income and prescription might become large if they belong to the referencegroup c such that highly educated, old age, living in an urban area. Then,they may not feel rich and so might hesitate to marry. And we assume thatthe category unmarried people is assumed to have no set of prescriptions
and the ﬁnancial cost of not marrying is much lower than that for marriage.
An individual’s self-image, represented by Ii , depends on the match between his or her behavior and characteristics and the prescription of ideals
for his or her category. This simple model focuses on the prescription thatmarried people have a certain minimum level of income. It also includes arandom error term ϵi with zero mean that aﬀects an individual’s self-image
for any category. Thus, an individual’s utility is described by
Ui = Ui (Yi , Ii )where

Ii = Ii (Yi , ci , P, ϵi )

∂Ui
∂Ui
> 0,
> 0,
∂Yi
∂Ii

(1)

(2)

That is, in general, an individual’s utility depends on his income and selfimage. Self-image, in turn, is a function of the interactions between an
individual’s income, the category with which he identiﬁes, the prescriptions
for that category, and a random error term. Suppose that the ﬁnancialprescription P for a married person is at least Yideal in a given referencegroup. Then, the identity payoﬀ for a married person isIi = Imarry − θ{max(0, 1 −YiYideal)} + ϵmarry ,(3)

where θ is a positive scalar describing the identity loss associated with falling
below the “marriage ideal.” The identity payoﬀ of not marrying is
Ii = Inot−marry + ϵnot−marry ,(4)

and assumes Inot−marry < Imarry , In other words, on average, a marriedperson who meets the necessary ideal has a higher self-image than a similar
person who is not married. In making the decision on whether to marry, anindividual compares the utility of marriage and being unmarried. A person

8will marry when the following condition holds:
Ii = (Imarry − Inot−marry ) − θ{max(0, 1 −YiYideal)} > ϵnot−marry − ϵmarry ,(5)

The gains to self-image through marriage tend to increase with the averagegain in self-image from marriage and the individual’s income. The gains
decrease with a higher “marriage ideal” and a higher penalty θ for deviatingfrom the norm. This framework provides some simple comparative statics.
The gain to marriage is increasing in Yi for Yi < Yideal :
θ
∂Ui
=
,
∂Yi
∂Yi

(6)

Similarly, an increase in the marriage ideal Yideal is associated with a Yi
decrease in the gain to marriage for low values of Yi :θ∂Ui
= − 2,∂Yideal∂Yi(7)

but there is no change in the gain for high values of Yi :∂Yi
= 0,
∂Yideal(8)

The model assumes that Yideal is the ideal income required for marriage.
However, the real level of income people perceive to be required for marriage
is unobservable.

5

Empirical method

As noted above, the main analysis assumes that a man sets the medianincome of his reference group to the minimum income required for marriage.
According to the theoretical model, the ratio of a male’s own income to themarriage ideal should aﬀect the marriage decision, but only for those below
the ideal. This study uses these two values to estimate the probabilityof whether a male is married. In order to interpret the eﬀect simply, thefollowing regression is used with a linear probability model. The baseline
speciﬁcation is as follows:Yi

M arriedieact = β0 + β1 · underi ·+ β2 · (1 − underi ) ·YiŶeactŶeact+β3 · under3 + β4 · X3 + σy + ϵieact ,(9)Here, M arriedieact indicates whether individual i in education group e, agegroup a, city group c, and year t has ever married, and Yi denotes individual9income. Then, Ŷeact is the ideal income for marriage in each category and isconsidered the median income of the reference group. In this model, Ŷeact
is a proxy variable for Ŷideal · underi as an indicator of when i’s income isbelow that of the reference group median, and takes the value one whenŶideal − Yi ≥ 0. Furthermore, Yi is the rate of i’s income to the referenceŶeactgroup median, representing the relative income scale, and Xi is a vector ofindividual characteristics including age, education, and living place. Lastly,
α and σy represent an intercept and a year dummy, respectively.
In this analysis, β1 is the coeﬃcient of interest and shows the eﬀectof relative income for those below the median on the probability of being
married, and β2 represents the eﬀect of relative income for those above themedian on the probability of being married. The parameters are a reduced
form of θ from the theoretical model described in Section 4. Based on thetheoretical framework and the ﬁndings of previous studies, we expect that
β1 will be positive and that β2 will be close to zero. This would mean thatmales are more likely to marry when their income approaches the referencegroup’s median income. As noted earlier, males consider the median income
as the target income to succeed in marriage.

6

Data

The data are micro data taken from the Employment Status Survey covering the period 1992–2002. The Japanese government surveyed people’s
employment status, including individual characteristics, every ﬁve years.
The survey employs the two-stage stratiﬁed extraction method, and is not a
census. This study uses a sample of males aged 20 to 39 years who currently
work because this period is generally believed to be when most people getmarried in Japan.
The advantage of these data is the large sample size, with close to 300,000
observations. The large sample size allows the data to be divided into reference groups with suﬃciently large dimensions of characteristics. In addition,
the survey provides detailed data on employment status, such as the company size, length of service, and system of employment, as well as data on
the individuals, including educational background, age, whether living in
one of the three biggest metropolitan areas, and so on.
However, there are two limitations of the data. First, the survey is
not a census of repeated cross-sectional data. Therefore, we do not know
the actual income level at the time of the marriage decision, and cannot
judge the timing of the marriage decision. This study analyzes the eﬀect ofrelative income on the probability of a male getting married. The second
limitation is that the income data are represented as interval data. Thisis problematic, given that we are concerned with how much someone earns
compared with similar individuals in order to determine the eﬀect of relative10

income. Therefore, we know only a range of income for each individual,
rather than an accurate income.
Table 2: Descriptive Statistics for income
Reference Group Median
Fraction Under Median
1992
1997
2002
1992 1997
2002
Age20–24
ElementaryHigh-schoolSome college
College+All

246.692248.749
231.684249.391244.129247.601
272.043248.85225.886248.595

221.753
223.318223.396223.838
223.076

0.594
0.521
0.564
0.532
0.556

0.538
0.542
0.511
0.499
0.477

0.61
0.579
0.57
0.513
0.554

Age25–29
ElementaryHigh-schoolSome college
College+All306.657
342.815344.816349.129335.854308.9
344.89345.867
351.007337.666

272.958
308.526
309.587347.712309.695

0.573
0.553
0.559
0.529
0.393

0.493
0.547
0.528
0.516
0.338

0.568
0.537
0.526
0.553
0.49

Age30–34
ElementaryHigh-schoolSome college
College+All

312.637
397.321399.312449.137389.602348.878400.86446.316514.696427.687

311.031394.376396.926449.375
387.927

0.505
0.603
0.512
0.576
0.52

0.565
0.49
0.54
0.534
0.411

0.484
0.632
0.574
0.569
0.547

Age35–40
ElementaryHigh-schoolSome college
College+All392.501505.873511.551519.815482.435

396.601510.15515.586592.142503.62349.505447.323494.948596.715472.123

0.707
0.687
0.618
0.461
0.638

0.627
0.631
0.519
0.552
0.525

0.587
0.568
0.611
0.571
0.582

To mitigate the second limitation, we estimate the parameters for theincome distribution using an interval regression, and use the expected values of the estimated parameters to denote income. An interval regression isa parametric statistical method that supposes a distribution for data. We
assume that the Japanese income distribution follows a log-logistic distribution, in general.2 in general. The goodness of ﬁt test suggests a log-logistic
2

Several studies examine functional forms for the income distribution in Japan. Atoda
et al. (1988) and Tachibanaki et al. (1997) estimate the income distribution in Japan,

11

Table 3: Descriptive Statistics
Reference Group Median
Total
1992
N = 292900 n = 106756

1997
n = 101677

2002
n = 85467

Ever married

0.51

0.53

0.5

0.49

Live in 3 metropolitan

0.34

0.34

0.36

0.32

Age20–24
Age25–29
Age30–34
Age35–39

0.2
0.26
0.26
0.28

0.21
0.21
0.26
0.29

0.21
0.21
0.25
0.26

0.17
0.17
0.29
0.27

Elementary

0.08

0.09

0.07

0.07

High-school

0.51

0.52

0.51

0.49

Some college

0.11

0.09

0.11

0.14

College+

0.3

0.29

0.31

0.3

Employed full-time

0.84

0.85

0.87

0.8

12

Figure 3: the proportion of the sample who are married
distribution is a signiﬁcantly good ﬁt, and the regression results show thateach parameter is signiﬁcant. Thus, we use the estimated values to calculate
the expected value of each individual’s income. Using these values, we calculate the median income for each social group. Table 2 shows the descriptive
statistics. Table 2 shows that there were no major changes to income during
the sample period.
Then, we regard these medians as the levels of ideals people are concerned
about, and deﬁne the ratio of individual income to the group median as theindividual’s relative income. Lastly, I exclude the bottom and top intervalsof the income range samples, which account for less than one percent of thetotal. Figure 3 shows the proportion of the sample who are married samples
after dividing the samples by income percentile category in the referencegroup. From Figure 3, as in the case of absolute income, an increase inrelative income has a positive correlation with marriage probability.

7

Results

The results for the overall sample are shown in Table 4. Column 1 shows thatthe log of absolute income has a positive eﬀect on marriage probability at a 1
percent level of signiﬁcance. Furthermore, the magnitude is relatively large.
These results are consistent with those of previous studies (e.g., Sasaki, 2017;
Watson and Mclanahan, 2011).
and suggest that it follows a log-logistic or Singh–Maddala distribution. Nishino(2012) also propose the log-logistic distribution.

13

Column 2 indicates that even though the log of own income is added as
an explanatory variable, the eﬀect of improving one’s position within thegroup on marriage probability is positive and signiﬁcant. This result is thatthe same as that of the simple case in which the value one is substitute for all
values underi in Eq (9). The marginal eﬀect of a man becoming relatively
richer within the reference group on his marriage probability does not change
and, thus, is a linear probability model. The coeﬃcients are also positiveand signiﬁcant at the one percent level. This means that an increase inrelative income raises the probability of getting married in this simple linear
model. Additional income raises the probability of being married by 4.9
percentage points, reﬂecting the relative increase in wealth. This coeﬃcientalso shows how much the marriage probability rises when the median incomeof a person’s group declines, even if his income is constant.
Column 3 displays the estimation results for the equation, controlling
for the logarithm of own income, that includes only β3 , denoting whether
the income exceeds the median value of the group. Because β3 is a negativevalue at the one percent level of signiﬁcance, a man is 2.9 percentage points
less likely to be married if his income falls below the reference group median.
Columns 4 and 5 show main results from the speciﬁcation in Eq (9) discussed in the previous section. In addition, Column 4 shows the estimation
results for subsamples of neighborhoods around a cut-oﬀ point within ﬁve
percent bandwidths. Then, all coeﬃcients are consistent with the implications of the theoretical model in Eq (6) and Eq (7). Moreover, β1 is positiveat the 10 percent signiﬁcance level and β2 is statistically suﬃciently smaller
than β1 and is not statistically signiﬁcant. Column 5, taking advantage ofthe huge sample size, contains a large dummy matrix controlling for absoluteincome ranks. Column 5 shows a slightly better ﬁt. The adjusted R-squared
is slightly larger than that of the other models. Thus, we treat this modelas the baseline model.
The ﬁndings are consistent with those of the theoretical model, with
β1 positive at the one percent level of signiﬁcance. Using this model, we
test the null hypotheses β1 = β2 = 0 and β1 = β2 , both of which aresigniﬁcantly rejected by the F-test at one percent signiﬁcance. Therefore, it
can be considered that the eﬀect of an increase in relative income changes
across percentile points. From this result, in the Japanese data, the medianvalue of income is an important index for a male’s marriage probability.
Furthermore, people tend not to marry unless they have suﬃcient income
relative to their reference group.
These results suggest that the median income of each reference groupis not appropriate as a proxy variable for the threshold income level forthe marriage decision. To ﬁnd a more appropriate proxy, we use various
percentile points instead of the median as Ŷeact , as discussed in detail later.3
3

Similarly to Watson and Mclanahan(2011), we use the median income of full-time

14

Table 4: The impact of relative income
all
(1)

(2)

(4)

(5)

Relative income
if under Ŷeact : β1

0.092∗(0.049)0.118∗∗∗(0.013)

Ratio income/ideal
if over Ŷeact : β2

0.056
(0.088)0.023∗∗∗(0.006)−0.029∗∗∗(0.002)−0.017(0.096)−0.079∗∗∗(0.011)0.231∗∗∗(0.003)0.541∗∗∗(0.029)if under Ŷeact : β3
0.048∗∗∗(0.005)

Ratio income/ideal

Log of income(3)0.257∗∗∗(0.002)0.215∗∗∗(0.005)✓

Income dummyN
Adjusted R2

293,900
0.322

293,900
0.322

293,900
0.322

30,4860.325

293,900
0.328

Notes: ∗∗∗ ,∗∗ and ∗ indicate signiﬁcance at the 1, 5 and 10 percent level.

15

Table 5: Estimation using poor subsamples (bottom of 50% in national
distribution)
poor
(1)

(2)

(4)

(5)

Ratio income/ideal
if under Ŷeact : β1

0.120∗∗∗(0.017)0.194∗∗∗(0.017)

Ratio income/ideal
if over Ŷeact : β2

0.055∗∗∗(0.014)0.041∗∗∗(0.014)−0.014∗∗∗(0.003)−0.071∗∗∗(0.017)−0.139∗∗∗(0.018)0.159∗∗∗(0.004)0.091∗∗∗(0.010)if under Ŷeact : β3
0.082∗∗∗(0.011)

Ratio income/ideal

Log of income(3)0.168∗∗∗(0.003)0.112∗∗∗(0.008)✓

Income dummyN
Adjusted R2

158,6070.223

158,6070.224

158,6070.224

158,6070.224

158,607
0.228

Notes: ∗∗∗ ,∗∗ and ∗ indicate signiﬁcance at the 1, 5 and 10 percent level.

16

Table 6: Estimation using rich subsamples (top of 50% in national distribution)
rich
(1)

(2)

(4)

(5)

Ratio income/ideal
if under Ŷeact : β1

−0.116∗∗∗(0.022)0.038∗(0.022)

Ratio income/ideal
if over Ŷeact : β2−0.154∗∗∗(0.010)0.012(0.009)0.004(0.004)−0.019(0.017)−0.007(0.018)0.320∗∗∗(0.006)

0.522∗∗∗(0.015)

if under Ŷeact : β3−0.148∗∗∗(0.009)Ratio income/ideal

Log of income(3)0.316∗∗∗(0.005)0.501∗∗∗(0.013)✓

Income dummyN
Adjusted R2

135,2930.151

135,293
0.153

135,2930.151

135,293
0.153

135,293
0.152

Notes: ∗∗∗ ,∗∗ and ∗ indicate signiﬁcance at the 1, 5 and 10 percent level.

17

Next, we analyze the impact of relative income on the lowest income
class, that is, those who are in the bottom 50 percent of the national income
distribution. In the same way as above, Column 1 of Table 4 shows therelation between the logarithm of absolute income and marital status. An
additional logarithm of absolute income raises the probability of being married by 16.8 percentage points. Column 2 suggests that the marginal eﬀect
of an increase in income is decomposed into a direct eﬀect of increasing log
of income (11.1 percent) and the eﬀect of becoming relatively richer within
the reference group (8.2 percent). Column 3 suggests that a man is lesslikely to be married by 1.4 percentage points if his income falls below thereference group median.
In the main estimation equation, Column 4 is consistent with the implications from the theoretical model. An increase in the ratio of a man’s
income to his reference group’s median income signiﬁcantly raise the probability of marriage below the group median, but not above it.
Column 5 use a large dummy matrix to control for absolute income
ranks.
This is the baseline model. Using this model, we test the null hypotheses
β1 = β2 = 0 and β1 = β2 , both of which are rejected by the F-test at theone percent level of signiﬁcance. We ﬁnd that there is a statistically largechange between β1 and β2 .
The results for the higher income group (i.e., the top 50 percent of thenational income distribution) is shown in Table 5. The results are similar
to the previous results.

8Discussion

Our results imply that the median income of each reference group seems
to be appropriate as a Yideal proxy variable for the threshold income level
for marriage decisions nationally, as in Watson and Mclanahan (2011). But
why does the median make sense? One possibility is the following. In these
data, the value of median income is similar to the earnings by the regular
employed. As mentioned earlier, an increase in young males’ non-regular employment rates has been widely discussed in Japan. Some previous studies(e.g., Sasaki 2016, 2017) suggests that an increase in young male non-regular
employment rates is a cause of the rising single rates in Japan. Furthermore,regular employment is an important social signal for Japanese men, in general, to be considered an independent members of society. Thus, Japaneseyoung males may be concerned about regular employees’ income levels as areference point and part of their marriage decisions.

employees in the reference groups as a prescription. In this case, the coeﬃcient values
are nearly the same.

18

To test this hypothesis and to ﬁnd an accurate proxy value, we use
various percentile points instead of the median, as in Table 7. First, Columns
1 and 2 show the results of a placebo test when the reference percentile
points Yideal are randomly generated by group. In that case, no coeﬃcientsare signiﬁcant values or very close to zero, have no meaning, and the modelﬁts are not good.
On the other hand, Column 3 and 4 use the mean for regular employment worker’s income as Ŷeact instead of the median. For low-income males,
the diﬀerences between the median value and mean income for regular employment workers is only 1,500–2,000 US dollars,4 on average. However, thecoeﬃcients are very sensitive to change in this range. We ﬁnd a better ﬁt
and a clearer interpretation of the theoretical model in Column 3 comparedwith the previous results. β1 is signiﬁcant and large relative to β2 , and β3
is negative and large, all at the one percent signiﬁcance level. From thisresult, the income level that low-income males set for marriage may be close
to the mean earnings for regular employment.5 In addition, they tend not
to marry unless they have suﬃcient income, based on the reference group.
Furthermore, Column 4 shows the results for the overall sample. The
results are similar to those of the low-income group. Does this answer thequestion as to why high-income people not marry? In general, it is known
that income follows a distribution in which a longer right tail means a higher
upper limit of income. Thus, the mean and median values diverge in reference groups with a high upper limit of income because, similarly to educationlevel, a few very high values can raise the group mean.6 Therefore, richer
people tend to hesitate to get married because there are more rich people intheir social group.
Although we have discussed males’ postponement of marriage timing,based on a males’ marital decision model, it is natural that females’ decisions
and action will also have an eﬀect. A theory states that females tend to
marry males whose income exceeds that of the average regular employment
worker. Relatively high-income males may be more attractive and selectedby females. However, we cannot determine whether the result is driven fromthe male or female side. To take this into partial consideration, we estimate
the parameters while controlling for female income information.
Columns 5 and 6 consider the mean income of a working female in thesame marriage market as Yeact , which partially solves the problem. Many
studies propose that females who wish to have children after marriage require a partner with suﬃcient economic status because pregnancy and childcare temporarily decrease their supply of labor. If this hypothesis is correct, income levels of females who belong to the same marriage market are
4

This is equal to approximately 200,000 Japanese yen.
This value is close to the median value of the own group.
6
Matsuura (2011) shows there are diﬀerences in the income elasticities of the educational investment for each group, deﬁned by parents’ educational level in Japan.
5

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Table 7: Estimation using various percentile points as Ŷeact
Marrytype
(1)

(2)

(3)

(4)

(5)

(6)

Ratio income/ideal
if under Ŷeact : β1

0.015∗∗(0.006)0.005(0.005)0.331∗∗∗(0.025)0.165∗∗∗(0.018)0.218∗∗∗(0.009)0.029∗∗∗(0.006)

Ratio income/ideal
if over Ŷeact : β20.001
(0.004)0.0004(0.001)−0.005(0.028)0.025∗∗∗(0.009)0.242∗∗∗(0.010)−0.014∗∗∗(0.003)

Under Ŷeact : β3

−0.008(0.007)−0.002(0.004)−0.350∗∗∗(0.029)−0.145∗∗∗(0.014)0.064∗∗∗(0.013)−0.055∗∗∗(0.007)N
Adjusted R2

158,607
0.227

293,900
0.327

158,607
0.228

293,900
0.328

158,6070.231

293,900
0.328

Notes: ∗∗∗ ,∗∗ and ∗ indicate signiﬁcance at the 1, 5 and 10 percent level.
(1) use Ŷeact generated from uniform random distribution by group. (2) use poor
subsamples and Ŷeact generated from uniform random distribution. (3) use the mean
for regular employment worker’s income of as Ŷeact .(4) use poor subsamples and themean for regular employment worker’s income (5) use the mean income of a working
female in the same marriage market as Ŷeact .

20

very important to the males’ marriage situation, and the tendency of estimated parameters should be similar to the main result. However, there isno diﬀerence between β1 and β2 , which is inconsistent with the theoretical
implication. This result implies that female income is not suitable as a reference point. From these results, we conclude that Japanese males, especially
low-income males, are concerned about other males’ income distribution,and so hesitate to get married.

9

Conclusion

In this research, we conduct an empirical analysis of relative income andmarriage using Japanese data. The results show that in Japan, as in Watson
and McLanahan (2011), among others, an increase in the relative income
level has a positive eﬀect on males’ marital status. For males in the lower
income group (and in a group of people with the same social characteristics
as his own), an increase in income greatly increases the probability of getting
married. However, this eﬀect drops signiﬁcantly after a threshold value. The
above result is consistent with the implications from Akerlof and Kranton’s(2000) theoretical model on behavior selection. Furthermore, in Japan, thekink point at which the eﬀect of relative income changes sharply is aroundthe mean income of regular employment within each reference group.
From the above results, we conclude that Japanese males refer to otherpeople’s income when making a marriage decision. This behavior is particularly evident in the low-income group. For Japanese males, the income
levels of the regular employed within the same social status are the reference points for marriage decisions. This conclusion is consistent with thoseof preceding studies on people’s behavior.
In addition, the reference income level people consider to be necessary
for marriage diﬀers for each reference group. This leads to the hypothesisthat people who have high social status and high income tend to be morereluctant to get married because there are more rich people around them.
These results explain today’s Japanese late marriage trend very well.

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