Discussion Papers In Economics
And BusinessComparing the role of height between men and womenin the marriage market
Eiji Yamamura
Yoshiro Tsutsui
Discussion Paper 16-20

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANComparing the role of height between men and womenin the marriage market
Eiji Yamamura
Yoshiro Tsutsui
Discussion Paper 16-20
July 2016

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANComparing the role of height between men and womenin the marriage market
Eiji Yamamura∗
Seinan Gakuin University, Japan

Yoshiro Tsutsui
Konan University, Japan*

Corresponding author. Department of Economics, Seinan Gakuin University, 6-2-92 Nishijin, Sawara-ku
Fukuoka 814-8511, Japan.
E-mail address: yamaei@seinan-gu.ac.jp
Tel: +81 92 823 4543; Fax: +81 92 823 2506
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ABSTRACTThis paper attempts to explore how the different roles of height between men and women in
the marriage market changed across generations. Using individual-level data from Japan, we
compared the effect of height on marriages between men and women, and investigated how theeffect of height on marriage changed across generations. Our key findings are: (1) For those who
were born before 1965, a 1% increase in height led to an approximately 0.3% increase in theprobability that men were married. Conversely, a 1% increase in height led to an approximately
0.3% decrease in the probability that women were married. (2) For those who were born in orafter 1965, a 1% increase in height led to an approximately 1.40% increase in the probability thatmen were married. However, the height effect disappeared for women. Japan experienced amiraculous economic development post-World War II, which resulted in changes in its economic
and social structure. These changes may change the role of height for Japanese women in themarriage market.
JEL classification: J12; J16
Keywords: Marriage market; Height; Preference change.

21. Introduction
It is widely acknowledged that physical features such as height contribute to success in thelabor market, because height is considered to reflect human capital and health conditions (e.g.,
Schultz 2002; Heineck 2005; Dinda et al. 2006; Case and Paxson 2008, 2009; Gao and Smyth2010; Cinnirella et al. 2011; Lundborg et al. 2014; Tao 2014; Sohn 2015a; Steckel 1995).1 Taller
people can earn more, and therefore they are more highly evaluated in the marriage market.2
Height exerts a positive effect on marriage success because of an increase in income (Sohn 2015b).
Previous research has provided evidence that tall men are advantaged in the marriage market (e.g.,
Fu and Goldman 1996; Harper 2000; Murray 2000; Belot and Fidrmuc 2010).3 Educational levelis also a key factor in the search for a partner in the marriage market because human capital as
captured by education is positively related to marginal labor productivity, and consequently
increases earnings. From this perspective, height is nearly similar to educational attainment
because both education and height indicate higher expected earnings.4
According to the assortative mating hypothesis, people with higher educations are preferredin the marriage market because they are anticipated to become higher earners; therefore, people
with higher education are more inclined to have more highly educated spouses (Scully 1979;
Boulier and Rosenzweig, 1984; Lam 1988). Thus, the husband’s earnings are positively related
not only to his educational level, but also to his wife’s educational level.5 This holds when we
consider the role of height in the marriage market (Sohn 2015b), i.e., the husband’s earnings are
positively related not only to his height, but also to his wife’s height.6
The role of height cannot be separated from its socioeconomic context (Steckel 2009).7 In
Italy, the relationship between the probability of marriage for men and male height was
monotonically positive in Treppo Carnico, whereas it was inversely U-shaped in Alghero(Manfredini et al. 2013). Furthermore, women place greater weight on the intelligence and raceof their partner, while men respond more to physical attractiveness (Fisman et al. 2006). Buunket al. (2008) found that male height was negatively correlated with jealousy in response to sociallyinfluential rivals, whereas female height was negatively correlated with jealousy in response tophysically attractive rivals. These findings suggest that key features to influence mate choice arethought to differ between men and women. Based on data from the United States (1979–1991),
shorter men were less likely to get married while taller women were less likely to get married (Fuand Goldman 1996).8 Murray (2000) used historical data from the United States to suggest thatmale height is positively related to marriage, while female height is not positively related tomarriage.9 Similarly, the effect of male height on marriage is opposite to that of female height on
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Height is also considered to reflect a stylish appearance and thus a kind of beauty. Beauty is observed
to be highly evaluated in the labor market (e.g., Biddle and Hamermesh 1998; Hamermesh et al. 2002;
Hamermesh and Biddle 2004; Borland and Leigh 2014).
2
Tall people can also enjoy benefits from acquiring political power (Yamamura et al. 2015).
3
Shorter men are less likely to be in a relationship (Herpin 2005).
4
It should be noted that height is nonlinearly associated with male abilities (Heineck 2009).
5
The cross-productivity effect is another possibility; i.e., education level increases individual
productivity and also possibly plays a critical role in improving spouse’s productivity through householdinteractions (Huang et al. 2009; Mano and Yamamura 2013).
6
Individuals’ earnings were found to be associated with their spouse’s body mass index (Chiappori et al.
2012).
7
Belot and Fidrmuc (2010) show that height distribution differences are important in explaining thephenomenon that Black men are substantially more likely to have white spouses than are Black women,
but the opposite is true for Chinese men and women.
8
Relatively shorter French men were less likely to be in a relationship than their taller counterparts(Herpin 2005).
9
Another study based on historical data from the United States did not find a robust positive relationshipbetween male height and marriage (Hacker 2008). In a German case, not only male height, but also
female height were observed to be positively related to marriage (Baten and Murray 1998).
3

marriage in the case of the United Kingdom (Harper 2000).
An interpretation of the contrary effect of male or female height on marriage is that men’s
evaluation of female height is contrary to women’s evaluation of male height. According to theclassical work by Becker (1991), the gender division of labor within a household increased
household productivity. Especially in societies where women have labor market disadvantages,
women have a comparative advantage in housework and become housewives. In contrast, men
tend to become full-time breadwinners in the labor market and do not do any housework. In
addition, if height is only valuable in the labor market, taller women are not preferred in themarriage market. In less-developed countries, such as Indonesia, Sohn (2016) analyzed the impactof height difference between husband and wife on happiness and found that women with a taller
spouse are more likely to be happy.10 This observation is consistent with the argument that genderdivision of labor becomes dominant in society because an increase in the number of women in
the labor market increases the risk of marital disruption (e.g., Preston and Richards 1975; Cherlin
1992). Apart from economic reasons, height is influential in the marriage market (Sohn 2015b).
In general, women are physically inferior to men. Inevitably, weaker women depend on physicallystronger men to survive under natural conditions. The female preference for taller men seems to
be intrinsic because male height reflects a physical advantage in the struggle for existence.
Conversely, the role of height is thought to depend on norms shared by members of society,although the norm may possibly evolve in the process of economic development (Hayami 1998,
2001). In developed countries, the socioeconomic gap between men and women in society has
been reduced (Fortin et al. 2015). Japan has experienced rapid economic development in the postWorld War II period. This miraculous economic growth has transformed the social structure andchanged women’s social status in Japan. Accordingly, the gender division of labor appeared to
decline and height preferences may have possibly changed. The role of height in mate choice may
also possibly differ between generations.
Preferences for height and family relationships are thought to change if socioeconomic
conditions change. Accordingly, the role of height has changed in the marriage market. However,
the existing literature did not consider the effect of the height difference between men and womenon the process of social and structural change (Sohn 2015b, 2016). The rapid economic
development in Japan enables us to investigate how the effect of height difference changed in along-term perspective. Thus, this paper used data from Japan to examine the different roles ofmale or female height on marriages and consider how and the extent to which these differenceschanged over generations. On the one hand, we found that for the older generation, height ispositively and negatively related to marriage for men and women, respectively. On the other hand,
we found that for younger generations, a positive relationship between male height and marriage
is persistent, while a negative relationship between female height and marriage disappeared. The
remainder of this paper is organized as follows. An overview of the data and empirical method ispresented in Section 2. Section 3 presents and discusses the major findings. Section 4 concludes.

2. Data and methods
2.1. Data
We used individual-level data from Japan gathered by the Survey of Life Satisfaction andPreferences provided by the Global Center of Excellence at Osaka University, which conducts the
Human Behavior and Socioeconomic Dynamics program. This periodical survey has been carriedout throughout Japan annually; this paper used the data from 2009–2013. A random-sampling
method is used to collect the data. The targets of the survey were male and female adults aged
between 20 and 69 years. Therefore, the socioeconomic conditions in which survey respondents
were raised varied according to the generation to which they belonged because socioeconomic
conditions have changed noticeably in the post-World War period in Japan. The data that we
obtained includes basic individual socioeconomic characteristics, such as age, sex, householdincome, number of family members, educational level, occupations, and residential place. In
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Men do not value women’s intelligence or ambition when it exceeds their own (Fisman et al. 2006).
4

addition, as key factors in this paper, the respondents’ height and marital status were also available
from the data.
Table 1 includes definitions of the variables used in this paper and their mean values based on
the whole sample, and the male and female samples. The mean value for Married is about 0.80
for men and women, which suggests that the rate of marriage for respondents is 80%. There is nodifference in the rate of marriage between men and women. The average value of Height is 169.1
cm and 156.1 cm for men and women, respectively. On average, men are 13 cm taller than women,
which reasonably reflects the biological height difference. The average value of University is 0.37
and 0.15 for men and women, respectively. The final education level indicated that 37% of men
and 15% of women were university graduates; therefore, more men completed higher education
than did women. The average of Own Income is 4,577 and 1,593 for men and women, respectively.
This income difference may be decomposed into two factors: first, the difference in wage rate
between men and women, and second, women are more likely to be part-time workers or fulltime housewives. Accordingly, women’s working hours are significantly fewer than men’s
working hours.
Figures 1 and 2 compare the height differences between married and unmarried men andwomen. In both figures, respondents are divided into the older generation (born before 1965) andthe younger generation (born in or after 1965). In 1964, the first Tokyo Olympics were held,
which is considered as the time when Japan emerged from a weaker stage of economic
development. In addition, calorie intake and nutrition differs between generations, which resultsin height differences. Furthermore, the Equal Employment Opportunity Law was enacted in 1985;
therefore, women born in 1965 were 20 years old when they graduated from college and soughtwork. Thus, women born after 1965 are likely to enjoy decreased gender discrimination in thelabor market. Hence, the socioeconomic conditions were reasonably different between the older
and younger generations. Accordingly, the effect of height on marriage may also differ between
generations.
Figures 1 and 2 show that the younger generation are taller than the older generation, which
may be because the older generation grew up in a period when Japan had not yet caught up withthe most developed countries. Furthermore, Figure 1 shows that married men were taller thanunmarried men were in both generations. The mean difference test suggests that the absolute tvalue is 1.73 and 3.32 for the groups born before 1965 and after 1965, respectively. Therefore,height differences between married and unmarried men are statistically significant for bothgenerations. In contrast, Figure 2 shows that married women were shorter than unmarried women
were for the older generation. However, it is interesting to observe that the height difference
between married and unmarried women disappeared for the younger generation. The meandifference test suggests that the absolute t-value is 5.80 for the group born before 1965 while it is0.24 for the group born in or after 1965. Therefore, the height difference is statistically significant
only for the older generation, which is consistent with findings in the United States (Fu andGoldman 1996; Murray 2000). This result does not persist for the younger generation, whichsuggests that socioeconomic changes meant that male height played no role in the marriage
market. However, this finding does not control for various factors that seemingly influence theprobability of marriage. Hence, in the following sections, we examine the influence of height on
the probability of marriage more closely by controlling for various factors.

2.2. Methods
To assess the effect of height on the probability that individuals are married, we adopted a
probit model. The estimated function takes the form:
Marriedit = α0 + α1Ln (Height)it + α2 Ln(Age)it + α3 Universityit + α4Ln(Own Income) + Y′iBi + Z
t + u it,where Marriedit represents a marriage dummy and the dependent variables for individual i and5year t, and α represents the marginal effect of independent variables. Considering independent
variables (see Table 1 and Figure 1), there are large gaps in height between men and women, andbetween the older and younger generations, even for the same sex. Because of different heightscales, it is inappropriate to compare the effects on male height with that on female height, or theheights of the older generation with that for the younger generation. For interpretation
convenience, the log value of Height is used to provide height elasticity (effect of 1% increase ofheight on marriage rate). Furthermore, there is a large gap in Own Income between men andwomen; therefore, the log value of Own Income should be used to compare the magnitudes.
However, the log value of Own Income is 0 when respondents are full-time housewives or
unemployed. In this case, the log value of Own Income cannot be calculated. Therefore, the log
value of (1+ Own Income) is used for calculations. For consistency, the log value of Age is used.
The key independent variable is Height. From the previous literature (e.g., Fu and Goldman
1996; Harper 2000; Murray 2000), the coefficient of Height is expected be positive and negative
for men and women, respectively. We attach importance to checking whether this expected effectof Height changed according to generations. Other than Height, economic conditions are captured
by Own Income. Education levels and age are controlled. Besides these variables, the vectors of
the control variables are denoted by Yit. Respondents lived in residential areas defined according
to types of local government, such as large city, medium city, small city, town, and village, which
are classified by the numbers of residential people. These control variables are dummies ofresidential government scale, dummies of residential prefectures, and dummies of occupation.
Year dummies are included to control for Z, which includes factors that commonly affected
respondents in t. The estimations were conducted based on the full sample, male sample, andfemale sample. A gender dummy is included for the estimation based on the full sample.

3. Results and discussion
The regression estimates for the probit model are reported in Tables 2–5. Table 2 presents theresults based on the full sample. After dividing the full sample into men and women, results basedon the male sample are presented in Table 3, while those based on the female sample are presented
in Table 4. In each table, the results based on the sample covering all generations are shown incolumns (1) and (2). The results based on the younger generation are presented in columns (3)and (4), while those for the older generation are presented in columns (5) and (6). Columns (1),(3), and (5) show the results when economic-related variables (Own Income and occupation
dummies) are included as the independent variables. There are observations for which economicrelated information was not available and therefore are not included in the estimations in columns(1), (3), and (5).11 Therefore, to include these observations in the estimations, we also conducted
estimations where we deleted economic-related variables as independent variables. These results
are reported in columns (2), (4), and (6). Naturally, the observations are larger in columns (2), (4),and (6) than those in columns (1), (3), and (5).
Table 5 shows the results of closer examination whether the height effect varies according to
generations. In this estimation, the full sample was divided into men and women, and interaction
terms between height (education) and generation dummies were included to show how the heighteffect differs among the younger generation.
First, we begin by interpreting the results in Table 2. The coefficient of Age is positive andstatistically significant at the 1% level in all columns. The probability of being married is larger
as one ages. The sign of the coefficient of University is positive for the older generation, which is
11

Owing to data limitations, Own Income is only available for the year when the surveys wereconducted. That is, we cannot examine the effect of Own Income in the year of marriage. Therefore,making a decision to get married inevitably affected Own Income. To take a typical example, getting
married caused women to retire from work to be full-time housewives in Japan. Hence, there is a reverse
causality between Own Income and the decision to get married, which leads to estimation bias. Careshould be taken when interpreting Own Income data. Furthermore, this bias is avoided by excluding Own
Income and job dummies.
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interpreted as suggesting that education reflects the human capital to gain higher earnings andincreases competitiveness in the marriage market. In contrast, the sign of the coefficient of
University is negative for the younger generation. The results of Own income are similar to thoseof University. This finding will be scrutinized in more detail in the discussion of the results shownin Tables 3–5.
We now focus on the results of the key variable Height when we interpret the overall results.
The coefficient of Height is positive and statistically significant at the 1% level in columns (1)and (2), which is line with the literature (e.g., Baten and Murray 1998; Sohn 2015a, 2015b).
However, once the full sample was divided into older and younger generations, the statisticalsignificance of Height disappeared for the older generation although the significance continued
to be observed in the younger generations. The results shown in Tables 3 and 4 explore the reason
why Height is unrelated to the probability of marriage for the older generation.
Table 3 shows that for the male samples, University and Own Income are positive for the older
generation. University is statistically significant in column (4), but its significance disappears
once Own Income is included in column (5). This can be interpreted that the male educational
attainment advantage does not exist in the marriage market when economic advantages caused byeducation are controlled for by Own Income. Considering the younger generation, statisticalsignificance for Own Income is also observed in column (5). However, statistically significant
negative signs for University are observed in columns (5) and (6). One possible interpretation ofthis finding is that more highly educated people are more likely have higher earnings and enjoy
the single life. Getting married possibly leads them to distribute money to family members andspend time involved in family life. The opportunity cost of marriage is larger for higher educatedpeople, who then postpone the timing of marriage even though they can get married at any time
if they want. Table 3 shows that the coefficient of Height is positive and statistically significant
at the 1% level in all columns. Furthermore, the absolute value of the Height coefficient is about
0.30 for the older generation, which implies that a 1% increase in male height leads to a 0.30%
increase in the probability of marriage. However, the absolute value of the Height coefficient isabout 1.40 for the younger generation, which implies that a 1% increase in height leads to a 1.40%
increase in the probability of marriage. That is, the effect of height is about three times larger forthe younger generation than for the older generation. One possible interpretation is that heightcurrently plays a greater role in increase in income. However, even after Own Income andoccupation dummies are controlled for, the effect of Height is distinctly larger for the younger
generation than for the older generation. Therefore, the height effect on marriage througheconomic channels is unlikely to cause the gap between these generations. Another possible
interpretation is that women took the initiative to choose marital partners in the marriage market
because the probability of men marrying depends on female preferences for taller men. However,this holds true only if male preferences are less likely to influence marriage for the younger
generation. Thus, it is necessary to examine whether the effect of female height on marriagechanges.
Table 4 indicates that University and Own Income are negative and statistically significant inall columns for the older and younger generations. The negative sign of Own Income can beinterpreted in different ways. Women with higher income have lower incentive to depend on maleincome and are less likely to get married. Another interpretation is derived from reverse causality;
people who cannot enjoy the benefits of getting married have a higher incentive to work hard andreceive higher earnings. That is, the causality of Own Income and dependent variables isambiguous. The negative sign of University can be interpreted as implying that higher educatedwomen are less likely to get married at a marriageable age because they give more importance totheir studies and work career than to married life.
The results of Height reported in Table 4 are noticeably different from those in Table 3. Incolumns (1) and (2), the coefficient of Height is not statistically significant, although it is positive.
For the older generation, as shown in columns (3) and (4), the coefficient of Height is negativeand statistically significant at the 1% level. Additionally, the absolute value of the Height
coefficient is about 0.30, which implies that a 1% increase in female height leads to a 0.30%
7decrease in the probability of marriage. Considering the results for the older generation in Tables
3 and 4 led us to argue that male preferences for shorter women has a sizable effect, which isalmost equivalent to the effect of female preferences for taller men. The opposite significant effectof height on marriage between men and women is considered to neutralize the effect of height onmarriage when male and female samples are combined as indicated in columns (3) and (4) inTable 2. Considering columns (5) and (6) in Table 4, it is surprising to observe that the statisticalsignificance of Height disappeared and its sign changed to positive. In our interpretation, the malepreference for shorter women does not influence the probability of marriage for women. The
combined result for the younger generation shown in Tables 3 and 4 implies that women have theinitiative in marriage because female preferences for taller men are dominant in the marriage. As
a whole, with respect to Tables 3 and 4, the R-square for the estimations of the younger generationis larger than that for the older generation. We interpret this finding as follows: For the older
generation, traditional Japanese values such as familism, where parents have great bargaining
power within the family, were dominant at their marriageable ages. Individual-level
characteristics were then less likely to influence their marriage decision-making. In contrast, theyounger generation is more able to make their own decisions than the older generation was
because individualism is pervasive and more influential than familism.
In Table 5, to examine how the height effect on marriage varies over age in more detail, we
used the full sample covering all generations and added interaction terms for each younger
generation dummies (AGE_20, AGE_30, and AGE_40) and Height (University). In this model,
the reference generations are those who are equivalent to 50 years old or older than 50 years old.
There is the possibility of reverse causality between Own_Income and decision to get married,
which results in estimation bias. Therefore, the effect of Own_Income is only included as a control
variable, but is not interacted with generation dummies. Ln(Height)*AGE_20,
Ln(Height)*AGE_30, and Ln(Height)*AGE_40 show positive signs in all columns. For men,
Ln(Height)*AGE_30 and Ln(Height)*AGE_40 are statistically significant. This means that taller
men in their 30s and 40s are more likely to enjoy advantages in the marriage market than are older
generations. For women, Ln(Height)*AGE_20 is statistically significant. This means that taller
women in their 20s are less likely to suffer disadvantages in the marriage market than generations
over 50 years old. It is interesting to observe that University*AGE_20, University *AGE_30, andUniversity *AGE_40 show negative signs in all columns and are statistically significant, with the
exception of University *AGE_40 in column (2). For both men and women, the absolutecoefficient values are larger when the generation dummies represent the younger generation. Thisimplies that the opportunity cost of marriage for higher educated people is larger for younger
generations. Furthermore, the absolute coefficient values for women are about three times larger
than for men. In our interpretation, the labor market conditions for women have been improved
drastically; this structural change distinctly increased the opportunity cost of marriage for women.
We found that more highly educated women have a smaller incentive to get married when they
have the opportunity to display their ability in the workplace because of the increase in women’s
employment status. The reduced difference in employment status between men and women led
women to attach more importance to work than to marriage. This is consistent with the argumentthat women’s increasing “independence” from men partly reduced the marriage rate in Japan(Raymo and Iwasawa 2005). In contrast, existing literature found that women are happier when
they were housewives and enjoying their husband’s higher income in Japan (Lee and Ono 2008).
However, Lee and Ono (2008) used a sample aged 20–69 years and did not compare the results
between generations. As we observed in this paper, there is a great difference between generationsin the decision-making for marriage. Therefore, it is valuable to extend this work to consider how
economic factors influence happiness by comparing different generations.
What we have considered thus far indicated that the height effect on marriage differed between
men and women and the difference varied between generations. One interpretation is that
preferences for the height of the opposite sex changed, which led the role of height to change. As
for the older generation, men preferred shorter women to maintain their physically dominantposition over their spouses who become housewives to support and increase their husbands’
8productivity. However, women preferred taller men to anticipate their husbands’ higher earnings
related to the height premium in the labor market. Such male and female preferences fit the modelof gender division of labor in the household where the husband works to earn money and the wife
becomes a homemaker (Becker 1991). However, socioeconomic conditions changed because of
the rapid economic development in Japan. Women’s employment status has improved and womenare more able to display her ability in the workplace than ever before. Therefore, the differencesin employment status between men and women have diminished. Such changes in socioeconomic
conditions reduced the differences between male and female preferences; e.g., the male preference
for female height possibly became similar to the female preference for male height. From this, we
conjecture that the male advantage and female disadvantage of higher height in the marriage
market might be reduced. Our estimation results show that the negative effect of female heightfor getting married disappeared in the younger generation. Thus, tall women are notdisadvantaged in the marriage market, at least in the younger generation. However, the positiveeffect of male height for the younger generation is larger than that for the older generation. Thissuggests that the male preference for female height changed while the female preference for maleheight did not change. Such a difference in the change in height effect between men and women
may be because of the differences in innate characteristics between men and women. Weaker
women instinctively prefer taller men because of the male physical advantage, which is valuable
for survival under natural conditions. Additionally, as Persico et al. (2004) found, taller parents
tend to have taller children, which enables their children to enjoy the benefit of the heightpremium in the labor market. The high intergenerational correlation of height naturally leads
women to prefer taller men. This is because women have a maternal instinct to care for theirchildren, which is not influenced by socioeconomic conditions.
Another possible interpretation of the findings of this paper is that men and women had equal
influence in matching partners in the marriage market for the older generation even though
women were at a disadvantage in Japanese society. In traditional Japanese society, parents’intentions influenced marriage decision-making. Therefore, women’s parents’ bargaining power
can be considered equal to men’s parents’ bargaining power. Naturally, there is no difference inheight effect between male and female heights. In the period when women’s social position
greatly improved, women were more able to work full-time in the workplace where men havedominated in former times. The gender division of labor within a household is not the typicalhousehold model in Japan. According to classical sociology literature (e.g., Parsons 1942; Persons
and Bales 1955) and more recent studies on bargaining within the couple (e.g., Presser 1994;
South and Spitze 1994), a lack of specialization caused a status competition to arise between
husband and wife. Furthermore, the improvement of social and employment status possibly
altered the balance of power between men and women. That is, women’s bargaining power
outweighed the men’s bargaining power, so that while the female preference for taller men
continues to play an important role in matching partners, the male preference for shorter women
does not. This implies that women of the younger generation have the initiative to select theirpartner in the marriage market.

4. Conclusion
A number of studies suggest that height is positively associated with the probability ofmarriage. Height can be considered to reflect the degree of ability to earn, which is an importantfactor when searching for a marital partner. The literature suggests that height has a greater
contribution to marriage for men than for women (Murray 2000). One possible interpretation isthat women’s earning ability is less valuable than other attributes for men if there is gender
discrimination in the labor market or if women have comparative advantages in household labor.
However, even after controlling for earning attributes, tall men are preferred by women (Sohn
2015b). Therefore, there might be noneconomic reasons that tall men have the advantage in thelabor market. For instance, the female preference for taller men is intrinsic. Furthermore, men in
the marriage market prefer shorter women; therefore, the effect of female height is opposite tothat of male height (Fu and Goldman 1996; Harper 2000). It is likely that the influence of height9

in the marriage market varies according to social structure and values shared in the society.
However, little is known about how the influence of height in the marriage market depends on
people’s values formed in the social structure.
Japan experienced rapid economic growth in the post-World War II period, which inevitablycaused changes in the social structure, and in turn changed the role of height in the marriage
market. We used individual-level data from Japan to examine how the difference in height effecton marriage between men and women changed from the older generation to the younger
generation.
Our key findings are: For the older generation, tallness increased the probability of men
marrying while it decreased that of women marrying. For the younger generation, tallness
continues to increase the probability of men marrying, but tallness does not affect the probabilityof women marrying.
These findings can be variously interpreted by considering the structural changes in society.
In older, more patriarchal times, men held control of the family and decision-making on family
issues. Both men and women shared traditional values based on a male-dominated culture.
Therefore, in families dominated by the husband, the husband is more likely to be a higher earner,and older and taller than his wife. Men are required to be strong and reliable to be competenthusbands. Thus, the gender division of labor was widely accepted. However, because of the rapid
economic development in Japan, its male-dominated culture declined and men had reduced
incentive to control the family. We can offer a possible interpretation that these results reflect thatthe male preference for shorter women has changed, while the female preference for tall men
persisted. The male preference for women mainly depends on the socioeconomic conditions,while the female preference for men is intrinsic and does not depend on the socioeconomic
conditions. Another possible interpretation is that the height difference between men and womenis a critical factor for the marital couple (Sohn 2015b; 2016); i.e., women prefer men taller thanthemselves. Inversely, men prefer women shorter than themselves. On the one hand, the female
preference for male height persisted in the marriage market. On the other hand, the malepreference for female height is influential for the older generation, but not for the younger
generation. Women’s bargaining power relatively increased through changes in the social
structure as a consequence of economic development. In other words, the improvement of thesocial position of women reduced men’s bargaining power in the marriage market.
Owing to limitations of the data, we cannot identify the reason why the positive effect of maleheight is persistent while the negative effect of female height disappeared. This issue remains tobe addressed in future research.

10

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12

Male
173
172
171

cm

170
169
168
167
166
165
Born efore 1965
Marry

Born in or after 1965
Unmarry

Figure 1. Comparison of heights between married and unmarried men.

13Female
159
158

cm

157
156
155
154
153
Born efore 1965
Marry

Born in or after 1965
Unmarry

Figure 2. Comparison of heights between married and unmarried women.

14

Table 1. Definition of variables used for estimation and mean values.
Definitions
Mean

Mean
(Male)
0.80

Mean(Female)
0.79

169.1

156.1

HeightValue of 1 is given if respondents are married, 0.79
otherwise 0 (%)
Height (cm)
162.1

Age

Respondent’s ages

52.0

52.5

51.5

University

Value of 1 is given if respondents graduated 0.25
from university or graduate school, otherwise
0 (%).
Respondent’s own income (thousands of yen) 3,073

0.37

0.15

4,577

1,593

Value of 1 is given if age of respondent is 0.05
between 20 and 29 years, otherwise 0 (%)
Value of 1 is given if age of respondent is 0.13
between 30 and 39 years, otherwise 0 (%)
Value of 1 is given if age of respondent is 0.23
between 40 and 49 years, otherwise 0 (%)

0.05

0.05

0.13

0.15

0.22

0.24

MarriedOwn Income
AGE_20
AGE_30
AGE_4015

Table 2. Determinants of marriage based on male and female samples (probit model).
(1)
(2)
(3)
(4)
All
All
Birth year <1965
Birth year <1965
0.29***
0.37***
0.04
0.07
Ln(Height)(5.00)(6.68)(0.76)(1.36)
0.37***
0.37***
0.17***
0.14***
Ln(Age)(46.5)(48.6)(10.9)(10.3)–0.02***–0.02***
0.006
0.01***
University(–4.64)(–4.29)(1.34)(2.66)
0.003**
0.005***
Ln(Own Income)(1.97)(5.08)
Occupation dummy Included
Not included
Included
Not included(5)
Birth year ≥1965
0.61***(3.29)
1.18***(31.4)–0.11***(–6.96)–0.01**(–2.54)
Included(6)
Birth year ≥1965
0.66***(3.86)
1.18***(36.7)–0.10***(–7.75)

Various dummies a

Included

Included

Included

Included

Included

Included

Pseudo R square

0.31

0.27

0.13

0.06

0.31

0.27

Observations

15,24019,3798,720

11,827

5,701

6,816

Not included

Note: Numbers in parentheses are z-statistics calculated using robust standard errors. *, **, and *** indicate significance at the 10%, 5%, and 1%
levels, respectively. Numbers above the numbers without parentheses indicate marginal effects. Ln(Own income) is log of “own income +1” because
there are 0 values and in this case log value cannot be calculated,a. Constant, year dummies, gender dummy, dummies of residential city scale, dummies of residential prefectures, and dummies of occupation are
included, but the results are not reported.

17

Table 3. Determinants of marriage based on male sample (Probit model).
(2)All
0.66***(8.17)
0.44***(38.6)
0.01(1.29)

Occupation dummy(1)All
0.51***(6.01)
0.42***(32.6)–0.02***(–2.68)0.03***(13.8)
Included(4)
Birth year <1965
0.37***(4.48)
0.26***(11.2)
0.03***(4.40)

Not included(3)
Birth year <1965
0.30***(4.10)
0.26***(11.4)
0.01(0.85)
0.02***(11.2)
Included

Various dummies a

Included

Included

Pseudo R square

0.37

Observations

7,865

Ln(Height)
Ln(Age)University
Ln(Own Income)(6)
Birth year ≥1965
1.46***(4.84)
1.49***(26.4)–0.05**(–2.57)

Not included(5)
Birth year ≥1965
1.41***(4.31)
1.29***(20.5)–0.10***(–3.83)0.11***(6.44)
Included

Included

Included

Included

Included

0.31

0.24

0.11

0.32

0.28

9,410

4,056

4,982

2,640

3,011

Not included

Note: Numbers in parentheses are z-statistics calculated using robust standard errors. *, **, and *** indicate significance at the 10%, 5%, and 1%
levels, respectively. Numbers above the numbers without parentheses indicate marginal effects. Ln(Own income) is log of “own income +1” because
there are 0 values and in this case log value cannot be calculated.
a. Constant, year dummies, dummies of residential city scale, and dummies of residential prefectures are included, but the results are not reported.

18

Table 4. Determinants of marriage based on female sample (Probit model).
(1)
(2)
(3)
All
All
Birth year <1965
0.01
0.03
–0.32***
Ln(Height)(0.09)(0.56)(–3.73)0.25***
0.29***
0.06***
Ln(Age)(26.1)(30.2)(3.02)–0.05***–0.06***–0.03***
University(–7.65)(–9.42)(–3.24)–0.01**–0.01***
Ln(Own Income)(–7.78)(–3.78)
Occupation dummy Included
Not included
Included(4)
Birth year <1965–0.36***(–4.40)0.04**(2.15)–0.03***(–3.10)(6)
Birth year ≥1965
0.14(0.71)
0.96***(25.6)–0.14****(–8.63)

Not included(5)
Birth year ≥1965
0.20(0.92)
0.89***(21.4)–0.13***(–7.02)–0.04***(–6.86)
Included

Not included

Various dummies a

Included

Included

Included

Included

Included

Included

Pseudo R square

0.37

0.26

0.17

0.06

0.43

0.30

Observations

7,330

9,907

3,358

5,162

3,004

3,735

Note: Numbers in parentheses are z-statistics calculated using robust standard errors. *, **, and *** indicate significance at the 10%, 5%, and 1%
levels, respectively. Numbers above the numbers without parentheses indicate marginal effects. Ln(Own income) is log of “own income +1” because
there are 0 values and in this case log value cannot be calculated,a. Constant, year dummies, dummies of residential city scale, and dummies of residential prefectures are included, but the results are not reported.

19

Table 5. Determinants of marriage: examination of generation effect (Probit model).
(1)
(2)
(3)
(4)
Male
Male
FemaleFemale
0.10
–0.10
0.50*
0.47*
Ln(Height)(0.27)(–0.27)(1.94)(1.84)*AGE_200.51**
0.56**
0.30
0.38*
Ln(Height)(2.27)(2.45)(1.47)(1.82)*AGE_30
0.54**
0.60***
0.22
0.22
Ln(Height)(2.34)(2.63)(1.06)(1.12)*AGE_40–0.07*–0.08**–0.20***–0.21***
University(–1.95)(–2.50)(–5.03)(–5.80)*AGE_20–0.04**–0.03*–0.05**–0.05**
University(–2.23)(–1.82)(–2.41)(–2.42)*AGE_30–0.03*–0.02–0.05**–0.04*
University(–1.75)(–1.16)(–2.31)(–1.90)*AGE_40
0.06
0.16–0.38**–0.41***
Ln(Height)(0.44)(1.19)(–2.43)(–3.08)0.004
0.03***–0.01–0.02
University(0.35)(2.64)(–0.98)(–1.45)–0.95
0.06–0.99**–0.99**
AGE_20(–0.47)(0.04)(–2.10)(–2.04)–0.99**–0.99***–0.99–0.99*
AGE_30(–2.37)(–2.62)(–1.57)(–1.592–1.00**–1.00**–1.00–0.99
AGE_40(–2.43)(–2.72)(–1.08)(–1.15)0.03***–0.02***
Ln(Own Income)(14.0)(–7.38)
Occupation dummy Included
Not included
Included
Included
Various dummies a

Included

Included

Included

Included

Pseudo R square

0.35

0.30

0.38

0.28

Observations

7,865

9,410

7,330

9,907

Note: Numbers in parentheses are z-statistics calculated using robust standard errors. *, **, and*** indicate significance at the 10%, 5%, and 1% levels, respectively. Numbers above thenumbers without parentheses indicate marginal effects. Ln(Own income) is log of “own income+1” because there are 0 values and in this case log value cannot be calculated.
a. Constant, year dummies, gender dummy, dummies of residential city scale, dummies ofresidential prefectures, and dummies of occupation are included, but its results are notreported.

17