ISSN (Print) 0473-453X

Discussion Paper No. 1153

ISSN (Online) 2435-0982

THE EFFECTS OF GENDER-SPECIFICLOCAL LABOR DEMAND ON
BIRTH AND LATER OUTCOMES

Mika Akesaka
Nobuyoshi Kikuchi

Thirdly Revised May 2024
Secondly Revised November 2022
Revised May 2022
December 2021

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

The Effects of Gender-Specific Local Labor Demand on
Birth and Later Outcomes∗
Mika Akesaka†Nobuyoshi Kikuchi‡
April 2024
Abstract

We study the effects of local labor market conditions during early pregnancy on birth andlater outcomes. Using a longitudinal survey of newborns in Japan, we find that improvementsin employment opportunities increase the probability of low birth weight and premature birth.
We also examine the effects of gender-specific labor market conditions. An increase in labordemand for women has a large negative effect on gestational age, especially for mothers who
gave birth at relatively young ages. However, we find little evidence of a lasting negative
effect of an increase in labor demand during early pregnancy on serious health conditions ordevelopmental delays in early childhood. Using prefecture-level panel data, we confirm thatthe negative effect on infant birth weight is not driven by selective fertility and mortality.

Keywords: labor market conditions, newborn health, low birth weight, recession.
JEL Classification: I10, J13, J16, J23, R11.

∗

We would like to thank Daisuke Adachi, Taiyo Fukai, Rei Goto, Chie Hanaoka, Charles Yuji Horioka, Hirokazu
Ishise, Ayako Kondo, Yoshifumi Konishi, Shiko Maruyama, Hitoshi Shigeoka, Emiko Usui, two anonymous referees,and seminar participants at Kobe University, Osaka University, Yokohama City University, the Tokyo Labor Economics Workshop, the Workshop on Empirical Moral Science, the Workshop on the Economics of Human Resource
Allocation, the Asian and Australasian Society of Labour Economics Conference, the Japanese Economic Association meeting, and the Japan Health Economics Association meeting for their helpful comments and suggestions.
All remaining errors are our own. Akesaka acknowledges financial support from JSPS KAKENHI (JP20H05631,
JP20K13511, JP21H04397) and the Joint Usage/Research Center for Behavioral Economics at the Institute of Social and Economic Research of Osaka University. Kikuchi acknowledges financial support from JSPS KAKENHI(JP18K12794, JP22K01534). The data in the Longitudinal Survey of Newborns in the 21st Century were provided
by the Ministry of Health, Labour and Welfare (MHLW). The views expressed here are our own and do not necessarilyreflect those of the MHLW.
†
Research Institute for Economics and Business Administration, Kobe University; 2-1 Rokkodai-cho, Nada-ku,
Kobe, Hyogo 657-8501, Japan. E-mail: mika_akesaka@rieb.kobe-u.ac.jp‡
Corresponding author: Faculty of Political Science and Economics, Takushoku University. Department of Economics, The University of Tokyo; 7-3-1, Hongo, Tokyo 113-0033, Japan. E-mail: nobuyoshi.kikuchi@e.u-tokyo.ac.jp.

1

Introduction

Understanding the impact of economic conditions on health is essential to estimating the costs ofeconomic fluctuations. It leads to contemplating the need for stabilization and redistribution policy
measures. The literature widely reports a statistical association between economic fluctuations andhealth, especially for men in their prime (e.g., Ruhm, 2000; Ruhm and Black, 2002; Sullivan and
von Wachter, 2009). However, little is known about its effect on other populations at different life
stages. It is important to examine its effect even on babies in utero as they would also be affectedby the impact of economic fluctuations on their parents. Maternal health, for example, worsened
during the Great Recession (e.g., Currie et al., 2015).
If economic shocks causally affect prenatal health conditions, then they also potentially influence
future health and welfare costs. As extant literature suggests, birth outcomes, as a measure ofprenatal health, have long-lasting effects on later life, ranging from health to labor market outcomes(see e.g., Currie, 2009; Almond and Currie, 2011). Despite the suggested relationship between
economic downturns and prenatal health, its causality and importance remain unclear, with theliterature reporting mixed results in this regard (improved: e.g., Aparicio et al., 2020; Dehejia and
Lleras-Muney, 2004; van den Berg et al., 2020; deteriorated: e.g., Alessie et al., 2018; De Cao et al.,2022; Kohara et al., 2019; Margerison-Zilko et al., 2017; Olafsson, 2016).
One reason for these inconsistent results is the focus on abstract measures of economic conditions,such as the total unemployment rate. This may mask the different effects of changes in labor market
conditions on mothers vis-à-vis fathers. For example, if employment opportunities for fathers,
who are often the breadwinners, decrease due to a recession, then it may drastically reduce thehousehold income. Consequently, infant health may be negatively affected (e.g., Lindo, 2011). On
the contrary, if there is a decline in mothers’ employment opportunities, infant health may actually
improve because of the reduced physical burden and absence of work-related psychological stress1

(e.g., Rossin, 2011). Changes in mothers’ and fathers’ employment opportunities have been reported
to have different impacts on children’s health (e.g., Page et al., 2019; Schaller and Zerpa, 2019) andmaltreatment status (e.g., Lindo et al., 2018). With respect to infant health, we would expect evena greater difference in the effects on fathers vis-à-vis mothers; however, these differences are yet to
be determined.
In this study, we aim to evaluate the impact of economic conditions on neonatal and infant
health using changes in gender-specific labor demand during early pregnancy. For neonatal health,
we use data on birth weight and gestational age from the population survey of all children born
during a specific period in Japan. Using our dataset’s panel structure, we further examine theimpact on children’s development and health status at ages one to four.
Based on the method proposed by Bartik (1991), we focus on predicted employment growth rates
instead of the common measure of unemployment rates in prior studies as a measure of economic
conditions. This is because the unemployment rate, by definition, is affected by labor supply, andthus cannot be separated from exogenous changes in labor demand (Page et al., 2019). Labor supply
decisions are likely to depend on the unobservable preferences and attributes of parents that impact
newborn health. Therefore, if we use unemployment rates as a proxy for labor market conditions,then these unobserved confounders could create a bias in the estimates.
We can summarize our results as follows. First, an increase in the overall predicted employment
growth rate statistically significantly reduces birth weight and weeks of gestation, and therefore
increases the probability of low birth weight and preterm birth. Second, we examine the differential
effects of an increase in the predicted employment growth rate for men and women, and find thatwomen’s employment has heterogeneous negative effects on newborn health across maternal age
at birth and educational attainment. Notably, the effect on gestational age is larger for newbornsof mothers who gave birth at age 24 or younger than for those who gave birth at age 25 or older.
Moreover, the effect on birth weight is larger for newborns of mothers who graduated from university2

or junior high school than for those who graduated from high school. Finally, we find that an increasein the predicted employment growth rate has a small and statistically insignificant impact on theprobability of hospitalization for illness at ages 1.5 to 4.5. Furthermore, we find little evidence ofsignificant negative effects on language development at age 2.5, the tendency to aggression at age
3.5, and the tendency to inattention and hyperactivity at age 3.5.
Our results are robust to concerns with pregnancy, infant mortality, and sample attrition. First,
using prefecture-level panel data, we confirm that changes in the predicted employment growth rates
during early pregnancy are significantly related to low birth weight, but not to pregnancy rates orneonatal and infant mortality rates. Second, adding prefecture- and municipal-level controls for
local availability of obstetrician and gynecology care and center-based childcare, we confirm that
our main results for birth outcomes are insensitive to these controls. Finally, we verify that changes
in the predicted employment growth rates during early pregnancy are not significantly associatedwith sample attrition or migration.
We use Japanese data for this study because its setting provides two advantages. First, under theJapanese universal health insurance system, the coverage and benefits do not rely on employment
contracts. Thus, the impact of a labor market shock does not include the effects of changes in accessto healthcare based on health insurance coverage. In a health insurance system in which coveragedepends strongly on employment status, such as in the US, changes in the labor market could also
affect coverage (see e.g., Schaller and Stevens, 2015).
Second, elective cesarean sections are unlikely to influence infants’ health because cesarean births
are not as prevalent in Japan as in other developed countries (see Figure A.1 in Appendix A). Borra
et al. (2019) and Schulkind and Shapiro (2014), for example, suggested that artificially accelerated
births due to institutional factors can negatively affect infants’ health conditions. If a greater
reliance on elective cesarean sections increases the likelihood of artificial birth date manipulation,then a low cesarean rate is less likely to cause an intended manipulation that confounds the effects3

of economic shock on birth outcomes.
Our study contributes to the literature in two ways. First, we examine the gender-heterogeneous
effects of labor market conditions on birth outcomes. Although information on whether effects onbirth outcomes occur through the employment channel of the father or mother is essential forpolicy interventions, studies in both developed countries (e.g., Dehejia and Lleras-Muney, 2004)
and developing countries (e.g., Baird et al., 2011; Bhalotra, 2010) have not fully examined theeffects by gender. Exceptions are De Cao et al. (2022) and van den Berg et al. (2020). De Cao
et al. (2022) found that both female and male unemployment rates have pro-cyclical effects on birthweight in the UK. Further, they found that the magnitude of the effect is much larger for the female
unemployment rate. By contrast, van den Berg et al. (2020) found the counter-cyclical effects in
Sweden. They suggested that an increase in the probability of very low birth weight due to higher
unemployment could only be attributed to the male unemployment rate.
Second, this study is one of the first to examine the impact of economic conditions during
pregnancy on child outcomes beyond the first year of life. Recent studies found that some events thatcause maternal stress, such as the passing of a mother’s close relatives during pregnancy (Persson
and Rossin-Slater, 2018) or exposure to the ravages of war (Lee, 2014), can have negative impacts
in the long run. However, the impact of economic conditions during pregnancy on child outcomes isstill not well understood. In this study, we observe no statistically significant relationship between
labor market conditions during pregnancy and developmental delays and serious health conditions
in early childhood. Our results are consistent with Maruyama and Heinesen (2020), who reported
that the low birth weight effect on infant health diminishes over time.
The remainder of this paper is organized as follows. Section 2 describes the background of thestudy. Sections 3 and 4 describe the data and provide an overview of the empirical framework,
respectively. Section 5 reports the results, and we present our conclusions in Section 6.

4

2

Background

2.1

Female Labor Force Participation

Women’s employment in Japan is characterized by low labor force participation rates relative otherdeveloped countries, wide variations across age groups, and low rates of full-time employment. In
Appendix A, Figure A.2 depicts the labor force participation rates of women aged 20–44. During
the survey period, the rates had been increasing in Japan but remained consistently lower than inalmost all other developed countries.
Figure A.3 shows the age profile of the labor force participation rates in the cross-section available
around the pregnancy periods of our main analysis. The rates were low for women in their teens dueto the high upper secondary school enrollment rate, but rose to the level of other developed countries
as women reached their 20s, declined in their 30s, and then rose in their 40s again. It is commonly
considered that Japanese female workers leave the labor force after marriage and childbirth, remainout while raising their young children, and then return as their children grow older.1
Figure A.4 shows the trends in the proportion of full-time workers. Japan has been in a downward
trend, with its level moving toward the lowest group. This trend implies that a certain number ofJapanese female workers are employed in part-time work, which is sensitive to economic fluctuations.
In Japan, full-time workers are basically regular workers rarely dismissed, while part-time workers
are non-regular workers who are usually on a fixed-term contract. Previous studies suggest thatfirms in Japan respond to economic shocks by adjusting employment through the turnover of nonregular workers (see, for example, Hijzen et al., 2015; Yokoyama et al., 2021).
1

For a more detailed discussion, see, e.g., Abe (2011) and Esteban-Pretel and Fujimoto (2020).

5

2.2

Prenatal Care

The basic prenatal care system in Japan is standardized and publicly funded based on the MHLW
guidelines under the law. The Maternal and Child Health Act requires that national and local
governments make efforts to maintain and promote the health of mothers and infants. Based on
the Act’s provisions, the MHLW issued a notice on the desirable standard for prenatal checkups,
including the recommended number and guidelines for items and procedures. Since 1996, thestandard number of prenatal checkups has been 13 or 14 (MHLW Notice No. 934 of February
27 in 1996). Until 2006, two prenatal checkups were uniformly subsidized using funding from anational tax allocated to local governments. Since 2007, the number of publicly subsidized prenatal
checkups gradually grew. In 2009, the MHLW noted that the standard 14 checkups should be
publicly subsidized (MHLW Notice No. 0227001 of February 27 in 2009), and a survey conductedin April 2010 confirmed that 14 checkups were publicly subsidized in all municipalities (MHLW
Notice No. 0608001 of June 8 in 2010, “The Results of a Survey on the Status of Public Fundingof Prenatal Checkups”).
The Act asks a parent or other family member to report the pregnancy to the municipality ofresidence. After submitting the pregnancy notification, the municipality must issue a Maternal andChild Health Handbook. This Handbook is a personal record of birth and infant health that includes
necessary information on childbirth, child care, and daily living precautions such as the timing andcontent of standard health check-ups, vaccinations, and nutritional intake. The municipality mustalso help a pregnant mother and her family members access maternal and infant health check-ups
at public expense, counseling services by public health nurses or midwives, and various information
services such as childbirth classes.
In addition, the public health insurance system subsidizes the cost of childbirth. It covers
only abnormal deliveries, including cesarean sections and aspiration deliveries associated with risk

6

factors such as threatened premature labor, placenta previa, and ectopic pregnancy. However, it also
provides lump-sum, no-means-tested benefits for all births (the Lump-sum Allowance for Childbirthand Childcare of Articles 101 and 104 of the Health Insurance Act).
Prenatal care in Japan appears to be at least comparable to that in other developed countries.
Figures A.5 and A.6 in Appendix A shows that maternal and neonatal mortality rates remain very
low in Japan, as in other developed countries.
A striking feature of the perinatal health situation in Japan is that the rate of low birth weights
is consistently higher than in other developed countries. In Figure A.7, over our sample period forindividual data analysis, the measure is trending upward, rising from about 8.6% in 2000 to about
9.6% in 2010, but decreasing very slightly since 2014. One reason for this trend of relatively highincidence of low birth weight may be the large proportion of underweight pregnant women under
the strict standards for pregnancy weight gain recommended by the Japanese Society of Obstetrics
and Gynecology (JSOG) based on the 1999 guideline (Nakabyashi, 1999).2 The JSOG stopped
recommending the 1999 guideline and revised the standards in 2019 (Kanayama, 2019), after oursample period.

3

Data

3.1

The Children’s Panel Survey

The main data source for this study is the Longitudinal Survey of Newborns in the 21st Century(LSN21) conducted by the Japanese Ministry of Health, Labour and Welfare (MHLW). It is a
population-wide survey of children born during January 10–17 in 2001, July 10–17 in 2001, andMay 10–24 in 2010. Our analyses are based on pooled five-wave panel data of these birth month2

For further explanation, see, e.g., Kato et al. (2021), Normile (2018) and Takemoto et al. (2016). See also Itohet al. (2018), who comment on Normile (2018). These authors suggest that commitment to the 1999 JSOG guideline
is not a major factor explaining the low birthweight trend.

7

cohorts. In the first wave, the parents/guardians of six-month-old children were contacted, withannual follow-ups on the same dates. The response rate for the first wave was over 87.8% andremained at around 90% in the follow-up surveys.
The LSN21 reports parents’ demographic and socioeconomic data. For mothers, we use theirage, educational attainment, and employment status one year before childbirth. Unfortunately,
marital status and whether the father was absent during pregnancy could not be determined fromthe survey. Therefore, we control for the father’s educational attainment with a missing dummy
variable to capture both nonreporting and father-absent effects. Note that in Japan, the proportionof births outside marriage is very low, and thus the effects of births outside marriage should be
limited.3
Our data also include birth date, birth length, birth weight, gestational age, multiple birth, andprimiparity. This birth-related information is derived from the merged data of the vital statistics
collected by the MHLW. The vital statistics are based on administrative vital records, including
birth registrations under the Family Register Act. A birth registration document is submitted to
the local government by a parent or other family member, accompanied by a birth certificate inwhich the birth-related information is verified by a doctor or midwife.
Appendix Table B.1 presents summary statistics for the outcome and control variables fromthe LSN21 with their definitions and measures of economic conditions explained in the following
sections. Following the literature, we restrict our data to single-birth children with mothers who
gave birth at ages 16–49. In our sample, over 48% mothers are primipara and over 70% were aged
between 25 and 34 at the time of childbirth. Among the mothers whose educational attainmentis available, the most, about 34%, completed high school, about 18% completed vocational school,
about 23% completed 2-year college, about 19% completed university (4-year college), and thefewest, about 5%, completed junior high school (i.e., finished compulsory schooling). Over 58% of
3

The proportion was about 1.2% in 1995, which increased slightly and has been around 2% since the 2000s. It is
one of the lowest among OECD countries, which averaged about 41% in 2018 (OECD, 2022a).

8

mothers were employed one year before childbirth.

3.2

Outcome Variables

We focus on three sets of outcomes. Our primary interest is in birth outcomes. We consider birthweight, birth length, and gestational age as measures of maternal-fetal health conditions. Following
De Cao et al. (2022), we calculate the ratio of birth weight to gestational age (in grams/weeks), asa proxy for fetal growth. To capture the negative aspects of fetal health, we create an indicatorof low birth weight (<2,500 grams), very low birth weight (<1,500 grams), preterm birth (bornat or under 37 weeks of completed gestation), very preterm birth (at or under 32 weeks), andsmall for gestational age (SGA) babies (below the 10th percentile of birth weight by gestational age
distribution).4
Second, we consider child health outcomes at ages 1.5–4.5. We construct an indicator of healthconditions based on the parent/guardian’s response, namely, whether the child was hospitalized
for an illness in the previous 12 months.5 We focus on hospitalizations rather than doctor visitsbecause the decision to see a doctor is the parent/guardian’s choice based on their preferences andconstraints, and it might not reflect the severity of the child’s health condition.
The third set of outcomes concerns child development and mental health measures. We create
three indices based on Yamaguchi et al. (2018): language development at age 2.5, tendency toward
aggression at age 3.5, and inattention and hyperactivity at age 3.5. In the survey, the respondents,
mostly the mothers, answer a set of binary questions about their child’s language development. The
items include whether the child could put together two-word sentences. These are equivalent to4

The reference percentile charts for birth weight at gestational age by gender are from Ogawa et al. (1998), whichwere the national standards until 2010. The main results are robust to applying the updated reference charts fromItabashi et al. (2014), which were adopted officially in 2011. See Ogawa et al. (1998) and Itabashi et al. (2014) for
more details on the construction of the reference charts.
5
In each wave of the survey, the parent or guardian indicates whether the child experienced any episodes of
illness or diseases such as asthma, atopic dermatitis, cold, congenital disease, conjunctivitis, convulsion, dermatitis,
diarrhea, eczema, food allergy, impetigo, influenza, intussusception, Kawasaki disease, measles, mumps, otitis media
and externa, pertussis, pharyngeal conjunctival fever, rhinitis, roseola, rubella, streptococcal infection, varicella,
and/or others.

9those included in the list of developmental milestones by the US Centers for Disease Control andPrevention, which pediatricians commonly use to measure child development. The respondents also
select all applicable items regarding the child’s disruptive, inattentive, and hyperactive/impulsive
behavior. These are comparable to those in the guidelines set by the American Psychiatric Association.6 We construct an index by totaling the number of selected items in each measure andstandardizing them to a Z-score with a mean of 0 and a standard deviation (SD) of 1.
Appendix Table B.1 reports that the average birth weight is about 3,037.4 grams, the low birthweight rate is about 7.72%, and the very low birth weight rate is about 0.51% in our sample.
Additionally, the table shows that the average gestational age is about 39.33 weeks. The proportionof infants born with a gestational age of 37 weeks or less is about 4.3%, and that of 32 weeks or lessis about 0.52%. Around 11.7% of the children in the sample were hospitalized due to illness at age
1.5, while it decreased as they got older to around 5.2% at age 4.5. Around 23.5% of the children
experienced hospitalizations for illness at ages 1.5–4.5.

3.3

Predicted Employment Growth Rate

We construct our measure of economic conditions, the predicted employment growth rate, as follows:

Dpt =

X

Gjt ×

j

Ejp0
,
Ep0

(1)

where Gjt is the annual growth rate of industry j in pregnancy period t based on the Japanese Research Institute of Economy, Trade, and Industry (RIETI) and Hitotsubashi University’s the Japan
Industrial Productivity (JIP) 2021 database; and Ejp0 /Ep0 is the share of employment of industry j
in prefecture p in base period 0 from the 1997 Employment Status Survey by the Statistics Bureau.7
This measure captures the demand-driven employment shocks that vary by 47 prefectures due to
6

For more detailed explanations of the indices, see Yamaguchi et al. (2018).
For a detailed explanation of the construction of the JIP database, see Fukao et al. (2007) and Fukao et al.
(2021).
710

predetermined differences in the distribution of employment opportunities across 24 industries.8
To capture the direct heterogeneous shocks to labor demand mothers and fathers face, we construct gender-specific labor demand conditions following Lindo et al. (2018), Page et al. (2019), andSchaller (2016). The gender-specific predicted employment growth rate is

Dpgt =

X

Gjt ×

j

Ejgp0
,
Epg0

(2)

where the subscripted g indicates the gender group; thus, the share of employment in a prefecture
is gender specific.
We define the pregnancy period as the first 22 weeks following conception, rather than the ninemonths or one-year pregnancy period used in previous studies. As the minimum gestational age
in this sample is 22 weeks, we avoid reflecting variations in economic conditions after birth on
those during pregnancy.9 This definition is consistent with the literature suggesting that the firstand second trimesters are significantly linked with birth outcomes (e.g., Kyriopoulos et al., 2019;
Margerison-Zilko et al., 2011, 2017).
By the definition of the pregnancy period, the differences in conception date are also a sourceof variation in the predicted employment growth rate. Figure B.1 in Appendix B suggests thatconception dates substantially vary within each cohort (144, 127, and 152 days for January 2001,
July 2001, and May 2010, respectively).
The JIP database’s employment records are an annual measure based on several statistical
8

The industry categories are agriculture; forestry; fisheries; mining; manufacturing of food, beverages, tobacco,and feed; manufacturing of textile mill products; manufacturing of chemical and allied products; manufacturing ofiron and steel, metal products; manufacturing of machinery, equipment; manufacturing of miscellaneous categories;
construction; electricity, gas, heat supply, and water; transport, information, and communications; wholesale trade;
retail trade; eating and drinking places; finance and insurance; real estate; services of living-related and personal;
services of business; services of medical, health care, and welfare; services of education; services of miscellaneous
categories; and government services.
9
The Maternal Health Act defines the period in which artificial abortions are allowed as “a period when theunborn child cannot survive outside the mother’s body.” Notice No. 55 of March 20, 1990 by the Ministry of Healthunder the Act specifies it as gestational age of 22 weeks or less. Hence, births under 22 weeks are basically treatedas stillbirths in neonatal care in Japan (Minakami et al., 2011, 2014).

11

surveys as of October 1, collected by the Statistics Bureau of the Ministry of Internal Affairs
and Communications for the official statistics. Consequently, in the spirit of Page et al. (2019),
we calculate the number of employees working on a given day by linear interpolation based on
employment as of October 1 in adjacent years depending on the day of pregnancy and taking the
average of the 22-week pregnancy period.
In Appendix Table B.1, the sample mean (standard deviation; SD) value of the predicted employment growth rate is -0.0139 (0.0044) overall, -0.0129 (0.0033) for women, and -0.0145 (0.0064)
for men. As our sample period coincides with a period of prolonged stagnation, the predictedemployment growth rates are negative. Figure C.1 in Appendix C plots the predicted overall employment growth rates and the actual employment growth rates from 2000 to 2018 at the prefecturelevel.10 We find a positive correlation between them, as the OLS estimate of the coefficient of thepredicted employment growth rate in the simple regression model is about 0.89. This result suggeststhat the constructed variable predicts the actual variations well.
Figure C.2 presents the distribution of predicted employment growth rates for men and women.
We find a large variation in the raw data of gender-specific predicted employment growth rates. A
key source of the differences between the female and male rates is attributable to heterogeneity inthe share of industry employment by gender and prefecture.
Figure C.3 plots the industry’s share of employment at the prefecture level for each industry
category. In a prefecture, if the industry’s employment contribution is equally important for both
men and women, then the prefecture would be on the 45-degree line. We observe two key featuresfrom Figure C.3. First, we find a gender-disproportional contribution to local employment acrossindustries. Second, each industry shows a substantial variation in the relative share of employment
exists across prefectures.
10

The actual employment growth rate is based on the RIETI and Hitotsubashi University’s Regional-level Japan
Industrial Productivity (R-JIP) Database 2021. For a detailed explanation of the construction of the R-JIP database,see Tokui et al. (2013) and Tokui and Makino (2022).

12

The construction of the predicted employment growth rate is similar to that of the shift sharetype instrumental variables. Goldsmith-Pinkham et al. (2020) show that the exogeneity conditionof the instrumental variables can be expressed in terms of the initial shares.11 Therefore, we follow Goldsmith-Pinkham et al. (2020) and calculate the Rotemberg weights to further assess thecontributions of each industry share to the variations of the predicted employment growth rates.
In Appendix D, Table D.1 reports the industries with the five largest Rotemberg weights aswell as their proportions among all positive weights. The results suggest gender differences in therelative importance of industries. For women, services of medical, health care, and welfare accountfor the greatest contribution, while for men, it is wholesale trade. Although the industry with thelargest contribution occupies a considerable part among the positive weights, especially for women,
the contribution is still dispersed across the other industries. This dispersion of the contributions
suggests that no single industry dominates the whole variation of the predicted employment growthrate. In Section 5.4, we revisit this point to examine whether only a single industry drives the main
results by removing the top industry from our original Bartik variable.
In Figure 1, we plot the residuals from a regression of the gender-specific employment growthrate on the birth month cohort fixed effects and prefecture fixed effects. Clustering on the 45-degreeline implies a lack of independent variation in male and female employment growth rates, except
for fixed differences. Figure 1 indicates a positive correlation between female and male employment
growth rates; however, it also shows a considerable variation off the 45-degree line. This suggeststhat independent variations in employment opportunities for men and women make it possible toidentify gender-specific effects.

11

The instrumental variables also satisfy the exogeneity condition if the growth shocks are exogenous. See Borusyak
et al. (2022) and Goldsmith-Pinkham et al. (2020) for more details.

13Predicted Female Employment Growth Rates-.004 -.002
0
.002
.004
.006
-.006

-.006

-.004 -.002
0
.002
.004
.006
Predicted Male Employment Growth Rates

Figure 1: Scatter plots of the predicted female and male employment growth rates
Notes: This figure displays the residuals of the predicted female and male employment growth rates after controlling
for the birth month cohort fixed effects and prefecture fixed effects for the regression sample. The data are binned
in intervals of 0.005 and weighted by the number of observations.

14

4

Empirical Framework

In the main analysis, we examine the effects of labor market conditions on birth and later outcomesat the individual level. We estimate the following model:

Yicpt = α + βDpt + θc + ψp + γXi + ϵipct ,(3)

where subscripts i, c, p, and t refer to the child, pregnancy period, prefecture, and birth monthcohort, respectively. In the data set, we can identify the prefecture where the child lived in thefirst-wave survey and define it as her/his local prefecture. Yipct denotes the outcome variable. Dpt
represents the overall employment growth rate, and β is its coefficient, our primary parameter ofinterest. Xi is a vector of dummy variables for individual controls, including the child’s gender,
mother’s first childbirth, mother’s age at the time of childbirth, mother’s employment status one
year before pregnancy, and parents’ educational attainment. θc indicates birth month fixed effects,
ψp denotes prefecture fixed effects, and ϵipct is an idiosyncratic error term assumed conditional mean
independent of Dpt .
To identify the effects of labor demand shocks for mothers and fathers separately, we include
both variables in the following regression model:

Yipct = α + βf Dpf t + βm Dpmt + θc + ψp + γXi + ϵipct ,(4)

where Dpgt represents the gender-specific employment growth rate, and the subscript g ∈ {f, m}refers to gender. Hence, the coefficient of the predicted female employment growth rate indicatesthe effect of an increase in the variable, while holding the predicted male employment growth rate
constant and vice versa.
We also investigate selective fertility using aggregate-level data before turning to the main results.

15

We estimate the following model:

Ypτ = α + βDpτ + ψp + ξτ + ϵpτ ,(5)where subscripts p and τ refer to the prefecture and (fiscal) year, respectively. Ypτ denotes theoutcome variable. Dpτ represents the overall employment growth rate, and β is its coefficient, ourprimary parameter of interest. ψp denotes the prefecture fixed effects, ξτ indicates the year fixed
effects, and ϵpτ is an idiosyncratic error term assumed conditional mean independent of Dpτ . We
also examine the effects of the gender-specific employment growth rate using a similar specification
to that in equation (4).
In all results tables, we estimate the model using ordinary least squares and multiply the coefficient estimates by 100 to represent the effect of a one-percentage-point increase in the predictedemployment growth rate. Following the literature, we cluster standard errors at a prefecture levelto account for the error term’s correlation across time periods within each prefecture.

5

Results

5.1

Fertility and Maternal Age

Here, we briefly assess the relationship between the predicted employment growth rate and fertility
across maternal ages. To do so, we estimate variants of equation (5) using prefecture-level panel datafrom 2000 to 2018. Because we use aggregate data available on an annual basis here, we average thepredicted employment growth rates over a one-year period. This one-year period of the economic
conditions variable is similar to that used in previous studies employing state- or prefecture-level
yearly panel data, such as Dehejia and Lleras-Muney (2004) and Kohara et al. (2019).

16

5.1.1Pregnancy, Birth, and Mortality Rates

In Table E.1 in Appendix E, we investigate the decision regarding fertility by estimating the impact of labor demand shock on pregnancy and childbirth. Columns (1) and (2) show a statisticallyinsignificant association between the predicted employment growth rates and pregnancy rate, suggesting that pregnancy decisions might be strongly unaffected by labor demand shocks.
In columns (3) and (4) in Panel A, the results suggest that the predicted overall employment
growth rate is negatively and statistically significantly associated with the birth rate. For example, in column (3), a one-percentage-point increase in the predicted overall employment growthrate reduces the birth rate by about 0.137 percentage-points (pps). The gender-specific predictedemployment growth rate is also negatively associated with the birth rate; however, the result is statistically insignificant. This pattern suggests a negatively associated decision for childbirth, while
the significance of the impacts remains inconclusive.
In columns (5)–(8), to further investigate the effects on childbirth, we estimate the impact on
the risks of stillbirth and perinatal mortality. The predicted overall and female employment growth
rates are positively associated with these rates, and the female rate is statistically significant. The
results suggest that an improvement in mothers’ employment opportunities increases the risk ofstillbirths, and thus decreasing the live birth rate.
In columns (9)–(12), we examine the effect of a labor demand shock on neonatal and infantmortality rates. We find no evidence of significant effects on mortality rates. The results confirm
that the negative effect on newborn health is not driven by sample selection related to changes inneonatal and infant mortality.
To summarize, an increase in the predicted female employment growth rate does not significantly
affect the deaths of live-born infants or the selection of pregnancies, but statistically significantly
increases the risk of fetal deaths during the perinatal period. Note that this selection into live births

17could potentially attenuate the deteriorating effect on newborn health, suggesting that fertility
selection was not the main driver of the observed negative effect, which we will show in the main
results of our study. If an improvement in female labor demand deteriorates the health of a pregnant
mother and her fetus, then it would increase the risk of stillbirth. This could lead to a higher average
health endowment of neonates because stillbirths truncate the lower tail of the health endowment
distribution. We may thus understate the true magnitude of the negative effect of the predicted
female employment growth rate on birth outcomes. Nevertheless, our estimates are informative
because they can be considered a lower bound on the magnitude of the negative effect.12

5.1.2

Composition of Maternal Age

Next, we examine the effects on the composition of mothers’ age at childbirth. When labor demand
for women increases, for example, women might refrain from giving birth due to an increase inopportunity costs. If relatively younger women were to change their fertility decisions more easily,a disproportionate composition in maternal ages could worsen the birth outcomes. This is because
childbirth at an older age is associated with a higher risk of low birth weight and premature births.
In columns (1) and (2) of Table E.2, we examine the effects of predicted employment rate
on average age of mothers at childbirth. The results show that the predicted overall and female
employment growth rates are negatively associated, and the male rate is positively associated withthe average age of mothers at childbirth. However, the estimates are statistically insignificant, andthe magnitude is small.
In columns (3)–(8), we further look into the composition change of the mother’s age at childbirth.
12

In our main analysis, we use a sample of all children, including only children, born in a specific period. We
control for the mother’s primiparity and age at childbirth and the child’s birth month fixed effects. Still, we payless attention to the mother’s endogenous choice of birth spacing and stopping. A few previous studies, such as
those by De Cao et al. (2022), Dehejia and Lleras-Muney (2004), and van den Berg et al. (2020), control for fertility
selection bias using the mother-fixed effect framework that compares differential exposures of unemployment rates
across siblings born to the same mother. This framework, however, can apply only to a selective sample of motherswith at least two children. It would therefore not correct the endogenous selection of both birth timing and spacing.
Further studies that account for birth spacing and fertility are needed.

18

We examine the impact on the number of births by mothers in a maternal age group per all births.
Columns (3) and (4) show that the predicted employment growth rate is insignificantly associatedwith the proportion of births by mothers who gave birth at ages 20 to 24. In columns (5) and (6),the proportion of mothers who gave birth at ages 25 to 34 is statistically significantly increased by
the predicted female employment growth rate, while it is reduced by the male rate. Along withthese effects, the proportion of mothers who gave birth at ages 35 to 44 is statistically significantly
reduced by the female rate and increased by the male rate. However, these proportions are notsignificantly associated with the predicted overall employment growth rate.
To summarize, we find no strong evidence that the predicted overall employment growth rateaffects the composition of mothers’ age at birth. The predicted female and male employment growth
rates do not significantly affect the youngest group of mothers, but they affect the composition of
the middle and oldest groups of mothers in opposite directions. An increase in the predicted female
employment growth rate reduces the number of births that include advanced-age births with ahigher risk of maternal complications.13 By contrast, an increase in the predicted male employment
growth rate increases the number of such births. This suggests that the compositional change may
positively (negatively) bias the estimate of the effect of an increase in the predicted female (male)
employment growth rate on newborn health.
In columns (9)–(12), we focus on mothers at first childbirth. We find that the predicted employment growth rate has no statistically significant impact on the average age at first birth andthe proportion of primiparous mothers, except for that of the predicted male employment rate incolumns (11) and (12). The results suggest that the composition of first-time mothers does not varysystematically over the economic fluctuations.
13

See, e.g., Frederiksen et al. (2018) and Kenny et al. (2013) for associations between advanced maternal age andadverse pregnancy outcomes.

19

5.2

Birth Outcomes

We investigate the effects of the predicted employment growth rates on child’s health outcomes using
the individual-level data. Table 1 shows the main results of the effects on child’s birth outcomes.
As evident in Panel A, improvements in employment opportunities are negatively associated withbirth weight, length and gestational age. Columns (1), (2) and (3) show that a negative association
between the predicted overall employment growth rate and the absolute value of the maternal-fetal
health measure; it corresponds to a decrease in birth weight by about 70.37 grams, in gestational
age by about 0.445 weeks and in fetal growth by about 1.12. We obtain a similar result with analternative measure of neonatal health. Column (4) suggests that a one-percentage-point increasein the predicted employment growth rate reduces the birth length by 0.4152 cm. These results are
statistically significant.
The negative impact on birth outcomes is remarkable when we focus on the lower tail of thebirth weight and gestational age distributions. Columns (5) and (6) show that a one-percentagepoint increase in the predicted overall employment growth rate is associated with a 4.31-pp increasein the probability of an infant having a birth weight of less than 2,500 grams (low birth weight)
and a 1.30-pp increase in the probability of having a birth weight of less than 1,500 grams (verylow birth weight). Columns (7) and (8) show that a one-percentage-point increase in the predicted
overall employment growth rate is statistically significantly associated with a 3.82-pp increase in
the probability of preterm birth (<37 weeks), a 1.35-pp increase in the probability of a very preterm
birth (<32 weeks).
In Panel B, we report the estimates based on our preferred specification that allows for thedisproportionate effect of changes in employment opportunities between men and women. The
results suggest that shocks to labor demand for women are the main driver of the negative association
between the predicted employment growth rates and birth outcomes shown in Panel A. A one-20

percentage-point increase in the predicted female employment growth rate reduces weight by about
66.67 grams, gestation by 0.5006 weeks, fetal growth by 0.8942, and length by 0.4477 cm. It
increases the probability of having an infant with a low birth weight by 3.18 pps, with a very low
birth weight by 1.42 pps, with a preterm birth by 3.42 pps, with a very preterm birth by 1.32
pps. These estimates are statistically different from zero. The coefficients of the predicted male
employment growth rate show the same tendency, but the magnitude is smaller and statisticallyinsignificant. The Wald tests of equality between the male and female coefficients indicate that thetwo are statistically insignificantly different at the 0.05 level, while the p-values in columns (2) and(4) are 0.0506 and 0.0519, respectively.
As reported in column (9) in Panel A, the predicted overall employment growth rate is positively,
but statistically insignificantly, associated with SGA. In Panel B, the predicted employment growthrate for women is negatively associated with an infant being born SGA. Column (9) also shows that
if male employment opportunities increase, fetuses are more likely to be born SGA. However, theseare statistically insignificant. The Wald test of equality between the female and male coefficientsshows that the coefficient of the male rate is statistically insignificantly different from that of thefemale rate.
Overall, we find a negative association between the predicted employment growth rates and birthoutcomes. The sign and magnitude of the negative effect are consistent for the predicted overall andfemale employment growth rates. This finding suggests that the shock to labor demand for womenappears to play an important role, although the difference between the female and male effects ismarginally statistically insignificant.
Furthermore, we might underestimate the negative effect of the predicted female employment
growth rate on newborn health. This is because an increase in the predicted female employment
growth rate could reduce the proportion of advanced-aged births with a higher risk of maternal
complications and thus poor newborn health, as suggested by the result of the change in the com21

position of maternal age at birth (Appendix Table E.2). The estimated negative effect is therefore
robust to this sample selection that biases the estimate toward a positive effect.
By contrast, an increase in the predicted male employment growth rate increases the proportionof births at older ages. This negative selection makes us overestimate the negative effect of thepredicted male employment growth rate on newborn health; thus, it may obscure the difference
between the effects of the predicted male and female employment growth rates.
The negative effect of local labor demand on newborn health is consistent with the resultsof recent studies, such as that of van den Berg et al. (2020), who find counter-cyclical effects of
the unemployment rate on birth weight, although they suggest that the effect stems mostly fromthe male unemployment rate. They show that a one SD increase in the male unemployment ratestatistically significantly reduces the probability of an infant having a very low birth weight by about
0.34 pps, and the female unemployment rate reduces it by about 0.14 pps; however, it is statisticallyinsignificant. The corresponding estimates of our study are about 0.05 (≈ 0.08 × 0.64) pps for the
male employment growth rate and about 0.47 (≈ 1.42×0.33) pps for the female employment growthrate.14
De Cao et al. (2022) suggest a greater effect of a shock on the female labor market than that of
the male labor market, which is similar to our findings. However, they find a pro-cyclical effect for
both the male and female unemployment rates in the UK. One caveat is that their and our regression
model specifications are different; they estimate the effect of the male and female unemployment
rates in a separate regression model, as does van den Berg et al. (2020). In addition, they findthat the overall unemployment rate has a counter-cyclical impact on birth weight in a region witha higher income level, which may be similar to the results from Sweden and Japan. This finding
calls for further analysis of the heterogeneity of the effects across the population subgroups.
14

The corresponding estimate is calculated as 100 · β̂g × 1SD pps, where g ∈ {f, m}. The coefficient estimate, β̂g ,is from column (6) in Table 1, which is the estimate for a one-percentage-point increase in the predicted employment
growth rate. The SD of the gender-specific predicted employment growth rate is from Appendix Table B.1.

22

23

-70.3677∗∗∗(16.7356)-12.5814(20.0805)
0.15873037.3793
83185

Male

-0.0155(0.1361)0.050639.329083185-0.5006∗∗∗(0.1320)-0.4450∗∗∗(0.0937)(2)
Gestation(weeks)-0.3431(0.3566)
0.426177.102083185-0.8942∗(0.4216)-1.1239∗∗∗(0.3111)-0.0185(0.1079)
0.051949.000483034-0.4477∗∗(0.1357)-0.4152∗∗∗(0.0901)(3)(4)
Fetal growth Birth length(grams/weeks)(cm)

0.0163(0.0129)0.51090.077283185

0.0318∗(0.0131)0.0431∗∗∗(0.0100)

0.0008(0.0053)
0.17190.005183185

0.0142∗∗(0.0051)

0.0130∗∗∗(0.0033)

0.0090(0.0126)0.25120.042883185

0.0342∗∗(0.0119)0.0382∗∗∗(0.0095)

0.0023(0.0053)
0.26360.005283185

0.0132∗(0.0051)

0.0135∗∗∗(0.0035)

(5)
(6)
(7)
(8)
Low birth weight
Premature birth<2500 grams <1500 grams <37 weeks <32 weeks

0.0139(0.0076)0.07090.088483185-0.0124(0.0080)

0.0028(0.0062)(9)
SGA

Notes: Standard errors clustered at the prefecture level are in parentheses. The columns of estimates in each panel are from separate regressions. Overall indicatesthe predicted overall employment growth rate. Female (Male) indicates the predicted female (male) employment growth rate. The coefficient estimates are for a
one-percentage-point increase in the predicted employment growth rate. P-value is based on the Wald test of equality between the female and male coefficients.
Mean indicates the sample mean level of the outcome variable. Child’s gender, mother’s first childbirth, mother’s age, mother’s employment status before pregnancy,
parental education, birth month cohort dummies, and a set of dummies for prefecture of residence are also controlled in the regression model but not reported.
Significance at 0.1%, 1%, and 5% levels are indicated by ***, **, and *, respectively.

p-value (βFemale = βMale )MeanObservations-66.6655∗∗(22.9492)

FemalePanel B: Gender-specific effects

Overall

Panel A: Overall effects(1)Birth weight(grams)Table 1: Predicted employment growth rates and birth outcomes5.2.1

Heterogeneity

In this section, we estimate the model allowing for heterogeneous effects across mothers’ age atchildbirth and mothers’ education levels.

Mothers’ Age at Childbirth The predicted employment growth rate could affect the birthoutcomes by changing the composition of maternal age at birth. Here, we examine how the effecton the birth outcomes varies across the maternal age at birth. We interact the maternal age groupdummy with the predicted employment growth rates and control variables. We divide the mothers
into three groups: mothers who gave birth at age 24 or younger, at age 25–34, and at age 35 orolder.
In Table 2, we focus on the effects on the key birth outcomes, which are birth weight andgestational age. We report the estimates of the coefficients of the predicted employment growth
rates based on the interaction model with the age 24 or younger group as the reference, along withthe average marginal effect (AME) of a one-percentage-point increase in the predicted employment
growth rate evaluated at each mother’s age group.
In column (1) of Panel A, we find negative effects of the predicted overall employment growthrate on birth weight, similar to the main results without interactions. In addition, the interaction
term for the age 35 or older group is statistically significantly negative. This suggests that themagnitude of the negative effect is statistically significantly greater for the age 35 or older groupthan for the age 24 or younger group.
Column (1) of Panel B reports the gender-specific effects on birth weight. The AMEs of thepredicted female employment growth rate are negative. Although the estimates suggest that themagnitude of the effect is the largest for the age 24 or younger group, we do not find statisticallysignificant differences in the effects across the age groups. The coefficient estimates of the male ratesuggest that those in the 25–34 and 35 or older age groups are statistically significantly different from24

those in the reference age group. However, the sign of the AME of the predicted male employment
growth rate is unstable due to the imprecise estimates. We only find a statistically significant
negative AME for the 35 or older age group. The test of equality across the gender-specific effects
suggests a statistical difference between the effects of the female and male rates for the age 24 oryounger group.
Column (2) shows the results of the effects on weeks in gestation. The AMEs of an increase in
the predicted overall and female employment rates are negative and statistically significant, except
for the female rate in the age 35 or older group. The magnitude of the effect is the largest in
the age 35 or older group for the overall rate and in the age 24 or younger group for the female
rate. Again, the AMEs of the male rate show an inconsistent sign of the effects across the mother’s
age groups. The AME of the male rate in the age 35 or older group is statistically significantly
negative. However, given the test for equality of the gender-specific effects, we cannot reject the null
hypothesis of equal effects in the age 35 or older group. We find statistically significantly different
gender-specific effects in the age 24 or younger and 25–34 groups.
The results suggest that the negative effect of an increase in the predicted overall and female
employment growth rates on the birth outcomes is robust to the alternative specifications thatallow for heterogeneity in the effects across the mother’s age at childbirth. Focusing on the genderspecific effects, the AMEs of the predicted female employment growth rate are consistently negative
for newborns of mothers who gave birth at age 34 or younger. Moreover, the AMEs for those of
mothers who gave birth at age 24 or younger are statistically significantly different from that ofthe predicted male employment growth rate. The magnitude of the effect of the predicted female
employment growth rate on gestational age is larger for the age 24 or younger group than for thoseof mothers who gave birth at age 25 or older. It is worth noting that the composition of the age 24
or younger group is not significantly affected by the predicted employment growth rate.

25

Table 2: Predicted employment growth rates and birth outcomes by mothers’ age(1)Birth weight (in grams)
Coef.
AME

(2)
Gestational age (in weeks)
Coef.
AME

Age < 25 (reference)-45.6023∗(20.0642)-45.6023∗(20.0642)-0.3745∗∗(0.1286)-0.3745∗∗(0.1286)

25 ≤ Age < 35

-16.8344(19.2240)-62.4367∗∗∗(16.3021)-0.0454(0.1294)-0.4200∗∗∗(0.0906)35 ≤ Age-84.5406∗(37.3994)-130.1429∗∗∗(34.6529)-0.2974(0.1771)-0.6719∗∗∗(0.1746)

Panel A: Overall effects
Overall:

Panel B: Gender-specific effects
Female:Age < 25 (reference)-119.4929∗∗(41.4487)-119.4929∗∗(41.4487)-0.8600∗∗∗(0.1973)-0.8600∗∗∗(0.1973)

25 ≤ Age < 35

46.3674(34.5549)-73.1255∗∗(24.0035)

0.3379∗(0.1548)-0.5221∗∗∗(0.1458)

35 ≤ Age73.2716(61.8611)-46.2213(47.0896)

0.5521∗(0.2502)-0.3078(0.1834)

Age < 25 (reference)

66.0880(37.7307)66.0880(37.7307)

0.4254(0.2301)

0.4254(0.2301)25 ≤ Age < 35

-66.1709∗(30.1872)-0.0829(22.5859)-0.4003∗(0.1964)0.0251(0.1424)

35 ≤ Age-153.3483∗∗(49.6322)-87.2602∗(39.0308)-0.8498∗∗(0.2815)-0.4243∗(0.1849)

Mean
Observations
p-value (Age < 25)
p-value (25 ≤ Age < 35)
p-value (35 ≤ Age)

3037.3793
83185

Male:

39.329083185
0.01910.08790.59700.0021
0.04450.7117

Notes: Standard errors clustered at the prefecture level are in parentheses. The columns of estimates in each panel
are from separate regressions. Overall indicates the predicted overall employment growth rate. Female (Male)
indicates the predicted female (male) employment growth rate. Coef. indicates the coefficient estimates based on
the regression model including the interactions of the predicted employment growth rate and mothers’ age group
dummies, where the mothers aged 24 or younger at childbirth group is the reference group. AME indicates theaverage marginal effect evaluated at each mother’s age group. The estimates are for a one-percentage-point increasein the predicted employment growth rate. Mean indicates the sample mean level of the outcome variable. P-value
is based on the Wald test of equality between the female and male marginal effects evaluated at each mothers’ age
group. Child’s gender, mother’s first childbirth, mother’s employment status before pregnancy, parental education,birth month cohort dummies, a set of dummies for prefecture of residence, and their interactions with the mothers’
age group dummies, and the mothers’ age group dummies are also controlled in the regression model but not reported.
Significance at 0.1%, 1%, and 5% levels are indicated by ***, **, and *, respectively.

26

Mothers’ Education Levels The literature suggests that during the Great Recession, the negative effects of economic fluctuations were concentrated among the less educated (e.g., Currie et al.,2015; Hoynes et al., 2012), whereas the heterogeneity in the effects across mothers’ education levels is undetermined for children’s health outcomes (Dehejia and Lleras-Muney, 2004; Page et al.,2019). Looking at a subset of mothers’ educational attainment, we approach a potential source ofthe heterogeneity in the effects that could be driven by changes in labor demand for women.
In Table 3, we examine the heterogeneity in the effects by mothers’ education level. Because
large differences in the point estimates are likely to occur by chance when using small subgroupsamples, we use the high school with the largest subpopulation as the reference group and test thestatistical significance of the interaction terms.
Column (1) reports the effects on birth weight. In Panel A, an increase in the predicted overall
employment growth rate statistically significantly reduces birth weight. The magnitudes of theeffects for the 2-year college and university groups are statistically significantly larger than that for
the high school group, and the AME is the largest for the university group.
If we focus on the gender-specific effects in Panel B, the effects of the predicted female employment growth rate are consistent with those of the overall rate; there are statistically significant
negative effects on the birth outcomes. The magnitudes of the effects are statistically significantly
larger for the junior high school and university groups than for the high school group.
For the predicted male employment growth rate, the effects are not significant due to the large
standard errors, and their sign is also unstable across subgroups. There are no statistical differences
between the high school group and other subgroups. The test of equality between the male andfemale coefficients suggests that the effects are statistically significantly different in the junior highschool and university groups.
Column (2) reports the effects on gestational age. For the overall effects in Panel A, the results
show similar patterns to the effects on birth weight. The negative effect of the predicted overall
27

employment growth rate is statistically significantly larger for the college and university groups
than for the high school group. For the gender-specific effects in Panel B, we find a negative AMEof the predicted female employment growth rate on gestational age. However, we find no statisticaldifferences in the effects across the mothers’ education levels. The test of equality suggests a
statistical difference between the effects of the female and male rates for the high school and college
groups.
In sum, we find that the negative effect of an increase in labor demand during pregnancy on birthweight is larger for more educated mothers. This pattern is common to the effects of the predicted
overall and female employment growth rates. The effect of labor demand for women is also larger
for mothers in the junior high school group than for those in the high school group. However, theshare of the junior high school group in the full sample is about 5% (Appendix Table B.1); hence,the contribution of the effect of labor demand for the subgroup to that for the full sample might belimited. In addition, we do not find statistically significant heterogeneous effects of labor demand
for men.
Therefore, the negative association between labor demand during early pregnancy and birthoutcomes appears to be mainly driven by changes in female employment opportunities for more educated mothers, especially those who gave birth at relatively young ages. This finding is consistent
with the patterns of the female labor force participation rate across ages in Appendix Figure A.3,
suggesting that more younger educated women are more likely to be attached to the labor market.
As our variable of changes in local labor demand proxies for changes in the local employment growthrate, it may mainly capture the extensive margin of labor adjustment.

28

Table 3: Predicted employment growth rates and birth outcomes by mothers’ educational attainment(1)Birth weight (in grams)
Coef.
AME

(2)
Gestational age (in weeks)
Coef.
AME

Panel A: Overall effects
Overall:Jr. high school-59.2667(50.7035)-81.8010(45.6643)-0.3667(0.1937)-0.6265∗∗∗(0.1729)High school (reference)-22.5343(18.6382)-22.5343(18.6382)-0.2597∗∗(0.0910)-0.2597∗∗(0.0910)

Vocational school-42.9175(35.7322)-65.4518∗(30.9420)-0.1556(0.1363)-0.4153∗∗(0.1228)2-year college-69.0616∗(29.8544)-91.5959∗∗(28.3129)-0.2488∗∗(0.0862)-0.5086∗∗∗(0.0983)

University or more

-110.6374∗∗(34.6341)-133.1717∗∗∗(35.2422)-0.3959∗(0.1747)-0.6556∗∗(0.1942)(continued)29

(continued)(1)
Birth weight (in grams)
Coef.
AME

(2)
Gestational age (in weeks)
Coef.
AME

Panel B: Gender-specific effects
Female:Jr. high school-185.0373∗(76.1202)-207.4647∗(78.4321)-0.4621(0.3641)-0.9625∗(0.3698)

High school (reference)-22.4274(30.9172)-22.4274(30.9172)-0.5004∗∗(0.1823)-0.5004∗∗(0.1823)

Vocational school-18.5666(44.1677)-40.9939(43.4996)

0.0738(0.1737)-0.4266∗(0.1885)2-year college-42.2476(45.0119)-64.6750(40.9735)-0.0766(0.1541)-0.5770∗∗∗(0.1475)

University or more

-151.3487∗∗(48.7077)-173.7761∗∗∗(47.5301)-0.1912(0.2200)-0.6916∗∗∗(0.1963)

Jr. high school

105.3390(56.3697)

102.6245(67.6619)

0.0735(0.2688)0.2573(0.2966)

High school (reference)-2.7146(26.9855)-2.7146(26.9855)

0.1838(0.1732)0.1838(0.1732)

Vocational school-31.3299(46.3939)-34.0444(47.6152)-0.2399(0.1745)-0.0561(0.1900)

2-year college-32.6390(46.3726)-35.3535(40.7640)-0.1946(0.1267)-0.0108(0.1297)

University or more

15.7959(37.2803)

13.0813(33.3395)-0.2810(0.2308)-0.0972(0.2354)

Mean
Observations
p-value (Jr. high)p-value (High)p-value (Vocational)
p-value (College)p-value (University)

3038.4258
74773

Male:

39.333374773
0.03020.71830.9351
0.69800.00950.06270.04770.30190.02580.1179Notes: Standard errors clustered at the prefecture level are in parentheses. The columns of estimates in each panel are from separate
regressions. Overall indicates the predicted overall employment growth rate. Female (Male) indicates the predicted female (male)
employment growth rate. Coef. indicates the coefficient estimates based on the regression model including the interactions of thepredicted employment growth rate and mothers’ education level dummies, where mothers with high school is the reference group. AME
indicates the average marginal effect evaluated at each mothers’ education level. The estimates are for a one-percentage-point increasein the predicted employment growth rate. Mean indicates the sample mean level of the outcome variable. P-value is based on the Wald
test of equality between the female and male marginal effects evaluated at each mothers’ education level group. Child’s gender, mother’s
first childbirth, mother’s age, mother’s employment status before pregnancy, parental education, birth month cohort dummies, a set ofdummies for prefecture of residence, and their interactions with the mothers’ education level dummies, and mothers’ education leveldummies are also controlled in the regression model but not reported. Significance at 0.1%, 1%, and 5% levels are indicated by ***, **,and *, respectively.

30

5.3

Later Outcomes

Next, we analyze the association between predicted employment growth rates and physical healthand mental development in early childhood. A large body of epidemiology literature suggeststhat low birth weight is associated with poor physical and mental health from infancy throughadolescence (e.g., McCormick et al., 1992; Saigal et al., 1996). However, anthropometric measures
such as low birth weight might reflect unmeasured genetic and socio-economic backgrounds (Almondand Currie, 2011). Due to potential paths and confounding from unmeasured factors, the observed
poor neonatal health does not necessarily imply that economic shocks affect future health outcomes.

5.3.1

Health Conditions

Panel A of Table 4 shows that improvements in labor market opportunities are positively associatedwith the probability of childhood hospitalization for illness, except when the child is 1.5 years old.
The estimates are statistically insignificant, and the magnitude is small. Even the largest impact ofthe predicted overall employment growth rate on children aged 4.5 is only about a 0.71-pp change.
We should be cautious about these statistically insignificant results. Because hospitalizations
are rare events, the test may lack statistical power. Therefore, to examine the robustness of theresult, we construct a cumulative measure of hospitalization at any time from age 1.5 to age 4.5.
Column (5) shows that impact of the predicted overall employment growth rate on hospitalizations
during ages 1.5–4.5 is small and statistically insignificant.
We also examine the gender-specific effects on childhood hospitalization for illness. Panel B
shows no statistically significant association between the predicted employment growth rates andhospitalization at ages 1.5–4.5. These results suggest that labor demand shocks during pregnancy
have no substantial impact on children’s health in early childhood.
In contrast to the effects of labor demand shock during pregnancy on newborn health, we findno evidence of a significant impact on children’s health later in life. Previous studies suggest that31

the current labor market fluctuations could have a negative impact on children and mothers’ health(e.g., Page et al., 2019; Schaller and Zerpa, 2019). These findings indicate that changes in employment opportunities are more likely to be relevant for health conditions in the short run.

Table 4: Predicted employment growth rates and hospitalization for illness(1)
at age 1.5(2)
at age 2.5(3)
at age 3.5(4)
at age 4.5(5)
at ages 1.5–4.5

0.0033(0.0081)0.0006(0.0059)

0.0071(0.0074)

0.0110(0.0130)

Panel A: Overall effects
Overall-0.0011(0.0090)

Panel B: Gender-specific effects
Female-0.0010(0.0120)-0.0036(0.0141)0.0082(0.0098)-0.0004(0.0101)-0.0003(0.0217)Male

0.0017(0.0148)0.91460.116973935

0.0078(0.0169)
0.70560.085671820-0.0061(0.0110)0.47700.058567919

0.0097(0.0120)0.6341
0.051564669

0.0145(0.0244)0.73940.234764669

p-value (βFemale = βMale )MeanObservations

Notes: Standard errors clustered at the prefecture level are in parentheses. The columns of estimates in each panel arefrom separate regressions. Overall indicates the predicted overall employment growth rate. Female (Male) indicatesthe predicted female (male) employment growth rate. The coefficient estimates are for a one-percentage-point increasein the predicted employment growth rate. P-value is based on the Wald test of equality between the female and malecoefficients. Mean indicates the sample mean level of the outcome variable. Child’s gender, mother’s first childbirth,mother’s age, mother’s employment status before pregnancy, parental education, birth month cohort dummies, anda set of dummies for prefecture of residence are also controlled in the regression model but not reported. Significanceat 0.1%, 1%, and 5% levels are indicated by ***, **, and *, respectively.

32

5.3.2

Language and Mental Development

Table 5 reports the association between labor demand during pregnancy and children’s language
and mental development. In column (1), Panel A shows that a one-percentage-point increase in theoverall employment growth rate is associated with a 0.0118-SD increase in the children’s language
development index. In Panel B, the corresponding estimate for women is a 0.0099-SD increase andfor men, it is a 0.0002-SD decrease. We see that the magnitude of all estimates is small, and thestandard errors are large.
Column (2) indicates the impacts on children’s tendency toward aggression. In Panel A, an
increase in the predicted overall employment growth rate is associated with a 0.0315-SD decrease but
is statistically insignificant. In Panel B, the predicted female employment growth rate is statisticallysignificantly associated with a 0.0744-SD decrease in children’s tendency toward aggression, but themagnitude of the effect is small. It is about a 0.0246-SD decrease for a one SD increase in thepredicted female employment growth rate (≈ 0.0744-SD× 0.33 pps). By contrast, the effect of thepredicted male employment growth rate is statistically insignificant and has the opposite sign.
Column (3) shows the impacts on children’s tendency toward inattention and hyperactivity. The
effect of the predicted overall and male employment growth rate is positive, while that of the female
is negative; however, they are statistically insignificant due to their large standard errors. Thus,
there are no significant effects of labor demand during pregnancy on these outcomes, except for
female labor demand on the aggression index.
To summarize, the results in Tables 4 and 5 suggest that the negative impact of a labor demand
shock on birth outcomes does not persist in the long run for subsequent outcomes. We find a statistically significant effect of an increase in the predicted employment growth rate only with respect
to that of the female rate, reducing the tendency toward aggression. At first glance, this seems
to contradict previous studies that found long-term negative effects of low birth weight; however,33

the result requires careful interpretation. In this study, we focus on the total effects of economic
fluctuations, not necessarily the effects only through low birth weight. In addition, our key identification variations differ from previous studies based on variations in fetal growth restriction in
twins that may have long-term effects. Maruyama and Heinesen (2020), using exogenous variations
in gestational age as an instrumental variable, found that the negative effect of low birth weightdiminishes over time. Our results are consistent with their findings as our estimates suggest thatthe main factor of low birth weight is a shorter gestation period.

Table 5: Predicted employment growth rates and child development(1)
Language development(2)
Aggression(3)
Inattention & Hyperactivity-0.0315(0.0449)

0.0102(0.0260)

Panel A: Overall effects
Overall

0.0118(0.0238)

Panel B: Gender-specific effects
Female0.0099(0.0328)-0.0744∗(0.0359)-0.0197(0.0551)

Male-0.0002(0.0303)
0.86260.000072770

0.0407(0.0609)
0.21540.000066876

0.0276(0.0567)
0.66660.000065329

p-value (βFemale = βMale )MeanObservations

Notes: Standard errors clustered at the prefecture level are in parentheses. The columns of estimates in each panel arefrom separate regressions. Overall indicates the predicted overall employment growth rate. Female (Male) indicatesthe predicted female (male) employment growth rate. The coefficient estimates are for a one-percentage-point increasein the predicted employment growth rate. P-value is based on the Wald test of equality between the female and malecoefficients. Mean indicates the sample mean level of the outcome variable. The outcome variable is standardized
to mean 0 and standard deviation 1. Child’s gender, mother’s first childbirth, mother’s age, mother’s employment
status before pregnancy, parental education, birth month cohort dummies, and a set of dummies for prefecture ofresidence are also controlled in the regression model but not reported. Significance at 0.1%, 1%, and 5% levels areindicated by ***, **, and *, respectively.

34

5.3.3Heterogeneity

We also examine the heterogeneous effects across the mothers’ age groups and education levels on
later outcomes. In Appendix F, Table F.1 presents the results from the model with the mothers’ age
at childbirth interactions. Column (1) shows that for children’s ever experienced hospitalizations
at ages 1.5–4.5, the magnitude of the impact is still small and statistically insignificant. Columns(2)–(4) present the results for children’s language development, tendency toward aggression, andtendency toward inattention and hyperactivity index. Across the mothers’ age groups, the interaction terms are statistically insignificant throughout. The AMEs of the overall and gender-specificeffects are also statistically insignificant and unstable.
Table F.2 reports the results from the model with the mothers’ education level interactions.
For the overall and gender-specific effects, few heterogeneity patterns stand out. One exception
is the effect on children’s language development. For example, for the junior high school group,
the difference in the effects of the overall rate relative to the high school group is statisticallysignificant. The magnitude of the AME is −0.3111-SD, which is about a 0.1369-SD decrease in thechildren’s language development index for a one SD increase in the overall rate (≈ −0.3111-SD×0.44 pps). We also find that the effect of the female rate for the 2-year college group is statisticallysignificantly larger than that for the high school group. However, the magnitude of the AME issmall (0.1501-SD), corresponding to about a 0.0495-SD increase for a one SD increase in the female
rate (≈ 0.1501-SD× 0.33 pps). In addition, we cannot reject the equality between the female andmale AMEs. We can reject the equality of the gender-specific effects only for the high school group.
Across the mothers’ age groups and education levels, the effects are basically small and their signis unstable. This finding confirms the lack of strong evidence that an increase in the predicted employment growth rate deteriorates children’s health conditions and development in early childhood,which are similar to the results in Tables 4 and 5.

35

5.4

Robustness Check

5.4.1

Model Specifications

Due to the sampling of newborns, our main results might be sensitive to unobserved factors that
affect fertility decisions. For example, if individuals from a certain socioeconomic group such as
skilled labor postpone or give up plans to have a child in response to an industry-specific shock tolabor demand, then the decision creates a systematic change in the composition of parents. It would
lead to a correlation between labor demand shock and the health conditions of newborns amonga group that chose to give birth. To mitigate this concern, we control for primiparity, maternalage, maternal employment status before childbirth, and parents’ educational level in the baseline
regression models.
One concern is that the control variables might rather bias the estimates if they are endogenous.
To investigate this possibility, we estimate the effects on birth weight and gestational age based on
different specifications in Table G.1 in Appendix G, where we add a control variable to the model
step-by-step. In column (1), we reproduce our baseline estimates from Table 1 for comparison. In
column (2), we estimate a simple model that controls only the birth month cohort and prefecturefixed effects. The signs and significance of the effects are quite similar to those of the estimates incolumn (1), while the magnitude of the effects is slightly smaller for birth weight in Appendix Table
G.1a.
Adding the child’s basic characteristics, gender, and primiparity returns results almost identicalto the baseline. Further, the results are basically robust to controlling for the additional individual
characteristics such as mother’s age at childbirth, parents’ education level, and mother’s employment
status one year before childbirth. Therefore, our main results are basically insensitive to the model
specifications after adding controls for individual characteristics.

36

5.4.2Prefecture and Municipality Controls

We further examine the sensitivity of the results by controlling for prefecture- and municipalitylevel variables that may be correlated with both labor market conditions and fertility decisions orchildren’s health. The prefecture control variable is the number of hospitals that offer obstetrics
and gynecology services per the number of women aged 20–44. The municipality control variables
are the ratio of the accredited childcare center capacity to the number of children aged 0–5 andshare of nuclear family households.15
We include these municipality controls because the literature suggests that local accessibility tocenter-based childcare could change the fertility rate, and the effect is pronounced in regions where
households are unlikely to include grandparents (e.g., Fukai, 2017). Although some of these regional
control variables may be affected by labor demand shocks, they could also capture unobserved factorsthat determine the selection for childbirths. Therefore, including them allows us to partly addressconcerns on selection on unobservables.
Appendix Table G.2 shows that the pattern and magnitude of the estimates are not sensitive toincluding prefecture and municipality control variables. Controlling for the regional variables also
does not affect the statistical difference between the effects of the female and male rates, with the
exception of the effects on gestational age (p-value = 0.0471 in column (6)). This suggests that our
main results are relatively robust to concerns about selective fertility and confounding related tothe unobserved regional factors.
In addition, we conduct an alternative robustness check suggested by Pei et al. (2019). They
point out that the estimated coefficient movement might be uninformative if the included control is anoisy proxy of the unobserved confounding factor. Therefore, following their suggestion, we conduct
a left-hand-side (LHS) and right-hand-side (RHS) balancing test under similar specifications to the15
The number of hospitals is from the Survey of Medical Institutions of the MHLW. The number of childcare
centers is from the Survey of Social Welfare Institutions of the MHLW. Population and household data are based on
the national census.

37

equations (3) and (4). In the LHS test, we regress each regional control on the predicted employment
growth rates. In the RHS test, we regress the predicted employment growth rate on the regional
controls.
In Appendix Table G.3, the estimate for the LHS test suggests a statistically insignificant association between the predicted employment growth rate and the regional control variable, except forthat between the predicted male employment growth rate and the share of nuclear family households. The p-values of the LHS joint balancing test suggest that we cannot reject the hypothesisthat the three controls are not correlated with the predicted overall and female employment growth
rates. Moreover, based on the RHS joint balancing test under the hypothesis that the three controls
are balanced, the p-values suggest that we cannot reject the null at any conventional significancelevel for the predicted overall, female, and male employment growth rates. These results suggestthat prefecture and municipal unobserved factors are unlikely to affect our main results, especiallyfor the effects of the predicted overall and female employment growth rates.

5.4.3

Sample Attrition

Another concern is a selection issue due to children endogenously being dropped from the analysis
sample for later outcomes. For example, if improvements in employment opportunities during
pregnancy worsen newborn health, then they could also increase infant mortality. In such a case, thissample selection might attenuate negative associations between labor demand shock and children’s
health in early childhood.
We address this concern by examining whether the predicted employment growth rate causes
sample attrition in early childhood. Here, the dependent variable is a dummy variable that equals
one if the child is dropped from the base regression sample until the follow-up survey. Appendix
Table G.4 shows that the estimates of the effects of predicted employment growth rates are statistically insignificant and with very small magnitudes in all columns. The results suggest that our38findings on health and development in early childhood are robust to sample attrition.

5.4.4

Migration

We might also be concerned about endogenous migration during pregnancy. For example, if mothers
who have a low risk of prenatal health issues are more likely to relocate in response to labor demandchanges during pregnancy, this selective relocation could provide significant associations between
labor demand changes and birth outcomes. To address this concern, we estimate correlations
between the predicted employment growth rates and relocation during pregnancy.
Although there is limited information on migration during pregnancy in the LSN21, only for theJanuary 2001 and July 2001 cohorts, we can identify whether the family had relocated because of
pregnancy or childbirth between one year prior to childbirth and when the child was 6 months old.
Appendix Table G.5 shows no statistically significant association between the predicted employment
growth rates and relocation during pregnancy. Hence, we find no strong evidence that selective
migration drives the main results.

5.4.5

Pregnancy Periods

In our baseline specification, we define the pregnancy period for the predicted employment growth
rates as the first 22 weeks of pregnancy to accommodate the minimum gestational age in our sample.
Here, we examine the sensitivity of the definition of the pregnancy period. In Appendix Table G.6,
we also present the estimates with 20 and 24 weeks for the pregnancy period. The estimates are
similar to the ones from the baseline specification, suggesting that the main results are insensitive
to the definition of the pregnancy period.

39

5.4.6

Rotemberg Weights

In this section, we address the concern that only a single industry may drive our main results. If
the variation in employment opportunities is not dominated by one industry with large Rotemberg
weights, then even if we omit the industry, we would still find negative effects of the predictedemployment growth rate on birth outcomes.
In Table D.1 of Appendix D, we reconstruct the predicted employment growth rate, by dropping
one industry in each row from all industries and normalizing the sum of the employment shares of
the remaining 23 industries to one. Columns of the effect and the standard errors (SE) report theestimates of the coefficient of the predicted employment growth rate based on the equations (3) and(4) using the reconstructed predicted employment growth rates.
We find that an increase in the predicted overall and female employment growth rate statisticallysignificantly reduces birth weight, which is consistent with the main results in Table 1. This result
suggests that the negative impact is not solely driven by a specific industry.

5.4.7

Prefecture-level Analysis

Finally, we re-examine the impact of the employment growth rate on birth weight using prefecturelevel panel data from 2000 to 2018. In Table E.3 of Appendix E, we confirm a significant impactof labor demand shocks on birth weight at the prefecture level. Columns (3) and (4) show thatthe predicted overall employment growth rates are statistically significantly associated with theproportion of low birth weight births. Columns (1)–(4) show that the female rates are statisticallysignificantly associated with the average birth weight and the proportion of low birth weight births.
These factors reduce birth weight, resulting in increasing the risk of causing low birth weight.

40

6Conclusion

This study examines the effects of local labor market conditions during early pregnancy on childbirth outcomes in Japan. Studies in developed countries produced mixed results, with recessions
having both better and worse outcomes for prenatal health. This study contributes to the ongoing
discussion by providing new evidence that the impacts of labor market conditions vary by gender.
We take advantage of the fact that men and women are often engaged in different industries tocapture gender-specific labor market conditions.
Our results demonstrate that improvements in labor market opportunities for women during
early pregnancy worsen birth outcomes. However, we do not observe a significant change in birthweight and gestational age when labor market opportunities for men improve. These results imply
that maternal employment rather than paternal employment, is a key factor in prenatal health indeveloped countries.
One possible mechanism is that an increase in labor demand for women increases work-related
stress. In addition, it increases the opportunity cost of time for health-related investment, suchas exercise and health checkups, and it could thus deteriorate the health conditions of pregnant
women. A heavy workload for pregnant women could increase stress-dependent health problems,
which might lead to deteriorating birth outcomes. For instance, based on a large-scale prospective
cohort study of pregnant women in Japan, Suzumori et al. (2020) found that long working hours
during pregnancy are associated with the risks of hypertensive disorders of pregnancy, and vacuum
or forceps delivery.
This result is consistent with our findings that the negative effect is larger for younger mothers
who gave birth at age 24 or younger, as younger women are more likely to be attached to the labormarket, and thus more likely to be in employed around the pregnancy. A larger magnitude of theeffect for more educated mothers is also suggestive because they might have a greater incentive to41

work during pregnancy than less educated mothers.
The present study uses changes in economic situations during the early stages of pregnancy.
The analysis suggests policy interventions during early pregnancy, targeting to improve prenatal
health conditions, consistent with the evidence that an expansion of maternity leave programs canimprove prenatal health conditions (e.g., Rossin, 2011). Note that, maternity leave is guaranteed
for only six weeks prior to the expected date of delivery under the current Japanese law (the Article
the Labor Standards Act).
We also investigate the impact of economic conditions during pregnancy on subsequent childhealth and development. Although previous studies demonstrate that maternal mental stress during
pregnancy worsens children’s future outcomes, the long-term impact of economic conditions during
pregnancy is still not well understood. In this study, we find little evidence that an increase in
the predicted employment growth rate during pregnancy deteriorates child health and development
several years later. This finding suggests that the negative effects of improved economic conditions
through a deterioration in birth outcomes could be obscured in early childhood.
This study has three limitations. First, we were unable to observe the actual employment statusduring pregnancy. We would need detailed data on maternal employment during pregnancy toidentify a potential channel of the impact of economic fluctuations better.
Second, we lacked access to information regarding maternal health behaviors during pregnancy,such as dietary intake for weight gain. Previous studies suggest that birth weight, energy intake,height, and body mass index shared declining trends over four decades in Japan (e.g., Kato et al.,2021; Maruyama and Nakamura, 2018; Normile, 2018; Ogawa et al., 2018). To control unknown
differences across cohorts, we include cohort fixed effects in the analysis. If pregnant women change
their health behaviors in response to economic shocks, then it would present an interesting mechanism to analyze.
Finally, for the outcome of child health and development, we were only able to track children
42aged 0 to 4.5. Labor demand shocks could influence health-related socioeconomic outcomes laterin life since poor neonatal health effects remain latent for many years, such as the effects on heartdisease, which become apparent in middle age (Almond and Currie, 2011). Future research needs
longer-term data to analyze the impact of economic status during pregnancy on future outcomes.

43References
Abe, Yukiko. 2011. “The Equal Employment Opportunity Law and Labor Force Behavior of
Women in Japan.” Journal of the Japanese and International Economies 25 (1): 39–55. 10.1016/
j.jjie.2010.06.003.
Alessie, Rob, Viola Angelini, Jochen O. Mierau, and Laura Viluma. 2018. “Economic
Downturns and Infant Health.” Economics & Human Biology 30 162–171. 10.1016/j.ehb.2018.07.
005.
Almond, Douglas, and Janet Currie. 2011. “Killing Me Softly: The Fetal Origins Hypothesis.”
Journal of Economic Perspectives 25 (3): 153–172. 10.1257/jep.25.3.153.
Aparicio, Ainoa, Libertad González, and Judit Vall Castelló. 2020. “Newborn Health andthe Business Cycle: the Role of Birth Order.” Economics & Human Biology 37 100836. 10.1016/
j.ehb.2019.100836.
Baird, Sarah, Jed Friedman, and Norbert Schady. 2011. “Aggregate Income Shocks andInfant Mortality in the Developing World.” The Review of Economics and Statistics 93 (3): 847–
856. 10.1162/REST_a_00084.
Bartik, Timothy J. 1991. Who Benefits from State and Local Economic Development Policies?.
Kalamazoo, MI: W. E. Upjohn Institute.
Bhalotra, Sonia. 2010. “Fatal Fluctuations? Cyclicality in Infant Mortality in India.” Journal of
Development Economics 93 (1): 7–19. 10.1016/j.jdeveco.2009.03.006.
Borra, Cristina, Libertad González, and Almudena Sevilla. 2019. “The Impact of Scheduling
Birth Early on Infant Health.” Journal of the European Economic Association 17 (1): 30–78.
10.1093/jeea/jvx060.
Borusyak, Kirill, Peter Hull, and Xavier Jaravel. 2022. “Quasi-Experimental Shift-Share
Research Designs.” The Review of Economic Studies 89 (1): 181–213. 10.1093/restud/rdab030.
Currie, Janet. 2009. “Healthy, Wealthy, and Wise: Socioeconomic Status, Poor Health in Childhood, and Human Capital Development.” Journal of Economic Literature 47 (1): 87–122.
10.1257/jel.47.1.87.

44

Currie, Janet, Valentina Duque, and Irwin Garfinkel. 2015. “The Great Recession andMothers’ Health.” The Economic Journal 125 (588): F311–F346. 10.1111/ecoj.12239.
De Cao, Elisabetta, Barry McCormick, and Catia Nicodemo. 2022. “Does Unemployment
Worsen Babies’ Health? A Tale of Siblings, Maternal Behaviour, and Selection.” Journal of Health
Economics 83 102601. https://doi.org/10.1016/j.jhealeco.2022.102601.
Dehejia, Rajeev, and Adriana Lleras-Muney. 2004. “Booms, Busts, and Babies’ Health.” The
Quarterly Journal of Economics 119 (3): 1091–1130. 10.1162/0033553041502216.
Esteban-Pretel, Julen, and Junichi Fujimoto. 2020. “Non-Regular Employment Over the Lifecycle: Worker Flow Analysis for Japan.” Journal of the Japanese and International Economies
57 101088. 10.1016/j.jjie.2020.101088.
Frederiksen, Line Elmerdahl, Andreas Ernst, Nis Brix, Lea Lykke Braskhøj Lauridsen,
Laura Roos, Cecilia Høst Ramlau-Hansen, and Charlotte Kvist Ekelund. 2018. “Riskof Adverse Pregnancy Outcomes at Advanced Maternal Age.” Obstetrics & Gynecology 131 (3):457–463. 10.1097/AOG.0000000000002504.
Fukai, Taiyo. 2017. “Childcare Availability and Fertility: Evidence from Municipalities in Japan.”
Journal of the Japanese and International Economies 43 1–18. 10.1016/j.jjie.2016.11.003.
Fukao, Kyoji, Hamagata, Sumio, Inui, Tomohiko, Ito, Keiko, Kwon, Hyeog Ug, Makino,
Tatsuji, Miyagawa, Tsutomu, and Nakanishi, Yasuo. 2007. “Estimation Procedures and
TFP Analysis of the JIP Database 2006.” RIETI Discussion Paper Series 07-E-003.
Fukao, Kyoji, Ikeuchi, Kenta, Inui, Tomohiko et al. 2021. “JIP Database 2018 no Kochiku(The Construction of the JIP Database 2018).” In Service Sangyo no Seisansei to Nihon Keizai:
JIP Database wo Mochiita Jissho Bunseki (Service Sector Productivity and the Japanese Economy:
An Empirical Analysis Using the JIP Database), edited by Fukao, Kyoji, Chap. 1 3–81, Tokyo,
Japan: University of Tokyo Press (in Japanese).
Goldsmith-Pinkham, Paul, Isaac Sorkin, and Henry Swift. 2020. “Bartik Instruments:
What, When, Why, and How.” American Economic Review 110 (8): 2586–2624. 10.1257/aer.
20181047.
Hijzen, Alexander, Ryo Kambayashi, Hiroshi Teruyama, and Yuji Genda. 2015. “TheJapanese Labour Market during the Global Financial Crisis and the Role of Non-Standard Work:45

A Micro Perspective.” Journal of the Japanese and International Economies 38 260–281. 10.
1016/j.jjie.2015.09.003.
Hoynes, Hilary, Douglas L. Miller, and Jessamyn Schaller. 2012. “Who Suffers during
Recessions?” Journal of Economic Perspectives 26 (3): 27–48. 10.1257/jep.26.3.27.
Itabashi, Kazuo, Fumihiro Miura, Ritei Uehara, Yoshikazu Nakamura et al. 2014. “NewJapanese Neonatal Anthropometric Charts for Gestational Age at Birth.” Pediatrics International
56 (5): 702–708. 10.1111/ped.12331.
Itoh, Hiroaki, Yukiko Kohmura-Kobayashi, Kenta Kawai, and Naohiro Kanayama.
2018. “Multiple Causative Factors underlie Low Birthweight.” Science, eLetter, October 14, http://science.sciencemag.org/content/361/6401/440/tab-e-letters.
Kanayama, Naohiro. 2019. “1997nen Nihon Sanfujinka Gakkai Shusanki-Iinkai ni yoru ‘Ninshinchudokusho no Eiyo Kanri Shishin’ (1) no Suisho no Teishi ni tsuite (Suspension of Recommendation of the 1997 Nutritional Management Guidelines for Toxemia of Pregnancy (1) by the
Perinatal Committee of the Japanese Society of Obstetrics and Gynecologyy).” Acta Obstetrica
et Gynaecologica Japonica 71 (8): 1248, (in Japanese).
Kato, Noriko, Catherine Sauvaget, Honami Yoshida, Tetsuji Yokoyama, and Nobuo
Yoshiike. 2021. “Factors Associated with Birthweight Decline in Japan (1980–2004).” BMC Pregnancy and Childbirth 21 (1): 337. 10.1186/s12884-021-03819-0.
Kenny, Louise C., Tina Lavender, Roseanne McNamee, Sinéad M. O’Neill, Tracey
Mills, and Ali S. Khashan. 2013. “Advanced Maternal Age and Adverse Pregnancy Outcome:Evidence from a Large Contemporary Cohort.” PLOS ONE 8 (2): e56583. 10.1371/journal.pone.
0056583.
Kohara, Miki, Midori Matsushima, and Fumio Ohtake. 2019. “Effect of Unemployment on
Infant Health.” Journal of the Japanese and International Economies 52 68–77. 10.1016/j.jjie.
2019.03.002.
Kyriopoulos, Ilias, Zlatko Nikoloski, and Elias Mossialos. 2019. “Does Economic Recession
Impact Newborn Health? Evidence from Greece.” Social Science & Medicine 237 112451. 10.
1016/j.socscimed.2019.112451.

46

Lee, Chulhee. 2014. “In Utero Exposure to the Korean War and Its Long-term Effects on Socioeconomic and Health Outcomes.” Journal of Health Economics 33 76–93. 10.1016/j.jhealeco.2013.
11.002.
Lindo, Jason M. 2011. “Parental Job Loss and Infant Health.” Journal of Health Economics 30(5): 869–879. 10.1016/j.jhealeco.2011.06.008.
Lindo, Jason M., Jessamyn Schaller, and Benjamin Hansen. 2018. “Caution! Men Not
at Work: Gender-specific Labor Market Conditions and Child Maltreatment.” Journal of Public
Economics 163 77–98. 10.1016/j.jpubeco.2018.04.007.
Margerison-Zilko, Claire E, Ralph Catalano, Alan Hubbard, and Jennifer Ahern. 2011.
“Maternal Exposure to Unexpected Economic Contraction and Birth Weight for Gestational Age.”
Epidemiology (Cambridge, Mass.) 22 (6): 855–858. 10.1097/EDE.0b013e318230a66e.
Margerison-Zilko, Claire E, Yu Li, and Zhehui Luo. 2017. “Economic Conditions During
Pregnancy and Adverse Birth Outcomes Among Singleton Live Births in the United States,
1990–2013.” American Journal of Epidemiology 186 (10): 1131–1139. 10.1093/aje/kwx179.
Maruyama, Shiko, and Eskil Heinesen. 2020. “Another Look at Returns to Birthweight.”
Journal of Health Economics 70 102269. 10.1016/j.jhealeco.2019.102269.
Maruyama, Shiko, and Sayaka Nakamura. 2018. “Why are Women Slimmer than Men in
Developed Countries?” Economics & Human Biology 30 1–13. 10.1016/j.ehb.2018.04.002.
McCormick, Marie C., Jeanne Brooks-Gunn, Kathryn Workman-Daniels, JoAnna
Turner, and George J. Peckham. 1992. “The Health and Developmental Status of Very
Low-Birth-Weight Children at School Age.” JAMA 267 (16): 2204–2208. 10.1001/jama.1992.
03480160062035.
Minakami, Hisanori, Yuji Hiramatsu, Mitsuhiko Koresawa et al. 2011. “Guidelines for
Obstetrical Practice in Japan: Japan Society of Obstetrics and Gynecology (JSOG) and Japan
Association of Obstetricians and Gynecologists (JAOG) 2011 Edition.” Journal of Obstetrics andGynaecology Research 37 (9): 1174–1197. 10.1111/j.1447-0756.2011.01653.x.
Minakami, Hisanori, Tsugio Maeda, Tomoyuki Fujii et al. 2014. “Guidelines for Obstetrical
Practice in Japan: Japan Society of Obstetrics and Gynecology (JSOG) and Japan Association47

of Obstetricians and Gynecologists (JAOG) 2014 Edition.” Journal of Obstetrics and Gynaecology
Research 40 (6): 1469–1499. 10.1111/jog.12419.
Nakabyashi, Masao. 1999. “Ninshin-chudokusho no Eiyo Kanri Shishin (Guideline of Nutritional
Management in Toxemia of Pregnancy).” Acta obstetrica et gynaecologica Japonica 51 (12): N507–
N510, (in Japanese).
Normile, Dennis. 2018. “Staying Slim during Pregnancy Carries a Price.” Science 361 (6401):
440–440. 10.1126/science.361.6401.440.
Ogawa, Kohei, Naho Morisaki, Haruhiko Sago, Takeo Fujiwara, and Reiko Horikawa.
2018. “Association between Women’s Perceived Ideal Gestational Weight Gain during Pregnancy
and Pregnancy Outcomes.” Scientific Reports 8 (1): 11574. 10.1038/s41598-018-29936-z.
Ogawa, Yunosuke, Toru Iwamura, Norikazu Kuriya et al. 1998. “Nihonjin no Zaitaibetsu
Shusshouji Taikaku Kijunchi (Birth Size Standards by Gestational Age for Japanese Neonates).”
Acta Neonatologica Japonica 34 624–632, (in Japanese).
Olafsson, Arna. 2016. “Household Financial Distress and Initial Endowments: Evidence from the2008 Financial Crisis.” Health Economics 25 43–56. 10.1002/hec.3426.
Organisation for Economic Co-operation and Development (OECD). 2022a. “Family Indicators, OECD Social and Welfare Statistics (Database).” https://doi.org/10.1787/efd30a09-en,
Accessed: April 18, 2022.
Organisation for Economic Co-operation and Development (OECD). 2022b. “HealthCare Utilisation, OECD Health Statistics (Database).” https://doi.org/10.1787/data-00542-en,
Accessed: April 18, 2022.
Organisation for Economic Co-operation and Development (OECD). 2022c. “Health
Status, OECD Health Statistics (Database).” https://doi.org/10.1787/data-00540-en, Accessed:
April 18, 2022.
Organisation for Economic Co-operation and Development (OECD). 2022d. “Labour
Force Statistics by Sex and Age, OECD Employment and Labour Market Statistics (Database).”https://doi.org/10.1787/data-00309-en, Accessed: April 22, 2022.

48

Page, Marianne, Jessamyn Schaller, and David Simon. 2019. “The Effects of Aggregate andGender-Specific Labor Demand Shocks on Child Health.” Journal of Human Resources 54 (1):37–78. 10.3368/jhr.54.1.0716.8045R.
Pei, Zhuan, Jörn-Steffen Pischke, and Hannes Schwandt. 2019. “Poorly Measured Confounders Are More Useful on the Left than on the Right.” Journal of Business & Economic
Statistics 37 (2): 205–216. 10.1080/07350015.2018.1462710.
Persson, Petra, and Maya Rossin-Slater. 2018. “Family Ruptures, Stress, and the Mental
Health of the Next Generation.” American Economic Review 108 (4-5): 1214–1252. 10.1257/aer.
20141406.
Rossin, Maya. 2011. “The Effects of Maternity Leave on Children’s Birth and Infant Health
Outcomes in the United States.” Journal of Health Economics 30 (2): 221–239. 10.1016/j.jhealeco.
2011.01.005.
Ruhm, Christopher J. 2000. “Are Recessions Good for Your Health?” The Quarterly Journal of
Economics 115 (2): 617–650. 10.1162/003355300554872.
Ruhm, Christopher J., and William E. Black. 2002. “Does Drinking Really Decrease in Bad
Times?” Journal of Health Economics 21 (4): 659–678. 10.1016/S0167-6296(02)00033-4.
Saigal, Saroj, David Feeny, Peter Rosenbaum, William Furlong, Elizabeth Burrows,
and Barbara Stoskopf. 1996. “Self-Perceived Health Status and Health-Related Quality of Lifeof Extremely Low-Birth-Weight Infants at Adolescence.” JAMA 276 (6): 453–459. 10.1001/jama.
1996.03540060029031.
Schaller, Jessamyn. 2016. “Booms, Busts, and Fertility Testing the Becker Model Using GenderSpecific Labor Demand.” Journal of Human Resources 51 (1): 1–29. 10.3368/jhr.51.1.1.
Schaller, Jessamyn, and Ann Huff Stevens. 2015. “Short-run Effects of Job Loss on Health
Conditions, Health Insurance, and Health Care Utilization.” Journal of Health Economics 43
190–203. 10.1016/j.jhealeco.2015.07.003.
Schaller, Jessamyn, and Mariana Zerpa. 2019. “Short-Run Effects of Parental Job Loss onChild Health.” American Journal of Health Economics 5 (1): 8–41. 10.1162/ajhe_a_00106.

49

Schulkind, Lisa, and Teny Maghakian Shapiro. 2014. “What a Difference a Day Makes: Quantifying the Effects of Birth Timing Manipulation on Infant Health.” Journal of Health Economics
33 139–158. 10.1016/j.jhealeco.2013.11.003.
Sullivan, Daniel, and Till von Wachter. 2009. “Job Displacement and Mortality: An Analysis
Using Administrative Data.” The Quarterly Journal of Economics 124 (3): 1265–1306. 10.1162/
qjec.2009.124.3.1265.
Suzumori, Nobuhiro, Takeshi Ebara, Taro Matsuki et al. 2020. “Effects of Long Working Hours and Shift Work during Pregnancy on Obstetric and Perinatal Outcomes: A Large
Prospective Cohort Study—Japan Environment and Children’s Study.” Birth 47 (1): 67–79.
10.1111/birt.12463.
Takemoto, Yo, Erika Ota, Daisuke Yoneoka, Rintaro Mori, and Satoru Takeda. 2016.
“Japanese Secular Trends in Birthweight and the Prevalence of Low Birthweight Infants during
the Last Three Decades: A Population-based Study.” Scientific Reports 6 (1): 31396. 10.1038/
srep31396.
Tokui, Joji, and Tatsuji Makino. 2022. “R-JIP Database 2021 No Suikeihoho to Bunsekikekka(The Esimation Method and Analytical Results of the R-JIP Database 2021).” RIETI Discussion
Paper Series 22-J-007, (in Japanese).
Tokui, Joji, Tatsuji Makino, Kyoji Fukao et al. 2013. “Compilation of the Regional-Level
Japan Industrial Productivity Database (R-JIP).” RIETI Discussion Paper Series 13-J-037, (inJapanese).
van den Berg, Gerard J., Alexander Paul, and Steffen Reinhold. 2020. “Economic Conditions and the Health of Newborns: Evidence from Comprehensive Register Data.” Labour Economics 63 101795. 10.1016/j.labeco.2020.101795.
Yamaguchi, Shintaro, Yukiko Asai, and Ryo Kambayashi. 2018. “How Does Early Childcare
Enrollment Affect Children, Parents, and Their Interactions?” Labour Economics 55 56–71.
10.1016/j.labeco.2018.08.006.
Yokoyama, Izumi, Kazuhito Higa, and Daiji Kawaguchi. 2021. “Employment Adjustmentsof Regular and Non-Regular Workers to Exogenous Shocks: Evidence from Exchange-Rate Fluctuation.” ILR Review 74 (2): 470–510. 10.1177/0019793919887961.

50Appendices (for Online Publication)
International Comparisons

0

.1

.2

.3

.4

A

ITA

USA

DEU

CAN

GBR

JPN

Figure A.1: Proportion of cesarean sections in live births
Notes: This figure displays the number of live births delivered by cesarean section per all live births in 2013.
Source: OECD (2022b).

51

% 90
SWE
CAN
80

GBR
USA
JPN

70

ITA

60

50
2000

2005

2010

2015
Year

Figure A.2: Female labor force participation rate
Notes: This figure displays the labor force participation rate of women aged 20–44.
Source: OECD (2022d).

52

%

2000

2009

SWE
CAN
GBR
USA
JPN
ITA

100

80

60

40

20

49

45
-

44

40
-

39

34

35
-

29

30
-

25
-

24

20
-

19

49

15
-

44

45
-

39

40
-

34

35
-

30
-

29

24

25
-

20
-

15
-

19

0

Age

Figure A.3: Female labor force participation rate by age cohort in 2000 and 2009
Notes: This figure displays the labor force participation rate of women by age cohort.
Source: OECD (2022d).

53

% 85
SWE
CAN
80

JPN
ITA

75

GBR

70

65
2000

2005

2010

2015
Year

Figure A.4: Proportion of female full-time workers
Notes: This figure displays the proportion of female full-time workers among female full- and part-time workers aged
20–44, defined based on the common definition of 30-usual weekly hours of work in the main job.
Source: OECD (2022d).

54

20
USA

Deaths per 100,000 live births

CAN
15

GBR
JPN
ITA

10

SWE

5

0
2000

2005

2010

2015
Year

Figure A.5: Maternal mortality rates
Notes: This figure illustrates the maternal mortality rates calculated as the number of maternal deaths with allcauses per 100,000 live births.
Source: OECD (2022c).

55

5
USA
CAN

Deaths per 1,000 live births

4

GBR
ITA

3

SWE
JPN

2

1

0
2000

2005

2010

2015
Year

Figure A.6: Neonatal mortality rates
Notes: This figure displays the neonatal mortality rates calculated as the number of deaths of children under 28 daysof age per 1,000 live births. The number of deaths counted has no minimum threshold of gestation period or birthweight.
Source: OECD (2022c).

56

% 10
JPN
USA

8

ITA
GBR

6

CAN
SWE

4

2

0
2000

2005

2010

2015

Year

Figure A.7: Low birth weight rates
Notes: This figure displays the proportion of low birth weight infants calculated as the number of live births lessthan 2,500 grams divided by the total number of live births.
Source: OECD (2022c).

57

B

Descriptive Statistics
Table B.1: Summary statistics
Mean

SD

Observations

Child birth outcomes
Birth weight (in grams)<2500 grams<1500 grams
Birth length (in cm)
Gestational age (in weeks)<37 weeks<32 weeks
Fetal growth (grams/weeks)
Small for gestational age

3037.3793
0.07720.005149.0004
39.32900.04280.005277.10200.0825416.6259
0.26690.07122.25781.54360.20240.07199.51690.2752

83185
83185
83185
83034
83185
83185
83185
83185
83185

Child health outcomes
Hospializations for illness
at age 1.5
at age 2.5
at age 3.5
at age 4.5
at ages 1.5–4.5

0.11690.0856
0.05850.05150.23470.32130.27980.23470.22110.4238

73935
71820
67919
64669
64669

Child language and emotional outcomes
Language development
Aggression
Inattention and Hyperactivity

0.00000.0000-0.0000

1.00001.00001.0000

72770
66876
65329

Predicted employment growth rates
OverallFemaleMale-0.0139-0.0129-0.01450.00440.00330.0064

83185
83185
83185
(continued)

58

Table B1: Summary statistics (continued)

Birth monthJanuaryMay
July

0.2722 0.4451 83185
0.4523 0.4977 83185
0.2755 0.4468 83185

Child’s characteristics
Girl
Mother’s first childbirth0.4827 0.4997 83185
0.4840 0.4997 83185

Mother’s age at childbirth
19 years or younger
20–24 years
25–29 years
30–34 years
35–39 years
40 years or older

0.0113 0.1058 83185
0.1085 0.3110 83185
0.3428 0.4746 83185
0.3585 0.4796 83185
0.1562 0.3630 83185
0.0227 0.1489 83185

Mother’s employment status 1 year before childbirth
Not work
Self-employed or Misc.
Part-time
Full-time

0.4165 0.4930 83185
0.0524 0.2228 83185
0.1903 0.3926 83185
0.3408 0.4740 83185

Mother’s education
Junior high school (Lower secondary)High school (Upper secondary)
Vocational
2-year college
University or higher
Misc. or missing

0.04680.30690.16530.20580.17400.10110.2112
0.46120.37150.40430.37910.3015

83185
83185
83185
83185
83185
83185

Father’s education
Junior high school (Lower secondary)High school (Upper secondary)
Vocational
2-year college
University or higher
Misc. or missing

0.06680.31900.12040.02890.3512
0.11380.2496
0.46610.32540.16760.47730.3175

83185
83185
83185
83185
83185
83185

59

60

.05

.04

.03

.02

.01

01apr2000

01may2000

01jun2000

January 2001

01jul2000

01oct2000

01nov2000

01dec2000

01jan2001

01feb2001

01jul2009

Figure B.1: Density of conception dates

01aug2000 01sep2000

July 2001

01aug200901oct2009

May 2010

01sep2009

Notes: This figure displays density of conception dates for the regression sample. The data are binned in intervals of 3 days.

01mar2000

001nov200901dec2009Predicted Employment Growth Rates-.06

Actual Overall Employment Growth Rates
-.04
-.02
0
.02
.04

.06

C

-.03

-.02

-.01
0
.01
.02
Predicted Overall Employment Growth Rates.03

Figure C.1: Scatter plots of the actual and predicted overall employment growth rates
Notes: This figure plots the actual overall employment growth rates and the predicted overall employment growth
rates from 2000 to 2018 at the prefecture level.

61

.1
0

.02

.04

.06

.08

Female (mean = -1.2946, s.d. = 0.3324)
Male (mean = -1.4463, s.d. = 0.6438)

-3

-2

-1

0

Figure C.2: Density of the predicted female and male employment growth rates (in %)Notes: This figure displays the density of the predicted female and male employment growth rates for the regression
sample. The data are in percentage units and binned in intervals of 0.05.

62Female

.15

.1

.05

0

0

0

0

Male

.1

.05
Male

Chemical and Allied Products

Manufacturing.05

Agriculture

.1

.1

.15

0

0

.05
Male

Iron and Steel, Metal Products

Manufacturing

.005
Male

Forestry

.1

.01

0

.05
Male

・

Transport, Information
and Communications

.05

0

.02
Male

.04

.06

.1

0

0

0

.05
Male

.1

.2

.2

.05

.15

Medical, Health Care and Welfare

Services.1
Male

Retail Trade

.1
Male

Machinery, Equipment

Manufacturing

Male

0

0

Male

.002

.02
Male

Misc.

.04

Manufacturing.001

Mining.06.003

Manufacturing

0

0
Male

.04

.1
Male

Construction

.02

.2

.06

Food, Beverages, Tobacco and Feed

0

0

.02
Male

.04

Education

Services

.05
Male

.06

.1

Eating and Drinking Places

0

0

.1
Male

Misc.

Services

Male

Finance and Insurance

.2

.05

.02

0

Figure C.3: Scatter plots of the share of industry employment for women and men

Female.04

Notes: This figure displays the base-period share of female and male employment in an industry in each prefecture by industry category.

Male

Business

Services

.05
Male

0

Fisheries.050Services

Living-related and Personal

0

Wholesale Trade

Female.05.05

Female.05

Female

0

.1

0

.1

.01

Female.005

0Female.05

0.1

Female.05

Female

0

.1

.05
0
.2
Female.1FemaleFemale
.04
.02
0

.06

.02
.1
Female.05000FemaleFemale
.1
0

.06

.2
0
.15
.1

0
.2

Female.1Female

.003
.002
.001
0
Female.04

Female

0

.06

.06
.04
.02
0
Female
.1
.05

0

.2

.1
Female.05
0.02
Female.01
Female

0
.03
.02
.01
0
.1
Female
.05
0

63

.05
Male

.1

0

0

0

Male

.02

.05
Male

Government Services.01

Real Estate

.01
Male

.1

.03

.02

Electricity, Gas, Heat Supply
and Water

0

Manufacturing
Textile Mill Products

D

Rotemberg Weights
Table D.1: Industries with the top five Rotemberg weights

Industry

Rotemberg

Share

EffectSE

0.25680.23320.18230.1521
0.09830.18190.16520.12920.10780.0696-47.1986 ***-95.7519 ***-70.8376 ***-79.6114 ***-62.3343 ***(12.8988)(20.8538)(17.2540)(17.9464)(15.1793)

0.33580.25460.16980.16640.07890.2520 -226.9838**
0.1910 -73.5662 **0.1274 -61.2954 **0.1248 -70.2298 **0.0592 -74.7830 **(82.1989)(24.2156)(19.6094)(24.0810)(24.5753)

0.31080.18800.13300.11900.1172

0.21830.1321
0.09340.08360.0823(21.1103)(19.1147)(18.3221)(19.8814)(19.0534)

Panel A: Overall
Wholesale Trade
Medical, Health Care and Welfare Services
Manufacturing: Textile Mills Products
Agriculture
Manufacturing: Miscellaneous
Panel B: Female
Medical, Health Care and Welfare Services
Manufacturing: Textile Mills Products
Wholesale Trade
Agriculture
Finance and Insurance
Panel C: Male
Wholesale Trade
Agriculture
Construction
Manufacturing: Textile Mills Products
Manufacturing: Miscellaneous

6.1827-15.7422-35.8255-5.3707-5.1732

Notes: This table reports the top five industries with the highest Rotemberg weights for the overall (panel A), female(panel B), and male (panel C) predicted employment growth rates. Rotemberg indicates the Rotemberg weights.
Contribution indicates the proportion of the Rotemberg weights in the sum of all positive weights, calculated basedon the sample for column (1) of Table 1. The computation routine is based on bartik_weight.ado provided byGoldsmith-Pinkham et al. (2020). The outcome variable is birth weight, the endogenous variable is the observed
predicted employment growth rate, and the instrumental variables are a set of the employment share of the industry
with the annual growth rate of the industry as weights. By construction, all Rotemberg weights sum to one. The
Effect column reports the estimates of the effect of the reconstructed predicted employment growth rate in which
we drop the industry of each row and re-normalize the sum of the 23 industries’ share of employment to be 1. SE
indicates the standard errors clustered at the prefecture level. The effects of the reconstructed predicted employment
growth rates are estimated using the specifications of equations (3) and (4). Significance at 0.1%, 1%, and 5% levels
are indicated by ***, **, and *, respectively.

64

E

Aggregate Analysis of Fertility and Maternal Age

The tables in this Appendix present the results of an analysis using prefecture-level aggregate data.
The definition of the outcome variables is as follows. The pregnancy rate is the number of reported
pregnancies per 1,000 women aged 20–44. The childbirth rate is the number of live births per 1,000
population. The stillbirth rate is the number of stillbirths of 22 or more gestational weeks per 1,000
total births. The perinatal mortality rate is the number of stillbirths of 22 or more gestational weeks
and infants who die within 7 days of birth divided by 1,000 total births. The neonatal mortality
rate is the number of neonates who die within 28 days per 1,000 live births. The infant mortality
rate is the number of infants who die within a year of birth per 1,000 live births.
The data source of the variables are as follows. The female population is from the Population
Census and Population Estimates of the Statistics Bureau. The number of pregnancies is fromthe Report on Regional Public Health Services and Health Promotion Services of the MHLW. The
number of live births, stillbirths, and neonatal and infant deaths, average age of mothers, numberof first-time mothers, average birth weight of single-birth infants, and the number of single-birthinfants born weighing less than 2,500 grams are from the National Vital Statistics.

65

66

-0.4676(0.4037)✓0.364157.2613893

0.3012(0.5306)-0.7691(0.6980)-0.3628(0.3860)

0.65288.2559893-0.0336(0.0575)-0.1058(0.1090)(4)✓0.85968.2559893-0.0430(0.0613)-0.0715(0.1066)-0.1168∗(0.0554)Birth-0.1370∗(0.0582)(3)

0.0662
3.4985893-0.1986(0.1560)

0.3749∗(0.1802)0.1165(0.1403)✓

0.06603.4985893-0.2002(0.1582)

0.3811∗(0.1792)

0.1187(0.1351)(5)(6)
Stillbirth0.04224.4130893-0.2861(0.1783)

0.4427∗(0.2013)

0.0919(0.1522)✓0.04614.4130893-0.2812(0.1788)0.4248∗(0.1941)0.0797(0.1464)(7)(8)
Perinatal mortality

0.12902.1732893-0.2836(0.1916)0.2414(0.1744)-0.0723(0.1496)✓0.18472.1732893-0.2686(0.1970)0.1863(0.1649)-0.1055(0.1477)(9)(10)
Neonatal mortality

0.41404.6747893-0.3182(0.3082)

0.1465(0.2925)-0.1957(0.2205)✓

0.53624.6747893-0.2935(0.3166)

0.0559(0.2764)-0.2494(0.2154)(11)(12)
Infant mortality

Notes: Standard errors clustered at the prefecture level are in parentheses. The columns of estimates in each panel are from separate regressions. Overall indicatesthe predicted overall employment growth rate. Female (Male) indicates the predicted female (male) employment growth rate. The pregnancy rate is the number ofreported pregnancies per 1,000 women aged 20–44. The birth rate is the number of live births per 1,000 population. The stillbirth rate is the number of stillbirths of
22 or more weeks gestation per 1,000 total births (live births and stillbirths of 22 or more gestation). The perinatal mortality rate is the number of stillbirths of 22
or more weeks gestation and the number of neonates dying before reaching 7 days of age per 1,000 total births (live births and stillbirths of 22 or more gestation).
The neonatal mortality rate is the number of neonates dying before reaching 28 days of age per 1,000 live births. The infant mortality rate is the number of deathsof children less than one year of age per 1,000 live births. These variables are in percentage. The coefficient estimates are for a one-percentage-point increase in thepredicted employment growth rate. P-value is based on the Wald test of equality between the female and male coefficients. Mean indicates the sample mean level ofthe outcome variable. Prefecture and year fixed effects are also controlled in the regression model but not reported. Controls indicate that the number of hospitals
offering obstetrics and gynecology services per women aged 20–44 are included in the regression model. Significance at 0.1%, 1%, and 5% levels are indicated by***, **, and *, respectively.

Controls

0.260857.2613893

0.3487(0.5253)

Male

p-value (βFemale = βMale )MeanObservations-0.9434(0.6787)

FemalePanel B: Gender-specific effects

Overall

Panel A: Overall effects(1)(2)
Pregnancy

Table E.1: Prefecture-level analysis on predicted employment growth rates and pregnancy, childbirth, and mortality rates

67-0.0076(0.0193)0.0289(0.0237)

0.222830.6655893

Male

p-value (βFemale = βMale )MeanObservations✓

0.161030.6655893

0.0310(0.0234)-0.0487(0.0359)-0.0120(0.0197)

0.11260.1182893

0.0029(0.0019)-0.0048(0.0031)-0.0009(0.0014)✓0.11040.1182893

0.0028(0.0019)-0.0046(0.0029)-0.0008(0.0012)✓0.01050.6679893-0.0108∗∗(0.0035)-0.0108∗∗(0.0036)0.01250.6679893

0.0158∗(0.0069)

0.0020(0.0035)

0.0156∗(0.0071)

0.0020(0.0036)0.00980.1985893

0.0071∗∗(0.0022)-0.0106∗(0.0047)-0.0017(0.0025)✓

0.00930.1985893

0.0072∗∗(0.0022)-0.0109∗(0.0047)-0.0019(0.0024)

(4)
(5)
(6)
(7)
(8)
Proportion of births by mothers
aged 20–24
aged 25–34
aged 35–44(3)0.615729.2068893

0.0048(0.0385)-0.0390(0.0512)-0.0308(0.0228)✓0.534629.2068893

0.0071(0.0386)-0.0472(0.0508)-0.0355(0.0219)(9)(10)
Average age offirst-time mothers

0.09740.4590893

0.0032(0.0017)-0.0046(0.0031)-0.0004(0.0017)✓0.08370.4590893

0.0032(0.0016)-0.0046(0.0030)-0.0004(0.0017)(11)(12)
Proportion of
first-time mothers

Notes: Standard errors clustered at the prefecture level are in parentheses. The columns of estimates in each panel are from separate regressions. Overall indicatesthe predicted overall employment growth rate. Female (Male) indicates the predicted female (male) employment growth rate. The coefficient estimates are for a
one-percentage-point increase in the predicted employment growth rate. Mean indicates the sample mean level of the outcome variable. P-value is based on the Wald
test of equality between the female and male coefficients. Prefecture and year fixed effects are also controlled in the regression model but not reported. Controls
indicate that the number of hospitals offering obstetrics and gynecology services per women aged 20–44 are included in the regression model. Significance at 0.1%,
1%, and 5% levels are indicated by ***, **, and *, respectively.

Controls-0.0409(0.0359)

FemalePanel B: Gender-specific effects

Overall

Panel A: Overall effects(1)(2)
Average age of
all mothers

Table E.2: Prefecture-level analysis on predicted employment growth rates and maternal age

Table E.3: Prefecture-level analysis of predicted employment growth rates and birth weight(1)(2)
Average birth weight(in grams)(3)(4)
Birth weight
below 2500 grams

Panel A: Overall effects
Overall-2.7232(1.4407)-2.0754(1.2492)

0.1264∗(0.0559)

0.1062∗(0.0501)

Panel B: Gender-specific effects
Female-6.4204∗(2.5899)-5.3448∗(2.3373)

0.2421∗(0.1057)

0.2090∗(0.1014)Male

3.0789(1.8905)

2.7858(1.8283)-0.0778(0.0936)-0.0687(0.0919)

p-value (βFemale = βMale )MeanObservations

0.03103028.0963
893

0.04723028.0963
893

0.10288.0676893

0.1452
8.0676893

Controls✓✓

Notes: Standard errors clustered at the prefecture level are in parentheses. The columns of estimates in each panel arefrom separate regressions. Overall indicates the predicted overall employment growth rate. Female (Male) indicatesthe predicted female (male) employment growth rate. The coefficient estimates are for a one-percentage-point increasein the predicted employment growth rate. Mean indicates the sample mean level of the outcome variable. Prefecture
and year fixed effects are also controlled in the regression model but not reported. Controls indicate that the numberof hospitals offering obstetrics and gynecology services per women aged 20–44 are included in the regression model.
Significance at 0.1%, 1%, and 5% levels are indicated by ***, **, and *, respectively.

68

F

Heterogeneity: Later Outcomes69

Table F.1: Predicted employment growth rates and later outcomes by mothers’ age(1)
Hospitalization
Coef.
AME

(2)
Language
Coef.
AME

(3)
Aggression
Coef.
AME

(4)
Inattention & Hyperactivity
Coef.
AME

Panel A: Overall effects
Overall:
Age < 25 (reference)

0.05340.0534-0.1170 -0.1170 -0.1025 -0.1025 -0.0083(0.0353) (0.0353) (0.1049) (0.1049) (0.0635) (0.0635) (0.0907)-0.0083(0.0907)25 ≤ Age < 35

-0.0434(0.0347)0.01000.16620.04920.0685-0.03400.0379(0.0175) (0.1090) (0.0251) (0.0747) (0.0456) (0.0937)

0.0296(0.0280)35 ≤ Age-0.0486(0.0456)

0.00480.0715-0.0455
0.1010-0.0016 -0.0303(0.0276) (0.1435) (0.0749) (0.0921) (0.0892) (0.0835)-0.0387(0.0553)

Age < 25 (reference)

0.06800.06800.03990.0399-0.0844 -0.0844 -0.0833(0.0770) (0.0770) (0.1268) (0.1268) (0.1201) (0.1201) (0.1390)-0.0833(0.1390)25 ≤ Age < 35

-0.0640(0.0716)0.0040-0.02640.01350.0169-0.0675
0.0875(0.0208) (0.1251) (0.0363) (0.1350) (0.0453) (0.1384)

0.0041(0.0671)

35 ≤ Age-0.0929(0.0775)-0.0248 -0.0065
0.0334-0.0443 -0.1287
0.0061(0.0347) (0.1518) (0.0754) (0.1481) (0.0885) (0.1666)-0.0773(0.0862)

Age < 25 (reference)-0.0060(0.0828)-0.0060 -0.1645 -0.1645 -0.0166 -0.0166
0.0766(0.0828) (0.1252) (0.1252) (0.1119) (0.1119) (0.1374)

0.0766(0.1374)

25 ≤ Age < 35

0.01620.01020.19940.03490.04700.0304-0.0534(0.0780) (0.0212) (0.1192) (0.0339) (0.1286) (0.0625) (0.1240)

0.0232(0.0626)

35 ≤ Age

0.03130.02530.0792-0.0853
0.14240.1258-0.0397(0.0800) (0.0290) (0.1752) (0.0966) (0.1451) (0.1053) (0.1447)
0.23470.00000.00000.0000
64669
72770
66876
65329
0.63640.36940.76270.86960.74280.33680.37730.45760.15400.0369(0.0750)

Panel B: Gender-specific effects
Female:

Male:

Mean
Observations
p-value (Age < 25)
p-value (25 ≤ Age < 35)
p-value (35 ≤ Age)0.54470.88090.4529Notes: Standard errors clustered at the prefecture level are in parentheses. The columns of estimates in each panel arefrom separate regressions. Hospitalization indicates the hospitalization for illness at ages 1.5–4.5. Overall indicatesthe predicted overall employment growth rate. Female (Male) indicates the predicted female (male) employment
growth rate. Coef. indicates the coefficient estimates based on the regression model including the interactions of
the predicted employment growth rate and mothers’ age group dummies with the mothers aged 24 or younger atchildbirth group as the reference group. AME indicates the average marginal effect evaluated at each mother’s
age group. The estimates are for a one-percentage-point increase in the predicted employment growth rate. Mean
indicates the sample mean level of the outcome variable. P-value is based on the Wald test of equality between thefemale and male marginal effects evaluated at each mothers’ age group. Child’s gender, mother’s first childbirth,mother’s employment status before pregnancy, parental education, birth month cohort dummies, a set of dummies
for prefecture of residence, and their interactions with the mothers’ age group dummies, and the mothers’ age group
dummies are also controlled in the regression model but not reported. Significance at 0.1%, 1%, and 5% levels areindicated by ***, **, and *, respectively.

70

Table F.2: Predicted employment growth rates and later outcomes by mothers’ educational attainment(1)
Hospitalization
Coef.
AME

(2)
Language
Coef.
AME

(3)
Aggression
Coef.
AME

(4)
Inattention & Hyperactivity
Coef.
AME

Panel A: Overall effects
Overall:Jr. high school

0.0172(0.0515)0.0142 -0.3785∗∗∗(0.0485) (0.1071)-0.3111∗∗(0.1148)0.0537-0.0021(0.1543) (0.1769)-0.0208(0.1155)-0.0096(0.1014)High school (reference)-0.0031(0.0194)-0.0031(0.0194)

0.0674(0.0530)

0.0674(0.0530)-0.0558 -0.0558(0.0555) (0.0555)

0.0112(0.0378)0.0112(0.0378)

Vocational school

0.0389(0.0236)0.0359(0.0259)-0.0598(0.0834)

0.0076(0.0539)0.0495-0.0063(0.0618) (0.0590)-0.0192(0.0753)-0.0080(0.0594)2-year college

0.0030(0.0303)-0.0001(0.0225)-0.0228(0.0948)0.0446(0.0560)

0.0051-0.0508(0.0563) (0.0367)

0.0361(0.0591)0.0472(0.0552)

University or more

0.03870.0356(0.0311) (0.0274)-0.0801(0.0747)-0.0127(0.0524)0.1088∗(0.0491)-0.0343(0.0609)-0.0231(0.0498)

0.0529(0.0697)(continued)71

(continued)(1)
Hospitalization
Coef.
AME

(2)
Language
Coef.
AME

(3)
Aggression
Coef.
AME

(4)
Inattention & Hyperactivity
Coef.
AME

Panel B: Gender-specific effects
Female:Jr. high school

0.04650.0562-0.2281 -0.3375∗ 0.1475(0.0848) (0.0747) (0.1493) (0.1472) (0.1756)

0.02890.1783(0.1973) (0.1844)

0.0645(0.1494)

High school (reference)

0.00970.0097-0.1094 -0.1094 -0.1185(0.0325) (0.0325) (0.0667) (0.0667) (0.0607)-0.1185 -0.1138(0.0607) (0.0700)-0.1138(0.0700)

Vocational school-0.0148 -0.0051 0.1685∗
0.05910.0878(0.0496) (0.0350) (0.0821) (0.0650) (0.0786)-0.03080.0593(0.0707) (0.1230)-0.0546(0.1403)2-year college-0.0205 -0.0108 0.2595∗ 0.1501∗
0.0278(0.0393) (0.0344) (0.1005) (0.0642) (0.0850)-0.09070.1399(0.0540) (0.1048)

0.0261(0.0706)

University or more

-0.0149 -0.0052
0.12870.01930.1284(0.0637) (0.0510) (0.0991) (0.0775) (0.0766)

0.00990.2179∗(0.0554) (0.1004)

0.1041(0.0846)

Jr. high school-0.0333 -0.0431 -0.1799 -0.0139 -0.0729(0.0887) (0.0792) (0.1258) (0.1280) (0.1877)-0.0143 -0.1425(0.2441) (0.1556)-0.0322(0.1314)High school (reference)-0.0098 -0.0098 0.1660∗ 0.1660∗
0.0586(0.0313) (0.0313) (0.0754) (0.0754) (0.0805)

0.05860.1103(0.0805) (0.0658)

0.1103(0.0658)

Vocational school

0.0597(0.0443)0.0499 -0.2277∗ -0.0617(0.0401) (0.0900) (0.0674)-0.0351
0.0235(0.0760) (0.0758)-0.0658(0.1031)0.0445(0.1248)

2-year college

0.0227(0.0335)

0.0129-0.2592 -0.0932(0.0367) (0.1317) (0.0802)-0.02420.0343(0.0880) (0.0481)-0.0846(0.0959)

0.0257(0.0685)

University or more

0.05140.0417-0.1911 -0.0251 -0.0088(0.0442) (0.0397) (0.1072) (0.0728) (0.0669)
0.23670.00000.0000
62764
69493
64781
0.49930.20160.74970.04550.43680.31920.72470.08400.58810.75250.0498 -0.2252∗(0.0836) (0.1023)
0.000063295
0.91540.19150.6856
0.19560.7512-0.1150(0.0727)

Male:

Mean
Observations
p-value (Jr. high)p-value (High)p-value (Vocational)
p-value (College)p-value (University)

0.7122
0.09320.70300.99780.1442

Notes: Standard errors clustered at the prefecture level are in parentheses. The columns of estimates in each panel are from separate
regressions. Hospitalization indicates the hospitalization for illness at ages 1.5–4.5. Overall indicates the predicted overall employment
growth rate. Female (Male) indicates the predicted female (male) employment growth rate. Coef. indicates the coefficient estimates basedon the regression model including the interactions of the predicted employment growth rate and mothers’ education level dummies withmothers who graduated from high school as the reference group. AME indicates the average marginal effect evaluated at each mothers’
education level. The estimates are for a one-percentage-point increase in the predicted employment growth rate. Mean indicates the
sample mean level of the outcome variable. P-value is based on the Wald test of equality between the female and male marginal effects
evaluated at each mothers’ education level group. Child’s gender, mother’s first childbirth, mother’s age, mother’s employment status
before pregnancy, parental education, birth month cohort dummies, a set of dummies for prefecture of residence, and their interactions
with the mothers’ education level dummies, and the mothers’ education level dummies are also controlled in the regression model but
not reported. Significance at 0.1%, 1%, and 5% levels are indicated by ***, **, and *, respectively.

72

G

Robustness Check

73

74

-70.3677∗∗∗(16.7356)

Control variables:Child’s genderFirst childbirth
Mother’s age
Parental education
Mother’s employment

✓
✓
✓
✓
✓

-12.5814(20.0805)
0.15873037.3793
83185

Male

p-value (βFemale = βMale )MeanObservations-66.6655∗∗(22.9492)

FemalePanel B: Gender-specific effects

Overall

Panel A: Overall effects(1)-15.1672(20.5727)
0.23423037.3793
83185-60.5110∗∗(22.2733)-67.0356∗∗∗(16.8330)(2)✓-15.2362(20.8492)
0.23093037.3793
83185-62.1862∗∗(22.9591)-68.5400∗∗∗(16.8919)

(3)

✓
✓

-13.3390(20.4577)
0.17813037.3793
83185-65.7344∗∗(23.0957)-70.0950∗∗∗(16.8826)(4)(a) Birth weight (in grams)✓✓✓-12.3671(20.1263)
0.15553037.3793
83185-67.1077∗∗(23.0485)-70.5402∗∗∗(16.7701)(5)

Table G.1: Sensitivity to the model specifications✓✓✓-13.8443(20.4414)
0.18733037.3793
83185-65.0406∗∗(23.0357)-69.9572∗∗∗(16.8627)

(6)

✓
✓
✓
✓

-12.6590(20.1192)
0.16103037.3793
83185-66.5557∗∗(22.9730)-70.3235∗∗∗(16.7390)

(7)

✓

✓
✓
✓

-12.3321(20.0974)
0.15433037.3793
83185-67.1674∗∗(23.0363)-70.5767∗∗∗(16.7614)

(8)

75

-0.4450∗∗∗(0.0937)-0.0105(0.1381)0.048039.329083185-0.5140∗∗∗(0.1365)-0.4521∗∗∗(0.0959)(2)✓-0.0103(0.1367)
0.046839.329083185-0.5101∗∗∗(0.1348)-0.4485∗∗∗(0.0954)(3)✓✓-0.0209(0.1400)
0.064739.329083185-0.4902∗∗∗(0.1340)-0.4398∗∗∗(0.0953)

(4)

✓
✓
✓

-0.0148(0.1354)0.048539.329083185-0.5023∗∗∗(0.1317)-0.4460∗∗∗(0.0938)

(5)

✓

✓
✓

-0.0209(0.1400)
0.065039.329083185-0.4898∗∗∗(0.1340)-0.4393∗∗∗(0.0952)

(6)

✓
✓
✓
✓

-0.0144(0.1356)
0.049139.329083185-0.5015∗∗∗(0.1319)-0.4449∗∗∗(0.0937)

(7)

✓

✓
✓
✓

-0.0161(0.1360)
0.050639.329083185-0.5011∗∗∗(0.1319)-0.4460∗∗∗(0.0939)(8)

prefecture of residence are also controlled in columns (3)–(8). Significance at 0.1%, 1%, and 5% levels are indicated by ***, **, and *, respectively.

(5). Column (8) adds a set of dummies for the mother’s employment status one year before childbirth to the model of column (5). Birth month cohort dummies and a set of dummies for theof dummies for the mother’s and father’s education level to the model of column (4). Column (7) adds a set of dummies for the mother’s and father’s education level to the model of column

a dummy for the mother’s first childbirth to the model of column (3). Column (5) adds a set of dummies for the mother’s age at childbirth to the model of column (4). Column (6) adds a set

Column (2) controls birth month cohort dummies and a set of dummies for the prefecture of residence. Column (3) adds a dummy for child gender to the model of column (2). Column (4) adds

individual controls that are included in the baseline specification based on the equations (3) and (4). Column (1) replicates the baseline result from columns (1) or (2) of Table 1 for comparison.

coefficient estimates are for a one-percentage-point increase in the predicted employment growth rate. Mean indicates the sample mean level of the outcome variable. Control variables indicate

G.1a and gestational age in Table G.1b. Overall indicates the predicted overall employment growth rate. Female (Male) indicates the predicted female (male) employment growth rate. The

Notes: Standard errors clustered at the prefecture level are in parentheses. The columns of estimates in each panel are from separate regressions. The outcome variable is birth weight in Table

Control variables:Child’s genderFirst childbirth
Mother’s age
Parental education
Mother’s employment
✓
✓
✓
✓
✓

-0.0155(0.1361)0.050639.3290
83185

Male

p-value (βFemale = βMale )MeanObservations-0.5006∗∗∗(0.1320)

FemalePanel B: Gender-specific effects

Overall

Panel A: Overall effects(1)(b) Gestational age (in weeks)

76-70.3677∗∗∗(16.7356)-12.5814(20.0805)
0.15873037.3793
83185

Male

✓

-11.7102(20.3038)
0.13403037.3793
83185-67.3072∗∗(21.7854)-70.7817∗∗∗(17.3370)✓✓-11.3076(20.4978)0.13633037.3797
82758-67.3430∗∗(22.1239)-70.7661∗∗∗(17.6351)-0.0155(0.1361)0.050639.329083185-0.5006∗∗∗(0.1320)-0.4450∗∗∗(0.0937)✓-0.0165(0.1405)
0.050339.329083185-0.5000∗∗∗(0.1277)-0.4515∗∗∗(0.0972)✓✓-0.0068(0.1424)0.047139.329582758-0.5073∗∗∗(0.1304)-0.4505∗∗∗(0.0977)

(4)
(5)
(6)
Gestational age (in weeks)

Notes: Standard errors clustered at the prefecture level are in parentheses. The columns of estimates in each panel are from separate regressions. Overall indicatesthe predicted overall employment growth rate. Female (Male) indicates the predicted female (male) employment growth rate. The coefficient estimates are for a
one-percentage-point increase in the predicted employment growth rate. Columns (1) and (4) show the baseline estimates. Mean indicates the sample mean level ofthe outcome variable. Child’s gender, mother’s first childbirth, mother’s age, mother’s employment status before pregnancy, parental education, birth month cohort
dummies, and a set of dummies for prefecture of residence are also controlled in the regression model but not reported. Prefecture controls include the number ofclinics and hospitals offering obstetrics and gynecology services per women aged 20–44. Municipality controls include the share of nuclear households and the ratioof the accredited childcare center capacity to the number of children aged 0–5. Significance at 0.1%, 1%, and 5% levels are indicated by ***, **, and *, respectively.

Prefecture controls
Municipality controls

p-value (βFemale = βMale )MeanObservations-66.6655∗∗(22.9492)

FemalePanel B: Gender-specific effects

Overall

Panel A: Overall effects

(1)
(2)
(3)
Birth weight (in grams)

Table G.2: Predicted employment growth rates and birth outcomes with controls for prefecture and municipality variables

Table G.3: Balancing test: Predicted employment growth rates and controls for prefecture andmunicipality variables(1)
Ob-gyn hospitals(2)Childcare(3)
Nuclear families

0.0569(0.0783)-0.0009(0.0107)0.0395(0.0244)

Panel A: Overall effects
Overall

LHS joint balancing test (p-value):
RHS joint balancing test (p-value):

0.42600.4783

Panel B: Gender-specific effects
Female-0.0961(0.1275)0.0003(0.0261)-0.0613(0.0418)Male

0.1300(0.1403)-0.0045(0.0298)

0.0851∗(0.0334)

LHS joint balancing test (p-value):
FemaleMale
RHS joint balancing test (p-value):
FemaleMale

0.39770.00810.36770.1515

Notes: Standard errors clustered at the prefecture level are in parentheses. The columns of estimates in eachpanel are from separate regressions for the LHS (left-hand-side) balancing test, where the dependent variable isthe number of hospitals that offer obstetrics and gynecology services per number of women aged 20–44 (ob-gyn
hospitals), the ratio of accredited childcare center capacity to the number of children aged 0–5 (childcare), and shareof nuclear family households (nuclear families). Overall indicates the predicted overall employment growth rate.
Female (Male) indicates the predicted female (male) employment growth rate. The coefficient estimates are for a
one-percentage-point increase in the predicted employment growth rate. Child’s gender, mother’s first childbirth,mother’s age, mother’s employment status before pregnancy, parental education, birth month cohort dummies, and aset of dummies for prefecture of residence are also controlled in the regression model but not reported. The p-valuesof the LHS joint balancing test are based on the F-test for the joint significance of the coefficients of the predictedemployment growth rates, stacking the equations of the three columns. The p-values of the RHS (right-hand-side)
joint balancing test are based on the F-test for the joint significance of the coefficients of the ob-gyn hospitals,
childcare, and nuclear families variables, where the dependent variable is the predicted overall, female, and male
employment growth rate. Significance at 0.1%, 1%, and 5% levels are indicated by ***, **, and *, respectively.

77

Table G.4: Predicted employment growth rates and sample attrition(1)
at age 1.5(2)
at age 2.5(3)
at age 3.5(4)
at age 4.5

0.0094(0.0064)-0.0025(0.0057)-0.0032(0.0061)

Panel A: Overall effects
Overall0.0012(0.0014)

Panel B: Gender-specific effects
Female-0.0008(0.0017)0.0060(0.0079)-0.0028(0.0085)-0.0045(0.0100)

Male

0.0020(0.0015)0.3257
0.096283185

0.0043(0.0094)
0.91610.121683185

0.0000(0.0074)
0.85130.169583185

0.0010(0.0085)0.74930.207183185

p-value (βFemale = βMale )MeanObservations

Notes: Standard errors clustered at the prefecture level are in parentheses. The columns of estimates in each panel
are from separate regressions. Overall indicates the predicted overall employment growth rate. Female (Male)
indicates the predicted female (male) employment growth rate. The coefficient estimates are for a one-percentagepoint increase in the predicted employment growth rate. The outcome variable is a dummy variable that equals one
if the child is dropped from the base regression sample until the follow-up survey. Mean indicates the sample meanlevel of the outcome variable. Child’s gender, mother’s first childbirth, mother’s age, mother’s employment status
before pregnancy, parental education, birth month cohort dummies, and a set of dummies for prefecture of residence
are also controlled in the regression model but not reported. Significance at 0.1%, 1%, and 5% levels are indicatedby ***, **, and *, respectively.

78

Table G.5: Predicted employment growth rates and relocation between one year prior to birth andwhen the child was 6 months old

Panel A: Overall effects
Overall

0.0439(0.0487)

Panel B: Gender-specific effects
Female-0.0070(0.0427)

Male

0.0441(0.0472)0.51060.110244989

p-value (βFemale = βMale )MeanObservations

Notes: Standard errors clustered at the prefecture level are in parentheses. The sample only includes cohorts born in
2001. Overall indicates the predicted overall employment growth rate. Female (Male) indicates the predicted female(male) employment growth rate. The coefficient estimates are for a one-percentage-point increase in the predictedemployment growth rate. The outcome variable is a dummy variable that equals one if the household relocatedbecause of the pregnancy or childbirth during the period from one year prior to childbirth to the first survey when
the child was 6 months old. Mean indicates the sample mean level of the outcome variable. Child’s gender, mother’s
first childbirth, mother’s age, mother’s employment status before pregnancy, parental education, birth month cohort
dummies, and a set of dummies for prefecture of residence are also controlled in the regression model but not reported.
Significance at 0.1%, 1%, and 5% levels are indicated by ***, **, and *, respectively.

79

80

-62.3154∗∗∗(15.5008)-11.3986(18.5427)
0.18183037.3793
83185

Male

-12.5814(20.0805)
0.15873037.3793
83185-66.6655∗∗(22.9492)-70.3677∗∗∗(16.7356)-7.4687(22.5033)
0.08853037.3793
83185-81.0547∗∗(25.4540)-78.1740∗∗∗(17.9427)-0.0038(0.1253)0.049339.329083185-0.4544∗∗∗(0.1214)-0.3929∗∗∗(0.0846)-0.0155(0.1361)0.050639.329083185-0.5006∗∗∗(0.1320)-0.4450∗∗∗(0.0937)

0.0123(0.1528)0.032939.329083185-0.5882∗∗∗(0.1491)-0.4957∗∗∗(0.1024)

(4)
(5)
(6)
20 weeks 22 weeks (base) 24 weeks
Gestational age (in weeks)

Notes: The pregnancy period is defined as the first 22 weeks of pregnancy in the main analyses, see Section 3.3. Standard errors clustered at the prefecture levelare in parentheses. The columns of estimates in each panel are from separate regressions. Overall indicates the predicted overall employment growth rate. Female(Male) indicates the predicted female (male) employment growth rate. The coefficient estimates are for a one-percentage-point increase in the predicted employment
growth rate. Mean indicates the sample mean level of the outcome variable. Child’s gender, mother’s first childbirth, mother’s age, mother’s employment status
before pregnancy, parental education, birth month cohort dummies, and a set of dummies for prefecture of residence are also controlled in the regression model but
not reported. Significance at 0.1%, 1%, and 5% levels are indicated by ***, **, and *, respectively.

p-value (βFemale = βMale )MeanObservations-58.8161∗∗(21.2969)

FemalePanel B: Gender-specific effects

Overall

Panel A: Overall effects

Def. of the pregnancy period:

(1)
(2)
(3)
20 weeks 22 weeks (base) 24 weeks
Birth weight (in grams)

Table G.6: Sensitivity to the definition of the pregnancy period in the predicted employment growth rates