Discussion Paper No. 789

LONG-TERM EFFECTS
OF LABOR MARKET CONDITIONSON FAMILY FORMATIONFOR JAPANESE YOUTH
Yuki Hashimoto
Ayako Kondo

September 2010

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

Long‐term effects of labor market conditions on family
formation for Japanese youth*
Yuki Hashimoto
Graduate School of Economics, the University of Tokyo, andResearch Fellow, Japan Society for the Promotion of Science
7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033 Japanee077016@mail.ecc.u-tokyo.ac.jp
Ayako Kondo (corresponding author)
Institute of Social and Economic Research, Osaka University
6-1 Mihogaoka, Ibaraki-shi, Osaka, 567-0047 Japan
akondo@iser.osaka-u.ac.jp
Phone: +81-6-6879-8584 / Fax: +81-6-6879-8583
September 1, 2010
Abstract
This study aims to examine how each cohort’s family formation is affected bylabor market conditions experienced in youth in Japan. Although deterioration
in youth employment opportunities has often been blamed for Japan’s
declining marriage and fertility rates, the effects of slack labor market
conditions on marriage and fertility are theoretically unclear. We estimate theeffects of regional labor market conditions on marriage and fertility,
controlling for nation-wide year effects and prefecture fixed effects, and findthe following. First, the male unemployment rate is negatively correlated withmarriage of women in the local labor market, although the correlation is weakand concentrated on the less educated group. Second, high school-educatedwomen who experienced a recession while entering the labor market are lesslikely to have children and tend to marry later. In contrast, a recession atentry to the labor market rather increases fertility among college-educatedwomen. The overall impact of labor market conditions experienced in youth
on family formation is relatively weak, compared to the substantial losses inearnings and employment stability documented by the existing studies.

*This research is supported by the Grant-in-Aid for Young Scientists (#21830056, PI:
Kondo). All the analyses based on the Employment Status Survey were implemented by
Ayako Kondo, under approval by the Statistics Bureau. We would like to thank Takahiro Ito,
Daiji Kawaguchi, Tomoko Kishi, Yukinobu Kitamura, Kohei Kubota, Fumio Ohtake, Masaru
Sasaki, Kengo Yasui, and participants of KISER summer conference, OEIO mini-conference,
Trans-pacific Labor Seminars, JEA 2010 spring meeting, NBER Japan project meeting, andthe seminars at Waseda University, the National Institute of Population and Social Security
Research, and Okayama University for their suggestions.
11. Introduction
As is the case in many countries, in Japan, the average age of the first marriage has
been rising, and the fertility rate has been falling for decades.

Simultaneously,

unemployment and non-regular employment among youth have been on the rise. Becauseof the concurrence of these events, the deterioration of youth employment has often been
blamed for declining marriage and fertility rates.

For example, Yamada (2007) argues that

under the custom that allows unmarried women to remain economically dependent on theirparents, the bleak economic prospects for young men, relative to their fathers’ generation,
have made marriage less attractive for young women.

On the basis of a web survey

regarding attitudes toward family formation, Matsuda (2009) also claims that income risks
wrought by a recession make young people reluctant to marry.

Since out-of-wedlock

childbirths are very rare in Japan, the decline in marriage has also been thought to lead to alower fertility rate.
Economic theories, however, do not predict that deterioration in youth employment
prospects necessarily leads to lower marriage and fertility rates.

A recession can have apositive substitution effect because worse employment opportunities for women lower theopportunity costs of marriage and childbearing. Indeed, empirical studies using data fromthe United States have established that worse opportunities for women in the local labormarket can actually increase marriage and fertility rates, and this effect tends to dominate thenegative income effects that derive from deteriorated male employment opportunities (Butzand Ward 1979, Schultz 1994, Blau, Kahn and Waldfogel 2000, Dehejia and Lleras-Muney,
2004, Kondo 2010).1

At the same time, Kondo (2010) shows that this increase in marriage

reflects an inter-temporal substitution and that the long-term marriage rate remains unaffected.
1

At the same time, earnings inequality among young men prolongs the marital search
process and delays the marriage timing of women in the local labor market (Loughran 2002,
Gould and Passerman 2003).
2

Her result suggests that counter-cyclical marriage and fertility rates in the United States may
depend on its high labor turnover rate, which makes it easier to return to work when a child isold enough.2

In countries with more rigid labor market structures, it may be more difficult

for women to find a job after child-bearing, and thus there may be less room forinter-temporal substitution between market work and family activities.3

Incidentally, thefertility rate is falling rapidly in such countries, including Japan and southern European
countries.4
Existing studies using Japanese data vary significantly in their conclusions, dependingon the data and methodologies used therein. Among the studies using the Census data, on the
one hand, cross-sectional analyses using Census 2000 data report a positive correlation
between employment opportunities for young men and contemporaneous marriage andfertility rates (Ogawa 2003, Ohta 2007, Kitamura and Miyazaki 2009).5

On the other hand,

Ogura and Dekle (1992) find a negligible effect of men’s earnings by applying thefirst-difference method to prefecture-level panel data from Census 1970–85 data.

Moreover,all of these studies report a modestly negative effect of female labor market opportunities on
marriage and fertility; therefore, the overall effect of a recession on the demographic trend
remains ambiguous. Studies on the effect of female labor market conditions using theJapanese General Social Surveys also vary in terms of their conclusions: Shimizu (2002)
finds a decrease in women’s earnings raises fertility, while Nozaki (2007) shows that an
2

At the individual level, Lindo (2010) finds that a husband's job displacement accelerates
child bearing in the short run but it reduces the total fertility in the long run, using data fromthe United States. Furthermore, Huttunen and Kellokumpu (2010) find similar effects in
Finland.
3
Indeed, Gutiérrez-Domènech (2008) finds that a high unemployment rate delays marriage
and fertility of Spanish women. She argues that potential mothers care not only of theircurrent employment status but also in their expected possibilities to work afterwards.
4
As Yamada (2007) points out, another similarity between Japan and southern European
countries is that unmarried youths tend to be allowed to depend financially upon theirparents.
5
Ermisch and Ogawa (1994) show similar results using time series data.
3

increase in the female unemployment rate delays marriage.

Lastly, using data from theJapan Panel Study of Consumers, Higuchi, Matsuura, and Sato (2007) find that theprefecture-level active job opening to vacancy ratio (yuko kyujin bairitsu) does not affect thefertility of married women.
Such a lack of consensus among existing studies can be attributed to the lack of an
appropriate dataset that provides individual-level information on fertility and marriage history
and has a sufficiently large sample size.

We aim to overcome this limitation by constructing

individual-level fertility history data from the Employment Status Survey (ESS),
supplemented by prefecture-level panel data of marriage histories from the Census and Vital
Statistics.

These datasets are augmented with the regional unemployment rates.

By

exploiting geographical variations in unemployment rates, we control for nation-wide timetrends in a flexible way. Further, the ESS’s large sample size allows us to investigate theeffect for subsamples divided by education.
Furthermore, we exploit the fact that labor market conditions in one’s
school-graduation year have persistent effects on that individual’s subsequent employment
and earnings in Japan—that is, a recession at graduation permanently lowers one’s prospects
for employment and earnings over the long term (Genda, Kondo, and Ohta, 2010).

It is

important to distinguish the effect of temporary income fluctuations and that of permanent
income changes because they have quite different implications for long-term demographic
trends. On one hand, a temporary decline in income may delay marriage and childbearing,in order to smooth consumption inter-temporarily, without changing the total number ofchildren the couple would eventually have. On the other hand, a decline in permanent
income may affect the total number of desired children and the number of people who will
never have a child.
Our estimates suggest that the unemployment rate is negatively correlated with the4proportion of married women, although the size of the effect is modest. Further, highschool-educated women who experienced a recession at entry to the labor market are lesslikely to have a child, although the effect for men in the same cohort is subtle. In contrast, a
recession rather increases fertility among college-educated women; this weak income effectamong more highly educated women is consistent with existing evidence that income loss
due to a recession at entry is smaller for college-educated men than for high-school educatedmen (Genda, Kondo and Ohta, 2010).

Nonetheless, the overall impact of labor market

conditions experienced in youth on family formation is relatively weak compared to thesubstantial losses in earnings and employment stability.
The current study is not the first to examine the effects of labor market conditions atentry to the labor market on marriage and fertility: Abe (1999), Higuchi and Abe (1999), andHiguchi (2001) investigate such effects using individual-level panel data and national-level
labor market indices.

They find that women who graduate from school in a year when labordemand is slack tend to marry and bear children earlier, while the contemporaneous
unemployment rate delays marriage and childbearing.

However, the use of national-level

labor market indices does not allow them to control for time trends in a flexible manner.
With controls for nation-wide year effects and region fixed effects, we aim to obtain estimatesthat are more robust to the trend changes.6

Another value-added feature of this paper is that

we investigate the effects for subsamples divided by education, thanks to the large sample
size of the ESS.

We limit our sample to women because the effects of the unemployment

rate on mobility and educational attainment, which are discussed in the Appendix, are notnegligible for male sample of the ESS.7
6

The rest of this paper is organized as follows.

To our knowledge, Ogura and Dekle (1992) is the only existing study to employ the same
strategy, but their analysis was limited to the contemporaneous relationship between labormarket conditions and marriage and fertility behaviors.
7
In the earlier version we have tried using the sample of men. The estimated effects of theunemployment rate on family formation were weaker, although these estimates may be
5The following section reviews background statistics of the Japanese youth labor market, as
well as demographic trends. The third section describes our data, while the fourth reports
our empirical findings. The final section provides concluding remarks.

2. Japanese youth labor market and demographic trends
The Japanese economy experienced prolonged stagnation in the 1990s and early 2000s.
Since Japanese firms prefer to reduce their number of new hires among school graduatesrather than fire existing workers, the cost of this economic stagnation was borne
disproportionately by young people who were transitioning from school to full-time work.
Consequently, in Japan, macroeconomic conditions in the year of graduation have very
persistent effects on one’s subsequent employment and earnings. Genda, Kondo and Ohta
(2010) show that a one percentage-point rise in the regional unemployment rate at graduation
lowers earnings in the subsequent 12 years by 7% for high school-educated men and by 3%
for college-educated men.

For high school graduates, a one percentage-point rise in theunemployment rate at graduation also lowers the employment rate by three percentage points
for over a decade afterwards.

This implies that the cohorts that entered the labor force in the

1990s and the early 2000s will suffer substantial and permanent loss in terms of their lifetime
income.
At the same time, Japan has experienced a rapid fall in its fertility rate.

The total

fertility rate fell from approximately 1.8 in the early 1980s to below 1.3 in the mid-2000s,
and Japan’s fertility rate is currently among the lowest in the world.

This fall in the fertility

rate can be attributed to increase in the number of women who will never marry.

The

average number of children borne by a married couple has stayed at around two since the1970s, while the proportion of women aged 25–39 who are not married increased from 18%
subject to biases owing to endogenous mobility and educational attainment.
6

in 1985 to 36% in 2005, according to the Census. Moreover, according to the Vital Statistics
Annual Report of 2003, less than 2% of all births were out of wedlock.

Thus, it is believedin Japan that a decline in marriage leads to a decline in fertility.
This coincidence of timing leads to the argument that the deterioration in employment
prospects for young men has accelerated the aging of the Japanese population—that is,
economic stagnation has increased the number of young men who cannot earn enough to
support their families, thus suppressing marriage and fertility rates.8

Indeed, as shown in

Figure 1a, the total fertility rate seems to relate to fluctuations in the unemployment rate, witha few years’ lag.

Additionally, at the individual level, non-regular employment andunemployment tend to delay marriage among Japanese women (Nagase 2002, Sakai and
Higuchi 2005, Kitamura and Sakamoto 2009).
However, because the unemployment and fertility rates are sustaining secular trendchanges, the observed negative correlation might have been a mere coincidence. Further,
the individual-level correlation between family formation and past employment status does
not necessarily mean an aggregate effect of macroeconomic shocks experienced in youth,
especially if some unobservable factors affect both labor supply and family formation.
Therefore, the causal link between the unemployment rate and the fertility rate might bemuch weaker than it appears in the aggregate time series.

Hence, the rest of this paper

explores the relationship between the unemployment rate and fertility with controls fornation-wide trends in as a flexible way as possible.

Also, inspired by Genda, Kondo andOhta (2010), who state that macroeconomic conditions at entry to the labor market have apersistent effect on subsequent employment and earnings, we focus on the effect of theunemployment rate in the year of graduation from school and the probability of being married8

It is often emphasized that the social norm in Japan expects men to support their spousefinancially. According the Eleventh Japanese National Fertility Survey in 1997, women in
Japan consider earning capacity one of the very important attributes of prospective spouse.
7

or having a child, as well as the effect of the contemporaneous unemployment rate.

3. Data
Since no single dataset in Japan provides the marriage and fertility histories of womenand has a sufficiently large sample size, we construct separate datasets for marriage andfertility.

First we construct a prefecture-level panel comprising the ever-married population,using data from the Population Census and the Vital Statistics.
individual-level fertility histories from the ESS.

Then we construct data ofNote that, although it provides informationnecessary to retrieve fertility histories, the ESS does not provide any information regarding
when the respondent married.

Therefore, in order to assess effect on marriage timing, wehave to rely on the annual panel of the total and married populations by gender at each age
for each prefecture, constructed from the Census and Vital Statistics. Both fertility andmarriage data are augmented with the regional unemployment rate taken from the annual
reports of the Labour Force Survey.
Our analysis of marriage begins with data from the quinquennial Population Census for 1980,
1985, 1990, 1995, 2000, and 2005, which provides total and never-married populations byeach age, gender, and prefecture. 1990 and 2000 Census also provides information byeducational background. Table 1a presents the summary statistics.
Then, we impute data for inter-Census years from the Census and the Vital Statistics
Annual Report as follows.

The total population is interpolated solely based on the Census,

by taking a weighted average of the previous and subsequent Census.

The underlying

assumption is that the age-specific migration rate does not jump at a certain age; thisassumption is not very plausible for those younger than 25, because many people move at theages of 18, 20, and 22 to enter college or start a job. Hence, our data include only people
older than 25.

The numbers of ever-married women are calculated by adding the number of8marriages reported in the annual Vital Statistics to the ever-married population reported in the
Census.

For this reason, the ever-married population fluctuates with the number ofmarriages reported in the Vital Statistics. The Appendix describes our data procedure in
more detail. Eventually, we have a panel dataset comprising the total and ever-married
populations, by gender, prefecture, and year of birth, for the birth-year cohorts of 1956–1978.
Table 1b shows the summary statistics.
The ESS, our source of fertility history data, is conducted by the Statistics Bureau ofJapan every five years; for this study, we use the surveys from 1992, 1997, 2002, and 2007.
The ESS asks about the basic demographic characteristics of all adult household members,
such as age, gender, and educational background; it also asks about employment status and, if
employed, job characteristics and earnings.

Furthermore, the ESS asks about marital status

and the relationship of the household head to each adult within the household, as well as theages of all children therein.

To retrieve the fertility history of each adult woman, we assume

that all children younger than 18 years live with their mother, and that no woman younger
than 18 or older than 45 years can have a child. Then, if a woman was fertile when a childin the same household was born, the woman is assumed to be the child’s mother.

The

Appendix describes the step-by-step process by which we dealt with households withmultiple adult women; ultimately, the fertility histories of more than 99% of the women in therelevant age group are retrieved.
For the main analyses, we use data of women who were 33–37 years old at the time ofsurvey.

This cut-off threshold is a result of the trade-off between accuracy and the length ofthe period to cover.

On one hand, since we cannot track childbirths that had occurred 18 or

more years prior to the survey, we cannot capture births performed by a woman who was 16
years old at the time of the birth and 34 years or older at the time of survey.

On the other

hand, given that the median age of mothers bearing their first children is 29, we cannot ignore9births performed by women in their early thirties.

Since teenage births account for less than

2% of all births in Japan, we set 37 as the maximum age of women to include; the lower
cut-off is set to 33 so that each survey can cover 5 cohorts.
Table 1c shows the summary statistics of data from the ESS.

We divide the sample byeducational background, because it affects the timings of marriage and fertility.

One thing

to note is that in Japan, junior college is a much more popular option for females than formales. 9

In contrast, males are much more likely to go to four-year colleges.

Also,skipping and repeating grades very rarely occurs in Japan; thus, although the ESS asks only
about the highest degree completed in the four categories, we can calculate the age at
graduation fairly accurately as follows: 15 for junior high school, 18 for high school, 20 forjunior college, and 22 for college. Since the unemployment rate may affect enrolment
decisions, we further discuss the effect of the regional unemployment rate on education
attainment in the Appendix.
As mentioned, the median age of mothers at their first child’s birth is 29. Figure 2
plots the age profile of the women who have at least one child, as well as the woman who
have ever married from the annual panel at the prefecture level.

As expected, women withlower levels of education attainment tend to have a child earlier. Further, a significant
number of women have not had a child by their mid-thirties, especially those who had
graduated from college.
As an index for labor market conditions, we use the regional unemployment rates for

9

Further, the category includes both regular junior colleges, which grant associate degrees,
and technical college (kosen), which offer five-year programs that grant high school diplomas
and associate degrees. In addition, some respondents had gone to two-year vocational
schools (senmon gakko) and seemed to classify themselves as "junior college" graduates,
probably because the certificates issued by such vocational schools are taken as being
equivalent to associate degrees.
1010 regions,10 as published in the annual report of the Labour Force Survey since 1974. As
the prefecture-level unemployment rates are not available, we assign the same unemployment
rate to each prefecture in the same region.

The primary reason we use the regional

unemployment rates despite this limitation is that, by using the same variable as Genda,
Kondo and Ohta (2010), we ensure that there are significant long-term effects on earnings
and employment.

This is important because, in order to merge the regional unemployment

rate with fertility and marriage data, we have to assume that the region of residence at
graduation or at a specific age is the same as the region of residence at the time of survey.11
Admittedly, there must be some measurement errors; however, Genda, Kondo and Ohta
(2010) demonstrate that such errors are not large enough to attenuate the effects on earnings,
even 12 years after graduation.

The summary statistics are shown in the last part of Table 1.

4. Empirical findings
Marriage
Let us first examine the contemporaneous correlation between the male unemployment
rate and the family formation behaviors by young women in the same region.

Specifically,

we obtain prefecture-level male unemployment rate data, as well as data on the proportion of
the married female population by age, from Census 1985–2005. The first panel of Table 2
shows the estimated coefficient of the male unemployment rate from the following
double-fixed effect model:
M apt     U pt  t   p   pt …(1),where Mapt is the proportion of married women in age category a in prefecture p in year t, and10

Hokkaido, Tohoku, Southern-Kanto, Northern-Kanto and Koshin, Hokuriku, Tokai, Kinki,
Chugoku, Shikoku and Kyushu. Annual unemployment rates at the prefecture level had notbeen available until the survey redesign in 2002 because the sample size for small prefectures
had not been large enough to calculate unemployment rates precisely.
11
The effect of unemployment rate on geographic mobility is discussed in the Appendix.
11

Upt is the male unemployment rate in prefecture p in year t.

We use both the unemployment

rate of all adult men and that of men in the same age group as the women.

The year dummy,

η, is included to control for changes in nation-wide trends, and the prefecture dummy μ
controls for the time-invariant characteristics of each prefecture.

As shown in Table 2a, themale unemployment rate does not have a statistically significant effect on the marriage rate ofyoung women in the same prefecture.
Furthermore, we calculate the proportion of the married population by educational
background (high school or less, junior college and college) from Census 1990 and 2000.
The second panel of Table 2 shows the correlation between the first difference of the male
unemployment rate and that of the female marriage rate by educational background and age
category, from the following linear regression:dYp     dU p   p …(2),As shown in Table 2b, the male unemployment rate has a modestly negative effect on
marriage of women who had attained high school or lower levels of education.

This implies

that a recession affects women with different educational background in opposite ways.
Next, let us examine the effect of the unemployment rate experienced upon entry to thelabor market using the prefecture-birth year level data from the Census and Vital Statistics.
Since the data is aggregated to the prefecture level, we employ the following linear
probability model instead of a probit or logit:
M pya   a   a 'U p , y   a ' X pty   ya   pa   pya , … (3)U p , y is supposed to be the unemployment rate at graduation.

However, we cannot calculate

the married population separately for each level of education because the Vital Statistics does
not ask educational background.

For this reason, we try the unemployment rates in the year

when the cohort was 18, 20 and 22 years old as U p , y , and instead of splitting the sample byeducation, we include in Xpty the ratio of women (or men) with no more than high school
12

education. Xpty also includes the male-to-female ratio in the population. In order to allow thecoefficients to vary with age, we estimate (3) separately for each age a.
The estimated coefficients are reported in Table 3.

A recession at graduation fromschools generally delays the marriage of the affected cohort of women.

Moreover, theunemployment rate at graduation from high school has weaker at first but more persistenteffect than the unemployment rate at graduation from college graduation.

Yet, the effect israther small; a one percentage point rise in the unemployment rate at entry to the labor market
lowers the probability of having ever married by the age of 35 by at most about 0.5
percentage point.
Overall, we find only modest evidence that deterioration in the employment
opportunities of young workers have decreased the marriage of young women.

The strong

negative correlation observed from the time series data is likely spurious; i.e., a recession is
not associated with a decline in the marriage rate. At the same time, we do not observe theacceleration of marriage timing due to a recession experienced in youth, which is observed in
the United States.

Our results are closer to the study by Gutiérrez-Domènech (2008), whofinds that a high unemployment rate delays marriage in Spain.

Her interpretation of thenegative effect of the unemployment rate on marriage formation is that potential mothers
worry about future employment opportunities after child bearing.

Although our data do notallow us to go beyond a mere speculation on this matter, the same argument might hold inJapan, especially for less educated women who tend to be in non-regular employment andhave difficulty in keeping a job during their maternity leave.

Fertility
Before detailed analyses using the ESS, let us explore the contemporaneous correlation
between the total fertility rate and the male unemployment rate at the prefecture level.
13

Specifically, we match the data of the male unemployment rate from the Census, which isused in Table 2, with the prefecture’s total fertility rate in the same year, as taken from theVital Statistics Annual Report.

Figure 3 presents scatter plots of 5-year differences of thetotal fertility rate and male unemployment rate.

Although the slopes are negative for all

periods and statistically significant for the periods 1990-1995 and 2000-2005, the size of theeffect is not very large: a one percentage-point rise in the unemployment rate lowers the total
fertility rate at most by 0.03 point, whereas the average unemployment rate is around 4% andthe total fertility rate is around 1.5.
Nonetheless, the effect may vary with educational background, as is the case for theeffect on marriage presented in Table 2b.

Thus, next we estimate the following probit model

for subsamples of the ESS divided by educational background:C ptyi  1 if

   'U p , y   p   y   ptyi  0 , ..(4)where Cptyi takes 1 if person i has a child and 0 otherwise.
residence, and y is the year of graduation.

Subscript p denotes the region of

The remaining random errors are denoted as εapt,

which may correlate within the region (i.e. standard errors are clustered by region).

Weestimate equation (4) separately for different ages and educational backgrounds.
Table 4 reports the estimated marginal effect of the regional unemployment rate in theyear of graduation on the probability of having ever had a child at the ages of 26, 28, 30, 32
and 34.

The unemployment rate at graduation has marginally significant negative effects on

the probability of having had a child by their mid thirties for less educated women; this isconsistent with the negative effect on marriage presented in Tables 2b and 3.

In contrast, theeffect of the unemployment rate at graduation is positive for more educated women, although
it is not statistically significant for most ages.
Further, to identify the effects of unemployment rates both at entry to the labor market
and at present, we estimate the following Cox’s proportional hazard regression for women14

who are aged 33–37 at the time of survey:
H it   (birthyi , ageit , edci ) exp(U p ,t 1   U p , y   p ) , ...(5)where H is the hazard rate of having the first child, U p ,t 1 is the unemployment rate in theprevious year in the region, and U p , y is the unemployment rate at graduation. Table 5
reports the estimated coefficients α and β for all women and for two subgroups byeducational background.
The upper panel of Table 5 shows that the unemployment rate in the year of
graduation delays the timing of first births for female high school graduates, consistent withthe findings presented in Table 4. Also, the effect is positive for college (including juniorcollege) graduates, although the coefficient is not statistically significantly distinct from 0.
The contemporaneous unemployment rate has a modestly negative effect on fertility for bothgroups.
We also allow α and β to vary with the woman’s age, by taking interaction ofunemployment rates with age category dummies.

The lower panel of Table 5 reports theeffect differentiated by four age groups: 18–22, 23–27, 28–32, and 33–37.

The effect ofcontemporaneous unemployment rate is positive for the youngest age category but turns
negative as women ages, regardless of their level of education. The effect of the graduation
year’s unemployment rate is consistently negative for high school educated women andpositive for college educated women.

Figure 4 presents histograms of the simulated numberof births for each age category and level of education, using the actual age-specific fertility
rates and estimated coefficients reported in Table 5b.12 Overall, a recession decreases fertility
for less educated women but increases fertility for more educated women. The aggregate
12

The number of births when a unemployment rate at graduation rises by 1% =exp(β)*#actual births in the data *(1-simulated # births for the younger category)/ (1-actual #births for the younger category). The number of births when a contemporaneous
unemployment rate rises by 1% = exp(α)*#actual births in the data.
15effect is subtle.
The positive effect for more educated women is consistent with the findings in the
United States.

For example, Dehejia and Lleras-Muney (2004) show that women who havea child during a recession are on average better educated than women who have a childduring a boom.

Also, Kondo (2010) finds that a recession experienced in youth accelerates

marriage and childbearing slightly, but it does not change the number of children eventuallyborn.

To the contrary, a recession experienced at entry to the labor market has a persistent

negative effect on fertility for less educated women.
The opposite effect across educational background suggests that the positive
substitution effect is stronger for more educated women, while the negative income effect isstronger for less educated women.

The substitution effect might work stronger for moreeducated women because they are more likely to have a full-time regular job if they are single,
and therefore the opportunity cost of maternity leave may be higher. Another possibility isthat the income effect is weaker for more educated women because they tend to marry withmore educated men, who suffer less from a persistent loss of earnings due to entering thelabor market during a recession.
An alternative explanation is that the effect of the unemployment rate at entry does not
attributable to the declined income but operates through the increase in non-regular
employment. 13

The non-regular employment is more prevailing among less educatedwomen than more educated women: according to the ESS, 53.1% of employed women in
their twenties with high school or less education are on non-regular employment, whereas
only 28.0% of those with junior college or more education are on non-regular employment.
13

Readers who are familiar with the ESS might wonder why we do not assess the effect ofobtaining a regular fulltime job upon graduation directly, because ESS 2007 provides
information of the first job after graduation. We have tried but found that the proportion of
women who obtained a regular fulltime job within two years since graduation is about 50%,
which is unrealistically low compared to other published statistics in Japan. Thus we gave
up using this information.
16

Also, it is known that the unemployment rate at entry to the labor market increases theprobability of having non-regular job in subsequent years (Kondo 2007, Genda, Kondo andOhta 2010). Moreover, at the individual level, non-regular employment and unemployment
tend to delay marriage among Japanese women (Nagase 2002, Sakai and Higuchi 2005,
Kitamura and Sakamoto 2009).

Furthermore, Iwasawa (2004) shows that women who had

non-regular job at the time of marriage tend to have fewer children than those who had
regular job or those who did not have a job. This may be because, as shown by Imada and
Ikeda (2006), most non-regular employees are not eligible for maternity leave and have moredifficulty in keeping their job after childbirth than regular employees. Gutiérrez-Domènech
(2008) argues a similar mechanism leads to a negative effect of the unemployment rate on
marriage and fertility in Spain, although she does not find the same pattern across educational
background as we find in Japan.

In addition to this lack of job security during the maternity

leave, as Nagase (2002) speculates in the discussion of her results, regular full-time
employment might have offered some sort of matching opportunity for young women andalso pressured them to marry by a certain age.14
Lastly, let us examine the effect on the probability of having two or more children.
Does the delay in the first childbirth lead to a lower probability of having a second child forhigh school or less educated women?

Table 6 suggests the answer is no. It shows that,conditional on having at least one child, the unemployment rate at graduation increases theprobability of having the second child even for less educated women.

This may be because

women who experienced slack labor market conditions at entry to the labor market are less14

One might think that the lack of childcare is another cause of the negative effect of arecession, because more married women try to work part-time to compensate declined
income of their spouses during a recession. We have tried adding the number of children on
the wait-list to public day care (taikijidou), although such data is available only for the 2000s
and thus our finding is only suggestive. Nonetheless we find that, even though the shortage ofchildcare does discourage child bearing, the estimated effects of the unemployment rate do
not change much.
17

likely to be able to keep their full-time job after her first childbirth15 and thus the opportunity
cost of having additional children is lower for them.

Nonetheless, we must bear in mind that

our sample is limited to those who were 33–37 years old at the time of survey, because wecannot track births that occurred more than 17 years prior to the survey. Since 20 % of
second and higher-order births are undertaken by women older than 33 years, the resultspresented in Table 6 should be considered only suggestive evidence.

5. Conclusion
We have shown that a high regional unemployment rate and a low employment rate of
the men in the same cohort lowers the probability of being married for young Japanesewomen.

In contrast, the aggregate effect of local labor market conditions on fertility is

subtle.

However, a further look by educational background reveals that the effect of

unemployment rate at entry to the labor market on fertility is negative for less educatedwomen and positive for more educated women.

The positive effect for more educatedwomen is consistent with the substitution effect typically observed by existing studies in the
United States.

The negative effect for less educated women may be attributable to astronger income effect, the lack of job security during the maternity leave, or the lack ofmatching opportunities for non-regular female employees.
In any case, the overall effect of labor market conditions experienced in youth onfamily formation is weak, despite the well-documented persistent effect of labor market
conditions at entry on subsequent employment and earnings.

15

Although it might have

Imada and Ikeda (2006) show that female non-regular employees who are not eligible for amaternity leave tend to quit right before the birth of their first children. Since the rate of
non-regular employment is strongly affected by labor market condition in the year of
graduation in Japan (Genda, Kondo and Ohta, 2010), it implies that cohorts who face a slack
labor market at entry to the labor market are less likely to continue to work at a full-time
regular job after child-bearing.
18

delayed marriage timing slightly, the deteriorated employment prospects for young Japanese
men is not likely to be the primary reason of the declining fertility.

This result implies that,even though recovering employment prospects of young Japanese is an important policy
challenge on its own, it is not likely to be an effective policy for increasing the fertility rate.

Appendix
A1 Total and ever-married population from the Census and Vital Statistics
For total population, for each prefecture and sex, we define the population of each age in
an inter-Census year as a weighted average of population of the same birth-year cohort in thenearest two Census years. For example, the population of age 28 in 1997 is defined asfollows:
Population _ age28 _ in1997
 0.6* Population _ age26 _ in1995  0.4* Population _ age31_ in 2000

Using seven waves of the Population Census, we construct annual panel spanning from 1975
to 2005.
This definition does not take into account different migration rates for different ages.
This omission may cause non-random measurement errors for the teens and the early twenties
because many of them move from rural areas to large cities to enter college or start a new job,
especially at the ages of 18, 20 and 22. Yet, we believe that such errors are negligible for the
population of age 25 or older;16 therefore, we focus on marital status at the age of 25 or

16

For 1991-2005, we tried an alternative definition that uses the annual population estimatesreported in jinko suikei nenpo. Since the annual estimated population is available only forfive-year age categories, we decomposed each five-year age cell into five single-year age
cells using the ratio obtained from nearest two Census waves. For example, the population of
age
28
in
1997
is
definedasfollows:
19

older.
For the ever-married population of unmarried, we use the number of marriages by age ofthe bride and groom registered in each prefecture, published annually as a part of the Vital
Statistics, in addition to the never-married population by single age, sex and prefecture takenfrom the Population Census. Using these data, first, we subtract the number of marriages
formed after the latest Census from the never married population reported in the Census. Let
us call this "forward imputation." Second, we add the number of marriages to be formed by
the next Census to the never married population reported in this next Census. Let us namethis "backward imputation." Lastly, we take a weighted average of the forward and backward
imputations. For example, the never-married population of age 28 in 1997 is calculated asfollows:
1. Forward imputation: the never-married population of age 26 in 1995 - the number ofmarriages with 27-year-old brides in 1996 – the number of marriages with 28-year-old
brides in 1997.
2. Backward imputation: the never-married population of age 31 in 2000 + the number ofmarriages with 31-year-old brides in 2000 + the number of marriages with 30-year-old
brides in 1999 + the number of marriages with 29-year-old brides in 1998.
3. Weighted average: 0.6*forward imputation + 0.4* backward imputation.
The ever-married population is equal to total population – never-married population.
For years prior to 1980, only the "backward imputation" is available because Census
1975 does not provide with the never married population by single age. Thus, we regress theimputed never-married population on the backward imputation and interaction terms of age

Population _ age28 _ in1997  EstPopulation _ age25 ~ 29 _ in1997 *Population _ age26 _ in1995
Population _ age31_ in1995
)/2
Population _ age23 ~ 27 _ in1995 Population _ age28 ~ 32 _ in1995
We confirmed that the difference between this alternative definition and the definition used in
the main analysis is negligible for population older than 24.
20(

dummies and dummies for years since the last Census, using the data after 1980 and
separately for each prefecture, to get the predicted never-married population for years prior to1980 based on the backward imputation. The underlying assumption is that the difference
between the forward and backward imputations depends only on the age and how far the last
Census was.

A2 Identification of each woman’s children
To identify the year of child births, we made several important assumptions. First, wefollow children up to 17 years old, assuming they live with their mother. The reason for thisassumption is that 18 is the age when people graduate from high school and start leaving theirparents’ home in Japan. This assumption is probably tolerable because about 97% of 16 and17 year-olds in the sample are children or grand children of household heads. The fraction ofheads and wives increases from less than 3% at the age of 15-17 to 12.7% at the age of 18,
and it keeps growing with the age thereafter. Regarding the measurement errors due todivorce, the probability of divorce within 15 years since marriage is about 20% in Japan.17
Once divorced, the custody went to the mother for 75% of the cases of divorces that occurredin the 1980s and 1990s, according to the Vital Statistics Report. Taking into account that theprobability of divorce is higher at the initial stage of marriage before the couple has a child,roughly speaking, the fraction of children whose step-mother is misidentified as their
biological mother is at most 5 %.
Second, we assume that a woman can have a child only between the ages of 18 and 45.
This assumption is based on the special issue of Vital Statistics Report in 2005: only less than
0.3% of women who were born in the period of 1953-1974 had a child prior to the age of 18,
and virtually no one had a child after she reached the age of 45. Thus, women who could
17

Based on the average of (# divorce within 15 years in year t)/(# marriage formed in yeart-10), where t = 2000, 1995 and 1990, based on the Vital Statistics.
21

have a child must be 18-62 years old at the time of survey.
Then, for each woman in the sample who was 18-62 years old at the time of survey, we
tried to determine her children’s age by the following process.
1) If there were not any children in the household, all adult women in the household areassumed not to have any children younger than 17.
2) If she was the only adult woman younger than 62 in the household and all children wereborn in the periods when she was fertile, they are assumed to be her children.
3) If there are more than one adult women younger than 62 in the household, we check theage differences between each adult woman and the children. Then, if the following
conditions were satisfied for one of the women, all children in the household are assumed
to be her children and the other women in the household are assumed not to have any
children younger than 17.
i.

She has married (incl. already divorced/widowed)ii.

All children was born when she was 18-45 years oldiii.

None of the children was born when the other women in the household were 18-45
years old.

Up to this point, about 97% of the women who were 18-62 years old at the time of survey are
identified as either a mother of all children in the household or not having any children
younger than 17.
For the remaining 3%, we further go on the following speculation:4) For all pairs of a child and an adult woman in the household, we checked whether thechild was born when the woman was 18-62 years old. Then, if every child in thehousehold has only one woman in the household who could be his/her mother, the woman
is assumed to be the child’s mother.
5) If the process 4) still can’t identify the mother of children, we checked if the women in22the household are a pair of mother and daughter(s). If so, theni.

If the youngest child in the household was born when the mother was older than 45,
we guess all children are the younger woman’s. That is, the older woman is a
grandmother of the children. To avoid contradiction, we check whether the oldest
child was born after the daughter got 18 years old.

ii.

If the oldest child in the household was born when the younger woman was younger
than 18, we guess all children in the household are the older woman’s. That is, theyounger woman is a sister of the children. To avoid contradiction, we check whether
the youngest child was born before the mother got 45 years old.

Appendix Table A1 shows how many are identified as each category.

A3

The effect of the unemployment rate on education and geographical mobility
If the educational attainment is affected by the unemployment rate, such endogeneity

may bias our result.

Thus we estimated the effect of the unemployment rate at the age of 18

on (1) the probability of not finishing high school, (2) the probability of going to either jr orfour year college, and (3) the probability of going to four year college.

As shown in the

Appendix Table A2, the effect is small and not statistically significant for women.

At thesame time, the effect is not negligible for men in the ESS. This is one of the reasons why
we do not use data of men.
In order to assign the unemployment rate at graduation to each cohort or individual, we
had to use the region of current residence as a proxy for the region of residence at the time ofgraduation. Since the ESS ask whether the respondent had moved since the year prior to thesurvey, we can calculate the annual rate of across-region migration. It is about 1.2% forwomen with high school or less education, 2.1% for those with junior college education, and3.7% for those with four year college education. It is true that more educated people are23

more likely to move across regions, but such a difference should not affect the sign of theeffect estimated by education. It would be more problematic if the effect of unemployment
rate on mobility were different across educational back ground. Appendix Table A3 showsthat the previous year's unemployment rate has a positive effect on migration where as theunemployment rate at graduation has a negative effect.

The existence of such effects is aconcern, but the effects are not much different across educational background.

Appendix

Table A3 also shows the effect for men, which is larger and quite different across educational
background.

This is another reason why we give up using data of men.

A4 Estimation without fixed effects
Although we believe that controlling for year- and region- fixed effects is necessary for
identification, it would be informative to present estimates without such controls.
Table A4 replicates Table 5a without such fixed effects.

Appendix

The estimates are not statisticallysignificant, which implies that the weak effects we have found are not due to adding too
many fixed effects.

24

A5 Estimation with the job opening to vacancy ratio
The job opening to vacancy ratio is another popular index of labor market conditions inJapan.

Thus we replaced the unemployment rate with the job opening to vacancy ratio andreplicate Table 5a.

As shown in Appendix Table A5, although the effect of previous year's

job openings to vacancy ratio is not statistically significant, the effect of graduation year's job
openings to vacancy ratio is in consistent sign (note that a positive job openings to vacancy
ratio means a boom, so the signs of coefficients should be opposite to those in Table 5a).

25

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28

Figure 1: Unemployment Rate and Total
Fertility Rate in Japan, 1973-2008
2.2

6.0%

2.0

5.0%

1.8

4.0%

1.6

3.0%

1.4

2.0%

1.2

1.0%

1.0

0.0%
1973 1975

1980

1985

1990

Total Fertility Rate

1995

2000

2005

2008

Unemployment rate

Source: Labour Force Survey (unemployment rate) and Vital Statistics (TFR).

29

Table 1: Summary Statistics
a. Prefecture Level Data from Census 1985-2005 / 1990 & 2000
By education, 1990 and 2000
Total
1985-2005 High school or less College or more
Sample size
235
94
94
Proportion of married women in age 25-29 0.56
0.62
0.47
Proportion of married women in age 30-34 0.81
0.84
0.79
Total fertility rate
1.56
Male unemployment rate (%)
4.72
4.23
b. Prefecture–cohort Level Marriage History Panel, from Census and Vital Statistics
Prefecture*birth-year cohorts, born 1956-1975
Sample size
940
Median age of marriage
26
Ratio of high school or less educated, women
57.8%
c. Fertility History Data from the Employment Status Survey
Women
Sample size (33-37 years old at the time of survey ) 147,724
By highest school graduated:
Junior high school
8,031 (6.9%)
High school
76,645 (51.8%)
Junior College (AA equivalent)
45,759 (30.9%)
College (BA) and graduate school
17,289 (11.7%)
Median age of first birth (incl. never have child)29
ｄ. Regional Unemployment Rates, 1974-2006
Unemployment rate
Residual net of prefecture and year dummies

Mean Sd
3.02
1.28
0.00
0.32

30

100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
18

19

20

21

22

23

24

25

26

% married (all women, Census + Vital Stats)

27

28

29

30

31

32

33

34

35

% have a child (High school or less, ESS)

% have a child (Junior college or more, ESS)

31

Table 2: Contemporaneous Correlations between the Male Unemployment Rate andMarriage and Fertility of Young Womena. Fixed Effect Regressions with Census 1985-2005% married in women aged
% married in women aged
Dependent variable:
25-29
30-34
Male unemployment rate
-0.002-0.003(based on same age group)[0.002][0.002]
Male unemployment rate
0.001
-0.002(based on total labor force)[0.003][0.001]
Observations
235
235
235
235
Note: Linear regressions with prefecture dummies and year dummies in the RHS. All cells
are weighted by female population in the corresponding age category and prefecture.
Heteroskedasticity -robust standard errors are in brackets.
b. First Difference between 1990 and 2000, % Married Women by Education
Highs school Jr college or Highs school Jr college oror less, aged more, aged or less, aged more, aged
25-29
25-29
30-34
30-34
Male unemployment rate
-0.0080.005-0.005***-0.003(based on age 15-64 all education)[0.007][0.006][0.001][0.003]Observations
47
47
47
47
Note: Linear regressions with no other controls than a constant term. Each observation isweighted by female population in the corresponding age category and prefecture.
Heteroskedasticity -robust standard errors are in brackets.

32

Table 3: The Effect of the Unemployment Rate at Graduation on the Proportion of
Women Who Have Ever Married by Specific Agesa. The Unemployment Rate at High School Graduation
Age
25
27
29
31
33
35
Unemployment rate in the year
-0.008 -0.007** -0.006*** -0.004* -0.003* -0.005*
when the cohort was 18 years old [0.006] [0.003][0.002][0.002] [0.002] [0.002]
Ratio of high school graduates-0.016 0.049
0.052* 0.050*** 0.013 -0.085[0.085] [0.054][0.027][0.014] [0.016] [0.079]
Male/female population ratio
0.175
0.105
0.071
0.03
0.044 0.127[0.194] [0.139][0.082][0.056] [0.052] [0.092]
Observations
940
940
940
893
799
705
b. The Unemployment Rate at Jr College Graduation
Age
25
27

29

31

33

35

Unemployment rate in the year
-0.012** -0.012*** -0.009*** -0.005** -0.004* -0.004***
when the cohort was 20 years old [0.005] [0.003][0.003][0.002] [0.002] [0.002]
Ratio of high school graduates-0.0290.034
0.042 0.045*** 0.01
-0.085[0.082] [0.053][0.027][0.016] [0.017] [0.076]
Male/female population ratio
0.203
0.136
0.095
0.05
0.061
0.149
[0.185] [0.129][0.076][0.056] [0.053] [0.096]
Observations
940
940
940
893
799
705
c. The Unemployment Rate at Four-year College Graduation
Age
25
27
29
31
33
35
Unemployment rate in the year
-0.011** -0.011*** -0.008*** -0.005** -0.002 -0.001
when the cohort was 22 years old [0.005] [0.004][0.003][0.002] [0.002] [0.003]
Ratio of high school graduates-0.021
0.046
0.055* 0.053*** 0.018 -0.07[0.080] [0.053][0.029][0.015] [0.018] [0.073]
Male/female population ratio
0.209
0.141
0.098
0.05
0.055 0.134[0.183] [0.127][0.075][0.054] [0.051] [0.090]
Observations
940
940
940
893
799
705
Note: Linear regression using birth year–prefecture level panel. Each observation is weighted
by female population of the birth year–prefecture cohort. All regressions include dummies forprefecture and dummies for birth year. Standard errors in the brackets are clustered by region.

33

Figure 3: Changes in the Prefecture-Level Total Fertility Rate and Unemployment Rate
1990-1995

0

1985-1990

slope = -0.027-.2(0.019)slope = -0.027-.4(0.008)***
2000-2005

slope = -0.001

slope = -0.021(0.013)(0.006)***

-.4

-.2

0

1995-2000

-2

0

2

4

-2

0

2

4

Change in the male unemployment rate (%)
Change in the total fertility rate

Fitted values

Graphs by year

Table 4: The Effect of Regional Unemployment Rate at Graduation on the Probabilityof Having a Child by a Specific Age
Marginal Effects from probit regressions
a. High School or Less Educated Women
Age
26
28
30
32
34
-0.037 -0.037 -0.009 -0.029* -0.063*
Marginal effect of[0.024] [0.031] [0.017] [0.016] [0.035]
graduation year’s u rate
76,581 76,581 76,581 76,581 60,282
Observations
b. Jr. College or More Educated Women
Age
26
28
30
32
34
0.007 0.036* 0.044 0.055 0.010
Marginal effect of[0.017] [0.021] [0.039] [0.042] [0.029]
graduation year’s u rate
62,743 62,743 62,743 62,743 49,482
Observations
Note: all regressions include dummies for education (high school graduates vs less, juniorcollege vs four year college), region, and year of graduation. Standard errors in the brackets
are clustered by region.
34

Table 5 The Effects of regional unemployment rate at graduation and in the previous
year on the hazard of first child’s birth
Cox’s proportional hazard model, 33-37 years old at the time of survey
a. Constant Effects across Different AgesAllHigh school College
Previous year’s-0.068**-0.084-0.050unemployment rate
[0.035][0.055][0.066]
Unemployment rate
-0.021-0.113**
0.081
in the year of graduation[0.067][0.053][0.093]Observations
1629267836572792695b. Varying Effects with AgePrevious year’s unemp. r.
* age 18-22
Previous year’s unemp. r.
* age 23-27
Previous year’s unemp. r.
* age 28-32
Previous year’s unemp. r.
* age 33-37
Graduation year’s unemp. r.
* age 18-22
Graduation year’s unemp. r.
* age 23-27
Graduation year’s unemp. r.
* age 28-32
Graduation year’s unemp. r.
* age 33-37
Observations

All
High school College
0.169***
0.108[0.065][0.071]0.018-0.055 0.176***[0.047][0.070][0.025]-0.116*** -0.114*-0.075[0.040][0.064][0.073]-0.107-0.07-0.087[0.139][0.187][0.133]-0.133-0.198[0.126][0.125]-0.034-0.116*0.024[0.063][0.068][0.103]0.022-0.091*0.15[0.079][0.052][0.107]0.142
0.009
0.294***[0.143][0.252][0.089]1629267836572515483

Note: The baseline hazard is stratified by birth year cohort and education, and the
proportional part includes region dummies. Standard errors in the brackets are clustered byregion.

35

Figure 4: Simulated Changes in the Number of First Births by Mother’s Agea. All women
50.0%
45.0%
40.0%
35.0%
30.0%

All average

25.0%
All if u at graduation
rises by 1%pt…

20.0%
15.0%

All if current u rises by
1%pt..

10.0%
5.0%

ss
le
ch
i ld

33
‐3
7

28
‐3
2

23
‐2
7

18
‐2
2

0.0%

b. High school or less
50.0%
45.0%
40.0%
35.0%
High school or lessaverage

30.0%
25.0%

High school or less if u
at graduation rises by
1%pt…
High school or less if
current u rises by 1%pt..

20.0%
15.0%
10.0%
5.0%

7

2

7

23
‐2

28
‐3

33
‐3

ch
ild
le
ss

2
18
‐2

0.0%

36

c. AA or BA
50.0%
45.0%
40.0%
35.0%

AA or BA average

30.0%
25.0%

AA or BA if u at
graduation rises by
1%pt…

20.0%
15.0%

AA or BA if current u
rises by 1%pt..

10.0%
5.0%

le
ss
ch
ild

33
‐3
7

28
‐3
2

23
‐2
7

18
‐2
2

0.0%

37

Table 6 Suggestive Evidence about the Effect on the Probability of Having the Secondand Higher-order Children
Probit marginal effects, women 33-37 year-old at the time of survey
Unconditional on the Number ofConditional on Having at Least
Children
One Child
High
High
All
CollegeAll
CollegeschoolschoolUnemployment
0.018-0.0160.049
0.033*** 0.026*** 0.033**
rate at graduation [0.026][0.014][0.040][0.008][0.007][0.017]
Observations
139,324
76,58162,743
100,319
57,13643,183
Note: all regressions include dummies for education (high school graduates vs less, juniorcollege vs. four year college), region, and year of graduation. Standard errors in the brackets
are clustered by region.

38

Appendix Table A1: Identification of Women’s Maternal Statusobs%2) the only woman in hh
396,8903) the only likely woman in hh
69,238
Identified as a mother 4) all children has one mother
6,151
5) i
3,079
5) ii
1,749
1) No child in the hh
841,684
Identified as having 2) too old or too young
4,283
no child younger than 3) the other woman is the mom
73,44617 at the time of 4) the other women are the mom
9,182
survey5) i turned out to be grandmother
1,805
5)

ii turned out to be sister

Not identified in either way

cum %
28.04
28.04
4.89
32.93
0.43
33.36
0.22
33.58
0.12
33.7
59.45
93.16
0.3
93.46
5.19
98.65
0.65
99.29
0.13

99.42

3,938

0.28

99.7

4,248

0.3

100

Appendix Table A2: The Unemployment Rate at the Age of 18 and Educational
BackgroundHigh
schooldrop out
Unemployment rate in the year

-0.002

Women

Men

Jr

Jr college

college + four year High school + four four year
four year college

drop outcollege-0.008year

collegecollege-0.0220.025**-0.043** -0.037

When the respondent was 18 year old [0.009] [0.013] [0.017][0.011][0.020]Observations

133,746

133,746 133,746

138,433 138,433 138,433[0.029]39

Appendix Table A3: Unemployment Rate and Across-region Mobility
Women
All
0.007***
Previous year’s[0.002]
Unemployment rate
-0.008***
Unemployment rate
In the year of graduation [0.001]

Men

High school College

All0.004**

0.008*** 0.012**

0.005

0.019***[0.002][0.002][0.005][0.004][0.004]-0.007**-0.007*** -0.022***-0.013***-0.020***[0.003][0.002][0.004][0.006]275,190

181,467[0.003]High school graduate

0.001

0.006***

dummy[0.003][0.002]

Jr college dummy

0.011**

0.028***[0.004][0.007]

Four year college dummy 0.031***0.065***[0.010][0.009]Observations

478,025

High school College

260,668

217,357

456,657

Appendix Table A4: Replication of Table 5a without Fixed Effects
All
High school College-0.0090.004-0.028Previous year’s[0.055][0.070][0.038]
Unemployment rate
0.033
0.008
0.066
Unemployment rate
[0.034][0.034][0.068]In the year of graduation
Observations
1629267836572792695
Notes: the same as Table 5a except that the baseline hazard is not stratified by birth year andregion dummies are not included.
Appendix Table A5: Replication of Table 5a using the Job Opening to Vacancy Ratios
instead of Unemployment Rates
All High school College-0.05-0.021-0.131
Previous year’s[0.248]
Job Opening to Vacancy Ratio [0.133] [0.094]
0.021
0.029*-0.023
Graduation year's[0.113]
Job Opening to Vacancy Ratios [0.018] [0.015]-0.05-0.021-0.131
Observations
Notes: the same as Table 5a except that unemployment rates are replaced by job opening tovacancy ratios.

40