Persistence of Natural Disasters on Children’s Health:

Evidence from the Great Kanto Earthquake of 1923

Kota Ogasawara∗

March 6, 2021

Abstract

This study uses a catastrophic earthquake in 1923 to analyze the long-term ef-fects of a one-off disaster on children’s health. I find that fetal exposure to Japan’sGreat Kanto Earthquake had stunting effects on girls in the devastated area. Dis-aster relief spending helped remediate stunting among boys by late primary schoolages, whereas it did not ameliorate girls’ stunting, suggesting a biased remediationmechanism before birth and compensating investment after birth. While the ma-ternal mental stress via strong vibrations played a role in the adverse health effects,the maternal nutritional stress via physical disruption also enhanced those effects.

Keywords: child growth; child stunting; Great Kanto Earthquake; long-run effect;

natural disaster;

JEL Codes: I18; I19; N35;

∗Department of Industrial Engineering, School of Engineering, Tokyo Institute of Technology, 2-12-1,Ookayama, Meguro-ku, Tokyo 152-8552, Japan (E-mail: ogasawara.k.ab@m.titech.ac.jp).I would also like to thank Dan Bogart, Neil Cummins, Fabian Drixler, Bernard Harris, Janet Hunter,Volha Lazuka, Kazushige Matsuda, Yukitoshi Matsushita, Stephen Morgan, Eric Schneider, AnthonyWray, and the participants at the CSG seminar (Chiba), EHES conference (Tubingen), Stunting confer-ence (LSE), AED seminar (Kyoto), KIER workshop (Kyoto), WEHC (MIT), EHS conference (Belfast),and Tokyo Tech seminar for their helpful comments on the paper. The work was supported by the TokyoInstitute of Technology (grant in 2015; 2019). I wish to thank Motoya Taura and Tatsuki Inoue for theiroutstanding research assistance. There are no conflicts of interest to declare. All errors are my own.

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1 Introduction

Natural disasters have significant and lasting impacts on economies. In Japan, the Great

Kanto Earthquake of 1923, an extremely huge quake with a moment magnitude scale of

7.9, was an unprecedented crisis, leaving 156,000 people killed, injured, or missing. The

massive earthquake of 1923 had positive long-term impacts on industries through the so-

called creative destruction mechanism, such as the technological upgrade of machinery and

the selection into efficient firms (Okazaki, Okubo, and Strobl 2019).1 However, a growing

body of the literature indicates that fetal exposure to disasters can impede normal human

development and thus lead to negative consequences on later-life health and socioeconomic

outcomes (Vellore 2018; Rosales-Rueda and Triyana 2019; Karbownik and Wray 2019).2

The weight of evidence from this literature implies that little is known about the adverse

long-term developmental effects of Japan’s 1923 earthquake.

To bridge this gap in the body of knowledge, the present study investigates the im-

pacts of fetal exposure to the Great Kanto Earthquake on children’s health. For this

purpose, I select Chiba prefecture, where the physical disruption was primarily caused

by strong vibrations, and establish a series of datasets on children’s health using a set

of physical examination records.3 Specifically, I construct school-level multidimensional

panel datasets on the height and weight both of primary school children aged 6–11 years

born between 1914 and 1929. Since my datasets have three-dimensional panel structures,

namely a data cube with school-year-age dimensions, I can control for the time-varying

unobserved factors of each school in contrast to cross-sectional and two-dimensional panel

data analyses (Balazsi, Matyas, and Wansbeek 2018). To better identify the impacts of

the earthquake, I further exploit the geospatial variation in the physical devastation and

then interact the variation with the children who potentially experienced the earthquake

in utero.

I find that fetal exposure to the Great Kanto Earthquake negatively affected the

growth of children. Primary school girls aged 9–11 exposed in utero in the area extremely

1See also Pereira (2009) for the positive long-term economic impacts of the Lisbon Earthquake of1755.

2See Almond and Currie (2011), Currie and Vogl (2013), and Prinz et al. (2018) for comprehensivereviews in the related literature.

3While Tokyo and Kanagawa prefectures were also greatly impacted by the earthquake hit, Chibaprefecture is considered to be the most appropriate research area. Tokyo experienced vibrations and anenormous fire at the same time, whereas Kanagawa suffered both vibrations and an enormous tsunami(Hunter 2014). These incidents complicate the identification because separating their effects is diffi-cult. Moreover, physical examination records for both affected prefectures are unavailable. However,fortunately, a set of records for Chiba prefecture are scattered but still remaining.

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affected by the earthquake with the maximum seismic intensity scale were roughly one

cm shorter than those in surrounding cohorts. Given this result, I attempt to distinguish

different pathways of adverse health effects (i.e., mental and nutritional stress). First, I

test whether disaster relief supplies can mitigate the adverse health effects on children by

nourishing a pathway in the physically devastated area in which the strongest vibration

brought about unavoidable mental stress. Second, I employ the regional heterogeneity in

seismic activity and market disruption to test which stress is more likely to be plausible

in the area with little physical damage. From these exercises, I find suggestive evidence

that while mental stress plays an important role in the adverse health effects, nutritional

stress could be another pathway that enhances the adverse health effects on the exposed

children in the physically devastated area.

This study contributes to the literature in the following three ways. First, it adds

evidence on the long-term health impacts of fetal earthquake exposure. Previous studies

have documented the adverse effects of fetal earthquake exposure on pregnancy outcomes

such as low birth weight (Glynn et al. 2001; Torche 2011; Kim, Carruthers, and Harris

2017). However, limited evidence is available on the lasting effects of fetal earthquake ex-

posure on later-life health.4 This study bridges this gap by examining the adverse effects

of fetal earthquake exposure on the development and diseases of the juvenile population.

It is the first to show the long-term human costs of the earthquake of 1923, given that

previous studies have predominantly analyzed its impacts on industries and market func-

tions (Imaizumi, Ito, and Okazaki 2016; Hunter and Ogasawara 2019; Okazaki, Okubo,

and Strobl 2019).

Second, by providing evidence on the ameliorating effects of disaster relief on child

stunting, this study contributes to the literature on the optimal timing of child invest-

ment (Heckman 2012). Empirical evidence on how the complementarities in parental

investments respond to early-life shocks is at best mixed (Almond and Mazumder 2013).

Within the limited volume of evidence, the recent study by Vellore (2018) finds that New

Deal-related spending can ameliorate the adverse long-term effects of Dust Bowl exposure

on human capital formation. The consistent evidence found in the present study implies

that prenatal adverse effects can be mitigated by the end of primary school. My results

also shed light on the heterogeneity in remediation effects with respect to types of disaster

4One exception is Caruso and Miller (2015), who test the link between fetal exposure to the AncashEarthquake of 1970 and self-reported disability status in adulthood, showing little evidence on such alink in modern Peru.

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relief. However, I find a clear gender imbalance in the compensating effects of the disaster

relief, which may be driven by the prenatal remediation mechanism and postnatal-biased

resource allocation regulated by the institutional context.

Third, this study enriches our understanding of the mechanisms behind the adverse

health effects of fetal health shocks. While a growing body of evidence documents the

long-term effects of early-life health shocks on human capital, identifying the pathways

of those effects has remained a challenge (Currie and Vogl 2013). I provide suggestive

evidence not only on the role of the maternal mental stress underlying the adverse health

effects of fetal earthquake exposure, but also on the enhancing role of maternal nutritional

stress in the physically disrupted area. This is in line with the call for future research

by Prinz et al. (2018), who suggest that mental health issues are becoming increasingly

important for understanding labor market outcomes in adulthood.

2 Background

2.1 Great Kanto Earthquake

The Great Kanto Earthquake hit the southern area of the Kanto district including the

seven prefectures shown in Figure 1: Tokyo, Kanagawa, Chiba, Saitama, Shizuoka, Ya-

manashi, and Ibaraki. Although both the physical and the human damage were con-

centrated on Tokyo and Kanagawa prefectures, Chiba prefecture was also considerably

affected not only by the main shock but also by the aftershocks.5 Roughly one in ten

households in Chiba were damaged by the earthquake.6 Figure 2a shows the spatial distri-

bution of the percentage distribution of affected households.7 The affected municipalities

were concentrated on the western coast (uchi-bo), especially in the counties of Awa and

Kimitsu, because this area includes the seismic fault plane named the Kamogawa-teichi

5While Tokyo was the largest prefecture with approximately four million inhabitants in 1922, Kana-gawa and Chiba were middle-sized prefectures with 1.36 and 1.34 million inhabitants, respectively (Statis-tics Bureau of the Cabinet 1924b, p. 347). The shares of the agricultural, industrial, and commercialsectors in Chiba at that time were 70%, 10%, and 10%, respectively (Statistics Bureau of the Cabinet1924a, pp. 26–27).

6At that time, most houses in Japan were typically built with wood. However, even the concretebuildings of the municipal offices were destroyed in highly impacted areas (Division of Social Affairs,Chiba Prefecture 1933a, pp. 88–96). Tokyo City Office (1925 p. 161) reported that 86.5% of householdsin Kanagawa were affected and almost half of those in Tokyo. This included being burnt, destroyed, orwater-damaged by the earthquake and subsequent fires and tsunami.

7The spatial distribution of victims is similar but shows more regionally smaller distribution patterns.Thus, I prefer to use the percentage distribution of affected households in my empirical analysis (seeFigure A.2 in Online Appendix A).

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fault zone (Takemura 2003).8 Overall, 53% of municipalities suffered physical disruption.

In affected areas, roads and railways were largely destroyed and the production of

newspapers, postal services, and telegraph and telephone services also completely stopped.

Topographical changes that made traveling difficult were observed in many municipalities.

Although railway services had largely restarted by the end of September 1923, passengers

still had to walk between heavily damaged sections.9 Further, roughly nine in ten post

offices including telegraph and telephone stations were damaged.10 Wells, the main water

source for people at that time, became contaminated by sand and salt, causing difficulties

in obtaining drinking water. Physical disruption was also observed in the agricultural and

industrial sectors.11 Because approximately 14,000 hectares of arable land were affected,

including damage to reservoirs, embankments, and irrigation equipment as well as the

upheaval or depression of ground surfaces, agricultural households struggled to sell their

products because trade partners were mainly in Kanagawa and Tokyo. The industrial

sector was affected not only by the physical damage but also by the crisis in the financial

system, as approximately 77% of banks in Tokyo were burnt down by the fire.

2.2 Possible Paths

Earthquakes lead to adverse health shocks to a fetus through two main paths. The first

path is maternal mental stress in pregnant women (Hibino et al. 2009). Prenatal maternal

stress, especially posttraumatic stress disorder, increases the risk of adverse pregnancy

outcomes (Yonkers et al. 2014). Glynn et al. (2001), for instance, investigate 40 pregnant

women who experienced an earthquake of a magnitude of 6.8 that occurred in California in

1994 during pregnancy or shortly after, finding that maternal stress experienced in early

pregnancy is associated with a shorter gestational period. Torche (2011) also investigates

the influence of acute stress exposure to the large Chilean earthquake of 2005 on birth

weight using birth registry data. She shows that maternal stress results in a decline

in birth weight and an increase in the proportion of low birth weight deliveries. Kim,

Carruthers, and Harris (2017) also provide evidence that psychological maternal stress

8Indeed, pictures taken in the aftermath of the earthquake show the unimaginable scale of devastationin Awa (Online Appendix B).

9Railway transportation was well developed by the early 20th century in Japan (Tang 2014; 2017).10See Division of Social Affairs, Chiba Prefecture (1933a, p. 151) and Chiba Prefecture (1925c, vol. 5,

p. 152).11Descriptions of the agricultural sectors are taken from the Division of Social Affairs, Chiba Prefecture

(1933a, pp. 126–130, p. 133, 136, pp. 141–143) and Chiba Prefecture (1924, p. 11, 103). The number ofcollapsed banks was obtained from the Bank of Japan (1986, p. 48).

5

from exposure to the Northridge earthquake of 1994 increased the likelihood of low birth

weight. Such low birth weight due to reduced gestational age and intrauterine growth

restriction can have adverse effects on the development of children (Victora et al. 2008;

Datta Gupta, Deding, and Lausten 2013).12

The second path is maternal nutritional deprivation, which can be driven by several

channels (Barker 1992; 1998). The first direct channel is either external injury or a

shortage of food because of shocks to transportation (Division of Social Affairs, Chiba

Prefecture 1933b). The second channel is indirect pecuniary shocks (Banerjee et al. 2010;

Bozzoli and Quintana-Domeque 2014). Declines in household income were caused not

only by shocks to agricultural production and/or sales but also by the destruction of

banks as described. Despite the short-run impacts, the third indirect channel is the

increased price of food and daily commodities that could decrease the relative wealth

of households (Hunter and Ogasawara 2019).13 The fourth channel is declining sanitary

conditions. Infection can significantly reduce fetus nutrition via inflammation, high fever,

lost appetite, vomiting, and compilations (Metzger et al. 1982; Tomkins et al. 1994).14

3 Empirical Analysis

3.1 Data

To estimate the impacts of fetal earthquake exposure on children’s health, I first assem-

bled school-level datasets of height and weight, given that child stunting is the best overall

indicator of the well-being of children (de Onis and Branca 2016). Height is the main

measurement of the overall health outcome of children, as this measure reflects accu-

mulated nutritional status and is associated with cognitive ability and long-term adult

health and socioeconomic outcomes (Fogel 1994; Case and Paxson 2008; Currie and Vogl

2013).15 Weight is used as a secondary measurement of children’s health, even though it

12See Stein et al. (2014) for a comprehensive summary of the association between parental disordersand offspring outcomes. See also Au Yeung et al. (2016) for a discussion on the association between lowbirth weight and the risk of disease.

13Although the government intervened to stabilize markets by issuing the Emergency Requisitioningand Antiprofiteering Ordinance within a week of the earthquake to deal with panic buying and rapidlyrising commodities prices, the retail prices of food and other necessities still increased after the earthquake(Division of Social Affairs, Chiba Prefecture, 1933a, pp. 293–296).

14A fifth possible channel would be the work burden caused by the reconstruction of cities and towns.However, this kind of heavy labor was more likely to be conducted by men than women.

15I do not use the height-for-age z-score of modern WHO standards to control for the age effects becausethe pubertal growth spurt of children in the early 20th century could have occurred at slightly older agesthan in modern healthy children. This would lead to a distorted height-for-age profile for my sampled

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might be sensitive to instantaneous effects.16 In light of these biological outcomes, Japan

is a good context for my study because of its comprehensive school physical examination

records. Since these physical examinations had to be conducted in April of each year

for all schools under the Gakuseiseito shintaikensa kitei (Official Regulations for School

Physical Examination) from 1897, most schoolchildren undertook one.

I collected an available set of the annual statistical reports named Gakko seitojido

shintaikensa tokei (Statistics of Physical Examinations for Students; SPES) compiled

by Chiba prefecture to construct primary school-year-age-level multidimensional panel

datasets. The primary school dataset includes children aged 6–11 measured between 1925

and 1935 (i.e., born between 1914 and 1929).17 My dataset on the primary school students

from all 434 schools covers approximately 95% of the juvenile population aged 6–11 in

Chiba at that time. Thus, the target population in the analyses using the dataset can be

regarded as almost the entire child population in the same age range in the prefecture.

I systematically divide my primary school samples into two groups to investigate the

heterogeneous stunting effects; 6–8 (early primary) and 9–11 (late primary). Panel A of

Table 1 reports the summary statistics on the biological outcomes.18 The average height

and weight of both exposed and unexposed cohorts seem to be very similar, implying the

importance of observing potential regional heterogeneity in the impacts of the earthquake.

In fact, Figure B.1 in Online Appendix B, which illustrates average height and weight by

cohort, gender, and area, with 95% confidence intervals, suggests that the stunting effects

may be concentrated in the physically devastated areas.

I used data on the physical damage from the official report for the Great Kanto Earth-

quake named Taisho shinsaishi (History of the Taisho Earthquake; HTE) published by

the Social Welfare Bureau of the Cabinet in 1926. Since the HTE surveyed all damaged

children (see Schneider 2019).16Moreover, the body mass index is not used herein because it is designed to capture the degree of

adult obesity. Child growth disturbs the measurement of obesity at different ages. This issue makes itdifficult to identify whether the observed child stunting comes from fetal shocks or just the timing ofchild growth. See Schneider (2019) for a detailed explanation of this mechanism.

17Since the academic term runs from April to March in Japan, children in the first grade of primaryschool are aged 6 and 7 and those in the final grade are aged 11 and 12. To ensure the consistency ofthe data structure, however, I refer to the range of ages in my primary students sample as 6–11 yearsthroughout this paper. The systematic data on the secondary schools are unfortunately unavailable. TheSPES indicates that the number of secondary schools in the physically devastated areas was indeed toosmall to conduct any statistical inferences.

18I excluded some observations because of outliers, missing values, and consolidations of the municipal-ities. Consequently, 99% of my school-year panels are balanced. I confirm that trimming my sample tokeep a balanced panel structure produces results virtually identical to those presented in the main resultsin Table 3 (not reported). This means that the particular subsets of my school panels do not disturb mymain results.

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households in Chiba by November 15, 1923, it provides a complete picture of the degree of

physical disruption at the municipality level (Division of Social Affairs, Chiba Prefecture,

1933a, pp. 412–420).19 I calculated the physical disruption rate (PDR) as number of

affected (collapsed and semi-collapsed) households per 100 households for each munici-

pality, as shown in Figure 2a.20 However, the distribution of the PDR is highly skewed, as

in the case of the effect of the Chernobyl radioactive fallout in Sweden (Almond, Edlund,

and Palme 2009). Hence, to relax the functional form assumption, I begin by systemati-

cally dividing municipalities into three categories according to the Japan Meteorological

Agency seismic intensity scale (JMA-SIS): JMA-SIS of 5, 6, and 7.21 Table 2 summarizes

the classification. The mean PDRs reported in this table suggest that the physical dis-

ruption was concentrated in the JMA-SIS7 area with a mean rate of approximately 80%.

A large number of affected municipalities were in the Awa and Kimitsu counties. While

the municipalities in the SIS6 area were moderately affected (approximately 23%), the

municipalities in the JMA-SIS5 area were hardly physically damaged by the earthquake

(approximately 0.4%). This distribution of damage suggests that the negative effects of

the earthquake were obvious in the JMA-SIS7 area. Figure 2b illustrates these regions.

3.2 Identification Strategy

I use the quasi-experimental estimation strategy that matches the exogenous shocks due to

fetal earthquake exposure with the corresponding birth cohorts. If the physical disruption

enhanced the earthquake stress placed on the fetus, a higher degree of disruption may

be associated with stronger stunting effects. I thus include the product terms between

19Since more than two months had passed after the earthquake, which occurred on September 1,the number of affected households cumulatively documented in the report should be accurate. Oneobservation (Nakagawa village in Kimitsu county) showed an unrealistically large value in the number ofaffected households. This was considered to be a misprint and was replaced with the figure based on thesurvey conducted by the prefecture on October 3, 1923 reported in the Division of Social Affairs, ChibaPrefecture (1933b, pp. 2–3).

20The number of households in each municipality is based on the 1920 Population Census. Accordingto the 1925 Population Census, the number of households increased from 259,026 in 1920 to 270,796 in1925 (Statistics Bureau of the Cabinet 1926). Assuming this increasing trend, total households around1923 might have increased by roughly 5,900, accounting for 17 households more per municipality from1920. This figure accounts for a 2% increase per municipality based on 1920 values. Thus, the data fromthe 1920 Population Census are considered to be plausible to use as a proxy for the number of householdsin municipalities around the time of the earthquake hitting.

21To calculate the JMA-SIS, I first predicted the collapse rate using the prediction equation proposedby Moroi and Takemura (1999) based on data on collapsed wooden houses from the Hyogoken-NanbuEarthquake of 1995: Collapse Rate = −1.61 + 0.46 × PDR + 0.0051 × PDR2, where the PDR is the physicaldisruption rate defined earlier. Then, the predicted collapse rate is converted into seven categories of theJMA-SIS based on Takemura and Moroi (2002): the rate greater than 30% is defined as 7 (shindo nana),greater than 1% and less than 30% is defined as 6 (shindo roku), greater than 0.1% and less than 1% isdefined as upper 5 (shindo go).

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the 1923 and 1924 birth cohort indicator variables and indicator variable for the JMA-

SIS7 area to allow the effects of earthquake stress to vary across areas.22 My baseline

specification is given as follows:

ysta = α+ β0I (YOB=1923)ta + β1I (YOB=1923)ta × SIS7 gs + β2I (YOB=1924)ta

+β3I (YOB=1924)ta × SIS7 gs + x′gcsta

γ + µsa + λt + esta(1)

where s indexes schools, t indexes survey years, and a indexes ages.23 The variable y is

either height or weight, I(·) is an indicator variable that equals one for children born in

1923 or 1924, SIS7 is an indicator variable for the JMA-SIS7 area, x is a vector of the

county birth-year-level control variables, µ is a school age-specific fixed effect, λ is a year

fixed effect, and e is a random error term.24

Since the earthquake hit on September 1, 1923, the physical and human loss mostly

occurred in that month.25 This timing suggests that children born between September

1923 and July 1924 experienced the earthquake in utero. This means that the 1923 birth

cohort includes those children impacted by the earthquake in utero because children born

in 1923 in my sample were individuals born between April 1923 and March 1924 as the

academic year starts in April and ends in March in Japan.26 In this vein, the 1924 birth

cohort also includes children born between April and July 1924 who experienced the

earthquake at early gestation. Thus, I also included the 1924 birth cohort variables in

equation (1). Since the proportion of affected children in the 1924 birth cohort was smaller

than that in the 1923 birth cohort, however, I expect that the adverse health effects should

be clearer in the 1923 birth cohort than in the 1924 birth cohort. Considering the feature

of exposure, I expect the estimated coefficients on the affected cohort indicators I(·) and

their area interaction terms I (·) × SIS7 to be negative.27

22I confirm that the effects of fetal earthquake exposure in the JMA-SIS6 area are identical to those inthe JMA-SIS5 area in the statistical sense. Online Appendix C.2.1 presents and discusses these results.

23The group variables gs and gcs indicate municipalities and counties, respectively. Although I use thenotation ta to indicates the year of birth for the sake of simplicity, it is identical to t− a. For instance,the county birth-year-level controls can be expressed as xgc

staor xgc

s,t−a.24Although earthquake severity is measured at the municipal level, I use data on the biological outcomes

at the school-year-age level to control for the unobserved factors of each school using the bilateral-specificfixed effect and exploit the full information around the JMA-SIS7 area. Despite this design, I find thatthe main results in Table 3 are mostly unchanged if I use the municipal-year-age-level datasets (notreported).

25While the share of extrinsic deaths in total deaths is normally around 1%, this share dramaticallyincreased to 27% in that month (Online Appendix A).

26The average pregnancy term was nine to ten months at that time in Japan (Tokyo City Office, 1926).Hereafter, I simply refer to the 1923 birth cohort instead of the 1923 academic year birth cohort forsimplicity.

27Drixler (2016) highlights the imprecision of birth data in prewar Japan. In light of this study, one

9

The timing and spatial distribution of the physical disruption must be exogenous to

improve the identification. While the timing of the earthquake was obviously unexpected,

the distribution of the disruption used was more likely to be dominated by the distribution

of the fault plane found after the earthquake, suggesting that the distribution is plausibly

exogenous.28 I also allow the unobserved factor of school to vary over age by introducing

a fixed effect (µ) in each school-age cell in all the specifications (Davis 2002).29 The ad-

vantage of this approach is that the school-specific unobservable preference on improving

children’s health can be controlled for.30 I further use the county birth-year-level vari-

ables at baseline, namely the fetal death rate,31 rice yield, coverage of doctors, and school

enrollment rate of the parental generation.32 These variables are included to control for

the potential mortality selection effects, wealth levels related to agricultural productivity,

accessibility to medical care around birth, and changes in parental characteristics across

cohorts, respectively (Bozzoli, Deaton, and Quintana-Domeque 2009; Brown and Thomas

2018).

To assess the potential spatial and school cohort-specific correlations, I intend to clus-

ter the standard errors at the 13-county level. For the statistical inference, however, I

adopt the wild cluster bootstrap-t method to deal with the issue of the small number of

must be careful about the potential age heaping in April. For example, children born in March 1923 mighthave been disadvantaged in their development, perhaps during their early primary school ages, comparedwith those born in April 1922. This implies that parents might have had an incentive to register theirchildren born in March 1923 as born in April 1923. However, I confirm that there was no such systematicage heaping in April using vital statistics (Statistics Bureau of the Cabinet 1925). In addition, while the1923 birth cohort includes children born after the earthquake, I confirm that postnatal exposure had nostatistically significant stunting effects on the affected birth cohorts (see the “Robustness” section).

28I also confirm that the distribution of the physical disruption did not depend on that of soil compactionmeasured as the spectral intensity values observed in 2011, suggesting that my key measure of earthquakestress does not depend on the potential agricultural productivity that might be correlated with children’sbiological standards. The map of soil compaction is based on boring data from 50,000 monitoring pointsand thus the spectral intensity values are measured in 250-m meshes. This means that the map couldshow nearly the same distribution of soil compaction in the early 20th century, even though the data areinvestigated in 2011. See Online Appendix B for the details.

29Since I introduce the fixed effect for each school-age cell, the identification depends on the withinvariation over the measured year. This means that the increasing trends in the growth of child heightshould be similar over school-age cells. I confirm that the growth patterns of the sampled children overthe measured years are similar (Online Appendix C.1).

30In addition, my approach can efficiently control for unobservable instantaneous shocks using the yearfixed effect (λ). Another advantage of including the year fixed effect is that the unobservable trends inpotential wealth and public health can be captured.

31The fetal death rate is the number of still births per 1,000 births. Data on fetal deaths as well as livebirths between 1913–1930 are taken from the SRCP and Vital Statistics for Municipalities (Online Ap-pendix B). I linearly interpolated the missing values for 1922–1924 and 1926–1929 using the values of1921, 1925, and 1930 given that the time-series plots of the rates in Chiba prefecture at that time werenearly linear (Figure C.2a in Online Appendix C.2.5).

32The rice yield is the volume of rice yield per hectare; coverage of doctors is the number of doctorsper 100 people; and the school enrollment rate of the parental generation is the 17-year lagged primaryschool enrollment rate from the year of birth. See Online Appendix B for the summary statistics anddata sources of these control variables.

10

clusters in the cluster-robust variance estimator (Cameron et al. 2008).33 The regression

equation is estimated separately for boys and girls for each developmental stage to inves-

tigate both gender differences in the effects and the potential catch-up growth against the

shocks (Steckel and Ziebarth 2016).34 The age bins are systematically divided into 6–8

(early primary) and 9–11 (late primary) because the growth pattern of children during

primary school ages were almost linear at that time.35

3.3 Main Results

Table 3 presents the results from my baseline specification denoted in equation (1). Panel

A lists the estimates of β for height. Columns (1)–(2) present the results for the boys,

whereas columns (3)–(4) present the results for the girls. In column (1), primary school

boys aged 6–8 born in 1923 are found to be approximately 0.21 cm shorter than the

surrounding cohorts. Column (2) shows a similar but slightly smaller stunting effects on

the primary school boys aged 9–11. These results imply that the stunting effects of fetal

earthquake exposure are likely to diminish in later years of primary schooling because of

the catch-up growth during primary school age (Frankenberg et al. 2017). The estimated

coefficients on the area interaction terms are statistically insignificant in columns (1)–

(2). When I look at the results for the girls, those stunting effects are greater in their

magnitude. Column (3) indicates that the girls aged 6–8 born in 1923 are 0.23 cm and 0.53

cm shorter in the non-JMA-SIS7 and JMA-SIS7 areas, respectively. Similarly, column (4)

indicates that the girls aged 9–11 born in 1923 are 0.23 cm and 0.84 cm shorter in each

33Therefore, my method controls for the correlation and heteroskedasticity within clusters. Since aschool is nested in a county, this means that both the dependency across observations of the samebirth cohort within a school and the dependency across schools within same county are allowed in myestimation. In addition, my method can deal with potential heteroskedasticity across clusters. I preferto cluster at the county level than the municipal level because regressors grouped at the county levelare used (Moulton 1986, 1990) and the correlation within the same county over time such as the schoolcohort-specific correlation may be problematic (Bertrand, Duflo, and Mullainathan 2004).

34I herein do not prefer to use the regression specification including the interaction terms betweengender and the key control variables because such a specification postulates a stronger assumption thatthe marginal effects of the other control variables uninteracted with gender are the same between boysand girls. As for the analytical weight for the regressions, I am, unfortunately, unable to use the dataon the number of inspected children for each age and school (Deaton 1997). However, since the numberof schools should have been set to reflect the size of municipalities, the number of inspected children ineach age cell should be largely similar across schools (Ministry of Education 1973). Thus, my analyticalresults should be robust to the weighting.

35See Online Appendix C.1 for the observed growth patterns of students. Although one must be carefulto simply apply the modern growth reference to historical child growth, the height-for-age z-sores of −2in the modern WHO growth reference show similar growth curves to those children (de Onis et al. 2007),supporting that this developmental-stage age bin makes theoretical sense. Despite this, we confirm thatour main results are robust to the use of alternative definitions of the developmental-stage age bin (OnlineAppendix C.2.2).

11

area.

Panel B of Table 3 shows the estimates of the effects on weight, following the same

column layout. Although the estimates in column (1) suggest that the primary school

boys born in 1923 are approximately 0.06 kg lighter than those in the surrounding cohorts,

those listed in column (2) are no longer statistically significant. This result is consistent

with the weak stunting effects on the boys reported in Panel A. Similar to the results on

the girls’ height, the affected birth cohorts among the primary school girls are slightly

lighter than those in the surrounding cohorts. Column (3) indicates that the girls aged

6–8 born in 1923 are 0.1 kg and 0.37 kg lighter in the non-JMA-SIS7 and JMA-SIS7 areas,

respectively. Similarly, column (4) shows that the girls aged 9–11 born in 1923 are 0.13

kg and 0.43 kg lighter in each area.

Overall, these results are consistent with the literature on early-life shocks on human

growth (Mazumder et al. 2010; Rosales-Rueda and Triyana 2019). The Great Kanto

Earthquake of 1923 had negative lasting effects on girls’ growth in the damaged area with

a maximum JMA-SIS of 7. I find that, however, while the boys slightly stunted in their

early primary school ages, the stunting effects were wiped out in the late primary school

ages. I return to this gender difference in the stunting effects in greater detail. Finally,

I do not find any meaningful impacts on the 1924 birth cohorts in a statistical sense. As

discussed, while the 1924 birth cohort includes children who experienced the earthquake

in utero during the first trimester, the proportion of those affected children in this birth

cohort is small compared with that in the 1923 birth cohort. This feature of assignment

can attenuate the estimated effects on the 1924 birth cohort.

3.4 Mechanisms

Next, I extend my discussion to the pathways (i.e., maternal mental and nutritional stress)

through which fetal earthquake exposure may affect children’s health. First, I investigate

what type of disaster relief spending mitigated the adverse effects of the earthquake on

the development of children. Second, I use the market disruption due to damage to the

railway transportation network to identify the potential stress behind these adverse health

effects.

12

3.4.1 Relief Effects

Soon after the earthquake, Chiba prefecture decided to provide disaster relief for affected

counties, namely Awa, Kimitsu, Ichihara, Higashikatsushika, Chosei, and Isumi, and ap-

proximately 12% of people received relief in these counties.36 To investigate the potential

compensating effects of the disaster relief, I use statistics on the county-level relief ex-

penses by expense item in the SRCP (Chiba Prefecture 1925a). Of the relief expenditure,

66% was used to provide food, 31% was for temporary housing, and 0.3% was for med-

ical treatment.37 Panel B of Table 1 shows the summary statistics of the relief expense

variables.

To assess the relief effects, I introduce the interaction terms between the measures of

relief expenses and indicators of affected cohorts into equation 1 as follows:

ysta = κ+ δ0I (YOB=1923)ta + δ1I (YOB=1923)ta × SIS7 gs + δ2I (YOB=1923)ta × Relief gcs

+ δ3I (YOB=1924)ta + δ4I (YOB=1924)ta × SIS7 gs + δ5I (YOB=1924)ta × Relief gcs

+ x′gcsta

θ + ηsa + νt + εsta

(2)

where Relief is the county-level per capita relief expenses (total, food, shelter, or medical

treatment). While medical treatment is obviously necessary for injured people, immediate

hunger relief intervention is the principal emergency response to sustain people in a severe

situation after a crisis. Temporary shelter is also an important emergency facility for

personal safety, climate protection, security, and resistance to infectious diseases after

large-scale disasters.38 Therefore, one can expect the estimates of the parameters of

interest (δ2; δ5) to be positive when the disaster relief had the capacity to compensate for

the negative effects of the earthquake. The regression equation is estimated separately

for each gender and age bin to investigate how relief spending ameliorated the negative

health impacts of the earthquake in detail.

36The relief effort particularly focused on Awa and Kimitsu, which accounted for 85% and 11% oftotal relief expenditure, respectively. Although total relief expenditure was 490,837 yen for the impactedyear, this dropped to 2,790 yen in the following year. This means that the relief was considered to bea temporal and one-shot investment for affected counties in 1923. This feature of public spending maysupport evidence that Japan was able to quickly recover from these shocks (Hunter 2014).

37Although the remaining 5% was used for the other miscellaneous goods, I do not use this categoryfor the regression analysis because the purpose of the relief is unclear and thus it is difficult to interpretthe results.

38See Pingali, Alinovi, and Sutton (2005) and the International Federation of Red Cross and RedCrescent Societies (2013). In Yorho village, for instance, 120 out of 600 households collapsed and thusthe victims had to spend 10 days living outside (Division of Social Affairs, Chiba Prefecture 1933b,p. 748).

13

Table 4 presents the regressions for testing the effects of the disaster relief.39 Panel

A (Panel B) lists the estimates of the relief effects on height (weight). Columns (1)–(4)

present the results for total, food, shelter, and medical treatment expenses, respectively.

Overall, I find that the disaster relief had compensating effects on child stunting to a

certain degree. Column (1) of Panel A shows that while the relief ameliorates the stunting

effects on the primary school boys aged 6–8 born in 1923, it does not have such effects

on the girls’ stunting. Furthermore, column (1) of Panel B indicates that the relief also

ameliorates the weight loss of the primary school boys aged 6–8, while such effects are

not observed for the girls. These results are consistent with the finding from Table 3:

while the boys’ stunting may have been improved throughout the primary school ages,

girls’ stunting was persistent. Comparing the estimates between the boys aged 6–8 and

those aged 9–11 indicates that the relief effects are statistically significantly positive only

at early primary school ages. This difference can be explained by the fact that the growth

rate of the boys becomes higher at late primary school ages than early ages (de Onis et

al. 2007).

To investigate what type of disaster relief can ameliorate the adverse effects of the

earthquake on children, I decompose the relief expenses into three subcategories in columns

(2)–(4). While relief expenses for food, shelter, and medical treatment had positive ef-

fects on both height and weight, the estimated effects are the largest in medical treatment,

followed in order by shelter and food (41.5, 0.38, and 0.17 in the first row in Panel A).

This result seems to be reasonable given the fact that medical treatment was intensively

provided for injured people in devastated areas, whereas relief interventions for food and

shelter were provided more broadly. I next discuss the magnitudes of these relief effects

estimated on the height of the primary school boys. The estimate in column (1a) of Panel

A suggests that a one standard deviation increase in disaster relief (Table 1) might have

increased the affected primary school boys’ height by roughly 0.1 cm at ages 6–8 on aver-

age. Given that the estimated stunting effect on the early primary school boys is 0.2 cm

as discussed, this magnitude implies that disaster relief might have ameliorated roughly

half of the stunting effects.40

39The estimated coefficients on the cohort dummies and area interaction terms (δ0; δ1; δ3; δ4) in equation2 are not reported in the same table because these estimates are largely unchanged from those in Table 3.

40 Similar calculations applied to food (column 2a), shelter (column 3a), and medical treatment (column4a) indicate that the potential compensating effect is roughly 0.1 cm for each expense. These magnitudesare considered to be reasonable. Average relief expenditure on food for treated people in Chiba prefecturewas 3.8 yen, whereas average expenditure for food in peasant households in September 1926 was 7.7 yenper family member. Although this is the most conservative calculation, it suggests that the relief mighthave provided nearly half of the monthly food needed. In this calculation, the number of family members

14

The foregoing results suggest that disaster relief could mitigate child stunting during

primary school ages by remediating adverse shocks in early life. A few points on the

heterogeneity of relief effects do, however, need to be discussed.

First, while my result suggests that the disaster relief might have played a role in nour-

ishing the primary school boys to a certain extent, I find that it had little compensatory

effect on the primary school girls. I discuss this gender bias in relief effects from the timing

dimension: prenatal or postnatal remediation. One plausible explanation of prenatal re-

mediation is the growing body of the literature supporting evidence that male fetuses are

more vulnerable to ambient stress in utero than female fetuses and thus positive selection

into birth is larger for boys than girls (Kraemer 2000). Given the selection effects on the

boys, my results imply that the disaster relief might have further mitigated the stunting

effects by reducing the “scarring” effects on the boys (Valente 2015).41 Another potential

explanation of postnatal remediation is the strong son preference in the family system (ie

seido) in prewar Japan. The prewar Civil Code regarded the first-born son as the next

head of the family (koshu) who had the right to inherit properties and dictatorial power

to allocate household resources (Ramseyer 1996; Hayashi and Prescott 2008).42 Conse-

quently, a gender bias against girls in intrahousehold resource allocations, especially in

educational attainment, was widely observed in prewar Japan (Hijikata 1994).43 From

this perspective, one must be careful about the fact that the 1923 birth cohorts include

children born after the earthquake hit but before (or during) the distribution of the relief.

Since a large part of the disaster relief may have been distributed in and after October

1923, children born in September might have benefitted from the relief in their postnatal

period (Division of Social Affairs, Chiba Prefecture 1933a, p. 420). Thus, the likelihood

of adverse health effects for girls might have been high given the institutions and customs

includes both adults and infants. These data are taken from the agricultural household surveys conductedbetween September 1926 and August 1927. Although the details of the sampling method are unavailable,the number of surveyed households was 670 and those households were sampled from nine prefecturesacross the Japanese archipelago: Yamagata, Saitama, Niigata, Nagano, Aichi, Hyogo, Hiroshima, Ehime,and Fukuoka. See Statistical Bureau of the Cabinet (1929 p. 420).

41The scarring effect indicates a shift of fetal health distribution to the left. Although the improvingeffects on the scarring effects may also be applicable for female fetuses, male fetuses should be moresensitive to nourishing than girls because they are more susceptible than female fetuses. In other words,if I consider the cumulative distribution function of fetal health endowments, male fetuses are more likelyto be distributed on the lower side of the distribution function than female fetuses (see Valente 2015).

42In fact, Article 970 of the Civil Code prioritized the of wedlock boy than the legitimate girl (withinthe same degree of relationship) in inheritance if the boy is recognized by his father (Cabinet OfficialGazette Bureau 1898, pp. 148–149).

43The most extreme case of biased resource allocation is that daughters were often sold by their parents(miuri) in depression periods given the higher return of boys in the labor market (Nakamura 1994,pp. 115–116).

15

in family systems favored boys. Second, neither the immediate hunger relief and shelter

protection nor medical treatment had ameliorating effects on the 1924 birth cohorts. A

potential explanation of this result is, as discussed, the attenuation effects due to the

smaller proportion of the affected children in the 1924 birth cohort than in the 1923 birth

cohort.

My finding on disaster relief for the Great Kanto Earthquake is consistent with the

recent economic history study by Vellore (2018) that reveals that childhood remediation

through New Deal-related spending can mitigate the adverse effects of Dust Bowl exposure

on socioeconomic outcomes and disability in adulthood. Since I use county-level relief

expenses as the relief variable, the estimated effects may underestimate the actual effects

of the relief. Despite this, the present study provides suggestive evidence that remediation

effects through disaster relief can mitigate stunting effects by the end of primary school

ages. While the magnitude of relief effects suggests that medical treatment has greater

remediation effects than food and shelter, the expenses for the latter items might have

been effective in terms of coverage. However, relief effects might be biased by gender

depending on the timing of remediation.

3.4.2 Mental and Nutritional Stress

The disaster relief results suggest that the ameliorating effects of relief might stem from

reducing the risk of maternal nutritional deprivation after the crisis. In this subsection, I

test whether the effects of mental and nutritional stress are more remarkable in the area

that experienced little physical disruption and received no disaster relief.

Mental stress is caused not only by the experience of strong vibrations and a stressful

life in the aftermath of the earthquake but also the physical disruption, whereas nutri-

tional stress is concentrated in physically damaged and market disrupted areas (Harada

et al. 2015). Considering this feature, I begin my analysis by trimming the sample as fol-

lows. First, I exclude all municipalities in the JMA-SIS6 and JMA-SIS7 areas and leave

the JMA-SIS5 area alone because it is difficult to disentangle mental and nutritional stress

in those areas that experienced physical disruption. Second, I drop municipalities that

received any disaster relief to eliminate its compensating effects. The sample reduces to

222 municipalities that include exposed children who experienced strong vibrations in

utero in the limited area with a JMA-SIS of 5 but received no disaster relief.44

44The JMA-SIS ranges from 1 to 7, meaning that a JMA-SIS of 5 is still a very strong vibration.At a scale of 5, people are frightened and feel the need to hold onto something stable. See the Japan

16

For the identification, I sort the locations where suffered market disruption due to dam-

age to railway transportation networks. Regarding railway transport in Chiba prefecture,

there were considerable losses of freight arrivals on the Noda line in Higashikatsushika

county and Kururi line in Kimitsu county.45 In fact, the annual tonnages of freight ar-

rivals on both lines dropped by more than half of those in previous years (Chiba Prefecture

1924, pp. 125–126). Since the main freight items were food, wood, and fuels, this suggests

that the regional markets near both lines were more likely to have been disrupted than

other areas and thus the alimentary deficiency might have occurred after the earthquake

hit (Chiba Prefecture 1925c, pp. 138–145). While railway transport suffered physical

damage, however, marine transport was not impacted by the earthquake. Indeed, Choshi

port, a famous export port for the fish industry located at the western end of the pre-

fecture, restarted to export goods to Tokyo soon after the earthquake (Chiba Prefecture

1924, pp. 150–151).46

Exploiting these facts, I test the pathways by estimating the following regression:

ysta = ϕ+ ζ0I (YOB=1923)ta + ζ1I (YOB=1923)ta × Railgs + ζ2I (YOB=1924)ta

+ζ3I (YOB=1924)ta × Railgs + x′gcsta

φ + ιsa + τt + εsta(3)

where Rail is an indicator variable for municipalities within 10 km of any municipality

including either the Noda line or the Kururi line.47 The estimates (ζ0; ζ2) are expected

to be statistically significantly negative when maternal mental stress to vibrations were

associated with adverse health effects. In addition, the estimates of the coefficients on the

area interaction terms (ζ1; ζ3) are statistically significantly negative when maternal nu-

tritional stress due to disrupted transportation networks had adverse effects on children’s

health. The combination of the estimates provides evidence of which pathway might have

played an important role in disturbing children’s growth in the limited JMA-SIS5 area.

Meteorological Agency (website).45Both lines served several populated provincial towns and villages such as Noda town, Funabashi town,

Chiyoda village, Kisarazu town, Makuta village, Obitsu village, and Kururi town (Chiba Prefecture 1924,pp. 52–53). As described in the “Great Kanto Earthquake” section, the other lines recovered relativelysoon after the earthquake in Chiba prefecture (Ministry of Railways 1927).

46Moreover, marine transport was not a major transportation method in Chiba at that time. In fact,the annual tonnages handled in the marine transport was much smaller than freight by railway transportin Chiba (Chiba Prefecture 1924, pp. 150–151).

47I set the radius to cover neighboring municipalities: 10 km is roughly the median of the 5th percentileof the distances from the origins (i.e., municipalities including railways) to the other municipalities. Mostmunicipalities within the radius are far from other railway networks, making it difficult for them to accessother regional markets. Given the mean distance to the nearest municipality is approximately 3 km, Icheck the sensitivity of my results by changing the diameter to 3 km range and confirm that my resultsremain similar (Online Appendix C.2.2).

17

The regression equation is estimated separately for each gender and age bin.

Panel A of Table 5 presents the results for height.48 Columns (1)–(2) list the estimates

for the boys, whereas columns (3)–(4) list the estimates for the girls. While the estimated

coefficients on the 1923 birth cohort dummies in columns (1), (3), and (4) are statistically

significantly negative, those on the interaction terms are not statistically significant. In

the same columns in Panel B, I find similar effects for the girls’ weight. While these results

may provide evidence of the role of mental stress, my estimates offer less clear arguments

on the stunting effects of nutritional stress.

One might worry about the proxy measure of market disruption used herein. A mar-

ket survey reported that the retail prices of daily food such as rice, wheat, miso, and

soy source increased after the earthquake in the regional markets of Chiba, which might

have temporarily reduced the nutritional intake of mothers.49 However, despite such mar-

ket responses, rural households might have benefitted from conservable vegetables for

self-consumption to compensate for the temporary market disruption.50 If this kind of

unobservable self-protection behavior by households played a role, my estimates would

understate the adverse effects of the disruption. While I acknowledge these potential is-

sues, the result obtained herein is consistent with the growing body of the medical and

economic literature that shows the maternal mental stress caused by the earthquake is as-

sociated with the adverse health outcomes at birth (Yonkers et al. 2014; Kim, Carruthers,

and Harris 2017).

3.5 Robustness

Thus far, I have documented the adverse effects of fetal earthquake exposure on children’s

health. Before discussing the findings of the present study, I conduct a set of exercises

to test the sensitivity of the main results in terms of the following concerns: sorting

issues owing to internal migrations; selection issues because of absenteeism, mortality,

48The estimates of ζ2 and ζ3 are statistically insignificant in all the regressions. To show the resultsconcisely, I do not report these estimates in Table 5.

49Systematic statistics on retail prices around the time of the earthquake are unavailable (Hunter andOgasawara 2019). Although my data on retail prices are also insufficient for use in the regression analyses,the retail prices in some regional markets on September 20 were reported (Division of Social Affairs, ChibaPrefecture 1933a, pp. 293–296). For instance, in the Matsudo market in Higashikatsushika, the maximumprice of rice involuntarily increased from 39 to 45 sen per sho (1.8 liter) after the earthquake hit. Anotherexample shows that in the Kururi market in Kimitsu county, the maximum price of rice increased from36 to 41 sen per sho and the maximum price of miso rose from 80 to 85 sen per kan (3.75 kg).

50Moreover, in the initial stage after the earthquake, there might have been a greater supply of foodin rural areas because of the damaged transport links in Tokyo and Kanagawa prefectures, as describedin the “Great Kanto Earthquake” section.

18

and fertility; the potential impacts of other historical events; and postnatal exposure to

the earthquake. I provide evidence that these concerns do not confound my findings.

Sorting Issues

The nature of the school-level data makes it difficult to capture the sorting of children

due to internal migration across municipalities. If the children exposed the earthquake

in utero in the devastated area moved to other areas after birth, the estimation would

suffer from attenuation bias. In this sense, my estimates of the adverse health effects are

a lower bound of the average treatment effects of fetal earthquake exposure. Therefore, to

understand the potential influence of the internal migration of schoolchildren, I compute

the proportion of children who live in their place of birth to total children at the same

age using the 1930 Population Census (Statistics Bureau of the Cabinet 1931). According

to the census, approximately 94% and 90% of children aged 0–9 and 10–14 were born

in their current municipalities, respectively.51 I regard the figures on children aged 0–9

as plausible for primary schoolchildren because the census figure for children aged 10–14

should be biased downward because of the existence of graduate children aged over 12

who could have jobs in other municipalities.52 This implies that most schoolchildren did

not leave their original places until finishing primary school, making the potential sorting

not a major issue in my analyses. This is indeed consistent with the historical fact that

the internal migration of schoolchildren in the interwar period was limited (Nakagawa

2001, p. 42).53

Selection Issues

First, the potential selection issue might have been arisen from children absent from school

on the date of the physical examination. If the children exposed to the earthquake in utero

have lingering health issues and thus were more likely to be absent from schools for health

reasons compared with other children, my estimates may suffer from downward bias, which

would understate the adverse health effects of fetal earthquake exposure. In addition, if

51The age ranges reported in the census are systematically divided into 0–9 and 10–14 years. Sincethose figures for boys and girls are similar, I present the average figures herein.

52These students were also unlikely to change their primary schools within each municipality (Hijikata1994, pp. 159–165).

53Since the data do not allow me to fully address the potential sorting effects because of the lack ofan appropriate measure of migration, I further test the impacts of potential omitted variable bias byemploying the method proposed by Oster (2019). I find that the suggested bounds of my estimates arenot far from my baseline estimates in Table 3 (Online Appendix C.2.4).

19

the absentees came from poor households and thus were unhealthier than other children,

my estimates would also understate the effects because of systematic positive selection.

Therefore, owing to a similar mechanism to the sorting effects, my estimates present a

lower bound of the true treatment effects of the earthquake. According to the official

reports of Chiba prefecture, however, the absenteeism rates for primary schools were

approximately 0.5% at that time, which is considered to be negligible (Chiba Prefecture

1932, p. 21). As shown in Panel A of Table 6, I therefore confirm that my baseline results

for the primary school children are unchanged if I control for both the municipal-year-level

primary school enrollment rate and the county-year-level primary school attendance rate

as proxies for the proportion of children with health issues.54

Second, the mortality selection is another important selection issue to be discussed.

If unhealthy fetuses and infants were less likely to survive into the sample, my esti-

mates would understate the impacts of the earthquake because survivors should have

been healthier than culled individuals. As discussed, I control for a part of the poten-

tial selection before birth by including the fetal death rate in the regressions. However,

since many unhealthy babies also died within the first 12 months of birth, I further check

whether infant deaths involuntarily increased after the earthquake hit using the time

series plots of infant mortality rates. I find no systematic increasing trend in infant mor-

tality rates after the earthquake in Chiba prefecture (Online Appendix C.2.5). Another

important phenomenon in terms of sample selection may be fertility selection after the

disaster (Dehejia and Lleras-Muney 2004). The fertility rate may involuntarily decrease

during the aftermath of the disaster because some of the population could not afford to

have children in such abnormal circumstances. In other words, children born during the

aftermath of the disaster might be more likely to belong in households that can provide

sufficient compensation for any adverse health effects in the circumstances. Although my

estimates would understate the adverse health effects if such fertility selection behavior

had been common among parents, I do not find any systematic reduction in the crude

birth rate during or after the year in which the earthquake hit (Online Appendix C.2.5).

54Table B.1 in Online Appendix B presents the summary statistics of both rates: across the sam-ple period, the school enrollment rate and school attendance rate were stable around 99% and 95%,respectively.

20

Other Prenatal Shocks and Postnatal Exposure

In Japan, Spanish influenza cases occurred between August 1918 and July 1920 and its

intensity (measured by the number of deaths) spiked in November 1918 and January

1920.55 This feature of pandemics implies that a large proportion of 1919–1920 birth co-

horts in my sample might have been affected by pandemic flu in utero. Another important

event during the sample period that might have influenced children’s health is the First

World War. In Panel B of Table 6, I include two indicator variables for these suspicious

cohorts.56 As shown, the estimated coefficients on the influenza pandemic and wartime

birth cohorts dummies are statistically insignificant in all cases. This result is consistent

with the fact that the influenza epidemics in Chiba prefecture had not been severe during

the epidemics in 1918–1920 (Statistics Bureau of the Cabinet 1921; 1922; 1923). As for

the impacts of the First World War, it is widely accepted in Japanese history that the

war did not change the daily lives of children in Japan and thus did not influence their

growth patterns (Kudo et al., 1976). These findings suggest that there is no significant

difference in these cohorts and reference birth cohorts in this setting.57

The regressions discussed thus far have aimed to capture fetal exposure to the earth-

quake. Evidence of the long-term effects of earthquake exposure suggests that postnatal

exposure, especially by the second year of life, to the great earthquake could also have

adverse effects on human capital accumulation (Caruso and Miller 2015). In Table 7, I

include an indicator of the 1922 birth cohort, which includes children who experienced the

earthquake roughly between 6 months old and 2 years old, in my baseline specification to

test the potential effects of postnatal exposure.58 While I find robust negative effects on

the 1923 birth cohort, I find little evidence of adverse health effects on the 1922 birth co-

55Representative historical studies investigating Spanish influenza in Japan include Rice and Palmer(1993) and Hayami (2006). Although the rice riots of 1918 might also have affected regional food prices,the riots in Chiba prefecture were small and thus had a negligible impact on the regional food prices(Shoji 1957, p. 36).

56 I include a 1919–1920 birth cohort dummy to control for the potential pandemic cohort effects.Regarding the wartime cohort effects, I include an indicator variable for the children in utero in wartimebetween 1915 and 1919 to control for the potential long-term impacts of wartime shocks on healthoutcomes (Lee 2014). When I include these indicators, the coefficients on the 1923 and 1924 birth cohortvariables are all measured relative to the small number of reference cohorts, which loses the advantageof averaging across many reference cohorts. In other words, if the coefficients are estimated relative tofew reference cohorts, one loses the ability to see whether outliers are generated because the earthquakecohort looks unique or because the small set of cohorts serves as a baseline. Given this result, I donot include the indicator variables for the pandemic and wartime cohorts in my baseline specifications(equations 1–3).

57As shown in Online Appendix C.2.3, I find that the estimates in Tables 3–5 are largely unchanged ifI include suspicious cohort indicator variables.

58Children born in January 1922 were exposed to the earthquake at 1 years and 9 months old, whereaschildren born in March 1923 were exposed at 7 months old.

21

hort, implying that postnatal exposure to the earthquake might not matter. This finding

is consistent with the evidence of the recent study by Rosales-Rueda and Triyana (2019),

who show that in-utero exposure to the Indonesian forest fires of 1997 had stunting effects

on children aged 10 and 17 years, while postnatal exposure at 2 years did not have such

persistent stunting effects. As discussed, children born between April and August 1923

included in the 1923 birth cohort experienced postnatal earthquake exposure in infancy.

These data may thus complicate the interpretation of the estimates of the 1923 birth

cohort variables, which could include potential negative effects via the postnatal health

shock. However, my results on the negligible influence of postnatal exposure support

the evidence that the estimated effects on the 1923 birth cohort variables in my baseline

results should capture the adverse effects of prenatal exposure to the earthquake rather

than postnatal exposure.

In Table 7, I also include an indicator of the 1925 birth cohort to run a placebo test.

Since the 1925 birth cohort was not exposed to the earthquake of 1923, the estimated

coefficient on this indicator variable should be statistically insignificant. If the estimates

were statistically significantly negative, the observed stunting effects could be systematic

decreasing trends in the height or weight of children. As shown in Table 7, the estimates

are all statistically insignificant, indicating that the estimated stunting effects do not

capture pre-trends in height and weight.59

4 Discussion

As discussed thus far, the Great Kanto Earthquake had negative consequences for chil-

dren’s health. I find that while both primary school boys and girls were stunted by fetal

earthquake exposure, the stunting effects were much clearer in girls than boys. My finding

on gender bias against girls is consistent with the study by Caruso and Miller (2015) that

finds stronger negative long-term effects of fetal exposure to the Ancash earthquake of

1970 on girls’ years of education. It is also consistent with medical evidence on the culling

mechanism before birth: since male fetuses are more vulnerable in utero than female

fetuses, there may be stronger positive selection into birth for boys than girls (Kraemer

59 If the estimated stunting effects do not capture such trends and can be regarded as the consequencesof fetal earthquake exposure, the mean heights and weights of children born in the surrounding yearsshould be similar after excluding the 1923–24 birth cohorts from the sample. In Online Appendix C.2.6,I confirm that the mean heights and weights of children born between 1918 and 1925 (excluding the1923–1924 birth cohorts) are indeed similar in the statistical sense using the specification including eachsurrounding birth cohort dummy.

22

2000). The ameliorating effects of the disaster relief on primary school boys can also help

explain these gender differences.

I find that the primary school girls aged 9–11 exposed to the earthquake in utero in

the JMA-SIS7 area were roughly 1 cm shorter than those in surrounding cohorts. While

this decline in height accounts for approximately a 0.2 standard deviation change (1/4.6)

in the average height of the primary school girls aged 9–11 (Panel A of Table 1), one must

be careful not to attach too much importance to the small stunting effects. Mazumder

et al.(2010) found that the U.S. birth cohort exposed to the 1919 pandemic influenza

experienced a 0.1 cm decline in final height on average, which is associated with a 5%

higher risk of cardiovascular disease in old age. Although they did not ascertain the causal

links between such stunting and the later-life risk of contracting diseases, a number of

medical and epidemiological studies including randomized intervention trials have found

similar associations between shorter stature in early life and suboptimal function later in

life (Dewey and Begum 2011). Furthermore, a growing body of the literature provides

further evidence on the links between child stunting and lower earnings later in life.

The comprehensive review by McGovern et al. (2017) finds that 0.5 cm child stunting is

associated with a decrease in wages of 2–3% on average. A recent height premium-based

calculation also suggests that almost 2 cm lower height translates into 1.6% lower earnings

for the affected population (Rosales-Rueda and Triyana 2019). While it is difficult to

apply these figures to the present case of prewar Japan since the context, treatment,

and economic environments are different, one must be careful about the possibility that

child stunting has lasting effects on later-life earnings. Another important fact is that the

medical evidence states that girls’ stunting has a risk of lasting adverse health impacts on

their offspring. A growing body of evidence has shown that maternal stunting restricts

the growth of the uterus and placenta and thus increases the risk of intrauterine growth

restriction, which causes insufficient neurological and intellectual development and the

shorter stature of their infants (Black et al. 2008). However, while my results have an

implication for the dynamic adverse health effects of fetal earthquake exposure, it is not

possible to assess these effects without access to data on both mothers and their children.

The long-run impact of maternal stunting on the health and socioeconomic outcomes of

their offspring is thus another avenue for future research.

This study provides suggestive evidence that the disaster relief might have played a

role in nourishing exposed primary school boys. As discussed, the gender bias against

23

girls in relief effects can be explained both by the reduction in scarring effects before

birth and by the potential institutional effects after birth via the prewar Civil Code (e.g.,

Hayashi and Prescott 2008; Valente 2015). The estimates suggest that relief expenses on

food and shelter might have ameliorated roughly half the stunting effects at early primary

school ages. It is also suggested that medical treatment had much greater ameliorating

effects on stunting, even though a limited proportion of people in the devastated area

benefitted from such treatment. These results thus add evidence to the recent literature

on the optimal timing of the remediation of early disadvantage (Heckman 2012; Vellore

2018). Specifically, they show that remediation effects may be able to mitigate stunting

effects by the end of primary school ages. In addition, they highlight the heterogeneity

in remediation effects with respect to types of disaster relief. The results are also in

line with those of Parman (2015), who shows that parents with a child exposed to the

1918 influenza pandemic in utero reallocated their resources to the child’s older siblings,

which led to higher educational attainment for these siblings. My results show that relief

effects can be biased by gender depending not only on the timing of remediation, but

also on parents’ resource allocation based on the strong son preference regulated by the

institution.

Given the unavoidable mental stress of the enormous exogenous shock, the role of

disaster relief indicates that nutritional stress is a plausible pathway of the adverse health

effects in the devastated area. In addition, among municipalities that experienced little

physical damage and disaster relief, I find suggestive evidence that maternal mental stress

due to vibrations might be more likely to be associated with stunting than nutritional

stress due to market disruption. That the earthquake can lead to maternal mental stress

is consistent with previous studies such as Kim, Carruthers, and Harris (2017). My results

further shed light on the role of the different pathways behind the adverse health effects

of fetal exposure to the earthquake. That is, while mental stress is an important pathway

underlying the adverse health effects, nutritional stress can be another pathway in the

physically devastated area, thus enhancing the adverse health effects on exposed children.

5 Conclusion

This study used a catastrophic earthquake from the 1920s to analyze the long-term effects

of a one-off disaster on children’s health. I found that fetal exposure to the Great Kanto

24

Earthquake had stunting effects on primary schoolchildren and that the magnitude of

such effects increased with the degree of earthquake stress. The disaster relief was found

to have compensating effects on stunting among boys by late primary school ages, even

though the magnitude of the effects varied with the types of relief expenses. Finally, while

mental stress might be associated with adverse health effects, nutritional stress might also

have enhanced the effects in the physically devastated area.

My evidence from industrializing Japan is, however, not without its limitations. First,

given the scarcity of individual-level data with the date and place of births in Japan, I

predominantly used school year-age-level datasets. Since this assignment can attenuate

the estimates, those obtained in this study should be considered to be the lower bounds

of the average treatment effects.60 Second, I used data that cover one prefecture in the

Kanto region, Chiba prefecture, implying that I rely on a limited number of observations

in the disrupted area to tease out the effects. Since there was no systematic rule in

editing the school physical examination records across prefectures, obtaining systematic

statistics on the school-level physical examination is difficult. Therefore, some reports are

often incomplete and mostly unavailable for the statistical analyses. Considering these

limitations, applying the exogenous variations due to earthquakes to a comprehensive

child-level dataset with a precise date of birth and place may thus be an avenue for future

work. While I acknowledge these limitations, this study does use a localized measure of

the severity of the 1923 earthquake. Municipal-level geospatial variations in the physical

devastation could improve the assignment by detecting the most impacted area in the

prefecture.

This study contributes to our understanding of the long-term effects of the great

earthquake on children’s health given that the lingering health impacts of earthquakes

on children have been neglected in the literature. It also offers suggestive evidence of

the importance of the remediation of early disadvantage via disaster relief and of the

potentially significant impacts of maternal mental stress on children in utero.

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25

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Documents, Statistical Reports, and Database

[1] Awa County Office. Awashinsaishi (History of Awa earthquake). [in Japanese] Chiba: AwaCounty Office, 1926.

[2] Bank of Japan. Nihonginko hyakunenshi (One hundred year history of the Bank of Japan).Tokyo: Bank of Japan, 1986. [in Japanese]

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[5] Chiba Prefecture. Chibaken tokeisho (The statistical report of Chiba prefecture, 1923 edition,volume.2). [in Japanese] Chiba: Chiba Prefecture, 1925a.

[6] Chiba Prefecture. Chibaken tokeisho (The statistical report of Chiba prefecture, 1923 edition,volume.3). [in Japanese] Chiba: Chiba Prefecture, 1925b.

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31

[16] Ministry of Land, Infrastructure, Transport and Tourism of Japan (database). Availableonline at the Ministry of Land, Infrastructure, Transport and Tourism of Japan (http://nlftp.mlit.go.jp/ksj/jpgis/datalist/KsjTmplt-N03.html) (last accessed on May 22, 2017).

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Tokyo: Tokyo City Office, 1926.

32

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le1:

Sum

mar

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Un

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Pan

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(cm

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811

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2.26

4.33

14,1

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chool

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r-ag

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7.47

4.13

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6.89

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7.43

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611

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7.92

4.10

1,26

912

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781,

296

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sth

esu

mm

ary

stati

stic

sfo

rth

eex

posu

revari

ab

les

an

dre

lief

exp

ense

sp

erca

pit

a.SIS

7is

an

ind

icato

rvari

ab

lefo

rm

un

icip

aliti

esin

the

JM

A-S

IS7

are

a(T

ab

le2).

Rail

isan

ind

icato

rvari

ab

lefo

rm

un

icip

aliti

essa

tisf

yin

gth

efo

llow

ing

con

dit

ion

s:(a

)lo

cate

din

the

JM

A-S

IS7

are

a,

(b)

did

not

rece

ive

any

dis

ast

erre

lief

,an

d(c

)lo

cate

dw

ith

in10

km

of

any

mu

nic

ipality

incl

ud

ing

eith

erth

eN

od

alin

eor

the

Ku

ruri

lin

e.E

ach

relief

vari

ab

leis

defi

ned

as

the

relief

exp

ense

div

ided

by

the

nu

mb

erof

peo

ple

(yen

).S

ou

rces

:B

iolo

gic

al

ou

tcom

ed

ata

are

from

the

SP

ES

(1925–1935

edit

ion

s).

See

Tab

le2

for

the

data

use

dto

calc

ula

teth

eea

rth

qu

ake

inte

nsi

tym

easu

re.

Data

on

the

railw

ay

dis

rup

tion

an

dre

lief

exp

ense

sp

erca

pit

aare

from

the

SR

CP

(1923

edit

ion

).

33

Tab

le2:

Cla

ssifi

cati

onof

Munic

ipal

itie

sby

JM

A-S

IS(s

hin

do)

Nu

mb

erof

Cou

nti

esm

ain

lyM

ean

of

the

dam

aged

Sei

smic

inte

nsi

tysc

ale

mu

nic

ipali

ties

incl

ud

edh

ou

seh

old

s(P

DR

in%

)JM

A-S

IS7

(sh

ind

on

ana)

23

Aw

a,

Kim

itsu

79.8

JM

A-S

IS6

(sh

ind

oro

ku

)46

Aw

a,

Kim

itsu

,Ic

hih

ara

,C

hose

i22.8

JM

A-S

IS5

(sh

ind

ogo

)275

Ch

iba,

Hig

ash

ikats

ush

ika,

0.3

9Im

ba,

Isu

mi,

Ich

ihara

,K

aij

yo,

Kato

ri,

Sanb

u,

Sou

sa

Note

s:T

he

PD

Ris

the

nu

mb

erof

aff

ecte

dh

ou

seh

old

sp

er100

hou

seh

old

s.T

he

JM

A-S

ISra

nges

from

on

e(m

inim

um

)to

seven

(maxim

um

).It

isca

lcu

late

db

ase

don

the

collap

sera

tepre

dic

ted

usi

ng

the

pre

dic

tion

equ

ati

on

of

Moro

ian

dT

akem

ura

(1999):

Collap

seR

ate

=−

1.6

1+

0.4

6×

PD

R+

0.0

051×

PD

R2.

Th

eJM

A-S

ISis

class

ified

base

don

Takem

ura

an

dM

oro

i(2

002):

ara

tegre

ate

rth

an

30%

isd

efin

edas

JM

A-S

IS7

(sh

ind

on

an

a),

gre

ate

rth

an

1%

an

dle

ssth

an

30%

isd

efin

edas

JM

A-S

IS6

(sh

ind

oro

ku

),an

dgre

ate

rth

an

0.1

%an

dle

ssth

an

1%

isdefi

ned

as

up

per

JM

A-S

IS5

(sh

ind

ogo).

No

mu

nic

ipaliti

esex

per

ien

ced

JM

A-S

IS1–4.

Follow

ing

the

offi

cial

class

ifica

tion

,JM

A-S

IS6

incl

ud

esb

oth

6-l

ow

er(r

oku

jyaku

)an

d6-u

pp

er(r

oku

kyo);

JM

A-S

IS5

incl

ud

esb

oth

5-l

ow

er(g

ojy

aku

)an

d5-u

pp

er(g

okyo).

Sou

rces

:D

ata

use

dto

calc

ula

teth

eea

rth

qu

ake

inte

nsi

tym

easu

res

are

from

the

HT

E(1

926)

an

dS

tati

stic

sB

ure

au

of

the

Cab

inet

(1926).

34

Table 3: Effects of Fetal Earthquake Exposure on Height and Weight

Boys Girls

(1) (2) (3) (4)Ages 6–8 Ages 9–11 Ages 6–8 Ages 9–11

Panel A: Effects on height1923 birth cohort −0.213* −0.196* −0.234* −0.233***

[0.020] [0.044] [0.010] [0.003]1923 birth cohort × SIS7 0.085 0.109 −0.296** −0.604***

[0.250] [0.798] [0.008] [0.005]1924 birth cohort −0.094 −0.086 0.117 −0.107

[0.212] [0.332] [0.236] [0.241]1924 birth cohort × SIS7 0.051 0.025 0.184 0.357

[0.812] [1.000] [0.164] [0.455]Panel B: Effects on weight1923 birth cohort −0.058* −0.041 −0.099** −0.131**

[0.016] [0.314] [0.008] [0.005]1923 birth cohort × SIS7 0.003 0.102 −0.269*** −0.303**

[0.938] [0.438] [0.002] [0.009]1924 birth cohort −0.004 −0.030 0.009 −0.052

[0.864] [0.490] [0.754] [0.051]1924 birth cohort × SIS7 −0.029 −0.006 −0.109 0.035

[0.996] [0.974] [0.204] [0.957]

***, **, and * represent statistical significance at the 0.5%, 1%, and 5% levels based on the p-values from thewild cluster bootstrap resampling method in brackets, respectively. The data are clustered at the 13-countylevel in the bootstrap procedure. The number of replications is fixed to 1,000 for all the specifications.Notes: The numbers of observations in columns (1)–(4) are 14, 139, 14, 133, 14, 145, and 14, 138, respectively.All the regressions include controls for the rice yield in the birth year; fetal death rate in the birth year;school enrollment rate of the parental generation; school-age-specific fixed effects; and year fixed effects.The null hypothesis of no joint significance of the affected cohort dummies and those area interaction termsare rejected based on the F -test at the 5% level in most specifications. The null hypothesis of no jointsignificance of the control variables is rejected based on the F -test at the 0.1% level in all the specifications.

35

Tab

le4:

Eff

ects

ofD

isas

ter

Rel

ief

onH

eigh

tan

dW

eigh

tby

Typ

eof

Rel

ief

Exp

ense

Rel

ief

exp

ense

sp

erca

pit

a

Tot

alF

ood

Sh

elte

rM

edic

altr

eatm

ent

(1a)

Exp

ense

s×

(1b

)E

xpen

ses×

(2a)

Exp

ense

s×

(2b

)E

xpen

ses×

(3a)

Exp

ense

s×

(3b

)E

xpen

ses×

(4a)

Exp

ense

s×

(4b

)E

xpen

ses×

1923

bir

thco

hor

t19

24b

irth

coh

ort

1923

bir

thco

hor

t19

24b

irth

coh

ort

1923

bir

thco

hor

t19

24b

irth

coh

ort

1923

bir

thco

hor

t19

24b

irth

coh

ort

Pan

elA

:E

ffec

tson

hei

ght

Boy

sA

ges

6–8

0.11

3[0

.022

]*0.

053

[0.6

22]

0.16

5[0

.012

]*0.

073

[0.6

20]

0.38

1[0

.038

]*0.

208

[0.5

68]

41.4

74[0

.048

]*20

.551

[0.5

64]

Age

s9–

110.

091

[0.9

46]

-0.0

54[0

.784

]0.

139

[0.7

62]

-0.0

77[0

.818

]0.

268

[1.0

00]

-0.1

92[0

.690

]34

.292

[0.9

14]

-22.

279

[0.6

58]

Gir

lsA

ges

6–8

-0.0

12[0

.982

]-0

.023

[0.8

26]

-0.0

11[0

.982

]-0

.026

[0.8

56]

-0.0

91[0

.968

]-0

.127

[0.6

82]

5.76

0[0

.990

]-1

5.00

0[0

.596

]

Age

s9–

110.

072

[0.5

87]

-0.0

42[0

.863

]0.

109

[0.5

63]

-0.0

53[0

.919

]0.

207

[0.6

35]

-0.1

97[0

.709

]35

.767

[0.4

47]

-20.

793

[0.6

67]

Pan

elB

:E

ffec

tson

wei

ght

Boy

sA

ges

6–8

0.02

9[0

.016

]*0.

032

[0.1

88]

0.04

2[0

.024

]*0.

051

[0.1

96]

0.09

7[0

.018

]*0.

080

[0.1

66]

15.9

93[0

.024

]*9.

356

[0.0

72]

Age

s9–

110.

048

[0.8

26]

0.00

5[1

.000

]0.

070

[0.7

10]

0.00

7[1

.000

]0.

155

[0.8

90]

0.01

1[1

.000

]19

.153

[0.6

36]

-0.6

87[0

.978

]

Gir

lsA

ges

6–8

0.00

2[1

.000

]-0

.022

[0.6

70]

0.00

3[1

.000

]-0

.031

[0.6

96]

0.00

9[1

.000

]-0

.085

[0.5

42]

10.1

09[0

.890

]-9

.491

[0.4

70]

Age

s9–

110.

056

[0.4

17]

-0.0

05[0

.995

]0.

079

[0.4

01]

-0.0

06[0

.997

]0.

202

[0.4

41]

-0.0

30[0

.973

]29

.010

[0.4

31]

-7.6

23[0

.923

]

*re

pre

sents

stati

stic

al

sign

ifica

nce

at

the

5%

level

base

don

thep-v

alu

esfr

om

the

wild

clu

ster

boots

trap

resa

mp

lin

gm

eth

od

inb

rack

ets.

Th

ed

ata

are

clu

ster

edat

the

13-c

ou

nty

level

inth

eb

oots

trap

pro

ced

ure

.T

he

nu

mb

erof

rep

lica

tion

sis

fixed

to1,0

00

for

all

the

spec

ifica

tion

s.N

ote

s:E

stim

ate

dco

effici

ents

onI(YOB=1923)×

Relief

an

dI(YOB=1924)×

Relief

ineq

uati

on

2are

rep

ort

edin

the

tab

le.

Th

enu

mb

erof

ob

serv

ati

on

sfo

rb

oys

(gir

ls)

aged

6–8

an

d9–11

are

14,1

39

(14,1

45)

an

d14,1

33

(14,1

38),

resp

ecti

vel

y.A

llth

ere

gre

ssio

ns

incl

ud

eco

ntr

ols

for

the

rice

yie

ldin

the

bir

thyea

r;fe

tal

dea

thra

tein

the

bir

thyea

r;sc

hool

enro

llm

ent

rate

of

the

pare

nta

lgen

erati

on

;sc

hool-

age-

spec

ific

fixed

effec

ts;

an

dyea

rfi

xed

effec

ts.

Th

enu

llhyp

oth

esis

of

no

join

tsi

gn

ifica

nce

of

the

contr

ol

vari

ab

les

isre

ject

edb

ase

don

theF

-tes

tat

the

0.1

%le

vel

inall

the

spec

ifica

tion

s.

36

Table 5: Effects of Fetal Earthquake Exposure on Height and Weight in the LimitedJMA-SIS5 Area

Boys Girls

(1) (2) (3) (4)Ages 6–8 Ages 9–11 Ages 6–8 Ages 9–11

Panel A: Effects on height1923 birth cohort −0.203* −0.168 −0.248** −0.225*

[0.020] [0.216] [0.008] [0.019]1923 birth cohort × Railway disruption 0.180 0.185 0.241 0.360

[0.732] [0.590] [0.056] [0.767]1924 birth cohort −0.120 −0.066 −0.130 −0.131

[0.248] [0.590] [0.160] [0.261]1924 birth cohort × Railway disruption 0.132 −0.038 −0.024 0.160

[0.480] [0.982] [0.914] [0.525]

Panel B: Effects on weight1923 birth cohort −0.051* −0.008 −0.103* −0.110*

[0.028] [0.996] [0.010] [0.039]1923 birth cohort × Railway disruption 0.081 −0.117 0.059 −0.060

[0.708] [0.480] [0.390] [0.799]1924 birth cohort 0.010 0.015 0.012 −0.052

[0.832] [0.784] [0.732] [0.083]1924 birth cohort × Railway disruption 0.087 −0.127 0.042 0.053

[0.154] [0.540] [0.608] [0.947]

** and * represent statistical significance at the 1% and 5% levels based on the p-values from the wild clusterbootstrap resampling method in brackets, respectively. The data are clustered at the 13-county level in thebootstrap procedure. The number of replications is fixed to 1,000 for all the specifications.Notes: The samples include municipalities receiving no disaster relief in the JMA-SIS5 area. The numbers ofobservations in columns (1)–(4) are 9352, 9348, 9353, and 9347, respectively. All the regressions include controlsfor the rice yield in the birth year; fetal death rate in the birth year; school enrollment rate of the parentalgeneration; school-age-specific fixed effects; and year fixed effects. The null hypothesis of no joint significance ofthe control variables is rejected based on the F -test at the 0.1% level in all the specifications.

37

Table 6: Effects of Fetal Earthquake Exposure on Height and Weight:Robustness of including Additional Control Variables

Boys Girls

(1) (2) (3) (4)Ages 6–8 Ages 9–11 Ages 6–8 Ages 9–11

Panel A-1: Effects on height1923 birth cohort −0.214* −0.197* −0.233** −0.233***

[0.020] [0.038] [0.008] [0.003]1923 birth cohort × SIS7 −0.084 0.109 −0.293* −0.607**

[0.238] [0.824] [0.014] [0.005]Primary school enrollment rate −0.081 0.055 −0.035 0.004

[0.122] [0.470] [0.524] [0.939]Primary school attendance rate −0.026 −0.006 0.042 −0.027

[0.476] [0.836] [0.172] [0.415]Panel A-2: Effects on weight1923 birth cohort −0.058* −0.041 −0.099** −0.131***

[0.018] [0.316] [0.008] [0.005]1923 birth cohort × SIS7 0.003 0.105 −0.268*** −0.304**

[0.926] [0.476] [0.002] [0.009]Primary school enrollment rate −0.019 −0.019 0.017 −0.003

[0.560] [0.340] [0.498] [0.943]Primary school attendance rate −0.004 0.029 0.012 −0.005

[0.794] [0.080] [0.264] [0.781]Boys Girls

(1) (2) (3) (4)Ages 6–8 Ages 9–11 Ages 6–8 Ages 9–11

Panel B-1: Effects on height1923 birth cohort −0.208* −0.197* −0.227** −0.234***

[0.018] [0.040] [0.010] [0.003]1923 birth cohort × SIS7 0.088 0.109 −0.293** −0.607***

[0.230] [0.804] [0.008] [0.005]Wartime birth cohorts 0.022 −0.015 0.097 0.149

[0.612] [0.770] [0.294] [0.131]Influenza pandemic birth cohorts −0.076 −0.004 −0.074 0.066

[0.388] [0.980] [0.340] [0.487]Panel B-2: Effects on weight1923 birth cohort −0.058* −0.040 −0.100** −0.136**

[0.012] [0.320] [0.008] [0.005]1923 birth cohort × SIS7 0.004 0.102 −0.269*** −0.308**

[0.922] [0.430] [0.002] [0.009]Wartime birth cohorts −0.043 0.047 −0.032 0.007

[0.074] [0.142] [0.376] [0.881]Influenza pandemic birth cohorts −0.009 −0.005 −0.008 0.094

[0.764] [0.880] [0.802] [0.097]

***, **, and * represent statistical significance at the 0.5%, 1%, and 5% levels based on the p-values from the wildcluster bootstrap resampling method in brackets, respectively. The data are clustered at the 13-county level in thebootstrap procedure. The number of replications is fixed to 1,000 for all the specifications.Notes: The numbers of observations in columns (1)–(4) are 14, 139, 14, 133, 14145, and 14138, respectively. Theestimated coefficients on the 1924 birth cohort dummy and its area interaction term are included in all the regressionsbut are not reported as those estimates are statistically insignificant in all the specifications. All the regressionsinclude controls for the rice yield in the birth year; fetal death rate in the birth year; school enrollment rate of theparental generation; school-age-specific fixed effects; and year fixed effects. The null hypothesis of no joint significanceof the control variables is rejected based on the F -test at the 1% level in all the specifications.

38

Table 7: Effects of Fetal Earthquake Exposure on Height and Weight: Testing thePotential Impacts on Surrounding Cohorts

Boys Girls

(1) (2) (3) (4)Ages 6–8 Ages 9–11 Ages 6–8 Ages 9–11

Panel A: Effects on height1922 birth cohort 0.056[0.226] -0.046[0.500] 0.056[0.574] 0.003[1.000]1922 birth cohort × SIS7 0.174[0.376] 0.441[0.478] 0.039[0.668] 0.019[1.000]1923 birth cohort -0.187[0.018]* -0.227[0.056] -0.192[0.028]* -0.261[0.009]**1923 birth cohort × SIS7 0.110[0.180] 0.158[0.468] -0.322[0.012]* -0.628[0.003]***1924 birth cohort -0.076[0.306] -0.124[0.338] 0.160[0.138] -0.148[0.231]1924 birth cohort × SIS7 0.077[0.782] 0.073[1.000] 0.158[0.136] 0.332[0.631]1925 birth cohort -0.012[0.898] -0.057[0.532] 0.061[0.334] -0.081[0.331]1925 birth cohort × SIS7 0.053[0.790] -0.050[0.824] -0.273[0.140] -0.326[0.151]

Panel B: Effects on weight1922 birth cohort 0.039[0.262] -0.005[0.898] -0.001[0.974] 0.002[1.000]1922 birth cohort × SIS7 0.085[0.488] 0.308[0.450] -0.087[0.352] -0.104[0.159]1923 birth cohort -0.040[0.022]* -0.054[0.226] -0.095[0.004]*** -0.158[0.003]***1923 birth cohort × SIS7 0.024[0.402] 0.155[0.480] -0.301[0.002]*** -0.325[0.003]***1924 birth cohort 0.010[0.824] -0.052[0.406] 0.016[0.630] -0.089[0.065]1924 birth cohort × SIS7 -0.008[1.000] 0.048[1.000] -0.141[0.216] 0.014[0.983]1925 birth cohort -0.007[0.806] -0.063[0.138] 0.027[0.294] -0.079[0.201]1925 birth cohort × SIS7 0.099[0.486] 0.213[0.482] -0.200[0.056] -0.113[0.327]

***, **, and * represent statistical significance at the 0.5%, 1%, and 5% levels based on the p-values from thewild cluster bootstrap resampling method in brackets, respectively. The data are clustered at the 13-county levelin the bootstrap procedure. The number of replications is fixed to 1,000 for all the specifications.Notes: The numbers of observations for each regression reported in columns (1)–(4) are 14, 139, 14, 133, 14, 145,and 14, 138, respectively. All the regressions include controls for the rice yield in the birth year; fetal death ratein the birth year; school enrollment rate of the parental generation; school-age-specific fixed effects; and year fixedeffects. The null hypothesis of no joint significance of the control variables is rejected based on the F -test at the0.1% level in all the specifications.

39

Ibaraki

Saitama

ChibaTokyo

Kanagawa

Yamanashi

Shizuoka

Affected areaMarginally affected area

Figure 1: Affected area and hypocenterNotes: The red circle shows the hypocenter of the earthquake. The affected area includes Tokyo,Kanagawa, and Chiba prefectures. The marginally affected area includes Saitama, Shizuoka,Yamanashi, and Ibaraki prefectures. Sources: Created by the author from Tokyo City Office(1925, p. 161). The location of the hypocenter was based on the official database of the JapanMeteorological Agency (database).

(42.5,98.8](21.1,42.5](3.6,21.1](1.5,3.6](0.6,1.5](0.3,0.6](0.2,0.3](0.0,0.2][0.0,0.0]

(a) Affected households (%)

JMA-SIS 7JMA-SIS 6JMA-SIS 5

(b) Intensity classification

Figure 2: Spatial distribution of affected households in Chiba prefectureNotes: The PDR, which is defined as the number of affected households (collapsed or semi-collapsed dueto the earthquake) per 100 total households, is illustrated in Figure 2a. The classifications of JMA-SIS5,JMA-SIS6, and JMA-SIS7 in Figure 2b include municipalities exposed to a JMA-SIS of 5 (shindo go), 6(shindo roku), and 7 (shindo nana), respectively. Sources: Calculated by the author from the Divisionof Social Affairs, Chiba Prefecture (1933b). Shapefile is based on the database of the Ministry of Land,Infrastructure, Transport and Tourism of Japan (database).

40

Appendices Online Appendices : For onlinepublication only (Supplemental materials for review)

Appendix A Background appendix

0

100

200

300

400

500

600

700

800

900

Num

ber o

f ext

rinsi

c de

aths

1 2 3 4 5 6 7 8 9 10 11 12Month

MaleFemale

(a) Number of extrinsic deaths

0

5

10

15

20

25

30

Prop

ortio

n of

ext

rinsi

c de

aths

(%)

1 2 3 4 5 6 7 8 9 10 11 12Month

MaleFemale

(b) Extrinsic deaths (%)

23456789

101112131415

Num

ber o

f ext

rinsi

c de

aths

per

10,

000

peop

le

1919 1920 1921 1922 1923 1924 1925 1926 1927Year

MaleFemale

(c) Extrinsic death rate

Figure A.1: Extrinsic deaths in Chiba around the Great Kanto EarthquakeNotes: The proportion of extrinsic deaths shown in Figure A.1b is defined as the number of extrinsic deaths relative tototal deaths. The extrinsic death rate shown in Figure A.1c is defined as the number of extrinsic deaths per 10,000 people.Sources: All figures are calculated by the author from the Statistics Bureau of the Cabinet (1922a–1928a).

Figure A.1a confirms the dramatic increase in extrinsic deaths in September 1923.

The striking fact is that the number of female extrinsic deaths is greater than that of

males. This tendency does not change if I divide the number of deaths by total male

and female monthly deaths in 1923 (Figure A.1b). Figure A.1c shows the extrinsic death

rate, defined as the number of extrinsic deaths per 10,000 people, in Chiba between

1919 and 1927. The extrinsic death rate for males was slightly higher than that for

females, reflecting the number of extrinsic deaths in outside workplaces. However, the

small disparity in the male and female death rates of 1923 suggests that women were more

likely to have been affected by the earthquake, which struck households around lunchtime

when most wives had cooked their family’s meal at home. This may have caused such

a gender disparity in the death rate. Figure A.2 shows the spatial distribution of the

number of deaths, missing people, and injured people due to the earthquake per 100

people (see also Online Appendix B). This figure shows a similar but spatially modest

distribution compared with that of affected households in Figure 2a. Indeed, while 28% of

municipalities (97 out of 349) had victims, 53% suffered household damage. The example

picture in Figure B.2 illustrates the devastation in Awa, confirming the validity of the

measure of earthquake stress (see the “Great Kanto Earthquake” section).

Appendix B Data appendix

The school-level primary school dataset used in this study is constructed from the SPES

published by Chiba prefecture between 1925 and 1936.61 The number of primary schools

61The SPES originally consisted of two reports that contained similar information: the Gakko seitojidoshintaikensa tokei published in 1925 and from 1927 to 1934 (data for 1925–1934) and the Seitojidoshintaikensa tokei published in 1936 (data for 1935). I uniformly refer to these publications as the SPES(1925–1935 editions) for simplicity. Some errata in these documents were corrected. For instance, if thesequence of height (in cm) from ages 6 to 11 was 109, 218, 123, 125, 128, and 130, I corrected the secondobservation to 118 because a height of 218 cm is clearly unrealistic for a seven-year-old and thus can beregarded as a typo.

1

(2.40,10.48](0.96,2.40](0.36,0.96](0.23,0.36](0.08,0.23](0.05,0.08](0.03,0.05](0.00,0.03][0.00,0.00]

Figure A.2: Spatial distribution of victims (per 100 people)in Chiba prefecture

Note: The number of victims is defined as the number of deaths, missing people, and injuredpeople due to the earthquake. Source: Calculated by the author from Division of Social Affairs,Chiba Prefecture (1933b).

from 1925 to 1935 was 429, 429, 429, 433, 434, 433, 433, 432, 428, 427, and 422, respec-

tively. According to the SPES and Population Census in 1935, approximately 95% of

primary school-aged children in Chiba were covered in the SPES datasets.62

Figure B.1 illustrates the raw relationship between schoolchildren’s average height and

weight by their year of birth.63 While there is a general increasing trend in height, reduced

growth is observed in the 1923 birth cohort, especially in the JMA-SIS7 area (Figure B.1a

and B.1b). Regarding the weight of children, reduced growth might be observed in the

1923 birth cohort in the JMA-SIS7 area (Figures B.1c and B.1d).

Figure B.3 shows a map of soil compaction in Chiba prefecture, based on boring data.

In this map, the areas with red (blue) meshes were more (less) likely to be affected by

the earthquake. The interesting fact is that the physical disruption in Figure 2a shows

the opposite distribution of affected households. This fact implies that my key measure

of earthquake stress does not depend on soil compaction, which can correlate with the

potential spatial distribution of industries and agricultural production. As discussed in

the “Great Kanto Earthquake” section, the distribution of physical disruption used is

more likely to be dominated by the distribution of the fault plane (Kamogawa teichi

active fault), which snakes across Awa county.

Finally, I list the sources of the documents used to construct the control variables. The

birth year fetal death rate, defined as the number of fetal deaths per 1,000 births. Data

on fetal deaths as well as live births between 1913–1921 are taken from the Statistical

Report of Chiba Prefecture (SRCP) (1913–1921 editions) published by Chiba prefecture

between 1915 and 1922. The missing values for 1922–1924 and 1926–1929 are linearly

62The number of examinees in the primary schools is taken from Physical Education Bureau, Ministryof Education (1942, p. 7; p. 23). The number of children is taken from Statistics Bureau of the Cabinet(1938, pp. 34–35).

63I used Kaitei shichoson binran (Handbook of Municipalities, revised edition) (Bunmeido 1915, pp. 1–27) to match the schools with the municipalities. The confidence intervals for the non-JMA-SIS7 areas aresystematically smaller than those for the JMA-SIS7 area because of the greater number of observationsin the former areas than in the latter area.

2

115.0115.5116.0116.5117.0117.5118.0118.5119.0119.5120.0120.5121.0121.5122.0122.5123.0123.5124.0124.5125.0125.5126.0

Aver

age

heig

ht o

f boy

s ag

es 6

to 1

1 (c

m)

1920 1921 1922 1923 1924Year of birth

SIS7 areaNon-SIS7 areas95% CI95% CI

(a) Height: PS-boys

115.0115.5116.0116.5117.0117.5118.0118.5119.0119.5120.0120.5121.0121.5122.0122.5123.0123.5124.0124.5125.0125.5126.0

Aver

age

heig

ht o

f girl

s ag

es 6

to 1

1 (c

m)

1920 1921 1922 1923 1924Year of birth

SIS7 areaNon-SIS7 areas95% CI95% CI

(b) Height: PS-girls

20.0020.2520.5020.7521.0021.2521.5021.7522.0022.2522.5022.7523.0023.2523.5023.7524.0024.2524.5024.7525.0025.2525.5025.7526.0026.2526.5026.7527.00

Aver

age

wei

ght o

f boy

s ag

es 6

to 1

1 (k

g)

1920 1921 1922 1923 1924Year of birth

SIS7 areaNon-SIS7 areas95% CI95% CI

(c) Weight: PS-boys

20.0020.2520.5020.7521.0021.2521.5021.7522.0022.2522.5022.7523.0023.2523.5023.7524.0024.2524.5024.7525.0025.2525.5025.7526.00

Aver

age

wei

ght o

f girl

s ag

es 6

to 1

1 (k

g)

1920 1921 1922 1923 1924Year of birth

SIS7 areaNon-SIS7 areas95% CI95% CI

(d) Weight: PS-girls

Figure B.1: Average height (in cm) and weight (in kg) of the primary school (PS)students by area and gender

Notes: Figure B.1a and B.1b show the average height of primary boys and girls, respectively. Figure B.1c and B.1d showthe average weight of primary boys and girls, respectively. The SIS7 area refers to the area extremely affected by theearthquake with the maximum seismic intensity scale (JMA-SIS7), whereas the non-SIS7 area refers to the JMA-SIS5–6areas reported in Table 2. Bootstrap percentile confidence intervals are illustrated. The number of replications is fixed to1,000. Sources: Calculated by the author from the SPES (1925–1935 editions).

interpolated by using the values of 1921, 1925, and 1930. Data on fetal deaths and

live births in 1925 and 1930 are taken from the Shichosonbetsu jinkodotai tokei (Vital

Statistics for Municipalities, 1925 and 1930 editions) published by the Statistics Bureau

of the Cabinet in 1927 and 1933, respectively. Data on rice yield (hectoliter per 0.1 ha)

and the number of doctors are taken from the SRCP (1913–1930 editions) published by

Chiba prefecture between 1915 and 1931. Data on the primary school enrollment rate

of the parental generation are obtained from the SRCP (1897–1912 editions) published

between 1899 and 1914.64 In the sensitivity analysis, I added the municipal-level primary

64 As described by Hijikata (1994, p. 13), the school enrollment rate is defined asSchool Enrollment Rate = 100 × (Students aged 6–13 + Graduates aged 6–13)/Children aged 6–13. TheVital Statistics of 1923 report that the average age at first marriage in Chiba was 25 years, implyingthat parents might have had their first child at 26 on average (Statistics Bureau of the Cabinet 1925a,pp. 18–21). Therefore, one can guess that the average age of parents is roughly 26 years. Given the yearof birth of the sampled children described above and primary school entrance age of 6, one may want touse the enrollment rates from 1894–1909, namely the 20-year lagged rate from the year of birth. Since the

3

Figure B.2: Devastation in Funagata town in Awa countySource: Division of Social Affairs, Chiba Prefecture (1933a).

Figure B.3: Soil compaction in Chiba prefectureNotes: Areas with the red (blue) meshes are more (less) likely to be affected by the earthquake.This map was created based on boring data in 2011 (approximately 50,000 observations). Thespectral intensity values are shown in 250-m meshes. Although the data are investigated in 2011,such wide meshes mean that the map could show nearly the same distribution of soil compaction inthe early 20th century. Source: Webpage of Chiba prefecture http://keihatsu.bousai.pref.chiba.lg.jp/hazadmap/ejk/pdf/yure/yure all.pdf, accessed on December 5, 2019.

school enrollment rate and county-level primary school attendance rate. Data on these

rates are taken from the SRCP (1925–1935 editions) published by the Chiba prefecture

between 1927 and 1937.

Appendix C Empirical Analysis Appendix

C.1 Trends in Height and Weight

Figures C.1a and C.1b present the heights of boys and girls by age and measured year,

respectively. Similarly, Figures C.1c and C.1d present the weights of boys and girls,

respectively. The trends of height and weight show near parallel translation over the

measured year for both genders, suggesting that the trends in child development are

data are severely limited before 1896, however, I use the 1897–1912 editions of the SRCP (17-year laggedrate from the year of birth) to establish the primary school entrance rate of the parental generation.

4

Table B.1: Summary statistics of the control variables

Unit Mean Std. Dev. NBaseline control variables

Fetal death rate in the birth year County-birth year 72.05 15.16 208Rice yield in the birth year County-birth year 30.91 4.36 208Coverage of doctors County-birth year 0.08 0.07 208School enrollment rate of the parental generation County-birth year 87.19 12.31 208

Additional control variablesPrimary school enrollment rate in the measured year Municipal-year 99.62 0.38 3769Primary school attendance rate in the measured year County-year 96.09 0.93 143

Notes: The fetal death rate is the number of still births per 1,000 births. Rice yield is the volume of rice yield per1 hectare. Coverage of doctors is the number of doctors per 100 people. The primary school enrollment rate andattendance rate are the shares of enrolled and attended children relative to total school-aged children. Sources:See Online Appendix B.

similar during my sample periods.

C.2 Robustness Exercises

C.2.1 Including Additional Area Interactions

Table C.1 shows the results from the alternative specification of equation 1 including the

additional interaction terms between the affected cohort dummies and indicator variable

for the JMA-SIS6 area. The estimated coefficients on the interaction terms with respect

to the JMA-SIS6 area are statistically insignificant in all cases. This means that the

effects of the earthquake on the affected cohorts are similar in the JMA-SIS5 and JMA-

SIS6 areas. This makes sense because the physical disruption in the JMA-SIS7 area was

massive relative to the JMA-SIS5 and -6 areas (Table 2).

C.2.2 Alternative Definitions of the Developmental-Stage Age Bin and Rail-

way Disruption

I systematically divided the age bins into 6–8 and 9–11 in the main text because the

observed growth patterns of height are mostly straight in the primary school ages, as

shown in Figure C.1. These bins are also consistent with the fact that the height-for-age

z-scores of −2 in the modern WHO growth reference show similar growth curves to those

children (de Onis et al. 2007). In Table C.2, I check the sensitivity to this definition of

developmental-stage age bins by slightly changing the bins to 6–9 and 8–11. The estimated

coefficients are similar to the results reported in Table 3, supporting the robustness of my

baseline results.

In Table C.3, I check the sensitivity of my results from equation 3 using the same

sample used in Panel A of Table 5 but changing the radius to a 1.5 km range. In Panels

A and B of Table C.3, I set Rail in equation 3 as an indicator variable for municipalities

within 8.5 km and 11.5 km of any municipality including either the Noda line or the

Kururi line, respectively. The results are largely unchanged from those reported in Panel

A of Table 5, supporting the robustness of my baseline results.

5

107108109110111112113114115116117118119120121122123124125126127128129130131132133

Hei

ght (

in c

entim

eter

s)

6 7 8 9 10 11Age

1925 1926 1927 19281929 1930 1931 19321933 1934 1935

(a) Boys’ height (6–11)

107108109110111112113114115116117118119120121122123124125126127128129130131132133

Hei

ght (

in c

entim

eter

s)

6 7 8 9 10 11Age

1925 1926 1927 19281929 1930 1931 19321933 1934 1935

(b) Girls’ height (6–11)

151617181920212223242526272829303132

Wei

ght (

in k

ilogr

ams)

6 7 8 9 10 11Age

1925 1926 1927 19281929 1930 1931 19321933 1934 1935

(c) Boys’ weight (6–11)

151617181920212223242526272829303132

Wei

ght (

in k

ilogr

ams)

6 7 8 9 10 11Age

1925 1926 1927 19281929 1930 1931 19321933 1934 1935

(d) Girls’ weight (6–11)

Figure C.1: Trends in the Height and Weight of ChildrenNote: Each figure shows the average height or weight of children in each measured year. Sources: Calculatedby author from the SPES (1925–1935 editions).

6

Table C.1: Effects of fetal earthquake exposure on height and weight:Including additional area interaction terms

Boys Girls

(1) (2) (3) (4)Ages 6–8 Ages 9–11 Ages 6–8 Ages 9–11

Panel A: Height1923 birth cohort −0.247** −0.219* −0.241** −0.253***

[0.010] [0.028] [0.010] [0.003]1923 birth cohort × SIS6 0.240 0.158 0.043 0.139

[0.158] [0.474] [0.746] [0.371]1923 birth cohort × SIS7 0.119 0.132 −0.290* −0.584***

[0.062] [0.752] [0.034] [0.005]1924 birth cohort −0.113 −0.092 0.132 −0.106

[0.132] [0.364] [0.222] [0.281]1924 birth cohort × SIS6 0.139 0.042 −0.109 −0.007

[0.210] [0.822] [0.402] [0.965]1924 birth cohort × SIS7 0.071 0.032 0.168 0.356

[0.746] [1.000] [0.142] [0.423]Panel B: Weight1923 birth cohort −0.068** −0.055 −0.111** −0.161***

[0.006] [0.136] [0.008] [0.003]1923 birth cohort × SIS6 0.070 0.094 0.082 0.207

[0.142] [0.220] [0.358] [0.235]1923 birth cohort × SIS7 0.013 0.116 −0.257*** −0.273*

[0.646] [0.404] [0.004] [0.015]1924 birth cohort −0.011 −0.044 0.007 −0.051

[0.658] [0.420] [0.866] [0.131]1924 birth cohort × SIS6 0.057 0.100 0.016 −0.005

[0.378] [0.398] [0.834] [0.967]1924 birth cohort × SIS7 −0.021 0.009 −0.106 0.035

[1.000] [1.000] [0.224] [0.885]

***, **, and * represent statistical significance at the 0.5%, 1%, and 5% levels based on the p-values from thewild cluster bootstrap resampling method in brackets, respectively. The data are clustered at the 13-countylevel in the bootstrap procedure. The number of replications is fixed to 1,000 for all the specifications.Notes: The numbers of observations in columns (1)–(4) are 14, 139, 14, 133, 14, 145, and 14, 138, respectively.All the regressions include controls for the rice yield in the birth year; fetal death rate in the birth year;school enrollment rate of the parental generation; school-age-specific fixed effects; and year fixed effects. Thenull hypothesis of no joint significance of the control variables is rejected based on the F -test at the 0.1%level in all the specifications.

7

Table C.2: Effects of fetal earthquake exposure on height and weight:Alternative definitions of the developmental-stage age bins

Boys Girls

(1) (2) (3) (4)Ages 6–9 Ages 8–11 Ages 6–9 Ages 8–11

Panel A: Effects on height1923 birth cohort −0.180* −0.173* −0.191*** −0.214***

[0.028] [0.025] [0.004] [0.002]1923 birth cohort × SIS7 0.075 0.134 −0.268* −0.596*

[0.218] [0.735] [0.022] [0.032]1924 birth cohort −0.030 −0.077 0.092 −0.080

[0.512] [0.347] [0.244] [0.234]1924 birth cohort × SIS7 −0.017 −0.031 0.266 0.349

[0.936] [0.100] [0.124] [0.310]Panel B: Effects on weight1923 birth cohort −0.043* −0.042 −0.086** −0.113***

[0.038] [0.207] [0.006] [0.002]1923 birth cohort × SIS7 0.027 0.093 −0.267* −0.264*

[0.200] [0.331] [0.016] [0.048]1924 birth cohort −0.005 −0.026 0.010 −0.046

[0.778] [0.519] [0.784] [0.088]1924 birth cohort × SIS7 −0.043 −0.028 −0.095 0.010

[0.730] [0.991] [0.126] [0.976]

***, **, and * represent statistical significance at the 0.5%, 1%, and 5% levels based on the p-values from thewild cluster bootstrap resampling method in brackets, respectively. The data are clustered at the 13-countylevel in the bootstrap procedure. The number of replications is fixed to 1,000 for all the specifications.Notes: The numbers of observations in columns (1)–(4) are 18, 851, 18, 845, 18, 857, and 18, 848, respectively.All the regressions include controls for the rice yield in the birth year; fetal death rate in the birth year;school enrollment rate of the parental generation; school-age-specific fixed effects; and year fixed effects.The null hypothesis of no joint significance of the control variables is rejected based on the F -test at the0.1% level in all the specifications.

8

Table C.3: Effects of fetal earthquake exposure on height and weight in the limitedJMA-SIS5 area: Alternative definitions of railway disruption

Boys Girls

(1) (2) (3) (4)Ages 6–8 Ages 9–11 Ages 6–8 Ages 9–11

Panel A-1: Effects on height1923 birth cohort −0.182* −0.184 −0.242*** −0.242**

[0.030] [0.162] [0.004] [0.007]1923 birth cohort × Railway disruption (8.5km) −0.078 0.482 0.215 0.705

[0.966] [0.546] [0.156] [0.529]Panel A-2: Effects on weight1923 birth cohort −0.048 −0.020 −0.104** −0.125*

[0.070] [0.922] [0.008] [0.013]1923 birth cohort × Railway disruption (8.5km) 0.050 0.017 0.084 0.141

[0.626] [0.904] [0.196] [0.737]

Panel B-1: Effects on height1923 birth cohort −0.203* −0.161 −0.247** −0.220*

[0.022] [0.218] [0.008] [0.021]1923 birth cohort × Railway disruption (11.5km) 0.161 0.103 0.206 0.274

[0.758] [0.680] [0.090] [0.773]Panel B-2: Effects on weight1923 birth cohort −0.047* −0.004 −0.104** −0.110*

[0.036] [1.000] [0.008] [0.039]1923 birth cohort × Railway disruption (11.5km) 0.031 −0.149 0.061 −0.054

[0.956] [0.256] [0.366] [0.829]

***, **, and * represent statistical significance at the 0.5%, 1%, and 5% levels based on the p-values from the wildcluster bootstrap resampling method in brackets, respectively. The data are clustered at the 13-county level in thebootstrap procedure. The number of replications is fixed to 1,000 for all specifications.Notes: Samples include municipalities receiving no disaster relief in the SIS5 area. The numbers of observations incolumns (1)–(4) are 9352, 9348, 9353, and 9347, respectively. Estimated coefficients on the 1924 birth cohort dummyand its area interaction term are included in all regressions but are not reported as those estimates are statisticallyinsignificant in all specifications. All the regressions include controls for the rice yield in the birth year; fetal deathrate in the birth year; school enrollment rate of the parental generation; school-age-specific fixed effects; and year fixedeffects. The null hypothesis of no joint significance of the control variables is rejected based on the F -test at the 0.1%level in all the specifications.

C.2.3 Including Additional Control Variables

Table 6 presents the results from specifications including additional control variables in

equation 1. In the same way, I show the results from specifications including the same

additional control variables in equations 2 and 3 in Tables C.4 and C.5, respectively. In

Table C.4, I report the results for the boys aged 6–8 given that all the estimates for the

other subsamples are statistically insignificant (see Table 4). As shown in Table C.4, the

results are largely unchanged from those reported in Tables 4.

C.2.4 Potential Influence of Omitted Variables

To investigate the potential influence of omitted variable bias, I present the results when

using the method proposed by Oster (2019). The maximum R-squared from a hypo-

thetical regression of either height or weight on both the observed and the unobserved

9

variables is assumed to be 1.3 × R-squared as recommended. The value for the relative

degree of selection on the observed and unobserved variables is also assumed to be one,

implying that selection into the unobservables is equal to selection into the observables.

Table C.6 shows the main results of Table 3 but adds the suggested bounds in parentheses.

Despite the conservative setting, the estimated bounds suggest weak attenuation bias in

my estimates; nonetheless, they are still close to the estimates in Table 3.

C.2.5 Time Series Plots of the Birth and Mortality Measures

404550556065707580859095

100105110

Num

ber o

f dea

th b

irths

per

100

0 bi

rths

1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929

(a) Fetal death rate

120125130135140145150155160165170175180185190195200205210215220

Num

ber o

f liv

e bi

rths

per 1

000

birth

s

1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929

(b) Infant mortality rate

252627282930313233343536373839404142434445

Num

ber o

f liv

e bi

rths

per 1

000

peop

le

1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929

(c) Crude birth rate

Figure C.2: Fetal death, infant mortality, and fertility rates in Chiba prefectureNotes: The fetal death rate is the number of deaths births per 1000 births. The infant mortality rate is the number ofinfant deaths (i.e., deaths within 12 months of birth) per 1000 live births. The crude birth rate is the number of live birthsper 1000 people. Sources: Statistics Bureau of the Cabinet (1920b–1930b).

Figure C.2 shows the time series plots of the fetal death, infant mortality, and crude

death rates between 1917 and 1929. The data on these rates are obtained from the

1917–1929 editions of the Vital Statistics of Japan (Statistics Bureau of the Cabinet

1920b–1930b). Figure C.2a and Figure C.2b show the decreasing trend of the risk of

fetal deaths and infant mortality during this period, as also shown by Schneider and

Ogasawara (2018). Although the infant mortality rate seems to increase slightly in 1923,

the increment was not greater as that observed in 1918, suggesting it is not systematic

mortality selection due to the earthquake. Figure C.2c shows no clear trend in the fertility

rate and thus does not support the evidence on fertility selection during and after the

earthquake of 1923.

C.2.6 Testing the Cohort Effects of Surrounding Cohorts

If the estimated stunting effects reported in the main text do not simply capture certain

trends, the mean heights and weights of children born in the surrounding years should

be similar when I exclude the 1923–24 birth cohorts from the sample. Each estimate of

the cohort effect in Table C.7 is obtained from a regression for the sample excluding the

1923–24 birth cohorts, which means that I run 80 regressions to obtain these estimates.

As shown, the estimated coefficients are not statistically significant in most cases.

10

Tab

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Eff

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11

Table C.5: Effects of fetal earthquake exposure on height and weight in the limitedJMA-SIS5 area: Robustness of including additional variables in the railway disruption

regressions

Boys Girls

(1) (2) (3) (4)Panel A Ages 6–8 Ages 9–11 Ages 6–8 Ages 9–11Panel A-1: Effects on height1923 birth cohort −0.205* −0.167 −0.246** −0.225*

[0.024] [0.216] [0.008] [0.019]1923 birth cohort × Railway disruption 0.184 0.181 0.235 0.361

[0.724] [0.598] [0.086] [0.769]Primary school enrollment rate −0.031 0.059 −0.053 −0.049

[0.522] [0.654] [0.466] [0.663]Primary school attendance rate −0.039 0.012 0.068 −0.063

[0.530] [0.908] [0.054] [0.103]Panel A-2: Effects on weight1923 birth cohort −0.051* −0.008 −0.102** −0.110*

[0.028] [0.994] [0.008] [0.039]1923 birth cohort × Railway disruption 0.081 −0.113 0.057 −0.057

[0.722] [0.496] [0.416] [0.815]Primary school enrollment rate −0.017 −0.035 −0.000 −0.069*

[0.560] [0.326] [0.972] [0.043]Primary school attendance rate 0.002 0.051 0.021 −0.005

[0.946] [0.230] [0.300] [0.859]Boys Girls

(1) (2) (3) (4)Panel B Ages 6–8 Ages 9–11 Ages 6–8 Ages 9–11Panel B-1: Effects on height1923 birth cohort −0.197* −0.162 −0.241** −0.233*

[0.018] [0.222] [0.010] [0.015]1923 birth cohort × Railway disruption 0.181 0.187 0.244 0.358

[0.728] [0.588] [0.056] [0.767]Wartime birth cohorts 0.048 0.064 0.208 0.096

[0.552] [0.474] [0.162] [0.253]Pandemic influenza birth cohorts −0.091 −0.070 −0.077 0.167

[0.480] [0.510] [0.528] [0.131]Panel B-1: Effects on weight1923 birth cohort −0.051* −0.006 −0.101* −0.117*

[0.022] [0.990] [0.010] [0.035]1923 birth cohort × Railway disruption 0.081 −0.116 0.060 −0.062

[0.704] [0.480] [0.390] [0.793]Wartime birth cohorts −0.039 0.066 0.001 0.037

[0.310] [0.094] [0.940] [0.635]Pandemic influenza birth cohorts −0.022 −0.015 −0.034 0.149

[0.634] [0.650] [0.520] [0.105]

** and * represent statistical significance at the 1% and 5% levels based on the p-values from the wild clusterbootstrap resampling method in brackets, respectively. The data are clustered at the 13-county level in thebootstrap procedure. The number of replications is fixed to 1,000 for all the specifications.Notes: Samples include municipalities receiving no disaster relief in the SIS5 area. The numbers of observations incolumns (1)–(4) are 9352, 9348, 9353, and 9347, respectively. The estimated coefficients on the 1924 birth cohortdummy and its area interaction terms are not reported because those estimates are statistically insignificant inmost specifications. All the regressions include controls for the rice yield in the birth year; fetal death rate inthe birth year; school enrollment rate of the parental generation; school-age-specific fixed effects; and year fixedeffects. The null hypothesis of no joint significance of the control variables is rejected based on the F -test at the0.1% level in all the specifications.

12

Table C.6: Effects of fetal earthquake exposure on height and weight:Sensitivity to the potential omitted variables

Boys Girls

(1) (2) (3) (4)Ages 6–8 Ages 9–11 Ages 6–8 Ages 9–11

Panel A: Effects on height1923 birth cohort −0.213* −0.196* −0.234* −0.233***

(−0.223) (−0.420) (−0.167) (−0.487)[0.020] [0.044] [0.010] [0.003]

1923 birth cohort × SIS7 0.085 0.109 −0.296** −0.604***(0.155) (0.036) (−0.182) (−0.689)[0.250] [0.798] [0.008] [0.005]

Panel B: Effects on weight1923 birth cohort −0.058* −0.041 −0.099** −0.131**

(−0.016) (0.163) (−0.054) (−0.259)[0.016] [0.314] [0.008] [0.005]

1923 birth cohort × SIS7 0.003 0.102 −0.269*** −0.303**(0.044) (0.043) (−0.220) (−0.347)[0.938] [0.438] [0.002] [0.009]

This table shows the main estimates reported in Table 3 but also presents Oster’s (2019) suggested boundsin parentheses. ***, **, and * represent statistical significance at the 0.5%, 1%, and 5% levels based onthe p-values from the wild cluster bootstrap resampling method in brackets, respectively. The data areclustered at the 13-county level in the bootstrap procedure. The number of replications is fixed to 1,000for all the specifications.Notes: The numbers of observations in columns (1)–(4) are 14, 139, 14, 133,14, 145, and 14, 138, respectively.All the regressions include controls for the rice yield in the birth year; fetal death rate in the birth year;school enrollment rate of the parental generation; school-age-specific fixed effects; and year fixed effects.The null hypothesis of no joint significance of the control variables is rejected based on the F -test at the0.1% level in all the specifications.

13

Table C.7: Effects of fetal earthquake exposure on height and weight: Testing thepotential impacts on surrounding cohorts excluding exposed cohorts

Boys Girls

(1) (2) (3) (4)Ages 6–8 Ages 9–11 Ages 6–8 Ages 9–11

Panel A: Effects on height1918 birth cohort −0.014 0.061 0.006 0.045

[0.802] [0.464] [0.949] [0.658]1919 birth cohort 0.017 −0.029 0.052 0.117

[0.676] [0.648] [0.371] [0.172]1920 birth cohort −0.108* 0.043] −0.118 −0.040

[0.048] [0.496] [0.129] [0.792]1921 birth cohort 0.024 0.024 0.021 −0.038

[0.680] [0.862] [0.737] [0.690]1922 birth cohort 0.076 −0.066 0.029 −0.090

[0.282] [0.526] [0.787] [0.392]1925 birth cohort 0.043 −0.073 0.101 −0.080

[0.542] [0.478] [0.147] [0.520]Panel B: Effects on weight1918 birth cohort −0.046 0.004 −0.055 0.015

[0.160] [0.932] [0.239] [0.796]1919 birth cohort −0.005 0.012 −0.006 0.022

[0.728] [0.492] [0.911] [0.650]1920 birth cohort −0.022 −0.005 −0.011 0.076

[0.080] [0.918] [0.669] [0.362]1921 birth cohort 0.018 0.003 0.034 −0.013

[0.450] [0.928] [0.239] [0.758]1922 birth cohort 0.035 −0.038 −0.012 −0.112

[0.388] [0.534] [0.647] [0.204]1925 birth cohort −0.009 0.039 0.028 0.073

[0.790] [0.428] [0.533] [0.160]

* represents statistical significance at the 5% level based on the p-values from the wild clusterbootstrap resampling method in brackets. The data are clustered at the 13-county level in thebootstrap procedure. The number of replications is fixed to 1,000 for all the specifications.Notes: Each estimate of the cohort effect is obtained from a regression for the sample excludingthe 1923–24 birth cohorts. The numbers of observations for each regression reported in columns(1)–(4) are 11, 548, 11, 580, 11, 555, and 11, 586, respectively. All the regressions include controlsfor the rice yield in the birth year; fetal death rate in the birth year; school enrollment rate of theparental generation; school-age-specific fixed effects; and year fixed effects. The birth cohortsbefore 1918 and after 1926 are not included in these analyses because they include individualsaged under four years, which is insufficient to calculate the mean birth cohort effects. The nullhypothesis of no joint significance of the control variables is rejected based on the F -test at the0.1% level in all the specifications.

14

References, Documents, Statistical Reports, and Database

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[4] Chiba prefecture (webpage). Available online at the Chiba prefecture (http://keihatsu.bousai.pref.chiba.lg.jp/hazadmap/ejk/pdf/yure/yure all.pdf) (last accessed on December 5,2019).

[5] Division of Social Affairs, Chiba Prefecture. Taishodaishinsai no kaiko to sonofukko (Rem-iniscences and reconstructions of the Taisho earthquake, volume 1). [in Japanese] Chiba:Division of Social Affairs, Chiba Prefecture, 1933a.

[6] Division of Social Affairs, Chiba Prefecture. Taishodaishinsai no kaiko to sonofukko (Rem-iniscences and reconstructions of the Taisho earthquake, volume 2). [in Japanese] Chiba:Division of Social Affairs, Chiba Prefecture, 1933b.

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[12] Schneider, Eric B, and Kota, Ogasawara. Disease and child growth in industrialising Japan:Critical windows and the growth pattern, 1917–39. Explorations in Economic History 69(2018), 64–80.

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[14] Statistics Bureau of the Cabinet. Nihonteikoku tokeinenkan (Statistical yearbook of theJapanese Empire, vol. 36–53). [in Japanese] Tokyo: Statistics Bureau of the Cabinet, 1918–1934.

[15] Statistics Bureau of the Cabinet. Nihonteikoku shiintokei (Statistics of causes of death of theempire of Japan, 1911–1927 editions). [in Japanese] Tokyo: Statistics Bureau of the Cabinet,1915a–1928a.

15

[16] Statistics Bureau of the Cabinet. Nihonteikoku jinkodotai tokei (The vital statistics of theempire of Japan, 1916–1933 editions). [in Japanese] Tokyo: Statistics Bureau of the Cabinet,1919b–1934b.

[17] Statistics Bureau of the Cabinet. Shichoson jinkodotai tokei (The vital statistics for munic-ipalities, 1925 edition). [in Japanese] Tokyo: Statistics Bureau of the Cabinet, 1927c.

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