Labor Market Inequality and Marital
Segregation in East Asia

Shoichi Sasaki

June 2018
Discussion Paper No.1822

GRADUATE SCHOOL OF ECONOMICS
KOBE UNIVERSITYROKKO, KOBE, JAPANLabor Market Inequality and Marital Segregation in East Asia

Shoichi SasakiAbstract
This study examines the effects of inequalities in male educational wages and gender—such as thegender wage gap and barriers for labor force participation of female workers—on the labor market. It
considers the effects of social infrastructure or public goods on marital segregation in Japan, South Korea,
Republic of China, and Taiwan. The theoretical hypothesis that Fernández et al. (2005) build is empiricallyanalyzed using individual data from East Asian Social Survey. The estimation results suggest that wage
and gender inequalities in the labor market, such as skill wage premiums for men and full-time rates formarried women, significantly affect marital segregation. These results show that policies to decrease
inequality in the parental generation can decrease future inequality.

Keywords: Wage premium, skilled workers, gender gap, marital segregation, assortative mating,hypogamy

JEL classification: J11, J12, J16, J24, J31, J71
Acknowledgments: I would like to thank Kazufumi Yugami, Naoki Mitani, Yasuhide Tanaka, 2018
Annual Meeting participants of Population Association of Japan, and the seminar participants at Kobe
University for their helpful suggestions and comments. Finally, I am grateful to the EASS committee forthe data provided.
Funding: This work is supported by JSPS KAKENHI (Grant number JP15K03513). The funding group
had no other involvement in the completion of this study.

 Research Fellow, Graduate School of Economics, Kobe University, 2-1, Rokkodai-cho Nada-ku, Kobe

City, Hyogo Prefecture 657-8501, Japan shoichi_sasaki@people.kobe-u.ac.jp
11. Introduction
This study examines the effects of male educational wage and gender inequalities—such as genderwage gap and barriers for labor force participation of female workers—on the labor market in East Asia. It
considers the effects of social infrastructure or public goods on marital segregation in Japan, South Korea,
Republic of China, and Taiwan.
The expansion of income gap, increasing poverty, hierarchy sorting, and their persistency in eachcountry and Taiwan, including Japan, is well understood. In fact, some researchers in western countries
empirically report that the rate of educational assortative mating has increased, as well as the tendency ofmarriage between men and women with similar income levels, which substantially widens the householdincome gap in numerous countries (e.g., Sweeney and Cancian, 2004; Schwartz, 2010; Torche, 2010).
Therefore, many studies focus on the mechanisms that determine marital tendencies between men andwomen with similar educational backgrounds and incomes. The economic theory models indicate thatmarrying a lower-educated or lower-income earning partner reduces household income for individuals who
have high educational background or high income. Therefore, they avoid such marriages, instead
preferring partners with similar educational background or income.
Therefore, I empirically determine whether the implications of this model are applicable to Japan,
Korea, China, and Taiwan, where few studies exist on this topic.
The reason for examining this area is as follows. The chosen countries and Taiwan accomplishedeconomic growth, dubbed “a miracle of Asia,” while also bearing differences in time and stage. Moreover,the popularization of higher education among workers continues to increase. Although the income gaps of
those countries is considered smaller than that of other developed countries, the International Monetary
Fund (IMF, 2006) recently stated that the gaps widened between skilled and unskilled workers or withrespect to educational backgrounds.1 Additionally, Japan, South Korea, Republic of China, and Taiwan are
regionally close, with similar cultural background underpinned by Confucianism.2,3 Their ratios ofassortative mating are traditionally higher than that of European countries or the United States. As opposed
to western countries, they also commonly have broad gender inequalities that are deeper rooted, including
stereotypical gender roles, labor market inequalities, and a considerable gender gap, as per World
Economic Forum (2017). However, they also have different political–economic systems, economic growthstages, income gap among men of different educational backgrounds, and labor markets for women.
Additionally, though the degree of Confucianism is different for each territory, they inhabit the same
cultural area.4 We identify this diversity through a comparison of the societies in terms of their similarities

1

IMF (2006) points out that the rise in wage premiums for skilled worker demand by the Skill-Biased Technological
Change (SBTC) caused wage inequalities in the East Asian countries after the 1990s.
2 A detailed discussion on the Confucianism zone can be found in the main religion hypothesis of Smits et al. (1998).
3 The average of the coefficient of partial correlation of educational years for married women is 0.519 in 15 European andAmerican countries of the 34 countries, excepting 15 former communist countries Fernández et al. (2005) analyzes.
However, the coefficients of correlation calculated using EASS data in this study are 0.475 (Japan), 0.710 (Korea),
0.671(Taiwan), and 0.563 (China), and thus, all coefficients are relatively high, except for Japan.
4 Sechiyama (1996) classifies East Asian society into four categories from the viewpoint of Confucian penetration andpolitical and economic system. He insists gender equality varies by society, such as China, Korea, and Taiwan.

2and differences (Takenoshita, 2011). Therefore, they are suitable objects for this comparative study.
Further, few studies on educational assortative mating exist for East Asian countries.
Therefore, it is important to verify whether the results are consistent with the economic theory
hypothesis from extant studies, and comparable with the results of studies on Europe and the United States.
Hereafter, I empirically analyze Fernández et al.’s (2005) theory hypothesis using comparable individual
data from Japan, South Korea, Republic of China, and Taiwan.
The remaining paper is organized as follows. Section 2 reviews extant literature. Section 3 presents
the theoretical hypotheses and estimation model. Section 4 describes the data. Section 5 reports anddiscusses the estimation results. Section 6 concludes the paper.
2. Literature review
Research on social sciences like economics or sociology has exhaustively examined assortative
marriage in individuals who closely resemble in terms of education background, race, and religion.
Particularly, trends in educational assortative marriage have been previously analyzed by country. Mare
(1991), Francesconi (1995), as well as Schwartz and Mare (2005), show that the rate of educational
assortative mating has been increasing in the United States. Blossfeld and Timm (2003) compare the trendof educational assortative mating internationally in cooperation with researchers in 13 developed countries
in Europe and the United States. They show that it increased in almost all countries, largely because men
tend to choose women of the same educational background owing to the increased labor force participation
rate for women and income dependence on women in the family budget. Smith and Park (2009) indicate
that, in 10 East Asian societies, educational homogamy tends to decrease with modernization, female
employment, and reduced Confucian influence. In Japan, the National Institute of Population and Social
Security Research (2003) and Miwa (2007) show that educational assortative mating tended to decrease
recently, while Kohara (2001), Higuchi et al. (2003), Abe (2005), and others find that the law of Douglas–
Arisawa does not hold, with the appearance of high educational or income couples.
We find that Korea and China exhibit the following trends in assortative mating. Han (2010) in Korea
and Park and Smith (2005) in China find that assortative mating increased in both countries. However,some studies also examine the tendency to marry within same educational background using certain
hypotheses.5 In economics, Becker (1974) considers theoretically how men and women match throughtheir marriage behaviors based on the household production theory. He concludes that, when the humancapital of men and women in the process of creating a household has a complimentary relation, assortative
mating, that is, marriage between men and women of higher human capital, in turn induces high utility foreach couple, as well as optimality for society. Additionally, Chadwick and Solon (2002) and Ermisch et al.

5Smits et al. (1998) reviews hypotheses on the reasons why the educational assortative mating is common to all countries
from a sociological viewpoint. These are classified roughly into modernization (economic growth), labor force participation,and dominant religion hypotheses. The modernization (economic growth) hypothesis posits the assortative mating rate has
an inverse U-shaped relationship with economic growth. Fernández et al.’s theoretical model (2005), which this study
follows, chooses “Romantic hypothesis” or labor force participation hypothesis. Their idea is that women strengthen theirpreferences for elements other than income, for example, chemistry and love, over the same education or income levels,
given the rising economic power of women relative to men.

3(2006) empirically confirm that assortative mating greatly affects the intergenerational transfer ofhousehold income based on sorting and income inequality among households through marriage and thetransfer of earning ability between generations through education. Further, researchers in Europe and theUnited States build a model that unified marriage behavior of the present generations and transfer of
earning ability to future generations to examine their effects on household income inequality. On thetheoretical side, Kremer (1997) insists that, even if the extent of marital segregation increases, it hardly
influences the income distribution and education.
Fernández and Rogerson (2001) build a simulation model of hierarchical stratification by educationacquisition and marital segregation between generations, concluding that income inequality widens withhierarchization. Fernández (2002) obtains a similar result after refining the model by introducing thequality of matching (i.e., chemistry or love) into the utility function of marriage, as well as the householdincome level. Fernández et al. (2005), whose model this study relies on, derive a theoretical hypothesis
wherein the extent of marital segregation increases according to the extent of the gender inequality. They
incorporate elements of gender inequality in the labor market in their model. Further, using cross-sectional
data of 34 European and American countries, they obtain an empirical result consistent with the theoretical
hypothesis. However, they have mainly two empirical problems. First, they cannot remove the unobserved
fixed effects of the countries and region individually because of the data’s cross-country-sectional nature.
Second, the range of the marriage markets they assumes is in country unit and too wide.
Therefore, this study uses multi-year data on local areas or economic blocks in Japan, South Korea,
Republic of China, and Taiwan to examine the effects of labor market inequality (e.g., income inequality
among educational backgrounds, gender gap in income, and entry barriers to the labor market) on theextent of marital segregation by controlling effects of unobserved factors characteristic to each country.
3. Theoretical hypotheses and estimation modelThis study is based on Fernández et al.’s (2005) model—a generation overlapping model with three
periods. The economic agents choose the amount of labor skill formation and their marriage partner in thefirst period. They work, reproduce, and raise children in the second period, with skilled workers repaying
educational investment expenses over this period. In the third period, they consume based on their income
minus educational loans, also assuming the role of monitors for repaying the educational loans of theirchildren. The next section explains the theoretical model with a focus on marital sorting in the first period.
3.1 Marital segregation model
3.1.1 Model setup
This study assumes no gender gaps, such as income gap between men and women in the labor market,
at first. Men and women obtain utilities from two elements on which their consumption is based—household income and quality of matching (chemistry or love)—according to Fernández et al. (2005). The
utility function of men and women is formulated as below:
V(I, q) = u(I) + q,

(1)
4

where I represents household income, and q, the amount of love (quality of matching), converted intomonetary value. Here, (s) represents the skilled workers, and (u), unskilled workers. If a worker has highskills, he/she obtains net income 𝑤
̃𝑠 ≡ 𝑤𝑠 − 𝑑, formed of wage 𝑤𝑠 minus the monetary cost to be askilled worker through educational investment d paid in the second and third periods. Then, an unskilledworker obtains wage 𝑤𝑢 in the second and third periods. Households are classified as combinations ofskill levels between husband and wife:62𝑤
̃𝑠 ,̃𝑠 + 𝑤𝑢 ,𝐼𝑖𝑗 = {𝑤2𝑤𝑢 ,𝑖𝑓 𝑖, 𝑗 = 𝑠𝑠
𝑖𝑓 𝑖, 𝑗 = 𝑠𝑢.
𝑖𝑓 𝑖, 𝑗 = 𝑢𝑢(2)

Each worker has two opportunities for matching in the marriage market. In the first meeting stage,
he/she meets a spousal candidate whose level of love is q with a random skill level, and then, leaves themarriage market if he/she accepts marriage. A worker who does not marry at the first meeting stage,proceeds to the second meeting, meeting another spousal candidate whose level of love is 𝜇,[0, 𝑞̅] withthe same skill level, finally deciding whether to marry.
The situation of a skilled worker is as follows. If he/she meets an unskilled lower wage worker with ahigh level of love (q) in the first meeting stage, he/she decides whether to marry the unskilled worker or askilled higher wage worker whose level of love is 𝜇 in second meeting stage. In other words, the skilledworker faces a trade-off between love and money.
Even if the skilled worker marries whichever, the utilities are indifferent when 𝑉𝑠𝑢 (𝑞) = 𝑉𝑠𝑠 (𝜇)holds. Here, as 𝑉𝑖,𝑗 (𝑞) ≡ 𝑢(𝐼𝑖,𝑗 ) + 𝑞 is true, we analytically calculate the level of q at which utilities
become indifferent, and derive the solution below:𝑞∗ = 𝑢(2𝑤̃𝑠 ) − 𝑢(𝑤̃𝑠 + 𝑤𝑢 ) + 𝜇.

(3)

The difference of expected amount of love 𝑞∗ − 𝜇 becomes equal to the loss of utility in the case of
marrying an unskilled worker 𝑢(2𝑤̃𝑠 ) − 𝑢(𝑤̃𝑠 + 𝑤𝑢 ); that is, 𝑞∗ refers to the amount of love thatcompensates the utility loss from the decreasing household income when a skilled worker marries anunskilled worker.
3.1.2 Wage inequality and marital sorting
Here, we analyze how exogenous changes in skilled and unskilled workers’ wages affect maritalsorting. From equation (3), we differentiate 𝑞∗ , and obtain:
𝜕𝑞 ∗
̃𝑠
𝜕𝑤
𝜕𝑞 ∗
𝜕𝑤𝑢

= 2𝑢′ (𝐼𝑠𝑠 ) − 𝑢′ (𝐼𝑠𝑢 ),= −𝑢′ (𝐼𝑠𝑢 ) < 0.

(4)

The rise in the wage of the unskilled worker decreases the extent of marital segregation by decreasing 𝑞∗ .
Then, the rise of skilled workers’ wages increases the degree of marital sorting if we assume a shape of theutility function with weak concavity according to Fernández et al. (2005).

6Society bipolarizes households of skilled worker couples as rich and the ones of unskilled workers as poor if marriages do
not occur across hierarchies.

5

From the above, the increase in the wage premium of skilled workers 𝑤
̃/𝑤
𝑠𝑢 increases the amount oflove needed to compensate the utility loss from a decreasing household income when a skilled worker
marries an unskilled worker. Then, the probability that skilled workers marry unskilled workers decreases,
while the degree of marital segregation increases.
3.1.3 Gender inequality and marital sorting
Next, we examine a model that includes gender inequality explicitly. Men spend all their productive
time in the labor market, while women decide how much time they devote for childcare and work in thelabor market. If we assume that time spent on childcare contributes to the child’s human capital
accumulation, the household utility function is described as follows:U(𝑤𝑚𝑖 , 𝑤𝑓𝑗 ) = 𝑙𝑛(𝑤𝑚𝑖 + 𝑤𝑓𝑗 𝑡𝑗 ) + 𝛽𝑙𝑛[(1 − 𝑡𝑗 )𝜓𝑗 ],(5)

where β < 1 and i, j ∈ {𝑠, 𝑢} refer to skill types. m and f represent men and women, while 𝑡𝑗 refers to
the time women of j type work; (1 − 𝑡𝑗 )𝜓𝑗 represents the child’s human capital level. Moreover, total
hours are standardized as 1. The first order condition of 𝑡𝑗 the household utility maximizes is described asfollows:
𝑤𝑓𝑗
𝑤𝑚𝑖 +𝑤𝑓𝑗 𝑡𝑗

−𝛽

1
1−𝑡𝑗

= 0 with 𝑡𝑗 ≥ 0,

(6)then optimal labor hours are described as follows:
𝑤𝑓𝑗 −𝛽𝑤𝑚𝑖, 𝑖𝑓 𝑤𝑓𝑗 > 𝛽𝑤𝑚𝑖
𝑡𝑗 = { 𝑤𝑓𝑗(1+𝛽).
0, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒(7)

Therefore, the hours a woman works in the labor market increase according to the rise in her wage
and decrease with the rise of the spouse’s wage. For men and women, the utility from marriage can be
described as U(𝑤𝑚𝑖 , 𝑤𝑓𝑗 ) + 𝑞𝑚𝑖 and U(𝑤𝑚𝑖 , 𝑤𝑓𝑗 ) + 𝑞𝑓𝑗 , respectively. Since we can categorize the main
discriminative practices against women in the labor market into the wage gap between men and womenand entry barriers to the labor market, their effects on marital segregation are derived.
First, decision-making regarding marriage for the skilled labor woman with a gender wage gap
between men and women is considered, then the level of love, which becomes indifferent between
assortative and non-assortative mating for skilled women, is:∗𝑞𝑓𝑠= μ + 𝜆′ [𝑈(𝑤𝑚𝑠 , 𝑤𝑓𝑠 ) − 𝑈(𝑤𝑚𝑢 , 𝑤𝑓𝑠 )]𝑤𝑚𝑠+ 𝑤𝑓𝑠= +𝜆′ [(1 + 𝛽)]𝑙𝑛 (𝑤𝑚𝑢 +𝑤𝑓𝑠),(8)

where 𝜆′ represents the probability of meeting a skilled man; the results in equation (7) are used as∗⁄𝜕𝑤𝑓𝑠 < 0 from equation (8), utility loss of the marriage with ahousehold utility. When obtaining 𝜕𝑞𝑓𝑠∗
non-skilled labor man decreases through a decrease of 𝑞𝑓𝑠
when the woman’s wage increases; that is,since the quantity of love that compensates decrease in household income from a skilled woman marrying
an unskilled man decreases in a society where the wage gap between men and women is small or thewages of high educational background women are relatively high. As such, the probability that a female
skilled labor woman marries an unskilled labor man increases, as does the extent of marital segregation.
6

Then, this study considers the effects of entry barriers to the labor market, for women, on women’smarriage behavior. We compare a society where women can choose work hours freely (i.e., 𝑤𝑓𝑠 > 𝛽𝑤𝑚𝑠 )with a society where they cannot choose the amount of labor supply freely. We then obtain the differencein the level of love at which the utilities between assortative mating and hypogamy, under which a skilled
woman marries an unskilled man, become indifferent:
𝑤𝑚𝑠 +𝑤𝑓𝑠∗∗
𝑞𝑓𝑠− 𝑞̃𝑓𝑠= 𝜆′ [(1 + 𝛽)𝑙𝑛 (𝑤𝑚𝑢 +𝑤𝑓𝑠𝑤) − 𝑙𝑛 𝑤 𝑚𝑠 ] < 0.
𝑚𝑢

(9)

The probability of women—who can work freely in the labor market—marrying unskilled men due todecreasing q* increases. In other words, the degree of marital segregation decreases with higher labor force
participation from married women.
From Fernández et al.’s (2005) model, we can predict that the extent of marital segregation increases
in a society where the gender gap, such as gender wage gap or the extent of entry barriers to the labormarket, is wider.
3.2 Estimation model
The hypothesis derived from the theoretical model is that increases in the skill premium and gender
gap in the labor market increase the extent of marital segregation by increasing the amount of love (𝑞∗ ) askilled woman accepts for marrying an unskilled man and decreasing the probability of marrying acrosshierarchies. For examining this theoretical hypothesis, this study defines a skilled woman as a university
graduate, and examines the effects of the skill premium and gender gap on the probability of choosing
educational hypogamy, under which a university graduate woman marries a lower educational backgroundman. For the empirical analysis, we divide Japan, South Korea, Republic of China, and Taiwan into 21
local areas/economic blocks7 to examine the validity of the hypothesis in the countries and region
individually, controlling for the fixed effects of these local areas/economic blocks.
An estimation model is derived as follows. From (3), a university graduate woman chooses hypogamy
when the q of the marriage partner exceeds 𝑞∗ . In other words, 𝑞 − 𝑞∗ > 0. Here, if 𝑞 − 𝑞∗ = 𝑦 ∗ holds,
she chooses hypogamy when 𝑦 ∗ > 0 holds. Conversely, she chooses assortative mating when 𝑞 − 𝑞∗ =𝑦 ∗ ≤ 0 holds. Therefore, this study estimates a probit model that takes the value 1 when a university
graduate woman chooses hypogamy and 0 otherwise (educational assortative mating):
𝑘∗
𝑘
𝑘
𝑘
𝑦𝑙𝑟𝑡
= 𝛼 𝑘 𝐴𝑙𝑟𝑡 + 𝛽 𝑘 𝑋𝑟𝑡+ 𝛾 𝑘 𝑍𝑟𝑡
+ 𝑅𝑟 𝛿 𝑘 + 𝜀𝑙𝑟𝑡
,

(10)

𝑘
𝑘∗
𝑘
𝑦𝑙𝑟𝑡
= 1 𝑖𝑓 𝑦𝑙𝑟𝑡
> 0, 𝑦𝑙𝑟𝑡= 0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒,𝑘∗where 𝑦𝑙𝑟𝑡
is a latent variable that takes the value 1 when a university graduate woman i, who lives inlocal area or economic block of country or region r, marries a man whose educational background is below𝑘university, and 0 otherwise. Additionally, 𝐴𝑙𝑟𝑡 is an age group dummy, 𝑋𝑟𝑡
a skill premium (wage𝑘inequality) index of each local area or economic block, 𝑍𝑟𝑡
a gender gap index, 𝑅𝑟 a dummy variable of𝑘local areas or economic blocks, and 𝜀𝑙𝑟𝑡
the error term.

7

The list of 21 local areas/economic blocks is provided in the Appendix 1.

7

In reference to Fernández et al. (2005), we use skill premiums of skilled workers, as opposed tounskilled workers (wage inequality among educational backgrounds), and gender gap as explanatory
variables. First, a university graduate man is defined as a skilled worker, while lower educational
background workers, as unskilled. Second, four educational wage inequality indices are set up,8 with four
variables—average annual income ratio of university graduate men and other lower educational
background men; “Wage ratio,” log of raw average wage of a university graduate men and other lower
educational background men; “Log of raw wage,” the coefficient of an university graduate man dummy
variable in the basic Mincer wage function; “Skill indicator, “ an expected earning rate per educational
year in the basic Mincer wage function; and “Mincer coefficient.” Four variables are also taken as genderindices—9average yearly income ratio of university graduate men and women; “Wage Gap,” men’s wage
premium for full-time work; “Male coefficient,” a labor participation rate of married women; and the fulltime worker ratio. Moreover, the GDP per capita of each local area or economic block is extrapolated as“GRDP,” a representative variable of economic development stage or living standard.
We assume the effects of each explanatory variable on hypogamy for university graduate womenbased on the theoretical hypothesis and conventional studies. First, we predict that only “Log of raw wage”
of lower educational background man (wage of unskilled workers) has a positive effect on the probabilityof hypogamy; all other indices have a negative effect on hypogamy based on the theoretical hypothesis.
GRDP per capita is assumed to have a positive effect on the probability of hypogamy based on theempirical results of Fernández et al. (2005). Next, we assume gender gap indices have negative effects on
the probability of hypogamy when they become wider. In other words, “Wage Gap” and “Male coefficient”
have negative effects, while the labor force participation rate of married women and the full-time worker
ratio have positive effects.
4. DataThis study uses data from 2006 and 2008 based on the individual data of the East Asian Social Survey(EASS), which analyzed Japan, Korea, China, and Taiwan, and was jointly conducted every two years
from 2006. EASS establishes common questions based on issues and concerns unique to East Asiansocieties, consistent with the Japanese General Social Surveys (GSS), Korea GSS, China GSS, andTaiwanese Social Change Survey, which are national-scale investigations that cover the adult population inline with the United States GSS. The data contain respondents’ (and spouse’s if married) sex, age, marital
status, educational level or years, job status, and yearly income, making them suitable for this study.
Further, these data can be subdivided by local area or economic block,10 as well as for countries/regions.
Therefore, the effects of the situation on each labor market of all local areas or economic blocks areanalyzed; that is, the men’s skill premium and gender gap on marital sorting. Additionally, we harmonize
8

The calculation method for wage inequality indices for men is explained in Appendix 2.
The calculation method for the gender gap indices is explained in Appendix 3.
10 This study uses six areas for Japan, according to the Statistics Bureau, Ministry of Internal Affairs and Communications;five areas in Korea, according to Shin (2009); three areas in Taiwan, according to Noda (1992) and more; and seveneconomic blocks in China, according to Kato (2003). This study also uses various wage inequality indices for each area andeconomic block. The list of all 21 areas and economic blocks is provided in Appendix 1.
98

the wages of the countries and region by converting local currencies to USD using the exchange rate forthe two respective years as per the American Federal Reserve Board (2012), and adjusting the 2006 prices
when calculating wage inequality and gender gap indices.
We use the observations for the two years for 26- to 45-year-old married university graduate men andwomen.11 In choosing the sample, it is necessary to avert the endogeneity problem of the wage premiumof university graduate workers and marital segregation. In Fernández et al.’s model (2005), first, each man
and woman of this generation decides his/her education level, then the ratio of university graduate men andwomen and wage premium of university graduates are determined in the society, thus theoreticallydetermining the degree of the marital segregation through marriage behaviors. Therefore, as per thetheoretical model, and for analysis, the economic agents has to be among the age groups that completed
their education investments in order to begin marriage behavior. In fact, the tendency to marry later in life
increases remarkably in Japan, South Korea, Republic of China, and Taiwan, with the average age of firstmarriage for men in urban areas exceeding 30 in Japan, Korea, and China. Therefore, sample selection bias
is possible for a young age-group (i.e., early 20s), which is why observations for the 26–45 age group areused. The descriptive statistics of the variables are shown in Table 1. The four men’s skill premium indicesrank from high to low as follows: China, Korea, Taiwan, and Japan. This is consistent with the results ofChan and England (2011), who decompose the factors of the gender wage gap in Japan, Korea, and Taiwan
using the same data. The labor force participation rate of married women is highest in China, followed by
Taiwan, Japan, and Korea. The full-time working rate for married women is highest in Taiwan, Japan,
China, and Korea in that order. Additionally, the GRDP per capita is highest in Japan, Korea, Taiwan, andChina in that order.
Table 1. Descriptive statistics of variables
Japan

Variable

Obs.

Mean

Std. Dev.

Min.

Max.

77

0.195

0.399

0

1

77

1.258

0.127

1.002

1.684

77

10.6730.243

10.16611.08277

10.547

0.187

10.13411.092Skill indicator

77

0.295

0.143

0.032

0.532

Mincer coefficient

77

0.091

0.222

0.053

0.126

Wage gap

77

1.991

0.660

1.000

3.004

Male coefficient

77

0.909

0.280

0.420

1.193

Hypogamy dummy
Wage ratio
Log raw wage (Log of averageannual income of university
Skill
wage
premium
indices

graduate men
Log raw wage (Log of averageannual income of other lower
educational background men)

11

This age-group is divided into the 26–35 and 36–45 age groups, and the skill premium (wage inequality) index isdeveloped to correspond to each age-group and gender gap indices. However, Fernández et al. (2005) use the 36–45 age
group.

9

GenderLabor market participant rate ofgap

married womenindices

Full-time worker rate of

77

0.583

0.109

0.294

0.742

77

0.445

0.218

0.045

0.681

GRDP per capita

77

37,757

4,147

28,16642,445Age-grade dummy (36-45 years old)

77

0.506

0.503

0

1

Obs.

Mean

Std. Dev.

Min.

Max.

232

0.142

0.350

0

1

232

1.768

0.388

1.128

2.450

232

8.543

0.341

7.042

9.163

232

7.996

0.209

7.611

8.276

Skill indicator

232

0.444

0.115

0.263

0.601

Mincer coefficient

232

0.097

0.025

0.027

0.123

Wage gap

232

1.766

0.473

1.056

3.243

Male coefficient

232

0.576

0.133

0.198

0.973

232

0.466

0.084

0.278

0.700

232

0.333

0.099

0.111

0.640

GRDP per capita

232

21,4762,510

16,35725,320Age-grade dummy (36-45 years old)

232

0.595

0.492

0

1

Obs.

Mean

Std. Dev.

Min.

Max.

45

0.422

0.499

0

1

45

8.323

0.365

7.378

9.229

45

7.777

0.399

7.090

8.497

45

0.599

0.214

0.280

0.984

Skill indicator

45

0.599

0.214

0.280

0.984

Mincer coefficient

45

0.123

0.029

0.052

0.157

Wage gap

45

1.368

0.277

0.905

2.000

GenderMale coefficient

45

0.328

0.118

0.199

0.572

gap

Labor market participant rate ofindicesmarried women

45

0.777

0.072

0.610

0.885

married women

Korea

Variable
Hypogamy dummy
Wage ratio
Log raw wage (Log of averageannual income of university
Skill
wage
premium
indices

graduate men
Log raw wage (Log of averageannual income of other lower
educational background men)

GenderLabor market participant rate ofgap

married womenindices

Full-time worker rate of
married women

China

Variable
Hypogamy dummy
Wage ratio
Log raw wage (Log of averageannual income of university
Skill
wage
premium
indices

graduate men
Log raw wage (Log of averageannual income of other lower
educational background men)

10

Full-time worker rate of

45

0.415

0.136

0.167

0.590

GRDP per capita

45

3,377

1,458

1,204

6,490

Age-grade dummy (36-45 years old)

45

0.400

0.495

0

1

Obs.

Mean

Std. Dev.

Min.

Max.

110

0.273

0.447

0

1

110

1.270

0.195

1.013

1.739

110

7.461

0.244

7.079

7.927

110

7.232

0.226

6.936

7.568

Skill indicator

110

0.336

0.145

0.102

0.463

Mincer coefficient

110

0.090

0.010

0.083

0.122

Wage gap

110

1.296

0.124

1.000

1.418

Male coefficient

110

0.322

0.068

0.240

0.436

110

0.719

0.030

0.645

0.806

110

0.586

0.069

0.468

0.721

GRDP per capita

110

17,125812

16,317

17,933Age-grade dummy (36-45 years old)

110

0.500

0.502

0

1

married women

TaiwanVariable
Hypogamy dummy
Wage ratio
Log raw wage (Log of averageannual income of university
Skill
wage
premium
indices

Gendergap
indices

graduate men
Log raw wage (Log of averageannual income of other lower
educational background men)

Labor market participant rate of
married women
Full-time worker rate of
married women

Moreover, the sample size is considerably smaller compared to the population of the countries orregion and in comparison to the original sample size of EASS, which is a remarkable tendency in China
and Japan particularly. The sample of university graduate women who are 26–45 years old, however, does
not have a sample selection bias concerning the university graduate and marriage rates. For example,
according to Figures 1 and 2, in China, the marriage rate of women is not as low, although the university
graduate rate is considerably lower, thus the decrease in sample size. Japan has lower university graduate
rates for women than Korea and Taiwan, as well as lower marriage rate for women compared to the others,
thus the sample size is small.

11

45.00%40.00%
35.00%
30.00%
25.00%

26-35 years old

20.00%

36-45 years old

15.00%
10.00%
5.00%
0.00%
Japan

Korea

China

TaiwanFigure 1. University graduate rates for women
Source: Author’s calculations using EASS (2006, 2008)

90.00%
80.00%
70.00%
60.00%
50.00%

26-35 years old

40.00%

36-45 years old

30.00%
20.00%
10.00%
0.00%
Japan

Korea

China

TaiwanFigure 2. Marriage rates for women
Source: Author’s calculations using EASS (2006, 2008)

Figures 3–5 present the relationships between the hypogamy rate for women and “Skill indicator,”“Wage gap,” and full-time worker ratio for married women in each local area and economic block using
scatter plots, where the red, blue, yellow, and green circles represent Japan, Korea, China, and Taiwan,
respectively. Moreover, the size of the circles expresses the scale of the sample in each local area or
economic block. The scatter plots show a negative relationship between skill premium and hypogamy ratein Japan, South Korea, Republic of China, and Taiwan. Further, the hypogamy rate does not have a12

correlation with “Wage gap.” Instead, a weak positive correlation is registered between the hypogamy rate
and full-time worker ratio of married women.

Figure 3. Scatter plot of the hypogamy rate for women and the skill indicator
The red, blue, yellow, and green circles represent Japan, Korea, China, and Taiwan, respectively.
Source: Author’s calculations using EASS (2006, 2008)

Figure 4. Scatter plot of the hypogamy rate for women and wage gap
The red, blue, yellow, and green circles represent Japan, Korea, China, and Taiwan, respectively.
Source: Author’s calculations using EASS (2006, 2008)

13

Figure 5. Scatter plot of the hypogamy rate of women and full-time worker rate for married women
The red, blue, yellow, and green circles represent Japan, Korea, China, and Taiwan, respectively.
Source: Author’s calculations using EASS (2006, 2008)

5. Estimation results and discussion
5.1 Baseline estimation results
First, this study estimates the effect of the increase in the wage premium of university graduate menon the probability of a university graduate woman choosing hypogamy using four educational wage
inequality indices for men. Table 2 presents the baseline estimation results.
Table 2. Effects of various wage premiums for men
Japan
Wage ratio

Korea-0.487( 1.957

0.240
)

**

(

-6.528Log of raw wage

( 2.911

Skill indicator
Mincercoefficient

)

**

(

)

(

(

0.209

0.691

)

**

(

0.405

)

***

(

(

1.727

)

(

1.547

)

*

(

0.225

)

(

***

(

0.992

)

(

**

(

11.786

1.171

1.628

)

(

0.347

)

(

9.050

Dependent variables: university graduate woman: choice of hypogamy=1, others=0
Observation numbers of each country and region are follows: Japan: 77, Korea: 232, China: 39, Taiwan: 110
Other explanatory variables are 36–45 years old dummy and dummy of local areas or economic blocks.
The second-step parentheses indicate standard errors modified by clustering based local areas or economic blocks.

14

)

)

0.644

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

)--1.977

8.648
)

)

1.801

-1.591
)

0.639
0.617

0.262

-4.335
)

0.300

0.872

-0.845-2.070-29.965( 33.647)

1.245-5.850( 2.907

0.186

Taiwan

0.200

0.543

4.873
( 2.969

China

)

***

Column 1 shows all four indices have a significant negative effect on the probability of hypogamy foruniversity graduate women, and are consistent with the theoretical hypothesis for Japan. Among them, theWage ratio and Log of raw wage have significant effects at the 5% confidence level. Column 2 indicates
three indices (except Log of raw wage) of university graduate men also have significant negative effects,
and are consistent with the theoretical hypothesis for Korea. Among them, the Log of raw wage of otherlower educational level men, Skill-indicator, and Mincer coefficient have significant effects at the 10%,1%, and 5% confidence levels, respectively. Column 3 demonstrates all four indices have no significant
effects for China. However, the coefficient signs of Log of raw wage of other lower educational level men
and Skill indicator are consistent with the theoretical hypothesis. Column 4 shows the Skill indicator has
negative significant effect at the 1% confidence level in Taiwan. The results show the effect of the Skill
indicator is almost significant in relation to the theoretical hypothesis in the countries/area examined. Thus,
the increase of the skill wage premium decreases the probability of hypogamy for university graduate
women, while the extent of marital segregation increases. This suggests that, because the other three
indices depend on age and years of job experience, it is appropriate to use the skill premium that controlsthem.
5.2 Alternative factor
Fernández et al. (2005) take the degree of economic development or standard of living as analternative factor that affects the extent of marital segregation, and examine their effects using GRDP per
capita as a proxy. Smits et al. (1998) also analyze the hypothesis wherein the educational assortative
mating rate has an inversed U-shaped relationship with economic development. Therefore, this study adds
GRDP per capita to the above baseline estimation according to these studies. Table 3 presents the results.
Table 3. Effects of various wage premiums for men with GRDP per capita
Japan
Wage ratio

Korea-5.020(

GRDP per capita
(

2.306

0.041
)

**

(

(

GRDP per capita(
Skill indicator

0.243

0.183

)

(

0.601

0.347
)

(

0.237

9.98E-

3.99E-

2.89E-

04

05

05

04

1.71E-

2.68E-

3.18E-

04

)

(

2.988

3.567

)

05

***

(

0.019
)

**

(

3.498
(

Taiwan1.40E--6.860Log of raw wage

China

(

(

-0.922

0.552

)

(

0.217
)

04

)

1.446

)

*

(

0.375

04

)

(

0.387

)

(

1.203

9.08E-

5.70E-

4.01E-

04

05

05

04

04
-6.868)(

7.62E-

)

05
0.269

(

1.68E04-1.669

15

)

3.295

1.66E-

1.90E-

)

0.023

0.124

1.206

2.89E-))(

5.24E05-1.766

)

)

(

2.490

GRDP per capita

)

(

1.558

(

(

)

(

0.649

4.35E-

3.83E-

04

05

04

)

6.48E-

(

)

05

*

2.06E-

(

)

)

0

*

(

-65.450

15.234)(16.294

1.41E-

9.67E-

4.77E-

04

04

05

04

2.11E-

3.18E-

3.01E-

04

(

)

05

***

(

***

)

05

1.51E-))

6.05E-(

10.816

2.95E+0

(

)

04

5.530

36.217

GRDP per capita

1.373

1.09E--30.900

coefficient

(

04

04

Mincer

)-1.31E-

2.06E-

(

**

)

04

1.10E-

(

04

)

***

)

***

Notes:
Dependent variables: university graduate woman: choice of hypogamy=1, others=0
Observation numbers of each country and region are follows: Japan: 77, Korea: 232, China: 39, Taiwan: 110
Other explanatory variables are 36–45 years old dummy and dummy of local areas or economic blocks.
The second-step parentheses indicate standard errors modified by clustering based local areas or economic blocks.
***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

The signs of the coefficients on GRDP per capita are positive in every country and region, except fora part of Japan, while having statistically positive significant effects especially in Korea and Taiwan. Thissuggests that the degree of marital sorting tends to decrease as living standards increase. However, foreducational wage inequality indices, the signs of Skill indicator and Mincer coefficient are opposite only in
Korea. Moreover, in Korea, multicollinearity between GRDP per capita and the explanatory variables
exists. For Japan and Taiwan, the skill premiums on the labor market have positive significant effects on
marital segregation by considering GRDP per capita an alternative hypothesis.
5.3 Gender gap
Table 4 shows the estimation results obtained by adding various gender gap indices as hypogamy
determinants for university graduate women considering the educational wage inequality index for men.
Here, we use the Skill indicator, which best fits the theoretical hypothesis with respect to the baseline
estimation of the four male income inequality indices, as does the educational wage inequality index.
Table 4. Effects of various gender gap indices with Skill indicator
Japan
Skill indicator

Wage gap

Skill indicator

Korea-4.827(2.873-1.622
)

*

(

0.707
(

0.601

3.618
0.656

0.572

)

(

0.471

***

(

(

0.389

0.872

-0.937
)

*

(

0.335
)

(

-2.450)

Taiwan-1.624)0.416-4.501
(

China

0.514

-0.536

***

(

0.798
0.211

16

)

2.059
)

(

-1.633
)

0.833

1.418

)

-0.417
)

**

(

0.687
4.543

)

Male
coefficient
Skill indicator

(

0.864

)

(

-6.243(

Labor market

2.817

1.142

)

*

(

-1.989)**(-1.128

0.480

0.983

)

(

-1.558
)

***

(

0.817

1.051

2.311

)

***

)

***

-2.051)(-0.8720.235-3.104

participation
rate formarried

(

2.257

)

(

1.035

)

(

2.364

)

(

6.648

)

women
-1.286Skill indicator(

Full-time

3.720-1.547
)

(

3.083

0.810

-1.324
)

*

(

1.221

1.137

-1.827)(-2.304

0.205

)

***

1.402

worker rate formarried

(

1.372

)

**

(

1.359

)

(

2.955

)

(

3.229

)

women
Notes:
Dependent variables: university graduate woman: choice of hypogamy=1, others=0
Observation numbers of each country and region are follows: Japan: 77, Korea: 232, China: 39, Taiwan: 110
Other explanatory variables are 36–45 years old dummy and dummy of local areas or economic blocks.
The second-step parentheses indicate standard errors modified by clustering based local areas or economic blocks.
***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

Among gender gap indices, the “Wage gap” between men and women is high in Japan and Korea as
per the descriptive statistics, but relatively low in China and Taiwan. However, this does not havesignificant effect, although the coefficient is positive for all Japan, South Korea, Republic of China, andTaiwan. Therefore, considering only wage, there are no significant effects of the advancement of women
on marital segregation.
Labor market penetration by women has a positive effect in promoting hypogamy for university
graduate women because the labor force participation rate of married women, as an index of entry barriers
to the labor market for women, has significant effects in no country or region. However, this index
includes support employees, such as part-timers and family employees. Yet, when the full-time worker rate
for married women increases, the probability of hypogamy for university graduate women tends toincrease in Japan, Korea, and Taiwan. Specifically, the effect of the full-time worker rate for married
women has a significant effect in Japan. In the examined East Asian countries and region, the probabilityof hypogamy for university graduate women decreases with a rising wage premium that, in turn, increases
the extent of marital segregation. For the gender gap indices, the extent of marital segregation decreases
with the probability of hypogamy for university graduate women when the full-time worker rate formarried women increases in every country, except China, and one region. Further, GRDP as the index ofdegree of economic development and standard of living in each local area or economic block has anegative effect of the extent of marital segregation through decreasing the probability of hypogamy. In
other words, economic development and standard of living affect the extent of marital segregation, while17

wage inequality for men and the opportunity of the full-time work as the gender gap decreases in the labormarket are also important factors for the extent of marital segregation.
5.4 Discussion
The results suggest that increase of wage premiums for university graduate men increases the extent
of marital segregation by decreasing the tendency of hypogamy for university graduate women. As
previously mentioned, increases in relative demand for skilled workers due to skill-biased technical change
increases wage premiums for university graduates. Like extant studies on Europe and the United States, thetechnical changes in each country and Taiwan also increase the extent of marital segregation by increasing
wage inequality, and thus, prompt educational or earning ability assortative mating in East Asia. However,
the effect of the gender gap on a labor market for high educational background women varies by country or
region. First, although wage inequality between university graduate men and women is highest in Japan,no significant results are obtained. Second, the results show that an increase in the full-time worker rate of
married women decreases the extent of marital segregation.
In the case of Japanese university graduate women, it is thought that promotion of full-time work, as
continuous employment which women are able to make use of the accumulated human capital, decreases
the degree of the marital segregation and wage inequality among households. The results on gender gap inJapan might be common to Korea and Taiwan to some extent, but are not statistically significant; that is,
the expansion of opportunities for full-time work for married women has a significant effect on marriage
behavior, rather than the wage gap between men and women. In other words, encouraging labor force
participation of women in consideration of quality, rather than quantity, of work is important in East Asia.
Interestingly, in China, all gender gap indices have no significant effects on marital sorting, probably
because China encourages an advanced society for women through its national economic system.
Additionally, since the gender wage gap for the high educational background group is small, the formationof a competitive labor market, similar to a capitalist society, might be developing (Ishizuka, 2010).
6. Conclusions
The expansion of income gap, increasing poverty, hierarchy sorting, and their persistency in eachcountry and region, including Japan, is well understood. Therefore, numerous studies focus on themarriage behavior of men and women, and the function of transfer of earning abilities between generations
thereof, from the perspectives of both economics and sociology.
First, this study theoretically examined the effects of male educational wage inequality and gender
gap—such as gender wage gap and barriers for the labor force participation of married women—on marital
segregation. Second, empirical examinations of the theoretical hypothesis are performed using data on
Japan, South Korea, Republic of China, and Taiwan. All accomplish remarkable economic growth so as tobe considered the “miracle of Asia,” but with widening income gaps between skilled and unskilled workers
or among educational backgrounds. Consequently, the study confirmed that the wage premium of
university graduate men has a positive significant effect on the extent of marital segregation, which is
18

consistent with extant studies on Europe and the United States. In regard to the effect of gender gap (e.g.,
wage or employment), the gender wage gap was found to have no significant effect, confirming that the
full-time worker rate for married women tends to decrease the extent of marital segregation in Japan,
Korea, and Taiwan.
Recently, scholars have noted that assortative mating causes inequality in the earning abilities of latter
generations through the transfer of household wages between generations (Chadwick and Solon, 2002;
Ermisch et al., 2006). Therefore, this study’s analytical results—that inequality in the labor market affects
marital segregation significantly—show that policies to decrease the inequality in parental generation canalso decrease inequality in future generations. In other words, a policy to reduce income inequality
between educational backgrounds in East Asia is necessary. However, because the educational wage
inequality is a problem due to the labor demand side, the effectiveness of the labor market might decrease
if direct policy interventions, such as the lower wage limits, are implemented for the labor demand side.
Therefore, it becomes important to adopt policies for the labor supply side, such as enhancement of skill
formation support for youth workers; that is, improving the quality of higher education, and increasing its
ratio. Additionally, the environmental improvement of the labor market for married women would allow
women to continue working full-time after child birth, thus decreasing the degree of marital segregation
through marriage behavior. Specifically, it would be effective to promote or support mechanisms thatenable work–family life balance in companies and society in general. In the part of East Asia on which thisstudy focused, the younger generation faces increasing inequality, such as educational wage inequality inthe labor market through the increase in the rate of non-regular workers. In conclusion, the challenge of
examining the effects of marital segregation on younger generation remains, which requires analysis of
more recent data.

Appendix 1. Local areas and economic blocks
Local areas and economic blocks are as follows:
Country/Area

Local area/
economic block
Hokkaido and Tohoku

Hokkaido, Aomori, Iwate, Miyagi, Akita, Yamagata,area

Fukushima, and Nigata prefectures

Kanto areaJapan

Prefecture/Metropolitan/City/County/Province

Chubu area

Kinki area

Tokyo metropolitan, Ibaragi, Tochigi, Gunma, Saitama, Chiba,
Kanagawa, Yamanashi, and Nagano prefectures
Toyama, Ishikawa, Fukui, Gifu, Shizuoka, Aichi, and Mie
prefectures
Shiga, Kyoto, Osaka, and Hyogo, Nara, and Wakayama
prefectures

Chugoku and Shikoku

Tottori, Shimane, Okayama, Hiroshima, Yamaguchi,

area

Tokushima, Kagawa, Ehime, and Kochi prefectures

19

KoreaKyushu and Okinawa

Fukuoka pref., Saga pref., Nagasaki pref., Kumamoto pref.,area

Oita pref., Miyazaki pref., Kagoshima pref., Okinawa pref.

Metropolitan area

Seoul metropolitan, Incheon et., Gyonggi province

Chung-Cheong area

Daejeon metropolitan, Chungcheing province

East-South area Daqing

Busan, Ulsan, and Deagu metropolitan, Gyeongsang province,area

Jeju-doHunan area

Gwangiu metropolitan, Jeolla province

Gangwon-do area

Gangwon province

Jing wing economic

Beijing, Tianjin, Heibei, Liaoling, Shandong and Hunnan

block

provinces

Sekichu–Tianshui

Shanxi, Inner Mongolia, Neimenggu, Leimonggu, Shaanxi

Economic blockprovince, Qinghai province, Ningxia Hui Autonomous Region

Northeast Economic
block
China

Yangtze River Delta
Economic block
Chengdu–Chongqing
Economic block
Central Economic block

Heilongjiang

Shanghai, Zhejiang and Anhui provinces

Hubei province, Chongqing, Sichuan province, Xizang
Fujian, Jiangxi, and Guangdong provinces, Hainan
Jinlin (Linan), Guangxi Zhuang Autonomous Region, Guizhou

North Economic blockprovince, Yunnan province, Gansu province, Xinjiang Uygur
Autonomous Region

North area

TaiwanCentral area

South area

Keelung city, Taipei city, New Taipei city, Taoyuan county,
Hsinchu city, Hsinchu county, Ilan county, Hualien county
Taichung city, Miaoli, Taichung, Nantou, Changhua, andYunlin counties
Chiayi, Tainan, and Kaohsiung cities, Chiayi, Tainan,
Kaohsiung, Pingtung, and Taitung counties

20Appendix 2. Wage premium indices for university graduate men
We explain the four wage premiums for university graduate men. Except “Log of raw wage,” three
indices are proposed according to Fernández et al. (2005). All four indices indicate increases when thewage rises. Additionally, this study uses each “Log of raw wage” for university graduate men and otherlower educational background men, which Fernández et al. (2005) do not use. The reason this study uses
each these indices is to examine the theoretical hypotheses derived from the model by separating the effectof increasingly skilled workers from the one of unskilled workers because we assume a shape of the utility
function with weak concavity when deriving the effect of the increase in skilled workers.
(1) “Wage ratio” represents the ratio of each average annually income of university graduate men andother lower educational background men who are 26–35 and 36–45 years old in each local area or
economic block.
(2) “Log of raw wage” represents each log of raw average annual wage of university graduate men

and other lower educational background men who are 26–35 and 36–45 years old in each local
area or economic block.
(3) “Skill indicator” represents the coefficient of university graduate men’s dummy variable in thebasic Mincer wage function, calculated by the following estimation model as 𝑎1 using the 30–60
age group sample of each local area or economic block for men:𝑙𝑜𝑔(𝑒𝑖 )=𝑎0 + 𝑎1 𝐼𝑖 + 𝑎2 (𝑎𝑔𝑒 − 𝑠𝑖 − 6) + 𝑎3 (𝑎𝑔𝑒 − 𝑠𝑖 − 6)2.
(4) “Mincer coefficient” represents an expected earning rate per educational year in the basic Mincer

wage function, calculated by the following estimation model as 𝑏1 using the 30–60 age groupsample of each local area or economic block for men:𝑙𝑜𝑔(𝑒𝑖 ) = 𝑏0 + 𝑏1 𝑠𝑖 + 𝑏2 (𝑎𝑔𝑒 − 𝑠𝑖 − 6) + 𝑏3 (𝑎𝑔𝑒 − 𝑠𝑖 − 6)2.

21

Appendix 3. Gender gap indices
We explain the four gender gap indices. “Wage gap” is made according to Fernández et al. (2005).
Additionally, we calculate the “Male coefficient” as men’s wage premium for all full-time workers. While
Fernández et al. (2005) build the labor market participation rate for women regardless of marital status,
this study uses the labor market participation rate and full-time workers rate for married women as moreconsistent variables with the theoretical model.
(1) “Wage Gap” represents an average annual income ratio of university graduate men and women

who are 26–35 and 36–45 years old in each local area or economic block, for which higher vales
indicate a wider gender gap.
(2) “Male coefficient” represents men’s wage premium for full-time work in the basic Mincer wage

function, calculated by the following estimation model as 𝑐1 using the 30–60 age group sampleof each local area or economic block for men:𝑙𝑜𝑔(𝑒𝑖 )=𝑐0 + 𝑐1 𝑀𝑖 + 𝑎2 (𝑎𝑔𝑒 − 𝑠𝑖 − 6) + 𝑎3 (𝑎𝑔𝑒 − 𝑠𝑖 − 6)2.
(3) Labor force participation rate is calculated as the ratio of those who participate on the labor market

and all married women. When its value increases, the gender gap becomes a less important entry
barrier to the labor market. It is an index to measure the degree of quantitative labor market entry
barriers for women regardless of employment type.
(4) Full-time worker ratio is calculated as the ratio of full-time workers and all married women. Whenits value increases, the gender gap becomes a less important entry barrier on the labor market for
full-time job. This index considers the quality of work in comparison with the labor participation
rate for married women.

22References
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