Empirical analysis of the effects of increasing wage
inequalities on marriage behaviors in Japan
Shoichi Sasaki

February 2017
Discussion Paper No.1705

GRADUATE SCHOOL OF ECONOMICS
KOBE UNIVERSITYROKKO, KOBE, JAPANEmpirical analysis of the effects of increasing wage inequalities on marriage
behaviors in Japan
Shoichi Sasaki
Graduate School of Economics, Kobe University,
Research Fellow, Graduate School of Economics,
Email: shoichi_sasaki@people.kobe-u.ac.jp
Abstract
This study examines the effects of inequalities in the lower, rather than the upper, tail ofwage distribution due to a declining labor market on marriage behaviors based on
gender in Japan. I apply a median-preserving spread to a marriage search model andthen empirically analyze the theoretical hypothesis for cross-gender marriage behaviors
using extensive individual Japanese data from the Employment Status Survey. The
theoretical and empirical results show that gender wage inequalities in the lower andupper tails have positive and statistically significant effects on increasing the probabilityof unmarried people among both genders. On the other hand, an increase in male nonstandard employment and unemployment rates have positive and significant effects on
the probability of unmarried women, even after controlling with wage inequality
indices. In addition, the median wage for women has a significant and negative effect on
the probability of unmarried men. These results highlight the need for policies topromote a shift from male non-standard to standard employment and increased wages in
the lower income class to raise marriage rates for both genders.
Key words: wage inequality, non-standard employment, marriage behavior, medianpreserving spread, two-sided search
JEL Codes: J12; J31; D31
Funding: This work was supported by JSPS KAKENHI [grant number JP15K03513].
The funding group had no other involvement in the completion of this study.

1

Introduction
The declining birthrate has become a serious problem in Japan, and a key reason
underlying this phenomenon is the tendency among the youth to remain single or delay
marriage. The declining birthrate causes a drop in the future workforce and has a great
influence on the sustainability of the social security system. Several studies have argued
that this tendency among the youth is related to the increasing rate of non-standard
employment1 among them (Nagase, 2002; Ohta, 2007; Sakai and Higuchi, 2005). In
addition, most children born are legitimate; thus, the marriage behavior precedes thebirth behavior in Japan. In fact, in the past 20 years, the rate of unmarried women has
gradually increased2 (Figure 1). Figure 2 shows that the male wage inequality in thelower tail has been consistently larger than that in the upper tail,3 and the former
continues to increase, whereas the later has shown signs of leveling off in the past 20
years. Thus, the former is more noteworthy than the latter in Japan.
Figure 1: Trends of the rate of unmarried women

The designation follows Kambayashi and Kato (2012).
The rate of unmarried 20–34-year old women rose from 47.2% in 1992 to 58.7% in 2012.
3
Female wage inequality in the lower tail also has been consistently larger than that in the upper tail.
12

2

Figure 2: Trends of the wage 90:50 and 50:10 percentile ratios for men

In fact, following the 1990s, the rate of unmarried Japanese women aged 20–34
years and that of non-standard employed Japanese men of the same age group havesimultaneously increased over the years (Figure 3). In addition, Figure 4 indicates thatthe rate of unmarried women and the wage inequality of men in the lower tail follow thesame trends. Therefore, it would be useful to examine the relationship between theincreasing rates of unmarried females and lower-tail male wage inequality in Japan toclarify the tendency among young women to remain single or delay marriage.

3

Figure 3: Trends of the rate of unmarried women and non-standard employment rate formen

Figure 4: Trends of the rate of unmarried women and 50:10 percentile ratios for men

The mainstream economic approach to examine the tendency among women toremain single or delay marriage is founded on Becker (1973). Becker performed a cost–benefit analysis on the basis of factors such as the declining benefits of marriage or theincreasing opportunity cost of marriage for women, including the popularization of4higher education and reduction of gender-based wage gaps.4 In particular, most studies
examine the influence of individual attributes such as age, educational background,income, and employment as factors influencing the probability of women remaining
unmarried.5 However, recent studies in the U.S. and other countries have examined
macroeconomic factors such as an increase in the non-standard employment rate andmale wage inequality in their marriage markets, even though such studies are limited in
the context of Japan owing to the lack of suitable statistical data.
Most U.S.- and Europe-based studies on the relationship between the rate ofunmarried women and macroeconomic factors are divided into two types and are
independent of each other. The first type focuses on the decreasing number or the rate ofmen who are suitable for marriage because of, for example, the declining labor market.
The second type concerns the changing male wage distribution, that is, the increasing
wage inequality among men. Given their close association, both issues must be
analyzed using an integrated analysis framework. Therefore, in this study, I first model
the relationship between the greater lower-tail male wage inequality and female
marriage behavior by applying a median-preserving spread. This framework allows
asymmetric distributional moves between the upper and lower tails. Data for the study
are Japanese individual data from the Employment Status Survey.
The remainder of this paper is outlined as follows. Section 2 reviews the extant
literature. Section 3 presents the theoretical hypothesis and estimation model. Section 4
describes the data. Section 5 reports and discusses the estimation results and Section 6
concludes.
Literature review
Some studies in the literature examine the relationship between the male labormarket and female marriage behavior. As mentioned, recent works examining theincreasing tendency among women to remain single or delay marriage are divided into
two types. The first is built on the concept that the number of desirable men who canmarry (the so-called “marriageable men”) is decreasing, while that of men with low
incomes is increasing owing to the declining male labor market. The second type isbased on the notion that increasing male wage inequality raises the expected value for
Oppenheimer (1997) suggests that the specialization model based on marriage gain is essentially anargument for non-marriage, not delayed marriage.
5
Stevenson and Wolfers (2007) also propose a hypothesis for the determinants of the rate of marriage,
including cohabitation, birth control and household technologies, change in the legal structure of marriage,
and shock to the matching function of the marriage market.
45

women to remain unmarried and continue their search for a partner.
Wood (1995) is an example of the first type and examines Wilson’s (1987)
hypothesis that fewer marriageable black men, due to the labor market decline in urban
areas, have contributed to the decreasing female marriage rates. He further suggests thatthe decreasing number of regularly employed men indicates an increasing number of
low-income earners and a simultaneous decrease in the number of marriageable men,which leads to higher rates of unmarried women.6 Blau et al. (2000), Brien (1997), andLichter et al. (1992) also find that the worsening male labor market conditions
negatively affect the female marriage rate. Oppenheimer et al. (1997) argue that theuncertainties of career entries lead to delayed marriage; in other words, those employed
in stop-gap jobs are less likely to marry than those with a career because the former is asign of career immaturity, uncertainty about long-term prospects, and low labor market
positions.
In Japan, Nagase (2002), Ohta (2007), and Sakai and Higuchi (2005) find that therate of marriage among those who worked as part-time employees (e.g., non-standard
employees) as their first job or are unemployed is lower than that of standard
employees; such employees also report delayed marriages. Hashimoto and Kondo(2012) also find that deterioration in the employment opportunities of young workers
contributes to the decreasing tendency to marry among young women. Sasaki (2016)
empirically examines the effects of an increase in gender-based, non-standard
employment rates on the increase in unmarried individuals of both sexes and presents
results consistent with the theoretical hypothesis. However, the data do not include
prefecture-level information for the marriage markets.
Examples of the second type of studies include Coughlin and Drewianka (2011),
Gould and Paserman (2003), Kuo (2008), and Loughran (2002). These studies examine
the search model hypothesis that the varying dispersion in male wage distribution
increases marriage reservation wage and the tendency to postpone marriages among
women, thus indicating a decrease in marriage rate for women. In particular, Loughran(2002) analyzes these issues for women aged 22–30 years using 90:50 and 50:10
percentile ratios of hourly wages for men aged 21–45 years as income inequality
indices. Using data from the U.S. census for 1970, 1980, and 1990, he finds that higher
male wage inequality in the upper, not the lower, tail indicates significantly delayed
marriages among women. Gould and Paserman (2003) address the same issues for
women aged 21–30 years using the standard deviation of weekly wages of full-time
Wood (1995) finds that the decline in marriageable men increased the rates of unmarried women by 3–
4%.
6

6

male workers aged 16–64 years as the main wage inequality indices, controlling for city
fixed effects. They argue that 25% of the decline in marriage rate among women from1970 to 1990 can be attributed to increasing inequality in male wages. Coughlin andDrewianka (2011) estimate the effects of increasing male wage inequality on female
marriage hazard rate and conclude that wage inequality promotes delayed marriages
among young women.
In Taiwan, where the rate of those unmarried is rapidly increasing, Kuo (2008)
examines the issues using data from the Manpower Utilization Survey for women aged
22–30 years for 1980–2005, controlling for geographic and linear-time fixed effects.
Kuo concludes that while the increasing inequality in male wages negatively affects themarriage rate among women, the effect is neither large nor significant. Kuo also showsthat, rather than the upper tail, the lower tail of male wage inequality has a larger
negative effect on female marriage rate by simultaneously estimating the 90:50 and
50:10 percentile ratios, without controlling for geographic and linear-time fixed effects.
This result differs from those based on U.S. data.
In Japan, researchers have rarely employed individual data to conduct marriage
market-level analysis because such data are difficult to obtain. Tachibanaki and Kimura(2008), among others, have used aggregate data based on official government statistics
instead of individual data and performed a simple empirical analysis without controllingfor various factors. Yugami and Sasaki (2011) conducted an analysis using individual
data from the Japanese General Social Survey; however, their estimation neither uses
the 90:50 and 50:10 percentile ratios owing to data limitations nor provides a clear
result for the effects of increasing non-standard employment rates.
Not all studies employ an analysis framework that connects both types of studies. In
addition, the mechanisms used for analyzing the increasing rate of unmarried women
differ based on the relevant cause, that is, fewer marriageable men or increasing malewage inequality. The former focuses on female behavioral responses to the lower tail ofmale wage distribution, while the latter considers the upper tail. Furthermore, the firstmechanism is yet to be clarified in detail using a micro-foundation analysis such as asearch model. Overcoming these limitations, the present analysis framework helps
explain the responses of female reservation wage and marriage probability toasymmetric wage distribution among men at the boundary of the lower and upper tails.
This phenomenon—higher inequality in the lower rather than the upper tail—is due tothe rise in the rate of non-standard employment and unemployment among men, given
the recent decline in Japan’s male labor market.
I first formulate an integrated analysis framework by applying a median-preserving
7

spread instead of a mean-preserving one to the marriage search model. The theoretical
hypothesis thus derived is based on more realistic wage inequality changes in Japan
compared with those in previous studies. An advantage of this framework is that it
explains consistently occurring phenomena in the youth labor and marriage markets. In
addition, I empirically analyze the theoretical hypothesis using large Japaneseindividual samples from the Employment Status Survey.
Theoretical hypothesis and estimation model
The theoretical model is based on Gould and Paserman (2003), Kuo (2008), andLoughran (2002). However, it differs from those in previous works in two ways. First, I
apply a median-preserving spread to a marriage search model to examine the effects of
the recent increase in lower-tail male wage inequality on female marriage behavior. The
asymmetric dispersion between the lower and upper tails can be attributed to the recent
increase in the non-standard employment rate among Japanese men. Second, I adopt theconcept of a two-sided search, because in the field of marriage behavior, theoreticalstudies addressing search behaviors of men and women tend to be mainstream (Blochand Ryder, 2000; Burdett and Coles, 1999; Chade and Ventura, 2002; Shimer and Smith,
2000). Thus, I first empirically examine the effects of lower-tail wage inequality andnon-standard employment rate for women on the rate of unmarried men, which, to thebest of my knowledge, is yet to be examined in the context of Japan.
Search model applying median-preserving spreadIn this subsection, I explain the effects of increasing male wage inequality on therate of unmarried women using the search model. The interpretation presented differsfrom that in previous studies. More specifically, non-standard employees are generally
low-wage earners, and, therefore, the higher the number of such employees, the lower
the mean male wage in the marriage market, and the greater the dispersion of wage
distribution in the lower tail. Thus, the change in male wage distribution is asymmetricbetween the upper and lower tails. However, the mean-preserving spread should beapplied only when the wage distribution spreads symmetrically around the mean wage
because it holds the mean constant. In fact, according to Loughran (2002), the meanpreserving spread does not hold when the wage inequality in the lower tail is higher
than in the upper tail.
To avoid this problem, this study applies Aldashev’s (2010) median-preserving
spread as an analysis framework to allow for asymmetric distributional changes
8between the upper and lower tails.7 I incorporate a median-preserving spread into amarriage search model, which, to the best of my understanding, no study has done till
date, and I derive a theoretical hypothesis for the effect on female reservation wages in
the case of median changes and increasing inequality in the upper or lower tails.
Deriving the effects of median changeFirst, I derive the effects of a median change on reservation wage.
The effects of an increase in the median of the male wage distribution on theacceptance probability of a marriage offer, 𝑝, is described as follows:
𝜕𝑝

𝐹′

𝑝

= 𝑤∗ [1 − 𝑝+𝑟] > 0,𝜕𝜃(1)where F is the distribution function of male wage w, which is conditional on individual
attributes such as age group, education, year, and prefecture. 𝑤 ∗ is the female
reservation wage. 𝜃 is the value to which the median of the wage distribution
increases. As a result, the higher the male median wage, the greater the acceptanceprobability of a marriage offer, and thus the lower the rate of unmarried women.
Effects of greater wage inequality in the upper and lower tails
When the female reservation wage is higher than the median of the male wage
distribution, a greater wage inequality in the lower tail does not affect the reservation
wage for women who adopt suitable search strategies. Therefore, the reservation wage
is assumed to be lower than the median.
The marginal effect of the dispersion in the upper tail of male wage distribution on
the probability of acceptance of a marriage offer is described as follows:
𝜕𝑝
𝜕𝜎𝑈

𝜕𝑤 ∗

= −𝜆𝐹 ′ (𝑤 ∗ ) 𝜕𝜎 − 𝜆𝐹𝜎𝑈 (𝑤 ∗ , 𝜎𝑈 ) < 0,𝑈(2)
where 𝑤 ∗ is reservation wage and 𝜎𝑈 is dispersion in the upper tail.
Here, 𝐹𝜎𝑈 (𝑤 ∗ , 𝜎𝑈 ) in the second term of the right-hand side is a partial
differentiation of the distribution function of the dispersion (𝜎𝑈 ) in the upper tail. Thus,
it is zero if the reservation wage is lower than the median, as assumed above. That is, agreater dispersion in the upper tail increases the reservation male wage (first term on
right-hand side) and the rate of unmarried women.
Labor economics studies using the median-preserving spread in a search model include Aldashev (2010)
and Möller and Aldashev (2007).
7

9

On the other hand, the probability of accepting a marriage offer, expressed as the sizeof dispersion (𝜎𝐿 ) in the lower tail, is described as follows:𝜕𝑝𝜕𝑤 ∗= −𝜆𝐹 ′ (𝑤 ∗ )− 𝜆𝐹𝜎𝐿 (𝑤 ∗ , 𝜎𝐿 ).
𝜕𝜎𝐿
𝜕𝜎𝐿
(3)
The right-hand side indicates the difference between the effect of a decline in thefemale reservation wage due to higher male wage inequality in the lower tail (first term)
and that of a variance in the male wage distribution below the female reservation wage
(second term). However, it is highly possible that the dispersion of male wage
distribution in the lower tail is so large that the latter effect is dominant, assuming thatthe increasing number of low-income non-standard employees among young Japanese
men is acceptable. Therefore, when the latter effect exceeds the reduction effect offemale reservation wage, the probability of accepting a marriage offer declines and therate of unmarried women increases. It is essential to examine this finding using a
positive analysis framework.
Estimation model
Here, I empirically examine the theoretical hypothesis mentioned above. The basic
empirical reduced-form specification for regressions is a probit model of a woman’s
propensity to remain single.
P(𝑦𝑖𝑗𝑎𝑙𝑡 = 1) = Φ (𝛽0 + 𝛽1 𝜃𝑗𝑎𝑙𝑡 + 𝛽2 𝜎𝑈𝑗𝑎𝑙𝑡 + 𝛽3 𝜎𝐿𝑗𝑎𝑙𝑡 + 𝛽4 𝐿𝑗𝑎𝑙𝑡 + 𝛽5 𝑍𝑗𝑎𝑙𝑡 +𝛽6 𝑋𝑖𝑗𝑎𝑙𝑡 + 𝜀𝑖𝑗𝑎𝑙𝑡 ),(4)
where 𝑦𝑖𝑗𝑎𝑙𝑡 is a dummy variable that takes the value of 1 if woman 𝑖 living inprefecture 𝑗 of age group 𝑎 with education 𝑙 at time 𝑡 has never been married, and0 otherwise. In Eq. (4), 𝜃, 𝜎𝑈 , and 𝜎𝐿 are the median, 90:50 percentile ratio, and50:10 percentile ratio, respectively, of male wages categorized by age group, education,
prefecture, and year in the context of a local marriage market.8 𝐿 is the vector of male
employment indices such as non-standard employment and unemployment rates
segmented by the abovementioned elements. Local marriage markets are categorized bythese factors because women tend to search for similar characteristics in marriage
The marriage markets are divided into 3 (age group) × 4 (education) × 47 (prefecture) × 5 (year) =2,820 cells.
810

partners, and thus they should respond most strongly to changes in wages andemployment indices of such men (Loughran, 2002).9 𝑍𝑗𝑎𝑙𝑡 is a vector of variables for
local marriage market conditions faced by women such as sex ratio, prefecture, andyear; 𝑋𝑖𝑗𝑎𝑙𝑡 is a vector of individual characteristics including age group, education, andhabitation dummy in large urban areas; and 𝜀𝑖𝑗𝑎𝑙𝑡 is the error term that captures
unobservable geographic and time-specific effects.
A key explanatory variable is the parameter for wage inequality in the lower tail, 𝜎𝐿 ,which is expected to be positive based on the search theory model with a medianpreserving spread. Parameter 𝜎𝑈 is expected to be positive, and 𝜃 is expected to be
negative. On the other hand, 𝐿 is expected to have no significant effect because 𝐿’s
effects can be absorbed by 𝜎𝑈 ’s effect. Sex ratio is defined as the male-to-female
population ratio in the marriage market by age group, education, prefecture, and year.
This variable can affect the prospect of marriage (Angrist, 2002; Fossett and Kiecolt,
1991; Hara, 2013; South and Lloyd, 1992); that is, the higher the ratio, the greater theadvantage for women in the marriage market, and, thus, the lower the probability ofwomen being unmarried. The prefecture dummy captures related unobserved
characteristics correlated with marriage behavior and inequality consistent over time,for example, marriage market fixed effects.10 The year dummy represents a common
time trend in marriages across individuals. The probability of a woman remaining
unmarried is probably higher in large urban areas given its weaker social norms
regarding marriage. In addition, according to previous studies, the tendency of womenremaining unmarried would be lower for higher age groups and those with lesser
education (Kuo, 2008; Gould and Paserman, 2003; Loughran, 2002). The standard
errors of the coefficients are modified by clustering with age groups, education levels,
prefectures, and years.
According to Gould and Paserman (2003), the abovementioned search model can beapplied in the case of marriage rates being negatively correlated with female wage
inequality. However, they suspect that the effect of female wage inequality on decisions
made by men is less than that of male wage inequality on those made by women.
Nevertheless, I also perform regressions for men’s decision to remain unmarried.
Data

Previous studies have found that educational assortative mating continues to be deeply rooted in Japan(Shida et al., 2000; Shimizu, 1990; Watanabe and Kondo, 1990).
10
The estimations in columns 2 and 4 in Tables 4 and 5 include a cross-term dummy with prefecture andyear to consider prefecture-specific linear trends.
911This study adopts data from Japan’s Employment Status Survey for 1992, 1997,
2002, 2007, and 2012. The data comprise vast individual information: age, sex,
educational attainment, employment status, wage, work hours, marriage status,
household composition, childbirth, age of children, prefecture, and size of city where
the individuals live. I restrict the sample to men and women aged 20–34 years because
the rate of unmarried individuals of both sexes declines sharply in these age groups;
however, this rate is shown to increase over time in Japan (Figure 1). The final sample
for analysis includes 583,207 women and 597,770 men.
I use an hourly wage to calculate the logarithm median wage11 and wage inequality
indices because the cyclical change in labor supply influences annual wage inequality(Loughran, 2002). I also estimate the employment indices and sex ratio in the marriage
market for each age group, education level, prefecture, and year. Female wages are
imputed to the regression results value by Heckit12 (Heckman, 1974) because many
women change their labor supply and employment status around the time of marriage orchildbirth. From similar reasons, I estimate the female employment indices using only
unmarried or non-childbirth samples. The descriptive statistics of the variables used inthis analysis are shown in Table 1.

Drawing on Genda (2007) and Lemieux (2006), I use the median of each wage grade as personal wage
and multiply it 1.4 times for the high-end annual wage group. The real wage is based on the 1992
consumer price index. From 1992, however, each wage group in the Employment Status Survey
questionnaire is further subdivided. Therefore, I standardize the wage groups according to those of 1992
and 1997, following Kambayashi (2010a), Shinozaki (2006), and Yugami and Sasaki (2011), to remove
the influence of class interval changes on the setup conditions of wage inequality indices. In addition, I
define the non-standard employment rate, as per Ohta (2007), in terms of all the employees and theunemployment rate as a proportion of the population.
12
The first step of the regression includes variables affecting the propensity of a woman’s labor supply
such as education, age group, employment status, marital status, childbirth, and age of the youngest child.
11

12

Table 1: Descriptive statistics of variables
Variables

Male age: 20–34 years

Female age: 20–34 years

Obs.

Mean

Std. dev.

Min.

Max.

Obs.

Mean

Std. dev.

Min.

Max.

20–24 years

583,207

0.319

0.466

0.000

1.000

597,770

0.317

0.465

0.000

1.000

25–29 years

583,207

0.329

0.470

0.000

1.000

597,770

0.328

0.469

0.000

1.000

30–34 years

583,207

0.315

0.477

0.000

1.000

597,770

0.355

0.478

0.000

1.000

Below junior high school graduate

583,207

0.072

0.258

0.000

1.000

597,770

0.046

0.209

0.000

1.000

High school graduate

583,207

0.490

0.500

0.000

1.000

597,770

0.485

0.500

0.000

1.000

Graduate from vocational school

583,207

0.085

0.279

0.000

1.000

597,770

0.260

0.439

0.000

1.000

Higher than college graduate

583,207

0.348

0.476

0.000

1.000

597,770

0.205

0.404

0.000

1.000

Unmarried probability

583,085

0.662

0.473

0.000

1.000

597,977

0.551

0.497

0.000

1.000

Logarithmic
hourly wage
in marriage
marketMedian (logarithmic)

582,823

7.002

0.271

3.798

9.448

375,744

6.993

0.213

6.119

7.610

90:10 percentile ratio

582,823

1.202

0.046

2.376

2.376

375,744

1.024

0.014

1.000

1.062

90:50 percentile ratio

582,823

1.092

0.028

1.000

1.579

375,744

1.004

0.007

1.000

1.044

50:10 percentile ratio

582,823

1.101

0.030

1.000

1.839

375,744

1.020

0.013

1.000

1.051

Non-standard employment rate

575,762

0.096

0.130

0.000

1.000

596,840

0.328

0.168

0.000

1.000

Regularly employed non-standard
employment rate

575,762

0.035

0.077

0.000

1.000

596,840

0.155

0.118

0.000

1.000

Irregularly employed non-standard 575,762
employment rate

0.031

0.070

0.000

1.000

596,840

0.126

0.081

0.000

1.000

Unemployment rate583,207

0.146

0.158

0.000

1.000

597,770

0.327

0.115

0.000

1.000

Sex ratio in marriage market

583,052

1.301

0.663

0.060

8.000

597,615

0.976

0.563

0.060

8.000

Dummy of habitation in large urban areas

315,338

0.331

0.471

0.000

1.000

320,640

0.337

0.473

0.000

1.000

1992

583,207

0.191

0.393

0.000

1.000

597,770

0.198

0.398

0.000

1.000

1997

583,207

0.198

0.399

0.000

1.000

597,770

0.196

0.397

0.000

1.000

Year dummy 2002

583,207

0.168

0.374

0.000

1.000

597,770

0.166

0.397

0.000

1.000

2007

583,207

0.146

0.353

0.000

1.000

597,770

0.166

0.372

0.000

1.000

2012

583,207

0.296

0.457

0.000

1.000

597,770

0.294

0.456

0.000

1.000

Age group
dummy
Education
dummy

Employment
conditions
in marriage
marketNote: The non-standard employment rate is the ratio of non-standard employees to all employees.
13Figure 5 depicts changes in the kernel distribution of 20–34-year-old males’
logarithmic hourly wage for five years (unwaged persons are excluded). The overall
wage distribution seems to be more skewed toward the upper side in 1997 comparedwith that in 1992. It then shifts to the lower side with a slightly higher lower tailinequality in 2012 than in 1992.
Figure 5: Changes in the kernel distribution of log hourly wages for men
Source: Employment Status Surveys, 1992, 1997, 2002, 2007, and 2012.

Estimation results and discussion
Baseline estimation results
I first estimate the effects of male wage inequality and employment indices on theprobability of a woman choosing to be unmarried. Table 2 presents the baseline
estimation results. I use the male 90:50 and 50:10 percentile ratios as the wage
inequality indices in the upper and lower tails of the wage distribution. In addition, nonstandard employment and unemployment rates for men are used as employment indicesfor the declining labor market.

14

Table 2: Marginal effects of various male employment indices
Male employment indicesNon-standard
employment
rate

Unemployment
rate

Regularly
employed
non-standard
employment
rate

Irregularly
employed
non-standard
employment
rate

Marriage market: calculated using age group, educational
background, prefectures, and years
Male wage 90:50

0.732

0.027

0.763

0.675

percentile ratio(0.389)*(0.523)(0.404)*(0.398)*

Male wage 50:10

0.839

0.500

0.729

0.836

percentile ratio(0.324)***(0.294)*(0.343)***(0.324)***

Male employment

0.174

0.811

0.157

0.259

indices(0.073)**(0.100)***(0.070)**(0.164)

Male median wage

-0.0830.045-0.073-0.082(0.108)(0.097)(0.108)(0.113)311,380

318,312311,380

311,380

No. of obs.
Notes:

1. Dependent variables (female): unmarried = 1, others = 0.
2. Male wage is a logarithmic value.
3. Other explanatory variables are female educational background dummy, female age groupdummy, sex ratio in marriage market, and female habitation dummy in large urban areas.
4. The second-step parentheses indicate standard errors modified by clustering on the basis of age
group, educational background, prefectures, and years.

5. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
Columns 1 and 2 show that the coefficients of the 50:10 percentile ratio havesignificant positive effects at the 1% and 10% confidence levels on the probability ofwomen remaining unmarried in both cases, which is consistent with the theoretical
hypothesis and similar to the results of a previous Taiwan-based study by Kou (2008).
The 90:50 percentile ratio in column 1 also has a significant positive effect at the 10%
confidence level on the probability of women remaining unmarried, which is also
consistent with the theoretical hypothesis and similar to the results of previous U.S.based studies (Gould and Paserman, 2003; Loughran, 2002). The coefficient of the
90:50 percentile ratio in column 2 is positive but does not have a significant effect. The
15

marginal effects of the 50:10 percentile ratio are larger than those of the 90:50 percentile
ratio in both cases. On the other hand, both the non-standard employment andunemployment rates for men have a significant positive effect at the 1% confidence
level on the probability of women remaining unmarried, which is contrary to
expectations. The coefficients for the male median wages have no significant effect.
The results in columns 3 and 4 demonstrate the effects of regularly and irregularly
employed non-standard employment rates. The 90:50 and 50:10 percentile ratios havesignificant and positive effects on the probability of women remaining unmarried inboth cases. The regularly employed non-standard employment rate has a significant
positive effect at the 1% confidence level on the probability of women remaining
unmarried, whereas the irregularly employed rate has no significant effect. The male
median wages also have no significant effect.
The control variables are statistically significant and have the expected sign: the rateof unmarried women increases with higher education, habitation in large urban areasand passing of time but declines in older age groups. The sex ratio has no significant
effect. These results indicate that women postpone their marriage on the basis of bothtails of men’s wage distribution and employment indices, such as non-standard
employment rate and unemployment rate.
Opposite direction effects
Here, I present the estimation results for the effects of wage inequality andemployment indices for women on the probability of men remaining unmarried inJapan, adapting the concept of a two-sided search model.
Table 3 presents the estimation results for the 90:50 and 50:10 percentile ratios andemployment indices for women. The coefficients for both the 90:50 and 50:10
percentile ratios have statistically significant and positive effects at the 1% confidence
level on the probability of men remaining unmarried in all four cases, which isconsistent with the theoretical hypothesis. The marginal effect of the 90:50 percentile
ratio is larger than that of the 50:10 percentile ratio, which is in contrast to the results inTable 2. This indicates that men postpone marriage depending on both tails of women’s
wage distribution. Furthermore, the marginal effects of both variables are larger thanthose in the case of men, which are in stark contrast to the prediction by Gould andPaserman (2002).

16

Table 3: Marginal effects of various female employment indicesFemale employment indicesNon-standard
employment
rate

Unemployment
rate

Regularly
employed
non-standard
employment
rate

Irregularly
employed
non-standard
employment
rate

Marriage market: calculated using age group, educational
background, prefectures, and years
Female wage 90:50

3.107

3.434

3.138

3.028

percentile(0.934)***(1.101)***(0.917)***(0.914)***

Female wage 50:10

2.114

2.280

2.071

2.069

percentile(0.739)***(0.748)***(0.720)***(0.729)***Female employment-0.039-0.075-0.062-0.016indices(0.070)(0.092)(0.090)(0.111)

Female median wage

-0.777-0.844-0.762-0.746(0.160)***(0.190)***(0.148)***(0.148)***

312,830

313,050

312,830

312,830

No. of obs.
Notes:

1. Dependent variables (male): single = 1, others = 0.
2. Female wage is a logarithmic value.
3. Other explanatory variables are male educational background dummy, male age group dummy,
sex ratio in marriage market, and male habitation dummy in large urban areas.
4. The second-step parentheses indicate standard errors modified by clustering on the basis of age
group, educational background, prefectures, and years.

5. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
The coefficients of female median wage have negative and statistically significant
effects at the 1% confidence level on the probability of men remaining unmarried in all
four cases, which is consistent with Fukuda’s (2013) result. Fukuda (2013) argues thatin Japan, women’s earnings have a positive effect on marriage for the post-1970 cohort,and, thus, wives’ economic contributions to the family are considered important. In
addition, Burgess et al. (2003) propose that higher wages among women have a “selfreliance effect” and “good catch effect.” The former effect means that women earn alivable wage that is independent of men, and the latter indicates that higher wages
increase the probability of women being considered a desirable marriage partner by17

men. This can be interpreted as the good catch effect exceeding the self-reliance effect,
which is contrary to findings presented in the literature (e.g., Becker, 1973; Burgess etal., 2003; Miyoshi, 2013). In all cases, female non-standard employment andunemployment rates have no significant effect.
As for the control variables, higher male education dummies, sex ratio, and later
year dummy have significant positive effects on the probability of men remaining
unmarried in all cases. In addition, older age group dummies have significant negative
effects on the probability of men remaining unmarried in all cases, thus supporting thetheoretical hypothesis.
Robustness check: estimation results including same-sex indices
Loughran (2002) adds female mean wages and employment rates as utility factors
for unmarried women to avoid an omitted-variable bias. Undoubtedly, these variables
are endogenous, and thus cannot be causally interpreted in these regressions. His aim isa robustness check, that is, whether the primary variables continue to have an effect on
female marriage propensities, even after controlling with these added variables.
Similarly, I add female median wages and employment indices in regressions for the
probabilities of women’s tendency to remain unmarried. I also add median wages andemployment indices for men to the regressions for their probability to remain
unmarried.
Columns 1 and 2 in Table 4 show the effects of the non-standard employment rate
among men, and columns 3 and 4 show the effects of their unemployment rate.
Columns 1 and 3 include only a prefecture dummy as a marriage market fixed effect.
Columns 2 and 4 are controlled with a prefecture and cross-term dummy, with theprefecture and year dummy being a marriage market-specific linear trend.
The 50:10 percentile ratio has positive and significant effects at the 1% confidence levelin all cases, even after adding median wage and employment indices for women. On theother hand, the 90:50 percentile ratio loses significant effects in all cases. Male
employment indices have significant and positive effects at the 1% confidence level inall cases, which is similar to the results presented in Table 2, and the marginal effects
are larger. By contrast, female employment indices do not have a significant effect in allcases. Male median wages have no significant effects in all cases, although female
median wages have negative and significant effects at the 1% confidence level in caseswith non-regular employment rates.

18

Estimation results including female indices
Table 4: Marginal effects of male and female employment indices
Male and female employment indicesNon-standard employment rate

Unemployment rate

Male wage 90:50

0.361

0.361

0.172

0.172

percentile ratio(0.491)(0.491)(0.447)(0.575)

Male wage 50:10

0.956

0.956

0.582

0.582

percentile ratio(0.353)***(0.353)***(0.285)**(0.194)**

Male employment

0.208

0.208

1.180

1.180

indices(0.080)***(0.080)***(0.123)**(0.123)***

Female employment-0.033-0.003-0.553-0.553indices(0.133)(0.133)(0.106)***(0.184)***

Male median wage

-0.145-0.145

0.099

0.099

(0.112)(0.112)(0.082)(0.331)-2.041-2.041-0.546-0.546(0.507)***(0.508)***(0.573)(0.182)Prefecture dummy

Yes

Yes

Yes

Yes

Prefecture × year

No

Yes

No

Yes

311,229

311,229

318,291

318,291

Female median wage

cross-term dummy
No. of obs.
Notes:
1. Dependent variables (female): single = 1, others = 0.
2. Male and female wages are in logarithmic value.
3. Other explanatory variables are female educational background dummy, female age groupdummy, sex ratio in marriage market, and female habitation dummy in large urban areas.
4. The second-step parentheses indicate standard errors modified by clustering on the basis of age
group, educational background, prefectures, and years.

5. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
Among the control variables, higher education, female habitation in large urban
areas, and passing of time have significant and positive effects at the 1% confidence
level on the probability of women remaining unmarried in all cases. The older age
group dummy has a significant and negative effect in all cases at the 1% confidence
level. In addition, including a marriage market-specific liner trend does not affect the19results.
In sum, the 50:10 percentile ratio and employment indices for men dominate theprobability of women remaining unmarried, even after controlling for female wage andemployment status. This confirms the robustness of the results.
Estimation results including male indices for probability of males being unmarried
Table 5: Marginal effects of male and female employment indicesFemale and male employment indicesNon-standard employment rate

Unemployment rate

Female wage 90:50

3.923

3.925

1.068

1.064

percentile ratio(1.072)***(1.072)***(1.043)(0.309)Female wage 50:10

1.787

1.787

0.386

0.386

percentile ratio(0.796)**(0.025)**(0.668)(0.563)

Female employment-0.012-0.012-0.371-0.371indices(0.067)(0.853)(0.067)***(0.067)***

Male employment

0.007

0.007

0.752

0.752

indices(0.089)(0.941)(0.081)***(0.081)***

Female median wage

-0.661-0.661-0.120-0.121(0.167)***(0.167)***(0.106)(0.536)-0.176-1.176-0.076-0.076(0.100)*(0.076)*(0.069)(0.269)Prefecture dummy

Yes

Yes

Yes

Yes

Prefecture × year

No

Yes

No

Yes

309,239309,239

313,045

313,045

Male median wage

cross-term dummy
No. of obs.
Notes:
1. Dependent variables (male): single = 1, others = 0.
2. Female and male wages are in logarithmic value.
3. Other explanatory variables are male educational background dummy, male age group dummy,
sex ratio in marriage market, and male habitation dummy in large urban areas.
4. The second-step parentheses indicate standard errors modified by clustering on the basis of age
group, educational background, prefectures, and years.

5. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

20

Table 5 shows that the 90:50 and 50:10 percentile ratios for female wages havesignificant and positive effects on the probability of men with a non-standard
employment status remaining unmarried, which is similar to the results presented inTable 3; however, they have no effect with unemployment rates. Female unemployment
rates have significant and positive effects, although non-standard employment rates for
women have no significant effect. Female and male median wages have significant andnegative effects on the probability of men remaining unmarried in the case of nonstandard employment rates. As for controlling variables, higher education and later
years have significant and positive effects on the probability of men remaining
unmarried in all cases. The older age group has a significant and negative effect in allcases. Sex ratios have a significant and positive effect on the probability of men
remaining unmarried in all cases. Considering a marriage market-specific linear trend
does not affect the results, which is similar to the results presented in Table 4.
In sum, female wage inequality indices and median wages for both genders
dominate the probability of men remaining unmarried in the case of non-standard
employment rates; on the other hand, both gender employment indices dominate theprobability of men remaining unmarried in the case of unemployment rates.
Alternative hypotheses
This section provides alternative hypotheses associated with the negative correlation
between wage inequality and the tendency to marry.
Change in preference for marriage
The first alternative hypothesis is that changes in wage inequality reflect changing
preferences for marriage among men and women (Gould and Paserman, 2003;
Loughran, 2002). Hashimoto (2009) mentions that men’s marriage aspiration strongly
and positively correlate with their social status, such as education, income, andoccupation, and therefore low income earners have a relatively low incentive to marry.
The larger the number of unmarried men who are lower income earners in the marriage
market, the higher the wage inequality, especially in the lower tail of the distribution. If
so, the possibility of a reverse causality or simultaneity is doubtful and the estimation
results contain a bias. To control this bias, I employ measures of 90:50 and 50:10
percentile ratios from my samples to restrict men who are currently married and aged
20–34 years. The basic results remain unchanged compared with those in Table 2.

21

Female wage inequality due to higher labor supply
The second hypothesis is that women may strategically increase labor supply to
exploit higher returns in response to higher female inequality, which is independent ofmale wage inequality. Thus, women may delay their marriage or risk-averse women
may work more to hedge against the increased risk of their partners’ wages. Gould andPaserman (2003) directly test whether women work more in areas with higher maleinequality. Using their approach, I attempt to test this hypothesis controlling for theirmarital status and whether they have children. The results of a probit for those working
full-time and a censored regression for weekly hours worked are consistent with theirfindings and reject the second hypothesis.
Asymmetric information
The third hypothesis is related to asymmetric information. Bergstrom and Bagnoli(1993) propose that high-type men marry later in life once their type is fully revealed
and distinguish themselves from low-type ones since asymmetric information about
earning ability exists in marriage markets. Therefore, the implication is that men who
are high-income earners might marry later than those who are low-income earners. I test
this hypothesis using the probit model for the effects of males’ log hourly wage on theprobability of them remaining unmarried. The other variables are identical to those in
Table 2. However, the coefficient for log hourly wages is negative and statisticallysignificant for the probability of men remaining unmarried, which is consistent with theprevious literature, and rejects third hypothesis.
Discussion
Effects of non-standard employment rate
From the results, we see that wage inequality indices have significant effects oncross-gender unmarried rates. In addition, male employment indices, non-standard
employment rate, and unemployment rate robustly dominate the probability of womenremaining unmarried.
Here, I explain the properties of non-standard employment, a common form of workfor the youth and a social issue that has recently emerged in Japan. Among young men,non-standard employees not only earn a lower income but are also subject to theprobability of income dispersion and unemployment probability that is three times
greater than that of standard employees, and, therefore, they face higher employment
and income risks (Abe, 2013). Sakaguchi (2011) argues that a division structure in
22which unemployment risk is centered on non-standard employees is stable in theJapanese labor market.
In addition, it is considered that Japanese companies manage human resources, forexample, develop abilities, on the basis of employee designation. Furthermore,companies tend to hire non-standard employees by designation and treat them as abuffer for labor adjustment because laying off standard employees costs far more in
Japan. Moreover, some studies have shown that designation has a greater influence on
working conditions and lifetime income than tenure (Kambayashi, 2013; Kambayashi
and Kato, 2012; Kawaguchi et al., 2011). Thus, it can be said that non-standard
employment reflects characteristics such as low income, uncertainty, and instability.
On the other hand, unemployed men in Japan are labeled as individuals with noprospect of employment or high income earnings, even in the long term. Thus,
employment indices such as the non-standard employment and unemployment rates cancapture the long-term uncertainty effects of employment, which a wage inequality index
fails to do. In other words, it is suggested that women predict long-term increases in low
income and unstable employment in the marriage market on the basis of a rise in the
non-standard employment rate, designation, and unemployment rate. As a result, they
postpone marriage, which is consistent with Oppenheimer et al.’s (1997) argument thatjob uncertainty delays marriage. However, men do not consider long-term aspects basedon female employment indices.
I now discuss non-standard employment. Standard and non-standard employment
are designation (“name at work”) types in the Employment Status Survey. Such aclassification method is peculiar to Japan and is uncommon in the U.S. and European
countries. In addition, the survey classifies positions according to employment
agreements; for example, workers with an agreed employment term of more than one
year are classified as regularly employed, and those with a term of less than one year aregrouped as being irregularly employed. Considering the classifications of designationand employment status on the basis of an agreed term, we group employment positions
into four categories: regularly employed standard employee, irregularly employedstandard employee, regularly employed non-standard employee, and irregularly
employed non-standard employee.
In Japan, the number of regularly employed non-standard employees has increased inrecent years (Kambayashi, 2010b). It has been recognized that the employment
agreement of regularly employed non-standard employment is often renewed so that theactual duration of the service is relatively long, although their wages remain low (Koyo̅
no Arikata ni Kansuru Kenkyukai [Meeting to Study Systems of Employment], 2009).
23

However, non-standard employment contracts, even if regularly employed, are notbeing renewed given the deteriorating labor market conditions in recent years.
Do the increasing rates of regularly and irregularly employed non-standard
employment have different effects on the rate of unmarried women? The results showthat only the regularly employed non-standard employment rate has a positive andsignificant effect on the rate of unmarried women. Therefore, even the term “nonstandard” is shared since the increasing regularly employed non-standard employment
rate for men could accelerate the rate of growing unmarried women, which is a concern.
Factors contributing to changes in marriage rate for both genders
Using the estimation results for the marginal effect of male wage inequality,
employment condition indices, and median wage, I attempt to predict the magnitude ofchange in the probability of women aged 20–34 years remaining unmarried between
1992 and 2012. Drawing on the estimated marginal effects presented in Table 2, Table 6
shows the extent to which changes in the rate of unmarried women can be explained by
the male wage inequality and trend of employment rate indices.
Table 6: Contribution of changes in wage inequality and employment indices onchanges in marriage propensity for females during 1992–2012
AveragechangeMarginal
effectPredictedchangeContribution△ Female-single rate

0.0948△ Male 90:50 percentile
ratio

0.015

0.732*

0.011

11.6%

△ Male 50:10 percentile
ratio

0.010

0.839***

0.008

8.9%

△ Male median wage

-0.254-0.083

0.021

22.2%

△ Male non-standard
employment rate

0.125

0.174**

0.022

22.9%

Note: Calculations are based on estimated coefficients for the four variables in column (1) of
Table 2.

The rate of unmarried women aged 20–34 years has increased by 9.48% in the past
20 years. During this period, the average changes in the 90:50 and 50:10 percentile
ratios of the male wage in the same age group show a 0.015 and 0.010 point increase. In
addition, the marginal effect coefficients are 0.732 and 0.939 when the above indices are24

simultaneously used as explanatory variables. Thus, the predicted changes are 1.1% and0.9% on the basis of the average changes multiplied by the coefficients of marginal
effect.
From these results, the changes in 90:50 and 50:10 percentile ratios for men explain
the 11.6% and 8.9% increase in the rate of unmarried women, respectively. Using asimilar calculation method, median wage and non-standard employment rate for men
can be used to predict the 22.2% and 22.9% increase in the rate of unmarried women,
respectively. These indices, except male median wage, have significant explanatory
powers. Table 7 shows that changes in the 90:50 and 50:10 percentile ratio, medianwage, and non-standard employment rate for women contribute 38.2% and 62.3%,−116.5%,13 and −26.9%, respectively, to the rate of unmarried men.
Table 7: Contribution of changes in wage inequality and employment indices onchanges in marriage propensity by males during 1992–2012
AveragechangeMarginal
effectPredicted Contributionchange△ Male-single rate

0.0407△ Female 90:50 percentile
ratio

0.005

3.107***

0.016

38.2%

△ Female 50:10 percentile
ratio

0.012

2.114***

0.025

62.3%

△ Female median wage

0.061

-0.777***-0.047-116.5%△ Female non-standard
employment rate

0.281

-0.039-0.011-26.9%

Note: Calculations are based on estimated coefficients for the four variables incolumn (1) of Table 3.
Conclusions
Clarifying the tendency among the youth to remain unmarried or delay marriage,
which is potentially the main cause for the declining birthrate in Japan, is an urgent
social need. Some recent studies in the U.S. and other countries have applied theimplications of search models to marriage behavior. Using a mean-preserving spread,they suggest that women delay marriage in response to the increasing inequality in the13

The contribution rate of female median wage becomes −73.6% when male median wage and non-employment rate
are included.

25

lower, rather than upper, tail of male wage distribution. However, previous analysis
frameworks have been unable to strictly analyze such phenomena as male wage
inequality in the lower tail than in the upper tail in Japan. In addition, conducting a
sophisticated study using Japanese individual data is difficult owing to data limitations.
In this study, I model the relationship between asymmetric changes in the male wage
distribution and female marriage behavior using a median-preserving spread framework.
This method allows us to analyze the effects of asymmetric inequality changes between
the upper and lower tails of wage distribution. In addition, I adopt the concept of a twosided search and estimate the dual-directional effects of the theoretical hypothesis inJapan using large individual data from Japan’s Employment Status Survey.
The results show that the wage inequality indices for both sexes have significant andpositive effects on cross-gender unmarried probabilities, which is consistent with thetheoretical hypothesis. However, male employment indices, such as non-standard
employment and unemployment rates, robustly dominate the probabilities of womenremaining unmarried. The non-standard employment and unemployment rate for men
can be interpreted as long-term low-income levels and job uncertainties, which
Oppenheimer et al. (1997) discuss in their study. Thus, it is suggested that women
predict a long-term rise in lower income and unstable employment in the marriage
market by considering an increase in non-standard employment and unemployment
rates by designation and therefore, delay marriage. To this effect, in Japan, the male
employment status based on designation and male wage inequality play equallyimportant roles in female marriage behaviors. As per the present marriage market
condition, men delay marriage in response to female wage distribution in both tails,
although they do not respond to female employment indices as a long-term marriage
market condition.
The policy implications derived from the results of the positive analysis are as
follows. It is essential to increase the low wage levels for both genders and support ashift from non-standard to standard employment for men by improving the labor market
environment. To this effect, an ability development program, employment training
improvement, and accurate evaluation systems for occupation abilities are necessary. An
additional requirement is improving income security. However, the challenge of
examining these influences on the so-called “lost-decade generation,” who were born inthe 1980s and face serious job scarcity, remains and needs the accumulation of more
recent data.
Acknowledgements:
26

I thank Kazufumi Yugami, Naoki Mitani, and Yasuhide Tanaka. I also thank theanonymous reviewers of this journal, the participants at the annual national conference of
The Japan Society of Household Economics, and the seminar participants at Kobe
University for their helpful suggestions and comments. Finally, I thank the Statistics
Bureau, Ministry of Internal Affairs and Communications for the Employment Status
Survey, for the data provided.

27

References
Abe, N., 2013. Jyakunensha no Chokumen suru Koyo̅・Shotoku Risk ni kansuru Ichi
Kousatsu – Panel data to Yobiteki Chotiku model wo Mochiita Dougaku bunseki [Anexamination of the employment and income risk that youth face: The dynamic
analysis using panel data and preliminary saving model]. Keizai Kenkyu̅ 64, 193203. [in Japanese].
Aldashev, A., 2010. Spatial Search and Commuting with Asymmetric Changes of theWage Distribution. The Labor Market Impact of the EU Enlargement, AIEL Series inLabour Economics, Berlin, Heidelberg: Springer-Verlag, 227-245.
Angrist, J., 2002. How do sex ratios affect marriage and labor markets? Evidence from
America’s second generation. The Quarterly Journal of Economics 117, 997-1038
Becker, G.S., 1973. A theory of marriage: Part 1. Journal of Political Economy 81, 813846.
Bergstrom, T. C., Bagnoli, M., 1993. Courtship as a waiting Game. The Journal of
Political Economy 101, 185-202.
Blau, F.D., Kahn, L.M., Waldforgel, J., 2000. Understanding young women’s marriage
decisions: the role of labor and marriage market conditions. Industrial and Labor
Relations Review 53, 624-647.
Brien, M.J., 1997. Racial differences in marriage and the role of marriage markets. The
Journal of Human Resources 32, 741-778.
Bloch, F., Ryder, H., 2000. Two-sided search, marriages, and matchmakers.
International Economic Review 41, 93-115.
Burdett, K., Coles, M.G., 1999. Long-term partnership formation: marriage andemployment. The Economic Journal 109, 307-334.
Burgess, S., Propper, C., Aassve, A., 2003. The role of income in marriage and divorcetransitions among young Americans. Journal of Population Economics 16, 455-475.
Chade, H., Ventura, G., 2002. Taxes and marriage: a two-sided search analysis.
International Economic Review 43, 955-985
Coughlin, T.P., Drewianka, S.D., 2011. Can rising inequality explain aggregate trends inmarriage? Evidence from U.S. States, 1977-2005. The B.E. Journal of Economic
Analysis & Policy 11, 1-31.
Fukuda, S., 2013. Earnings in marriage formation in Japan. The Annals of the American
Academy of Political and Social Science 646, 107-128
Fossett, M.A., Kiecolt, K.J., 1991. A methodological review of the sex ratio: alternatives
for comparative research. Journal of Marriage and Family 53, 941-957.
Genda, Y., 2007. Jyakunen Mugyo no keizaigakuteki Saikento̅ [Economic
28

backgrounds of non-employed youths in Japan]. The Japanese Journal of Labour
Studies 567, 97-112. [in Japanese]
Gould, E.D., Paserman, M., 2003. Waiting for Mr. Right: rising inequality and declining
marriage rates. Journal of Urban Economics 53, 257-281.
Hara, T., 2010. Sapporo-Shi no Haigu̅ Kankei Betsu Jyun idou Ritsu 2000 Nen-2005
Nen no Suikei ［Estimation of the net mobility rate each marital status in Sapporo
city from 2000 to 2005]. Journal of Design and Nursing 4, 41-49. [in Japanese]
Hashimoto S., 2009. Mikonshasou ni okeru kekkon Ishiki no hendo̅ to Shakai・Kaisouteki・Youin: Panel Logit model ni yoru Aspiration Bunseki [Social status andmarriage aspiration among unmarried young people in Japan: a panel logit model
analysis of social consciousness]. Discussion Paper Series University of Tokyo
Institute of Social Science Panel Survey. No. 29. [in Japanese]
Hashimoto, Y., Kondo, A., 2012. Long-term effects of labor market conditions onfamily formation for Japanese youth. Journal of the Japanese and International
Economies 26, 1-22
Heckman, J.J., 1974. Shadow prices, market wages, and labor supply. Econometrica 42,
679-694.
Kambayashi, R., 2010a. Jyōyō Hiseiki Roudousha no Shosou [Several aspects on nonregular employees in recent Japan]. Global COE Hi-Stat Discussion Paper Series 120.
[in Japanese]
Kambayashi, R., 2010b. 1980 Nen Ikō no Nihon no Rōdō Jikan [Labor hours from the
1980s onward in Japan]. In: Higuchi, Y. (Ed.). Rōdō Shijyō to Shotoku Bunpai［Labor market and income distribution], Tokyo, Japan, Keio University Press, 159197. [in Japanese]
Kambayashi, R., 2013. Hiseiki Roudo̅sha [Non-standard employee]. Nippon Roudo̅
Kenkyu̅ Zassi 633, 26-29. [in Japanese]
Kambayashi, R., Kato, T., 2012. Good jobs, bad jobs, and the recession: lessons fromJapan’s Lost Decade. IZA Discussion Paper Series. No. 6666.
Kawaguchi, D., Kambayashi, R., Hara, H., 2011. Seishain to Hiseiki・Shain wo
Wakatsu Bunsuirei: Hiseiki・Roudousha no Shoso̅ [Divide between standard
employee and non-standard employee: The diverse aspects of workers]. Mimeograph.
[in Japanese]
Koyō no Arikata ni Kansuru Kenkyū Kai (Sato. H. et al.), 2009. Seiki・Hiseiki
Nigenron wo Koete Koyō Mondai no Nokosareta Kadai [Beyond the dualism of
regular and non-regular employment: the remaining challenge of the employment
issue]. [in Japanese] http://www.works-i.com/pdf/r_000118.pdf (accessed January 22,29

2017).
Kuo, Y.C., 2008. Wage inequality and propensity to marry after 1980 in Taiwan.
International Journal of Business and Economics 7, 231-248.
Lemieux, T., 2006. Increasing residual wage inequality: composition effects, noisy data,or rising demand for skill? American Economic review 96, No.3, 461-498.
Lichter, D.T., McLaughlin, D.K., Kephart, G., Landry, D.J., 1992. Race and the retreat
from marriage: a shortage of marriageable men? American Sociological Review 57,
781-799.
Loughran, D.S., 2002. The effect of male wage inequality on female age at firstmarriage. The Review of Economics and Statistics 84, 237-250.
Miyoshi, K., 2013. Nippon ni okeru Roudo̅・Shijyo̅ to Kekkon・Sentaku [Labor
market and marital choice in Japan]. Nippon Roudo̅ Kenkyu̅ Zassi. No. 638, 33-42.
[in Japanese].
Möller, J., Aldashev, A., 2007. Wage inequality, reservation wages and labor market
participation: testing the implications of a search-theoretical model with regional
data. International Regional Science Review 30, 120-151.
Nagase, N., 2002. Jyakunensō no Koyō no Hisekika to Kekkon Kōdō [Marriage, timing
and the effect of increase in non-standard employment among the youth in Japan].
Journal of Population Problems 58, 22-35. [in Japanese]
Ohta, S., 2007. Raifu Ibento to Jakunen Rōdō Shijyo－‘Kokusei Chōsa’Kara Mita
Shingaku・Kekkon・Shussan Kōdō [Life event and youth labor market – behaviorof education, marriage, and child-bearing examined from the National Census in
Japan]. In: Tachibanaki, T., (Ed.), Nihonkeizai no Jisshō Bunseki – Ushinawareta
Jyūnen wo Norikoete [The empirical analysis of Japanese economy － Through thelost decade], Tōyō Keizai Shinpō Sha, 217-238. [in Japanese]
Oppenheimer, V.K., 1997. Women’s employment and the gain to marriage: thespecialization and trading model. Annual Review of Sociology 23, 431-453.
Oppenheimer, V.K., Kalmijin, M., Lim, N., 1997. Men’s career development andmarriage timing during a period of rising inequality. Demography 34, 311-330
Sakai, T., Higuchi, Y., 2005. Freeter no Sonogo － Shūgyō・Shotoku・Kekkon・
Shussan [The long-term effect of the past unstable employment status]. The Japanese
Journal of Labour Studies 535, 29-41. [in Japanese]
Sakaguchi, Y., 2011. Sitsugyo̅ Risk no Su̅sei Bunseki [Trend analysis of theunemployment risk]. Sociorogy 55, 2-12. [in Japanese]
Sasaki, S., 2016. Nippon ni okeru Jyakunenso̅ no Koyo̅・Kankyo̅ no Akka to
Kekkon・ko̅do̅ ni Kansuru Jisho̅・Bunseki [Aggravation of youth employment
30

environment and marriage behavior in Japan]. Journal of Household Economics 43,
31-41. [in Japanese]
Shida, K., Moriyama, K., Watanabe, H., 2000. Kekkon Shijyo̅ no Henyo̅[Transfomation of marriage market]. In: Moriyama, K. (Ed.). Nippon no Kaisou
System 4 Gender・Shijyo̅・Kazoku [Stratification system in Japan 4: Gender,
Market, and Family]. University of Tokyo Press. 159-176. [in Japanese]
Shimer R., Smith L., 2000. Assortative matching and search. Econometrica 68, 343-369.
Shimizu, H., 1990. Gakureki・Kekkon・kaisou Saiseisan [Education, marriage, andreproduction of stratification]. In: Kikuchi, S. (Ed.). Gendai Nippon no Kaisou
Kouzou 3 Kyo̅iku to Shakai Duo [Stratification system of contemporary Japan 3:
Education and social mobility]. [in Japanese]
Shinozaki, T., 2006. Wage Inequality in Japan, 1979-2005. The Japanese Journal ofLabour Studies 3, No.4, 4-22.
South, S.J., Lloyd, L.M., 1992. Marriage opportunities and family formation: further
implications of imbalanced sex ratios. Journal of Marriage and Family 54, 440-451.
Stevenson, B., Wolfers, J., 2007. Marriage and divorce: changes and their driving
forces. Journal of Economic Perspectives 21, 27-52.
Tachibanaki, T., Kimura, K., 2008. Kekkon to iu Kazoku Keisei [Family formation bymarriage]. In: Tachibanaki, T., Kimura, K. (Ed.). Kazoku no Keizaigaku, Tokyo,
Japan, NTT Press, 90-94. [in Japanese]
Yugami, K., Sasaki, S., 2011. Shotoku Kakusa to Jyosei no Kon-inritsu: Chīki no
kekkon Shijyō no Siten Kara [Income inequality and female marriage rate: the effectsof local market conditions on marriage decisions]. The Kokumin-Keizai Zasshi 204,
65-79. [in Japanese]
Watanabe, H., Kondo, H., 1990. Kekkon to kaisou Ketsugo̅ [Marriage and stratification
combination]. In: Okamoto, H., Naoi, M., (Eds.). Gendai Nippon no Kaisou Kouzou
4 Jyosei to Syakai Kaisou [Stratification system of contemporary Japan 4: Womenand social stratification]. 119-145. [in Japanese]
Wilson, W., 1987. The Truly Disadvantaged: The Inner City, the Underclass, and Public
Policy. Chicago, Illinois, The University of Chicago Press.
Wood, R., 1995. Marriage rates and marriageable men: a test of the Wilson hypothesis.
The Journal of Human Resources 30, 163-193.

31