Discussion Paper Series

University of Tokyo

Institute of Social Science
Panel Survey
東京大学社会科学研究所 パネル調査プロジェクト
ディスカッションペーパーシリーズ

Understanding Courtshipamong Japanese YouthHiroshi ISHIDA(Institute of Social Sciences, University of Tokyo)

October 2011
No.52

INSTITUTE OF SOCIAL SCIENCE

東京大学社会科学研究所
UNIVERSITY OF TOKYOInstitute of Social Sciences
University of Tokyo
Panel Survey Project Discussion Paper Series No.52
October, 2011

Understanding Courtship among Japanese YouthHiroshi Ishida(Institute of Social Sciences, University of Tokyo)

The permission to use the panel data is obtained from the Research Planning Committeeof the Japanese Life Course Panel Surveys. This research was supported by
Grant-in-Aid for Scientific Research (S) (numbers 18103003 and 22223005) from theJapan Society for the Promotion of Science (JSPS). The research support in
conducting the panel surveys from the Institute of Social Science, University of Tokyo,
and the Outsourcing Company is gratefully acknowledged. An earlier version of thepaper was presented at the Conference on Transitioning to Adulthood in Asia: Courtship,
Marriage, and Work, University Hall Auditorium, National University of Singapore,
July 21-22, 2011. I am grateful to Satoshi Miwa, Akira Motegi, Koji Yamamoto, andthe participants at the conference for helpful comments.

1

Understanding Courtship among Japanese Youth:

Hiroshi Ishida(Institute of Social Sciences, University of Tokyo)

Abstract
This study examines courtship and activities leading to courtship among Japanese youth.
Courtship is an important topic because it influences the chances of eventual marriage.
The analyses of the Japanese Life Course Panel Survey show 42 percent of young
unmarried people without a partner actively sought a dating partner during the past year.
The most popular activity among both men and women was asking friends for
introductions, followed by drinking parties. Men tended to engage in partner-search
activities when they were highly educated, had a full-time job, intended to get married,and had opportunities to meet the opposite sex. Among women, the intention to marry
was the key factor in predicting the likelihood of partner-search activities, in addition tofamily assets. The effect of the partner search on the chances of finding a partner
appears to be greatest for the men and women least likely to engage in partner searches.
Keywords: courtship, dating, Japan, youth, propensity score matching2

1. INTRODUCTION
Young people experience a number of events that mark a transition to adulthood.
Graduating from school and getting a first job (transitioning from school to work),
finding a partner and getting married (transitioning through courtship to marriage), andchildbirth and family formation (transitioning to parenthood) are among the mostsignificant events. Many young people experience all these events, but some do notundergo these processes. Furthermore, these transitions do not always occur in anorderly fashion.
Japanese youth, like those in many other countries, face the same transitions toadulthood. Most Japanese young people experience transitional events in an orderly
fashion, but notable changes have occurred in recent years. One of the most dramatic
demographic changes to attract the attention of the academics, policy makers, and themass media in Japan is the change in marriage behavior: the new habit of delaying
marriage and declining marriage rate (Retherford, Ogawa, and Matsukura 2001; Tsuya
and Bumpass 2004; Raymo and Ono 2007; Raymo and Iwasawa 2008a; Shirahase2009).
The proportion of people who are single by age-group and survey year in Japan isshown in Figure 1ab. The dark bars on the right represent 2005, the most recent period.
The trend indicates an increasing proportion of single people in the 25 to 29 and 30 to
34 age groups for both men and women. Until the 1970s, the rate for the 25-29 age
group was 46 percent for men and 18 percent for women, but the rate for the same agegroup in 2005 was 71 percent and 59 percent, respectively. The increase for the 30-34
age group has been even more dramatic: from 8 percent in 1950 to 47 percent in 2005
among men and from 6 percent to 32 percent among women.
Figure 2 shows the trends in the rate of the never-married reflecting the approximate
proportion of adults still single at 50. The rate has increased sharply since the 1980s,
especially for men. The numbers indicate that about 16 percent of 50-year-old
Japanese men, almost six times the rate of 1980, are single in 2005. Figure 3 presents
the trend in the mean age at which Japanese marry for the first time. The mean age has
been gradually increasing for both men and women since 1975, reaching 31 for men and29 for women by 2005. Although the mean age has been going up recently, themajority (58 percent) of women were married between the ages of 20 and 29 and the
majority (59 percent) of men were married between the ages of 25 and 34 in Japan in
2008. These figures are higher than are those of many Western nations (United
Nations 2010). The most popular ages of marriage for both men and women are stillconcentrated in the limited age range considered the most appropriate by the Japanese(Brinton 1992).
Despite the apparent increase in delayed marriages and the lowered marriage rate, noclear trend towards a weakening of interest in marriage or intention to marry is apparent
among the Japanese young people. A survey of Japanese single people aged 18 to 34
conducted by the National Institute of Population and Social Security Research reports3

no clear decline in the proportion of young men and women who intend to marry.
Even in 2007, 87 percent of male and 90 percent of female youths replied that theywould eventually get married. However, when they were asked if they would like to
get married within the year, 56 percent of males and 49 percent of females replied thatthey would not. These percentages have remained virtually unchanged since 1982
(National Institute of Population and Social Security Research 2007b; Japanese Cabinet
Office 2011).
One of the main reasons for the renewed attention given to the marriage behavior ofJapanese youth is that it relates directly to fertility and the aging of the national
population. While the correlation between marriage and fertility has been weakening
in many industrializing nations (Kiernan 2001; Heuveline, Timberlake, and Furstenberg
2003), marriage and childbirth are still closely related events in Japan, despite theincreasing incidence of premarital cohabitation, delayed marriage, and divorce (Raymo,
Iwasawa, and Bumpass 2004; Raymo and Iwasawa 2008a). The most significant
demographic change taking place in contemporary Japan is the declining fertility rate
(Hodge and Ogawa. 1991; Atoh 2001). Fertility is relevant to the shifting balance
between the working population and the retired generation and to the shrinking
demographic base of support for the nation’s social security system. Japan’s fertility
rate has been declining since around 1970. During the last 40 years, the rate has beenrunning below the replacement rate of 2.08, represented by the bold line in the figure(National Institute of Population and Social Security Research 2010)
There are two main reasons for the decline in fertility: the decline in the number ofchildren born to married couples, and the decline in the marriage rate. The trend
which became most apparent in the recent period is the decline in the proportion of
married couples with more than three children. The average number of children permarried couple was 2.83 in 1962, but only 2.09 in 2005 (National Institute of Populationand Social Security Research 2010). This has had a direct effect on the fertility rate.
Nonetheless, Japanese couples continue to plan to have at least one child. A report by
the National Institute of Population and Social Security Research (2007a) shows the
distribution of the ideal number of children among married couples. The most popular
responses were two (46 percent in 2005) and three children (42 percent in 2005), while
those who did not want children comprised only three percent in 2005. Therefore,
virtually all Japanese couples want to have children, and most eventually have them.
They have recently begun, however, to have fewer of them.
The other reason for the declining fertility rate concerns marriage. As out-of-wedlock
babies are still extremely rare in Japan, marriage and childbirth are closely linked, andshying away from marriage will result in a fewer number of babies born. Figure 3
shows the proportion of births outside of marriage by country. The proportion ofout-of-wedlock babies in Japan was about two percent in 2008, far below the rates inWestern industrial nations.
Further evidence of young people’s reluctance to give birth outside of marriage is theincreasing trend towards non-marital conceptions and bridal pregnancies (when a4

couple’s first child is born within the first eight months of marriage). The Japanesevital statistics special report shows that about one in four first-born children wereclassified as a product of a bridal pregnancy in 2009. Furthermore, 80 percent of thefirst babies of mothers aged 15 to 19 and 60 percent of first babies of mothers aged 20
to 24 were conceived before the women were married (Ministry of Health, Labour andWelfare 2011). Raymo and Iwasawa (2008b) claim that the increase in bridal
pregnancies since the 1970s has been highest among women with relatively little
education.
There is a sudden increase in the interest in marriage and dating among the mass media,
and even among government officials and politicians. Special agencies are being set
up by local governments to assist the young people with their mate-searching activities.
They do not employ or endorse the old match-making system, but provide young people
with socializing opportunities and parties, in order to boost marriage and eventuallybirth rates. Despite the increasing attention given them by the media, we know very
little about Japanese dating behavior and the processes that lead to marriage (Sato,
Nagai, and Miwa 2010).
This paper focuses on courtship and activities leading to courtship. It examines theprocesses by which young people find prospective marriage partners and the factors
affecting their partner-search activities. The paper will examine the following threequestions: (1) How prevalent are the activities leading to finding a partner? (2) Who isactively engaged in searching for a partner? (3) Does actively searching for a partner
affect the actual probability of finding a partner?

2. DATA, VARIABLES, AND METHODSThis study uses the Japanese Life Course Panel Surveys (JLPS), which consist offollow-ups on youth who were aged 20 to 34 in 2007 and the middle-aged who were 35
to 40 in 2007. The first wave of the JLPS was conducted in Japan between January
and April of 2007. Using the electoral and resident registries, the youth andmiddle-aged samples were drawn from the population of the respective age groups.
Respondents were first sent a letter explaining that they were chosen at random andasking them to take part in the survey which involves follow-ups. If they agreed totake part in the first survey but refused to be followed-up, they were not asked toparticipate in the survey. The need to consent to take part in the follow-up surveys
undoubtedly reduced the response rate, but it was hoped that the consent would increase
the retention rate. The questionnaires were distributed by mail and picked up by thestaff of a professional survey company. The response rate for the youth sample was
34.5%, yielding 3367 responses, and the response rate for the middle-aged sample was
40.4%, yielding 1433 responses.
The second wave of the JLPS was conducted from January to March of 2008. All the
respondents who returned their questionnaires in 2007 were followed-up. The
questionnaires were sent by mail and were collected by the staff of a professional survey
5company. The retention rate was 80.7% for the youth sample yielding 2719 responses,
and 86.9% for the middle-aged sample yielding 1246 responses. We will use thecourtship status of waves 1 and 2, so our analysis is restricted to the respondents whocompleted the second wave. For the details of the survey, please see Ishida et al. 2008,
Ishida et al. 2009, and Yamamoto and Ishida 2010.
The main variables used in this paper relate to the presence of a partner andpartner-search activities. Both waves 1 and 2 of the JLPS asked unmarried
respondents whether they had a dating partner at the time of the survey (courtship status
variables). Table 1 shows the cross-tabulation of the presence of a dating partner forwaves 1 and 2. Wave 1 contained 1285 non-married respondents without a partner (65percent of the total respondents) while 699 respondents had a partner (35 percent).
The chances of having a partner in wave 2 were greatly affected by the presence of apartner in wave 1. A total of 69 percent of respondents who had a partner in wave 1
had a partner in wave 2, and 12 percent of them had gotten married. These proportions
among the respondents who did not have a partner in wave 1 were much lower (14percent and 1 percent, respectively). Our analysis of courtship will be restricted to the
respondents who did not have a partner in wave 1 because we wish to examine thetransition from not having a partner to having a dating partner.1 The variable relating
to the partner-search was asked of all unmarried respondents. In wave 2, these
respondents were asked to report all activities they had done in order to find a partner
over the past year.
Other variables which are likely to influence both the likelihood of engaging in
partner-search activities and the chances of finding a partner are considered in our
analysis.2 The first set of variables relates to the respondents’ social background.
The JLPS wave 1 asked a number of questions about socio-economic backgrounds
when the respondents were about 15 years old. These questions are: father’s social
class (categorized as white-collar class, self-employed, and others (the last category is
the base)), father’s attendance to institutions of higher education, mother’s attendance toinstitutions of higher education, the number of books at home, the living standard
compared with other households (that is, whether it was above average), parents’ homeownership, possession of a private room, possession of a piano, parents’ possession ofsummer house, whether the respondent was brought up in a warm home environment,
whether the parents were ever unemployed, and whether the parents were ever divorced.
The second set of variables pertains to the respondents’ highest level of educational
attainment: high school graduation or less (base category), post-secondary vocational
school, junior college, and university/graduate school. The third set of variables is
1

We exclude from our analysis eight respondents who did not have a partner in wave 1
and got married in wave 2 because these respondents were not asked the question about
their partner-search activities between waves 1 and 2.
2 Because we combine the youth sample and the middle-aged sample, we categorized
respondents into four groups: those aged 20 to 24, 25 to 29, 30 to 34, and 35 to 40 atwave 1. The last group was used as the base category in the analysis.
6related to labor market experience: respondent’s entry employment status (full-time
employment or not), entry occupation (white-collar job or not), employment status inwave 1 (full-time employment or not), occupation in wave 1 (white-collar job or not),individual income in wave 1, and whether the respondent worked long hours (10 hours
or more per day) in wave 1. The fourth kind of variable is co-residence with parents(whether the respondent lived with a parent in wave 1). Finally, the fifth set ofvariables involves the circumstances of the respondents’ partner-search and marriage:
whether there were many of the opposite sex whom the respondent could talk andwhether the respondent intended to get married in the future. The positive responses tothese two questions were coded with a score of 1; otherwise, they were given 0.
The methods used in this paper are logistic regression and propensity score matching
technique. Since sub-classification matching using the propensity score is not a
popular method (Rosenbaum and Rubin 1983; 1984), it is described in detail in theanalysis section.

3. ANALYSIS
Partner-search activities
We begin our analysis with the distribution of partner-search activities. The proportionof unmarried respondents who had engaged in any kind of partner-search activities
between waves 1 and 2 was 42 percent. Men (at 43 percent) and women (at 41
percent) had very similar percentages. It is hard to judge whether these figures arehigh or low without any benchmark, but they are probably lower than what people
would have imagined given the prevalence of the notion of partner-search incontemporary Japan.
Table 2 shows the various activities in which unmarried youth engaged while trying tofind a partner.3 The most popular activity was asking friends for introductions. Morethan half of the men and women who engaged in at least one activity used this method.
Taking part in drinking parties was another popular activity for both men and women.
Using the internet and mobile phones was not a popular method, and only five percentof the men and women who did actively search a partner used commercial dating
services.
Determinants of partner-search activities
We next examine the determinants of the partner-search activities: to discover whatkinds of people were active in searching partners. Table 3 presents the results of thelogistic regression predicting the likelihood of engaging in partner search activities.
Among men, age, education, current employment, marriage intention, and opportunities
to meet the opposite sex all had significant effects. Men aged 20 to 24 were more
3

The question was in multiple choice formats, so respondents are asked to report all theactivities they engaged in. The proportion was calculated using the number of
respondents who chose at least one activity as the denominator.
7

likely to search for a partner than men of other age groups. None of the social
background variables exerted significant direct effects on the partner-search activities ofmen. Men with higher education (either junior college or university) were more likely
to engage in partner-search activities than those with only high school education or less.
Men who had a full-time job during wave 1 were more likely to search actively for apartner than those who had a part-time job. If men declared the intention to getmarried in wave 1, they were more likely to engage in a partner search. Finally, menwith many or a few opportunities to meet and talk to the opposite sex were more likely
to engage in a partner search.
Among women, those aged 25 to 29 were more likely to engage in partner-search
activities than those of other age groups. Education or labor market experience did nothave any significant effect. However, family assets (private room or second house)
exerted significant direct effects. When a woman had a private room while growing
up and her family had a summer house, she had a greater likelihood in engaging in apartner search. In addition, just like men, intention to get married in wave 1 had astrong effect. Women who intended to get married in wave 1 were more likely tosearch for a dating partner between waves 1 and 2 than those who did not.4
Counterfactual framework of assessing the effect of partner-search
Next, we will examine whether searching actively for a potential partner really made a
difference to the likelihood of finding a partner.5 In order to address this question, we
will use a counterfactual framework that closely follows the logic of an experimental
design (Rubin 1974; Rosenbaum 1995). We define “causal effect” as the potential
difference in outcomes obtained between two settings. One setting is the outcome
obtained after an individual received the treatment, and the other is the outcome
obtained after the same individual did not receive the treatment. Of course, the same
individual cannot simultaneously be exposed to the treatment and not. This is what
Holland (1986) called the “fundamental problem of causal inference.”
What we would ideally like to examine is that the difference in the outcomes between
the two individuals who are exactly the same, except for the treatment, so that we knowthat the difference in the outcomes is due only to the treatment. In order to avoid thefundamental problem and still address the issue of causal inference, we will use themethod of matching treated individuals (equivalent to treatment in an experiment) with
non-treated individuals (equivalent to control in an experiment). This will ensure thatthe treated are, on average, identical to the non-treated, based on observed
characteristics which are determined prior to the treatment (before the experiment
begins). In a randomized experiment, both observed and unobserved characteristics of
4

We did not distinguish the causal ordering among control variables because all thesevariables are determined prior to treatment (see below). However, social background,
education, and entry employment are determined prior to characteristics in wave 1 andare likely to influence wave 1 characteristics.
5 See Miwa (2010) for an earlier attempt to estimate the effect of partner search on thelikelihood of finding a partner.
8the treatment and control groups are assumed to be identical on average. In ourobservational study, we can match the individuals in the two groups based only on
observed characteristics (Rubin 1977; Rosenbaum 1995).
The counterfactual framework requires that we specify the treatment, outcome, andcontrol variables used to match the individuals in treatment and control. Figure 4
describes our analytic strategy for estimating the causal effect of treatment within the
counterfactual framework.6 Our treatment consists of partner-search activities between
waves 1 and 2, and our outcome is the presence of a dating partner in wave 2. Ourcontrol variables are the observed characteristics of the individuals prior to treatment.
They include social background variables, education, labor market experience up until
wave 1, co-residence with parents in wave 1, and marriage intention and opportunities
to meet the opposite sex in wave 1.
Our analytic strategy requires the following steps. First, we estimate the probability ofreceiving treatment by running a binary logistic regression model predicting theprobability of engaging in partner-search activities using observed characteristics of theindividual prior to treatment. These probabilities comprise the estimated propensity
scores for each individual in our study. Second, we examine the distribution ofpropensity scores for the treatment and control groups, making sure that theirdistributions follow our expectations: the treatment group should have, on average,
higher propensity scores than the control group. Third, based on the propensity scores,
treatment cases are matched to control cases so that the two have the balanced
distributions of the covariates. Of the possible methods of matching cases, we use theexact matching and sub-classification methods (Rosenbaum and Rubin 1983; 1984).
We match treatment and control cases exactly on gender and then group individuals into
strata of estimated propensity scores, so that treatment and control cases have very
similar, if not identical, propensity scores. Fourth, we examine to make sure that thetreatment and control groups have balanced distributions of covariates within the strata.
Fifth, we examine the heterogeneity in the treatment effect. We ask whether the effectof treatment (that is, the effect of the partner search) is the same for individuals withdiffering propensities for engaging in partner-search activities. Following Brand andXie (2010), we distinguish between positive selection effect and negative selection
effect.7 For the positive selection effect, the effect of the partner search is mostpronounced among individuals who are most likely to engage in partner-search
activities, because these individuals are likely to be fully committed to finding a partner
and aware of the benefits of actively looking one. The presence of a negative effectimplies that the effect of the partner search is strongest among individuals who are least
likely to engage in partner-search activities probably due to a very low probability of
6This figure and the procedure are constructed following the discussion by Harding(2003).
7 For examples of the positive and negative selection, please see, for example, Bryk,
Lee, and Holland (1993), DiPrete and Engelhardt (2004), Morgan (2001), and Tsai andXie (2008).
9

their finding a partner (especially when they do not actively search for one);partner-search activities are likely to make a large difference for these people.
Propensity score matching
Figures 5a and 5b present the distributions of propensity scores for the treatment groupand the control group among men and women. In estimating propensity scores, we
included in the logistic regression a wide range of covariates that are available in thesurvey (as described in the variable section). As expected, among both men andwomen, the distribution of the propensity scores for the treatment group is skewed
toward the higher end, while that for the control group is skewed toward the lower end.
These distributions imply that the individuals in the treatment group tend to have ahigher probability of engaging in partner-search activities than those in the control
group. The other important observation is that there is not much overlap between thetwo distributions at the very high and low ends. In other words, we are not able tomatch individuals in treatment group with very high propensity score with those in thecontrol group, and we do not have any matching cases in the treatment group where thepropensity score is very low.
Based on these propensity scores, we constructed four propensity score strata as shownin Table 4. In constructing the strata, we excluded the non-matching cases with very
high and very low scores, restricting our analysis to the region of common support.
The region of common support is the overlap region containing both treated and control
cases.8 Table 4 shows that the mean propensity scores are not significantly different
between the treatment and the control group within each stratum for both men andwomen. The table also shows that the mean scores for both the treatment and control
groups are very different across strata.
We can now assume that treatment and control cases are the same based on observed
characteristics within the strata. Figures 6a and 6b show the main findings of our study.
The effects of engaging in partner-search activities on the likelihood of finding a partner
in wave 2 are shown in separate propensity score strata. Let us begin with men (Figure6a). The effects of partner-search activities among men who are likely to engage insuch activities (those in strata 2, 3 and 4) are negative: the active search for a partner
actually appears to reduce their chances of finding one, rather than to increase them.
Since the effects in strata 2 to 4 are negligible, we may probably assume that there is noapparent effect for men who are likely to engage in partner-search activities. However,among men who are least likely to engage in partner-search activities (those in stratum1), we find a positive effect. This finding is consistent with the negative selection
effect which suggests that partner-search activities make a large difference for thoseleast likely to engage in such activities.
For women (Figure 6b), we find a similar story. While the effect of the partner searchis negligible or negative among women most likely to engage in such activities (those in8

By adopting the common support region, we excluded 11 male cases and 40 female
cases.
10

strata 3 and 4), the positive effect of the partner search is most pronounced among
women who are less likely to search for a partner (those in strata 1 and 2). Again, we
find that the effect of treatment is not homogenous across people in different propensity
score strata. An active search for a dating partner seems to increase the likelihood offinding one only for those who are least likely to engage in that activity. 9
Figures 7a and 7b present the chances of finding a partner by treatment and propensity
score strata for men and women. The differences in the percentages between those
who did and did not engage in active search for a partner were used to compute thefigures in Figure 6. These final figures show that for the men and women who did not
actively search for a partner (those in the control group), their chances of finding apartner is clearly reduced when they were least likely to engage in partner-search
activities (see scores in stratum 1). So for the men and women least likely to search
for a partner, their chances of finding one are very low if they did not actually look for apartner. The benefit of an active partner search is the greatest among the men andwomen least likely to search for a partner; for them the likelihood of finding a partner isincreased through partner-search activities. In short, we find evidence of aheterogeneous treatment effect and a negative selection effect.

SUMMARY AND CONCLUSIONSThis paper has focused on the question of courtship and activities leading to courtship.
Courtship, or the existence of a stable dating partner, is an important topic because it
influences the chances of eventual marriage. The result from the analyses of theJapanese Life Course Panel Survey showed that 69 percent of the respondents with apartner in wave 1 still had a partner and 12 percent were married a year later. For
respondents without a partner in wave 1, the chances of having a partner or getting
married decreased sharply to 14 percent and 1 percent, respectively. Courtship is an
important first step on the path to marriage. The major question then deals with howthese young people go about finding a partner.
This paper first examined the prevalence of partner-search activities among Japaneseyouth. Our analyses of the Japanese Life Course Panel Survey indicated that 42
percent of young unmarried people without a partner actively sought a dating partner
during the past year. The most popular activity among both men and women was
asking friends for introductions, followed by drinking parties. Using internet, mobile
phones, and commercial dating services were not popular at all.
When we asked what kinds of people actively searched for a partner, we found that men
between 20 and 24 and women between 25 and 29 were more likely to search for apartner than were men and women of other age groups. Men tended to engage inpartner-search activities when they were highly educated, had a full-time job, intended
9

For both men and women, none of the effects of partner-search is significant at the 5
percent level because neither stratum has a large number of cases.
11

to get married, and had opportunities to meet the opposite sex. Among women, theintention to marry was the key factor in predicting the likelihood of partner-search
activities, in addition to family assets.
We examined the effect of partner-search activities on the likelihood of finding a partner
within the counterfactual framework. We defined treatment as partner search activities
between waves 1 and 2 and outcome as the presence of a dating partner in wave 2. We
tried to assess whether there were heterogeneous treatment effects. The results suggestthat the effects of the partner search were not homogenous across groups with differing
likelihoods of engaging in partner-search activities for both men and women. The
benefit (that is, finding a partner) appears to have been greatest for the men and womenleast likely to engage in partner searches. Among those likely to look actively for apartner, the active search did not produce a positive impact on their chances of landing adating partner.
More than 40 percent of young Japanese were engaged in activities to find a dating
partner. Nonetheless, our analyses suggest that we should not overestimate the effectof partner-search activities on the likelihood of finding a partner. The youth most
likely to engage in partner-search activities tend to be more successful in finding
partners than are those least likely to engage in them, regardless of whether or not they
actively looked for a partner. In contrast, among people least likely to engage inpartner-search activities, their chances of finding a partner were very low if they were
not actively looking for a partner. The effect of partner search seems to be mosteffective for this group, and an active partner-search is likely to make a large difference
for these people.
We understand that our analyses have several limitations. First, the use of thecounterfactual framework based on propensity score matching does not control forunobserved covariates which may affect the treatment and control groups (Rosenbaum
1995; Rosenbaum and Rubin 1983; 1984). If the two groups differ by unobserved
causal factors (such as personality traits), our estimates of the effect of treatment(partner-search activities) on the outcome (finding a partner) may be biased and may
ignore heterogeneity caused by unobserved covariates. Second, we were not able todisaggregate the effect of partner-search by the types of partner-search activities.
Ideally, we would like to know the effectiveness of the type of activities, but the numberof cases for each individual activity was too small to conduct disaggregated analyses (cf.
Miwa 2010). Third, if many more people become actively looking for a partner,
including those who are least likely to engage in such activity, the negative selection
effect may not be present. The effect of partner-search is likely to be dependent on thenumber of people who actually engage in active search.
Finally, we can draw several implications from our analyses. Having a partner has astrong influence on the chances of eventual marriage. Among respondents who had apartner in wave 1, about a quarter were married in wave 2 or 3, while the same
percentage was substantially lower at 2 percent among respondents who did not have apartner in wave 1. Therefore, in order to boost marriage rate and eventually birth rate,
12

courtship formation is the key process.
We learned from our analyses that partner-search activities increased the chances offinding a partner for young people least likely to search for a partner, so there arebenefits of search activities for some people. We also learned that good education andfull-time entry employment were important factors increasing the likelihood ofengaging in partner-search activities among men. Furthermore, for both men andwomen, intention to get married was the strongest predictor of whether people actively
look for a partner. Although education and labor market positions did not exert direct
effect on the likelihood of active partner-search among women, they affected women’s
intention to get married; Women with university education and white-collar
employment were more likely to have intention to get married than those without.10
Therefore, securing early socio-economic foundation of young people is closely linked
to intention to get married which in turn affects courtship formation and marriage
prospects. All these results imply that earlier life events such as education andemployment are correlated with later events about family formation. Successful
completion of higher education and entry into full-time or white-collar employment
seem to have long-lasting influence on courtship and marriage prospects. Courtshipand marriage among Japanese youth must be understood within the sequence of earlier
life events and transitions.

10This observation is based on the analysis of running a logistic regression of marriage
intention on all other covariates observed prior to the treatment (table not shown).

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16

Figures and Tables

Source: Population Census of Japan (Statistics Bureau, various years)

17Source: Latest Demographic Statistics 2010 (National Institute of Population and Social
Security Research 2010)

Source: Statistical Abstract of the United States 2011(U.S. Bureau of the Census. 2011)

18

19

20

21

22

Table 1. Presence of A Partner at Wave 1 and Wave 2
Wave 2
Wave 1no partner partner
marriedtotal
no partner
1103
174
8
1285
(85.8)(13.5)(0.6)(100.0)partner
133
485
81
699
(19.0)(69.4)(11.6)(100.0)

Table 2. Proportion of Non-married Respondents Who Used Various Activities
To Find A Partner by Gender
Male
FemaleAsk friends for introduction
55
59
Take part in drinking parties
45
44
Ask colleagues and boss
23
21
Take part in enrichment lessons and hobby meetings
15
17
Take courses and join club activities
14
9
Use internet and mobile phone14
7
Ask parents and siblings
8
12
Take part in arranged dates
5
6
Use dating services
5
523

Table 3 Logistic Regression Predicting the Likelihood of Engaging in Partner Search Activities
Men
Women
Standard
Standard
Variables
Estimate
error
Estimateerror
Age group
20-24 years old
0.598 +
0.332
0.088
0.337
25-29 years old
0.192
0.309
0.524 +
0.319
30-34 years old
0.097
0.286
0.020
0.324
Father's classwhite-collar-0.091
0.234
0.085
0.261
self-employed
0.050
0.256
-0.413
0.293
Father's education
0.181
0.259
-0.271
0.263
Mother's education
0.272
0.276
0.370
0.277
Number of books-0.0730.062-0.0190.072
Living standard-0.234
0.237
0.113
0.262
Home ownership
0.078
0.228
-0.111
0.263
Assetsown room-0.1530.217
0.408 +
0.240
summer house
0.517
0.698
1.453 +
0.771
piano
0.146
0.220
-0.331
0.227
Warm home
0.072
0.120
0.138
0.125
Parent not unemployed-0.0460.249-0.4180.290
Parent not divorced-0.358
0.376
0.457
0.423
Education
post-sec vocational
0.486 +
0.282
-0.4150.324
junior college
1.749 *
0.868
0.190
0.324
university
0.543 *
0.247
0.020
0.300
Entry job full-time
0.166
0.261
0.267
0.267
Entry job white-collar-0.0950.250-0.1890.290
Wave 1 job full-time
0.828 **
0.284
0.039
0.281
Wave 1 job white-collar-0.471 +
0.243
0.311
0.290
Wave 1 long working hours
0.231
0.217
0.029
0.281
Income
0.001
0.001
0.127
0.104
Wave 1 co-residence
0.303
0.227
0.130
0.258
Wave 1 marriage intention
1.442 **
0.226
1.834 **
0.259
Wave 1 opportunity to meet
0.551 **
0.189
0.271
0.204
Constant
Log-likelihood chi-square
Probability
sample size

0.938
727.0976
0.000
615

Note: The estimate of income is multiplied by 100.
+ p < .10; * p < .05; ** p< .01 (two-tailed tests).

24

0.785

-0.929615.4121
0.000
533

0.703

Table 4. Mean and the Number of Cases by Propensity Score Strata
Mean
Cases
StrataTreatment
Control Treatment
Controlt-testMen[.00, .20]
0.142
0.135
8
86
0.407
[.20, .40]
0.300
0.293
51
108
0.678
[.40, .60]
0.508
0.503
112
101
0.549
[.60, 1.00]
0.704
0.685
92
45
1.607
Women
[.00, .20]
0.132
0.114
15
104
1.563
[.20, .40]
0.322
0.304
30
77
1.416
[.40, .60]
0.497
0.491
89
94
0.724
[.60, 1.00]
0.685
0.684
86
35
0.104

25

東京大学社会科学研究所パネル調査プロジェクトについて
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