DP2022-27

The Great Influenza Pandemic in Japan:
Policy Responses andSocioeconomic Consequences
Masato SHIZUME

Revised October 14, 2022

The Great Influenza Pandemic in Japan
Policy Responses and Socioeconomic Consequences
Masato Shizume*(First draft: June 2022)(Second draft: October 2022)
Abstract
This paper explores the socioeconomic consequences of the 1918-1920 Great Influenza
Pandemic (GIP) in Japan. First, it reviews the chronological and geographical patterns of thediseaseʼs spread and policy responses by the government. It then employs panel analyses totest the quantitative effects of the pandemic on socioeconomic indicators such as population
growth, factory employment, wage, capital formation, and income. The study finds that 1)
Japan was hit by the pandemic twice, once in the winter of 1918-1919 and again in the winterto spring of 1919-1920, with the urban population facing a greater risk to life because of
greater exposure to the virus, while the rural population was more likely to succumb to thedisease when infected, 2) the pandemic seemed to have a noticeable effect on socioeconomic
activities in the short and medium terms, suggesting a trigger of population outflows andsubstitution of labor by capital without any adverse effect on income, and 3) the government
response included medical and public health measures but not economic measures. Though
the GIP was similar to COVID-19 in terms of epidemiological patterns, it was very different
in terms of human agency and socioeconomic consequences.

* Professor, Faculty of Political Science and Economics, Waseda University, and Research
Fellow, Research Institute for Economics and Business Administration, Kobe University. (email: masato.shizume@waseda.jp) I thank Masahiko Shibamoto, Pierre Siklos, andparticipants in the 91st Annual Conference of Socio-Economic History Society of Japan (April
2022) for helpful comments. All remaining errors are of my own.
12

1. Introduction
The outbreak of COVID-19 has shed new light on the Great Influenza Pandemic (GIP),
which began in 1918. In this paper, I revisit the GIP in Japan to draw lessons for COVID-19,
focusing on the similarities and differences between the two pandemics. To this end, I look at
the pattern of the diseaseʼs spread and its socioeconomic impact using an underutilized panel
dataset compiled by contemporary experts.
According to a contemporary estimate by the Public Health Bureau (PHB) of theHome Ministry of Japan, over forty percent of the Japanese population was infected andalmost one percent of the population lost their lives to the disease (PHB 1922). Though Japanwas far removed from the main warring nations of Europe and the United States, whichbecame the initial epicenters of the disease toward the end of WWI, its death toll wascomparable to those countries (Hayami 2006, Athukorala and Athukorala 2020).
Since the outbreak of COVID-19, many scholars have studied past pandemics.
Among various others, the GIP has increasingly been attracting the attention of scholars due
to its similarities with COVID-19 in terms of its infectious patterns and its spread acrossborders. Meanwhile, the socioeconomic impacts of the GIP and COVID-19 seem to have bothsimilarities and differences.
There are two strands of research. The first strand tries to capture the overall picture ofsocioeconomic consequences by surveying global trends. Athukorala and Athukorala (2020)
reviews existing studies on the GIP, shows the global and regional estimates of mortality by
the disease, and discusses the demographic and socioeconomic consequences. Arthi andParman (2021) surveys the literature on the long-term effects of the GIP on health, labor, andhuman capital, while also touching on the Great Depression in the 1930s, another major eventthat had adverse global socioeconomic consequences. Using cross-country panel data, Barroet al. (2020) estimates the economic effects of the GIP on GDP and consumption as well as
real return on stocks and short-term interest rates. Beach et al. (2020) explores the effects ofboth the GIP itself and of the policy measures in response to it on health and human capitalthrough mortality, fertility, and economy. Siklos (2022) tests if the GIP led to a reversal intrade and financial globalization trends. Jorda et al. (2021) compares long-term
macroeconomic after-effects of pandemics, including the GIP, and major wars, by looking at
the natural rate of interest. Bloom et al. (2020) studies five major infectious diseases,
including the GIP, and argues that while major epidemics and pandemics can take enormous
human tolls and impose staggering economic burdens, early and targeted interventions may
mitigate these adverse effects.
The second strand digs deeper into the cases of specific countries to take a more detailed
look at the socioeconomic effects of the GIP and policy responses to it. For the United States,
3

Fourie and Norling (2020) tests the effect of the GIP on household income with city level data
to find that cities with higher mortalities saw greater declines in real income, while nonpharmaceutical interventions (NPIs) by local governments had little correlation with
consumer behavior. Correia et al. (2020) argues that the pandemic caused short- andmedium-term economic disruption, while NPIs may have reduce disease transmission without
further depressing economic activity. On the other hand, Velde (2020) claims that the impactof the GIP on the U.S. economy, such as on industrial output or manufacturing employment,
if any, was short-lived. Chapelle (2020) suggests that NPIs had no significant impact on citiesʼ
population, output and employment growth. For regions other than the United States, Bascoet al. (2020) argues that the effect of flu-related excess deaths on real wages was large andnegative but short-lived in Spain, and that the effect on returns to capital was insignificant.
Karlsson et al. (2014) reports that in Sweden, regions with higher incidence rates had rising
poorhouse rates and declining capital returns compared with those with lower incidence rates,
but no discernible effect on per capita earnings.
Country-specific studies make detailed observations, but in most cases, only death-toll
data is available, not data on case numbers. In this regard, Japan is special because the publichealth authorities1 maintained data on both case numbers and the death toll. This paper is thefirst attempt to perform a case study of Japan using the underutilized prefectural panel dataon case numbers and death toll to explore the socioeconomic consequences of the GIP. In
doing so, this study tries to add some insight to the second strand of research, country-specificcase studies, to capture the dynamics of infectious diseases and their socioeconomic outcomes.
The paper finds that 1) Japan was hit twice by the pandemic, once in the winter of 19181919 and again in winter to spring 1919-1920, that the urban population was more exposedto the virus and faced a greater risk to life, while the rural population, once infected, was more
likely to succumb to the disease, 2) the pandemic seemed to have a noticeable effect onsocioeconomic activities in the short and medium terms, suggesting a trigger of population
outflows and substitution of labor by capital without any adverse effect on income, and 3) the
government response included medical and public health measures but no significant
economic measures. Overall, even though the GIP was similar to COVID-19 in terms of
epidemiological patterns, it was very different in terms of human agency and socioeconomic
consequences.
The rest of this paper is structured as follows: Section 2 reviews the chronological andgeographical patterns of the diseaseʼs spread, and reviews the policy responses by the1Another country which had both types of data was Sweden. Karlsson et al. (2014) explores

the effects of the GIP on inequality, capital income, and household earnings.
4

government; Section 3 uses panel analyses to test the quantitative effects of the pandemic onsocioeconomic indicators such as population growth, factory employment, wage, capitalformation, and income; and Section 4 concludes by interpreting the implications of theempirical results and suggesting potential directions for future research.
2. The Great Influenza Pandemic in Japan
2.1 Chronology of the Pandemic
The GIP emerged in 1918 and spread worldwide through 1920. The mobilization anddemobilization of troops for WWI played a crucial role in spreading the disease while otherforms of physical contact also contributed (Crosby 1989). The first wave started in army
camps in the Midwest of the United States in the spring of 1918, got transmitted to Europe
with the U.S. troops, and had spread to North Africa, India, China, and Australia by July ofthat year. The second wave started in late summer in France and spread across the world. The
third wave was reported in many places around the world from winter through spring of 19181919. Japan experienced two waves, one in the winter of 1918-1919 and again in winter tospring 1919-1920, later than in the epicenters of North America and Europe, probably
because of its remote location. A report titled Ryūkōsei Kanbō (Influenza) published by the
PHB stated that “with the busy traffic of ships and frequent communications of commerce, it
was inevitable for this country to escape the pandemic. The pandemic showed signs ofprevalence from late August through early September 1918, three to four months later thanin Western Europe, and immediately spread nationwide.” (PHB 1922: 84)2
According to Athukorala and Athukorala (2020), the global death toll of the GIP was
37-46 million, with a mortality rate of 2.0-2.5 percent of the world population, four to fivetimes the casualty rate of WWI. Japan recorded 453-517 thousand deaths, with a mortality
rate of 0.8-0.9 percent of the population. The mortality rate in Japan was slightly higher thanin Europe and Anglo America (0.5-0.7 percent) and lower than in most of other parts of theworld.3
Table 1 shows three estimates of identified cases and/or deaths due to the GIP in
Japan during 1918-1920. The first is (case numbers and deaths) from PHB (1922), the second2

PHB (1922) contains an extensive analysis of the diseaseʼs spread and the social and policy

responses to counter it in Japan and other countries with a wealth of data.
3

British India was reported to have suffered 16,700-18,500 thousand deaths (a mortality

rate of 5.5-6.1 percent of the population), Africa 2,207-2,268 thousand (3.5-3.6 percent),and Latin America 900-1,053 thousand (1.1-1.5 percent). Chinaʼs death toll was uncertain,
with various estimates putting it between 4,000 and 9,500 thousand (0.8-2.0 percent).
5is (deaths) from Hayami (2006), and the third is (deaths) from Richard et al. (2009).4
Table 1. Estimates of case numbers and deaths in Japan due to the Great Influenza Pandemic
Case
Case* (a)Death* (b)Incidencefatality

rate**

rate

Populationmortality
rate***(a/b)**PHB (1922)
Aug. 1918-July 1919

21,168.4

257.4

37.58

1.22

0.46

Oct. 1919-July 1920

2,412.1

127.7

4.28

5.29

0.23

Total

23,580.5

385.0

41.86

1.63

0.68

Oct. 1918-May 1919

n.a.

266.5

n.a.

n.a.

0.47

Dec. 1919-May 1920

n.a.

186.7

n.a.

n.a.

0.33

Total

n.a.

453.2

n.a.

n.a.

0.80

Oct. 1918-May 1919

n.a.

299.7

n.a.

n.a.

0.54

Dec. 1919-May 1920

n.a.

181.8

n.a.

n.a.

0.33

Hayami (2006)

Richard et al. (2009)
Monthly estimate

Annual estimate

Total

n.a.

481.5

n.a.

n.a.

0.87

1918-1920

n.a.

532.1

n.a.

n.a.

0.97

Sources: PHB (1922); Hayami (2006); Richard et al. (2009)
Footnote 1: Case numbers and deaths are in thousands.
Footnote 2: The incidence rate is per 100 population at the end of 1917, and the case mortality
rate is per 100 cases.
Footnote 3: The population mortality rate is per 10,000 population at the end of 1917.
The three estimates show a similar trend but different levels. PHB (1922) estimatesthat the number of cases amounted to around 24 million, or 42 percent of Japanʼs population,at the end of 1917, while the number of deaths amounted to 385 thousand, or 0.8 percent of
the population. It states that the first wave began in August 1918 and lasted until July 1919,
and that the second wave lasted from October 1919 to July 1920.5 These estimates are basedon reports from individual prefectures. Hayami (2006) estimates a death toll of 453 thousand,or 0.8 percent of the national population. Richard et al. (2009) estimates a death toll of 482

4

Athukorala and Athukorala (2020) takes data from Hayami (2006) and Richard et al.

(2009).
5

While the report also suggests another wave from August 1920 to July 2021, this paper

excludes it from the GIP because of its small magnitude. Hayami (2006) and Richard et al.
(2009) similarly exclude it from the GIP.
6

thousand, or 0.9 percent of the population, based on monthly data, and 532 thousand, or 1.0
percent of the population, based on annual data. Hayami and Richard et al. base theircalculations on excess mortality and do not estimate case numbers.6
Both Hayami (2006) and Richard et al. (2009) claim that the first wave lasted from
October 1918 to May 1919, while the second wave lasted from December 1919 to May 1920.
Both estimate that the peak of the first wave was in November 1918, followed by a smaller
peak in February 1919, and that of the second wave was in January 1920.
While Hayami (2006: 235-236) notes a possibility of underestimation7 with regard
to the PHB estimates, a distinctive feature of the latter is the availability of not just the deathtoll, which is commonly available also for other countries, but also case numbers by prefecture.
This enables the study of the dynamics of infection with and death from the disease. A
quantitative analysis using the PHB data is provided later in this section.
Figure 1. Number of articles in national newspapers
250

Jan. 1920 (223)
200

Asahi

YomiuriFeb. 1919 (137)150

Nov. 1918 (121)

100

50

0
1917

6

1918

1919

1920

1921

While Hayami (2006) counts excess mortality due only to respiratory diseases, Richard etal. (2009) counts excess mortality due to all diseases. As a result, the death toll of the latterexceeds the former, suggesting greater vulnerability to and risk of mortality from influenza
for patients with various types of underlying diseases.
7

Hayami (2006: 234-236) guesses that the PHB may have excluded figures that werecounted as deaths due to reasons other than influenza or classified as deaths due tounknown reasons, and that case number and death surveys in some prefectures may havebeen incomplete.
7Sources: Asahi Shimbun Database Kikuzo II Visual; Yomiuri Shimbun Database Yomidasu

Rekishi-Kan
Articles in the national newspapers, Asahi Shimbun and Yomiuri Shimbun, confirm
that people were well informed of the situation (Figure 1). During the first wave, the numberof articles mentioning “kanbō,” “ryūkan,”8 or “influenza” peaked once in November 1918 andrebounded in February 1919. As early as July 1918, newspapers were reporting on the spreadof the GIP overseas. Starting October that year, they reported clusters in domestic military
camps and schools. During the second wave, the number of articles mentioning the GIP
peaked in January 1920.9.
2.2 Policy Responses
Acknowledging the need for preventing the spread of the disease and minimizing casualties,
the Japanese government tried to take various measures to counter the pandemic based on
the latest developments in public health and medicine research and measures taken by othergovernments. Measures included quarantining the infected, nationwide campaigns toencourage people to wear masks, gargle regularly, and avoid large gatherings, and thefacilitation of medical treatments and trials for the development of vaccines (PHB 1922: 110).
In January 1919, the Home Ministry printed 50 thousand copies of guidelines for theprevention and treatment of influenza and distributed them as a part of a national campaign.
The protocols in these guidelines are applicable even in the 21st century. They stipulated:
To prevent infection:
1) Do not get close to a patient, suspected patient, or persons who are coughing2) Avoid places with large gatherings of people3) Always wear a mask in crowded spaces, such as train carriages; if you do not have amask, cover your nose and mouth with a handkerchief, towel, or some other cloth4) Gargle regularly with salted water or warm water; or better yet with a gargle
If infected:
1) If you feel a cold coming on, go to bed immediately and call a doctor
2) Isolate from other people as far as possible except to receive nursing care3) Do not go out without the permission of your doctor even if you feel recovered

8“Kanbō” and “ryūkan” (an abbreviation of ryūkōsei-kanbō) are Japanese words for

influenza.
9

Hayami (2006) provides samples of individual articles.
8For your safety:
1) Keep your home and surroundings clean and open up doors and windows on sunnydays2) Sun your bedding and bedclothes on all sunny days
3) Be cautious even when you feel healthy4) For the sake of public health, make sure to cover your nose and mouth with ahandkerchief, towel, or other cloth when coughing or sneezing5) Sanitize the materials such as dishes, cloth, and bedding attached by spits or snottersof a patient by burning, boiling, or with a disinfectant
A major difference between the GIP and COVID-19 in terms of policy measures isthat there were no lockdowns, social distancing mandates, or other major non-pharmaceuticalmeasures to curb socioeconomic activities during the former. As PHB (1922: 186-187)
reflects, “Although we noticed that gatherings of large numbers of people were the mostdangerous, and that counter measures against them were critical, it was the most difficult totake such actions.” Also, effective vaccines were unable to be developed during this pandemic(PHB 1922: 341)10.
Accordingly, people commuted and traveled as usual even during the pandemic. The
number of passengers of the National Railway (NR) remained on an upward trend throughthe pandemic seasons, only temporary dropping during the initial stages, in the fall 1918
(Figure 2).

10Against the backdrop of rapid and widespread infections, medical research laboratories

competed to develop vaccines, and some laboratories, claiming to have successfully
developed a feasible vaccine, began immunization programs. However, in the event, these
efforts proved ineffective as the virus had not yet been discovered and the cause of thedisease was only discovered in the 1930s. PHB concluded, “At the present time, we are stillin the experimental stages of developing a vaccine for influenza” (PHB 1922: 389).
9Figure 2. Number of railway passengers per day (the National Rail)
1,400,000

deviation from the trend (percentage points, RHS)
railway passengers (SA, LHS)

1,200,000

5040trend (LHS)
1,000,000

30Apr.
800,000

20

Feb.
600,000

10

400,000

0Aug. Nov.

200,000

‐10

Feb.

0

‐20
1918

1919

1920

Source: Railroad Statistics, Railroad Authority, various issues
Footnote: Seasonally adjusted and trend data were obtained using the X-12ARIMA method.
The Ministry of Railways 1918 annual report (MOR 1920: 2-3) stated that “inNovember 1918, people took into account the influenza outbreak and refrained from travelingto some extent, but following news of the ceasefire of the Great War, people were tempted totravel near and far, and to both urban and rural destinations.” The following yearʼs annual
report (MOR 1922: 1) noted that the boom continued in 1919 and early 1920 without
mentioning the effect of the pandemic.
Passenger trends in 1918-1920 are sharply contrasted with those during COVID-19.
The number of passengers, which declined by a fourth in spring 2020, had not yet fully
rebounded as of June 2022.

10Figure 3. Number of railway passengers per month
2,500,000

120

2,000,000

90
deviation from the trend

1,500,000(parcentage point, RHS)
trend (LHS)

60

1,000,000

monthly railway passengers

30(thousand, SA, LHS)

Feb.
Apr.

500,000

0

-30

0
2018

2019

2020

2021

2022

Source: Railroad Transport Statistics, Ministry of Land, Infrastructure, Transport andTourism, various issues
Footnote: Seasonally adjusted and trend data were obtained using the X-12ARIMA method.
Another big difference compared with COVID-19 is that the government did nottake on the responsibility of introducing economic stimulus policies. This is conceivably
because the government had taken virtually no action to restrict economic activity in the firstplace. Expenditure toward infectious diseases increased from JPY 1.0 million in fiscal 1917 to
JPY 3.2 million in fiscal 1919, i.e., from 0.14 percent to 0.27 percent of the total expenditureof the general account of the national government. This pattern of government expenditure
was in sharp contrast to that toward rehabilitation and reconstruction following the Great
Kanto Earthquake of 1923, which totaled JPY 1.4 billion in the eight years from fiscal 1923 to
fiscal 1930, averaging JPY 181 million per year, or 13 percent of the total expenditure of thenational government. In fact, responding to the domestic economic boom following WWI, theBank of Japan tightened its monetary policy and raised its official discount rate four times,
from 5.11 percent in October 1918 to 8.03 percent in December 1919, to cool off the economy(Figure 4).

11

Figure 4. Wholesale prices and the Bank of Japanʼs official discount rate
350

1934‐1936 average=100

Percent

12

300

10

250

8

200

6

150

4

100

2

50

0
1913

1915

Pandemic period

1917

1919

Wholesale prices (LHS)

1921

1923

Official discout rate (RHS)

Sources: The Bank of Japan (1986, 1987)
2.3 Quantitative Analysis of the Pandemic
2.3.1 Factors Affecting the Spread of the Disease
This subsection uses a quantitative analysis of the pandemicʼs pattern to explore factors that
affected the spread of the disease. The standard formula is:𝑋𝛼𝛼 𝐷𝑒𝑛𝑠𝑖𝑡𝑦

𝛼 𝑆𝑡𝑎𝑟𝑡𝐷𝑎𝑡𝑒

𝜀 (1),

where 𝑋 is a dependent variable denoting the status of infection in prefecture i(deaths/10,000 population, cases/100 population, and deaths/100 cases), 𝐷𝑒𝑛𝑠𝑖𝑡𝑦 is the
population density of prefecture i, and 𝑆𝑡𝑎𝑟𝑡𝐷𝑎𝑡𝑒 is the date of the start of the pandemic inprefecture i. Table 2 reports descriptive statistics for the variables in the equation.

12

Table 2. Descriptive statistics for variables in Equation (1)Mean
Deaths/10,000 population in 1918-1919

Max
46.15

Min

SD

85.39

# of obs.

13.67

14.41

47

Deaths/10,000 population in 1919-1920

20.90

41.44

4.07

8.94

47

Cases/100 population in 1918-1919

39.05

61.04

18.27

12.61

47

Cases/100 population in 1919-1920

3.55

20.59

0.45

3.65

47

Deaths/100 cases in 1918-1919

1.26

2.70

0.63

0.45

47

Deaths/100 cases in 1918-1920

7.95

18.51

0.82

3.20

47

Population density in 1918*

3.83

26.76

0.39

4.73

47

Start date in 1918

Oct. 4

Nov. 5

Aug. 516.03

43Start date in 1919

Nov. 14

Dec. 15

Sept. 15

18.84

40

Footnote: Population density is given in terms of thousand population per square ri, as of
October 1918. One square ri is the equivalent of 15.42 square kilometers.
Sources: PHB (1922); Statistical Yearbook of the Empire of Japan, various issues
Table 3. Regression results on the influenzaʼs spread in 1918-1919
Dependent variablea) Deaths/pop.

b) Cases/pop.

c) Deaths/cases

1918-1919

1918-1919

1918-1919

Coefficients of independent variables
Constant

1333.492 *(Pop. density in 1918
(thousands)

781.585 )1551.365 **(-0.1480(

Start date in 1918

0.1656 )0.1141 )(-0.3090(-0.1880(731.857 )-25.5390.4174 )0.0154(-0.2208 **(0.1069 )24.219 )0.0228 )0.0039(0.0035 )F-statistic

2.18

2.83

0.98

R-squared

0.06280.1251

0.056744

44

44# of observations

Footnote 1: Figures in parenthesis are robust standard errors.
Footnote 2: * denotes significance at 10 percent; ** denotes significance at 5 percent.
Table 3 shows the results of OLS regressions for the first wave, during the 19181919 season. The start date coefficient is negative and statistically significant on
cases/population at the five percent level, indicating that the infection was more widespreadwhere the first infection happened sooner. This suggests that the areas that were hit early
faced the disease unprepared, e.g., the Home Ministryʼs national campaign not yet having
arrived in the prefecture.
13

The population density coefficients were not statistically significant in all theregressions. This may be interpreted to mean that there was no significant difference between
urban and rural areas, with high infection rates nationwide. As noted in PHB (1922: 86), “inmost prefectures, the disease broke out in the cities first and then spread to the surrounding
villages.”
Table 4. Regression results on the influenzaʼs spread in 1919-1920
Dependent variablea) Deaths/pop.

b) Cases/pop.

c) Deaths/cases d) Deaths/pop.

e) Cases/pop.

1919-1920

1919-1920

1919-1920

1919-1920

1919-1920

Coefficients of independent variables
Constant

72.619(Pop. density in 1918
(thousands)

107.045(0.6731 ***(Start date in 1919

0.1065 )0.0660 )113.550 )-183.086(

0.3988 ***(-0.0075(

Cases/pop. In 1918

479.254 )0.0568 )0.0156 )--69.960(-0.2753 ***(-0.0145(147.195 )0.0464 )0.0202 )134.592(

0.6760 ***(0.0265(481.769 )0.1224 )0.3693 ***(-0.0072(-

0.0664 )0.0988 )0.0564 )-0.0180(

0.0054(122.753 )0.0167 )-0.0558(

0.0337 )F-statistic

20.17

31.82

25.47

12.84

46.1

R-squared

0.19170.35140.22020.19180.3818# of obs.

40

40

40

40

40

Footnote 1: Figures in parentheses are robust standard errors.
Footnote 2: *** denotes significance at 1 percent.
Table 4 shows the results of OLS regressions for the second wave, during the 19191920 season. The population density coefficients are statistically significant at one percent
level in all the regressions. Interestingly, the sign of the coefficient on cases/population ispositive, and that on deaths/cases is negative. This suggests that people had a higher risk ofinfection in the urban areas but, once infected, were more likely to receive better medical carethan in the rural areas. The population density coefficient on deaths/population is positiveand statistically significant at the one percent level, indicating that urban residents were more
vulnerable to contracting the disease with a higher risk of infection (although, of course, withchances of receiving better medical care).
The start date coefficients in 1919 were not statistically significant in all theregressions, suggesting that people were prepared for the disease by the time the second wave
began. Also, the coefficient on cases/population during the previous wave (1918-1919
season) was not statistically significant, suggesting that herd immunity did not work,
presumably due to the virus mutating.

14

Table 5. Regression results on influenzaʼs spread in 1918-1920
Dependent variablea) Deaths/pop.

b) Cases/pop.

c) Deaths/cases

1918-1920

1918-1920

1918-1920

Coefficients of independent variables
Constant

2215.046 ***(Pop. density in 1918
(thousands)

643.621 )1745.979 **(731.167 )0.6828 ***(Start date in 1918

0.2017 )0.0940 )(0.1080(-0.3143 ***(-32.0270.3808 )0.0141(-0.2489 **(0.1068 )30.096 )0.0187 )0.0049(0.0044 )F-statistic

9.49

2.73

1.03

R-squared

0.13720.12440.0524# of obs.

44

44

44

Footnote 1: Figures in parentheses are robust standard errors.
Footnote 2: ** denotes significance at 5 percent; ***denotes significance at 1 percent.
Table 5 shows the results of OLS regressions for the entire pandemic, spanning
1918-1920 and including both the aforementioned waves. As in the case of the second wave
during 1919-1920, the population density coefficient is statistically significant at the one
percent level in all the regressions. The sign of the coefficient on cases/population is positive,
and that on deaths/cases is negative. The population density coefficient on deaths/population
ratio is positive and statistically significant at the one percent level, indicating that urban
residents were more at risk of contracting the disease and dying of it than rural residents
despite being more likely to receive better medical care.
The state date coefficients in 1918 are negative and statistically significant on
deaths/population and cases/population at the one percent and five percent levels,
respectively. This indicates that the people and the government were unprepared when the
pandemic first broke out, which resulted in a higher number of infections and deaths throughthe two waves.
3. Socioeconomic effects of the pandemic
This section explores the socioeconomic effects of the pandemic based on a quantitative
analysis using panel data. The standard formula is:
𝑌,

𝛽

𝛽 𝑋,

𝛽 𝑋,

𝐹𝐸

𝜀 , (2),where dependent variable 𝑌 , is the socioeconomic outcome for prefecture i during time t,15

independent variable 𝑋 ,and 𝑋 ,

represent the treatment effect of disease severity inprefecture i during the pandemic period on outcomes in 1919-1920 and 1921-1922
respectively, 𝐹𝐸 is the fixed effect for prefecture i. The dependent variables used are theannualized total population growth, the annualized natural population growth, the annualizedsocial population growth, the annualized growth in factory employment, and the annualized
growth in paid-in capital. 11 The independent variables used are the deaths/population,cases/population, and deaths/cases ratios, which are the dependent variables in equation (1).
The data is in a balanced panel including 44 prefectural levels and three periods ‒ prepandemic (1914-1918), pandemic (1919-1920), and post-pandemic (1921-1922) ‒ giving
132 observations. The main focus of interest is to see whether the severity of the pandemic as
an external shock affected regional economic activities during and after the GIP. Growthduring 1921-1922 is used for the post-pandemic period to exclude the effect of another big
external shock, the Great Kanto Earthquake of 1923.
Considering the endogeneity problem in 1919-1920, that is the possibility that thedependent variable 𝑌 , and independent variable 𝑋 ,might have been affected by somecommon factors, I employ instrument-variable (IV) regressions instead of OLS. By doing so,
I intend to control such hidden factors and isolate the effect of the independent variable.
Having observed that the spread of the disease had been affected by the start date of thepandemic in 1918, and assuming that the start date of the pandemic may not have affected
the dependent variables, I use the start date of the pandemic in 1918 as IV for 𝑋 ,. Whenemploying IV regressions, I exclude the three prefectures (nine observations) for which thestart date was missing from the panel, thereby getting 132 observations12.
Table 6 reports descriptive statistics of socioeconomic outcomes at the prefecturallevel. It shows that the average natural population growth was stable at the 1.0-1.1 level acrossprefectures through the sample period, while the average social population growth fluctuated,starting at slightly under zero percent during 1914-1918, shrinking to minus one percent
during 1919-1920, and returning to around zero percent during 1921-1922.13 The average

11

Natural population growth is calculated by subtracting the number of deaths from thenumber of births. Social population growth is extrapolated by subtracting the natural
population growth from the total population growth. Conceptually, social population growth
could be also obtained by subtracting outflow from inflow, but data limitations do not allow
a direct calculation.
12

I exclude Gunma, Chiba and Tokushima Prefectures.

13

While this paper does not go into the details of social population growth, fluctuations insocial population growth were caused by migrations from one prefecture to another, as well16

factory employment growth was over 10 percent during 1914-1918, thanks to the WWI boom,
fell to three percent during 1919-1920, and rebounded to six percent during 1921-1922. The
average real wage for laborers and carpenters declined in 1914-1918, recovered over 20
percent in 1919-1920, and continued to grow at a reduced pace in 1921-1922. The averagepaid-in capital growth started at 13 percent during 1914-1918, surged to 38 percent during
1919-1920, and fell to 8 percent during 1921-1922. The average real income shrank by one
percent in 1914-1918 and showed double-digit growth afterward.
Table 6. Descriptive statistics of socioeconomic outcomes

AverageMin.

Population growth, 1914-1918

0.0073-0.0123

Max. Std. Dev. # of obs.

Population growth, 1919-1920-0.0022-0.0534

0.03190.0158

47

Population growth, 1921-1922

0.0111-0.0035

0.04840.009747

Natural population growth, 1914-1918

0.01050.00380.02030.003247

Natural population growth, 1919-1920

0.01020.00340.03280.005147

Natural population growth, 1921-1922

0.0111-0.0035

0.04840.009747

Social population growth, 1914-1918-0.0034-0.0251

0.02870.010347

Social population growth, 1919-1920-0.0128-0.0589

0.02330.016247

Social population growth, 1921-1922-0.0011-0.01390.03150.0096

47

Factory employment growth, 1914-1918

0.10250.02140.28180.0562

47

Factory employment growth, 1919-1920

0.0265-0.77540.19360.143447

0.03610.010047

Factory employment growth, 1921-1922

0.0568-0.05380.27530.0581

47

Real laborer wage growth, 1914-1918-0.0049-0.0853

0.05880.0374

13

Real laborer real wage growth, 1919-1920

0.22300.02290.38390.0877

13

Real laborer real wage growth, 1921-1922

0.0275-0.1008

0.12100.0674

13

Real carpenter real wage growth, 1914-1918-0.0335-0.0680-0.00070.021913

Real carpenter real wage growth, 1919-1920

0.20710.08180.3221

0.061013

Real carpenter real wage growth, 1921-1922

0.0428-0.0118

0.1596

0.051913

Paid-in capital growth, 1914-1918

0.1185-0.1960

0.45180.095647

Paid-in capital growth, 1919-1920

0.3235-0.3359

1.05360.211047

Paid-in capital growth, 1921-1922

0.0725-0.8004

0.38070.1527

47

Real income growth, 1914-1918-0.0119-0.10070.14420.0527

47

Real income growth, 1919-1920

0.25320.04380.38350.0744

47

Real income growth, 1921-1922

0.1940-0.0024

0.33900.0772

47

Footnote 1: All figures are expressed as annualized changes in logs.
Footnote 2: Data used for calculating total population growth during 1914-1920, natural
population growth during 1914-1922, and paid-in capital growth during 1914-1922 is as ofthe end of year; data used for calculating total population growth during 1921-1922 is as of

as by emigration from Japan to other parts of the world, including colonies.
17

October 31 1920 and September 1 1923; social population growth during 1921-1922 isextrapolated using the above total population growth and natural population growth figuresfor 1921-1922; data used for calculating factory employment growth during 1914-1922 is theaverage of daily data within the year. For population data, the source underwent a base change
in 1920, using data from the first national census on October 1, 1920, and compiling the
population as of September 1, 1923, the day of the Great Kanto Earthquake.
Source: Statistical Yearbook of the Empire of Japan, various issues
Table 7. IV regression results on annualized total population growth
Independent variablea) Deaths/pop.

b) Cases/pop.

c) Deaths/cases

Coefficient of independent Variables
Constant

0.00763 ***(Deaths/population ratio
to 1919-1920 growth(

0.00089 )0.00754 ***(0.00081 )-0.00015 ***(Deaths/population ratio
to 1921-1922 growth

0.00077 )0.00828 ***0.00003 )0.00006 ***(0.00001 )Cases/population ratio-0.00025 ***to 1919-1920 growth(

Cases/population ratio

0.00006 )0.00006 ***to 1921-1922 growth(

0.00001 )Deaths/cases ratio-0.00594 ***to 1919-1920 growth(

Deaths/cases ratio

0.00133 )0.00234 ***to 1921-1922 growth(

0.00044 )2

Wald chi Statistics

168.80147.7357.75

R squared

0.19460.14400.2062

132

132

132

# of obs.

Footnote 1: Figures in parentheses are robust standard errors.
Footnote 2: ***denotes significance at 1 percent.
Footnote 3: Prefecture-level fixed effects are not reported.
Table 7 shows the results of IV regressions on annualized total population growth.
Our main interest is in the treatment effect of the infection, which is described in three forms,
namely deaths/population ratio, cases/population ratio, and deaths/cases ratio, on total
population growth. All the treatment variables had a statistically significant negative effect on
total population growth during the pandemic and a positive effect in the post-pandemic years.
Although statistically significant, the aforementioned effects were small. When thedeaths/population ratio in a prefecture rose by one over 100 population, the total population18

grew by -0.015 percentage points fewer annually in the 1919-1920 period and by 0.006
percentage points more annually in 1921-1922 period. When the cases/population ratio roseby one over 100 population, the population grew by -0.025 percentage points fewer in 19191920 and by 0.006 percentage points more in 1921-1922. When the deaths/cases ratio roseby one over 10,000 population, the population grew by -0.594 percentage points fewer in
1919-1920 and by 0.234 percentage points more in 1921-1922.
Table 8. IV regression results on annualized natural population growth
Table 8. IV regression results on annualized natural population growth
Independent variablea) Deaths/pop.

b) Cases/pop.

c) Deaths/cases

Coefficient of independent Variables
Constant

0.01057 ***(Deaths/population ratio
to 1919-1920 growth(

0.00047 )0.00996 ***(0.00070 )0.00000(

Deaths/population ratio
to 1921-1922 growth

0.00062 )0.01111 ***0.00001 )0.00001(

0.00002 )Cases/population ratio-0.00002

to 1919-1920 growth(

Cases/population ratio

0.00002 )-0.00001

to 1921-1922 growth(

0.00003 )Deaths/cases ratio

0.00022

to 1919-1920 growth(

Deaths/cases ratio

0.00042 )0.00120

to 1921-1922 growth(0.00106 )2

2419

2160.22

1.31

R squared

0.00250.00170.0124# of obs.

132

132

132

Wald chi Statistics

Footnote 1: Figures in parentheses are robust standard errors.
Footnote 2: ***denotes significance at 1 percent.
Footnote 3: Prefecture-level fixed effects are not reported.
Total population growth is resolved into two components ‒ natural population growth andsocial population growth. The natural population growth refers to the number of births lessthat of deaths. The social population growth refers to the number of immigrants from otherregions less that of emigrants to other regions.
Table 8 shows the results of IV regressions on annualized natural population growth.
19None of the treatment variables had statistically significant effects on natural population
growth during or after the pandemic period.
Table 9. IV regression results on annualized social population growth
Independent variablea) Deaths/pop.

b) Cases/pop.

c) Deaths/cases-0.00253 ***-0.00316 ***Coefficient of independent Variables
Constant-0.00306 ***(Deaths/population ratio
to 1919-1920 growth(

0.00096 )(0.00092 )-0.00015 ***(Deaths/population ratio
to 1921-1922 growth

0.00086 )0.00004 )0.00003 ***(0.00001 )Cases/population ratio-0.00024 ***to 1919-1920 growth(

Cases/population ratio

0.00006 )0.00002

to 1921-1922 growth(

0.00001 )Deaths/cases ratio-0.00590 ***to 1919-1920 growth(

Deaths/cases ratio

0.00149 )0.00138 ***to 1921-1922 growth(

0.00046 )2

Wald chi Statistics

18.95

7.4

31.90

R squared

0.15090.12060.1529# of obs.

132

132

132

Footnote 1: Figures in parentheses are robust standard errors.
Footnote 2: *** denotes significance at 1 percent.
Footnote 3: Prefecture- and year-level fixed effects are not reported.
Table 9 shows the results of IV regressions on annualized social population growth.
All the treatment variables had statistically significant negative effects on social population
growth during the pandemic; the deaths/population ratio and deaths/cases ratio had positiveeffects on it after the pandemic. When the deaths/population ratio rose by one over 100
population during the pandemic, the social population growth decreased by -0.015 percentage
points annually in 1919-1920, and increased by 0.003 percentage points annually in 19211922. When the cases/population ratio rose by one over 100 population, the social population
growth decreased by -0.024 percentage points annually in 1919-1920. The effect in 19211922 was not statistically significant. When the deaths/population ratio rose by one over
10,000 population, the social population growth decreased by -0.590 percentage points
20

annually in 1919-1920 and increased by 0.138 percentage points annually in 1921-1922.
As a whole, the effect of the spread of infections on regional differences in population
growth was statistically significant but small. The negative effect during the pandemic waslarger than the recovery following the pandemic. The regional differences in effect weremainly driven by social factors, namely people moving from one region to another, not by
natural factors, namely changes in the birth or mortality rates.
Table 10. IV regression results on annualized growth of factory employment
Independent variablea) Deaths/pop.

b) Cases/pop.

c) Deaths/cases

Coefficient of independent Variables
Constant

0.10387 ***(Deaths/population ratio
to 1919-1920 growth(0.01460 )0.10539 ***(0.01224 )-0.00122 ***(Deaths/population ratio
to 1921-1922 growth0.01407 )0.10372 ***0.00044 )-0.00069 ***(0.00023 )Cases/population ratio-0.00191 ***to 1919-1920 growth(

Cases/population ratio

0.00070 )-0.00107 ***to 1921-1922 growth(

0.00039 )Deaths/cases ratio-0.05032 ***to 1919-1920 growth(

Deaths/cases ratio

0.01666 )-0.02965 ***to 1921-1922 growth(0.00729 )2

Wald chi Statistics

114.05116.7416.74

R squared

0.07290.0586

0.1134# of obs.

132

132

132

Footnote 1: Figures in parentheses are robust standard errors.
Footnote 2: *** denotes significance at 1 percent.
Footnote 3: Prefecture-level fixed effects are not reported.
Next, I look into the impact of the pandemic on the labor market. GIP-induced crossprefectural migrations could have affected the labor supply in the prefectures in question. My
hypothesis here is that a prefecture hit more severely would see greater outflows of population,
resulting in labor shortages, a relative decline in employment, and higher wages. To test thishypothesis, I run two set of regressions; the first one uses growth in factory employment as
the dependent variable and pandemic severity as the treatment variable, and the second one
21

uses wage growth as the dependent variable and pandemic severity as the treatment variable.
Table 10 shows the results of IV regressions on annualized growth of factory
employment. In all regressions, the treatment variables representing the severity of thepandemic had significant negative effects on the growth of factory employment during andafter the pandemic. When the deaths/population ratio rose by one over 100 population,
factory employment grew by -0.122 percentage points fewer in 1919-1920 and by -0.069
percentage points fewer in 1921-1922. When the cases/population ratio rose by one over 100
population, it grew by -0.191 percentage points fewer in 1919-1920 and by -0.107 percentage
points fewer in 1921-1922. When the deaths/cases ratio rose by one over 10,000 population,
it grew by -5.032 percentage points fewer in 1919-1920 and by -2.965 percentage points fewer
in 1921-1922. These results suggest persistent negative effects of the pandemic on the factory
employment.
Tables 11 and 12 present the results of IV regressions on annualized wage growth
for laborers (unskilled workers) and carpenters (skilled workers), respectively.14
In all regressions in Table 11, the treatment variable representing pandemic severity
had significant positive effects on the growth of unskilled worker wages during the pandemic
and significant negative effects after the pandemic. The absolute magnitude of these effects
was larger during the pandemic than after it. Where the deaths/population ratio rose by one
over 100 population, laborer wage growth increased by 0.392 percentage points in 1919-1920
and decreased by 0.117 percentage points in 1921-1922. Where the cases/population ratiorose by one over 100 population, it increased by 0.707 percentage points in 1919-1920 anddecreased by 0.248 percentage points in 1922-1922. Where the deaths/cases ratio rose by one
over 10,000 population, it increased by 13.201 percentage points in 1919-1920 and decreasedby 3.323 percentage points in 1921-1922.
In all regressions in Table 12, the results are qualitatively similar to those in Table
11, but the magnitude is smaller and the statistical significance is lower. The treatment
variable representing pandemic severity had significant positive effects on the growth ofskilled worker wages during the pandemic, and negative effects in general after the pandemic,
while the effect of the deaths/cases ratio was statistically insignificant. This may reflect thesectoral segmentation of labor markets between skilled and unskilled workers with laborers
being more closely attached to factories than carpenters.

14

Wage data is given for 13 cities and has been obtained from the Statistical Yearbook of the

Empire of Japan and deflated by the GNP deflator taken from Ohkawa and Shinohara(1979).
22

Table 11. IV regression results on annualized growth of real laborer wages
Independent variablea) Deaths/pop.

b) Cases/pop.

c) Deaths/cases

Coefficient of independent Variables
Constant

0.09354 ***(Deaths/population ratio
to 1919-1920 growth(

0.01641 )0.08832 ***(0.01777 )0.00392 ***(Deaths/population ratio
to 1921-1922 growth

0.01659 )0.09902 ***0.00070 )-0.00117 ***(0.00036 )Cases/population ratio

0.00707 ***to 1919-1920 growth(

Cases/population ratio

0.00115 )-0.00248 ***to 1921-1922 growth(

0.00071 )Deaths/cases ratio

0.13201 ***to 1919-1920 growth(

Deaths/cases ratio

0.02416 )-0.03323 ***to 1921-1922 growth(

0.01148 )2

Wald chi Statistics

32.48

36.41

40.85

R squared

0.70660.76480.689639

39

39# of obs.

Footnote 1: Figures in parentheses are robust standard errors.
Footnote 2: *** denotes significance at 1 percent.
Footnote 3: Prefecture-level fixed effects are not reported.

23

Table 12. IV regression results on annualized growth of real carpenter wages
Independent variablea) Deaths/pop.

b) Cases/pop.

c) Deaths/cases

Coefficient of independent Variables
Constant

0.06428 ***(Deaths/population ratio
to 1919-1920 growth

0.01164 )(0.01088 )0.06477 ***(0.01664 )0.00412 ***(Deaths/population ratio
to 1921-1922 growth0.06951 ***0.00054 )-0.00046 *(0.00027 )Cases/population ratio

0.00746 ***to 1919-1920 growth(

Cases/population ratio

0.00080 )-0.00116 **to 1921-1922 growth(

0.00050 )Deaths/cases ratio

0.13593 ***to 1919-1920 growth(

Deaths/cases ratio

0.02092 )-0.01578

to 1921-1922 growth(0.01005 )Wald chi2 Statistics

30.56

42.42

55.81

R squared

0.77110.84860.748539

39

39# of obs.

Footnote 1: Figures in parentheses are robust standard errors.
Footnote 2: * denotes significance at 10 percent; ** denotes significance at 5 percent; ***denotes significance at 1 percent.
Footnote 3: Prefecture-level fixed effects are not reported.
Interestingly, after the pandemic, the more severely affected prefectures saw arelative increase in social population growth (Table 9), a continued relative decline infactory employment (Table 10), and a drop in wages (Table 11), suggesting the loosening of
the labor market in the post-pandemic period.
My hypothesis, based on the above findings and the behavior of factory owners, isthat factory owners may have been forced by the pandemic to substitute labor with capital;i.e., faced with labor shortages for two consecutive years (1919-1920), they may haveinstalled new machines to take the place of unskilled labor, and this may have had a lasting
impact on labor market.

24

Table 13. IV regression results on annualized growth of paid-in capital
Independent variablea) Deaths/pop.

b) Cases/pop.

c) Deaths/cases

Coefficient of independent Variables
Constant

0.10111 ***(Deaths/population ratio
to 1919-1920 growth(0.02091 )0.11023 ***(0.01877 )0.00344 ***(Deaths/population ratio
to 1921-1922 growth0.01958 )0.09887 ***0.00075 )-0.00020(

0.00025 )Cases/population ratio

0.00546 ***to 1919-1920 growth(

Cases/population ratio

0.00119 )-0.00020

to 1921-1922 growth(

0.00040 )Deaths/cases ratio

0.13401 ***to 1919-1920 growth(

Deaths/cases ratio

0.02807 )-0.01894

to 1921-1922 growth(

0.01644 )2

Wald chi Statistics

36.18

44.67

25.61

R squared

0.28770.31300.2803# of obs.

132

132

132

Footnote 1: Figures in parentheses are robust standard errors.
Footnote 2: *** denotes significance at 1 percent.
Footnote 3: Prefecture-level fixed effects are not reported.
In this section, I test my substitution hypothesis by looking at capital formation. Table 13
shows the results of IV regressions on annualized growth in paid-in capital. All the treatment
variables had statistically significant positive effects on growth in paid-in capital in 1919-1920,
but no statistically significant effect in 1921-1922. When the deaths/population ratio rose by
one over 100 population, the capital grew more by 0.344 percentage points more annually in
1919-1920. When the cases/population ratio rose by one over 100 population, it grew by 0.546
percentage points more annually in the same period. When the deaths/cases ratio rose by one
over 10,000 population, it grew by 13.401 percentage points more annually in the same period.
Although we need a careful interpretation because we only have employment figures forfactories, while capital figures are for all industries, and because we havenʼt dug into causality
figures, the results above suggest some substitution effect between labor and capital.15

15

In the panel regressions above, I omit time dummies as time dummies are statistically25

Table 14. IV regression results on annualized growth of real taxable income
Independent variablea) Deaths/pop.

b) Cases/pop.

c) Deaths/cases-0.00318-0.00813

Coefficient of independent Variables
Constant-0.00527(

Deaths/population ratio
to 1919-1920 growth(0.00997 )(0.01496 )0.00383 ***(Deaths/population ratio
to 1921-1922 growth

0.01180 )0.00030 )0.00288 ***(0.00029 )Cases/population ratio

0.00596 ***to 1919-1920 growth(

Cases/population ratio

0.00040 )0.00443 ***to 1921-1922 growth(

0.00039 )Deaths/cases ratio

0.15781 ***to 1919-1920 growth(

Deaths/cases ratio

0.01534 )0.11954 ***to 1921-1922 growth(0.01388 )2

Wald chi Statistics

468.25538.08109.98R squared

0.6476

0.67900.5010

131

131

131

# of obs.

Footnote 1: Figures in parentheses are robust standard errors.
Footnote 2: *** denotes significance at 1 percent.
Footnote 3: Prefecture-level fixed effects are not reported.
Next, I look at the pandemicʼs effect on income. To do so, I have compiled taxable income
data from the Statistical Yearbooks of the Empire of Japan and deflated it by the GNP
deflator taken from Ohkawa and Shinohara (1979). Table 14 shows the results of IV
regressions on annualized growth in taxable income. All the treatment variables had
statistically significant positive effects on income growth during and after the pandemic.
Where the deaths/population ratio rose by one over 100 population, annual income growthincreased by 0.383 percentage points in 1919-1920 and by 0.288 percent points in 19211922. Where the cases/population ratio rose by one over 100 population, it increased by
0.596 percentage points and 0.443 percentage points in the respective periods. Where thedeaths/cases ratio rose by one over 10,000 population, it increased by 15.781 percentage

insignificant in all cases.
26

points and 11.954 percentage points in the respective periods.16 These results do not imply
that the pandemic promoted income growth. Rather, they may imply that prefectures withthe potential for rigorous growth grew more despite the pandemic. In other words, the GIP
did not encumber regional economic growth.
4. Conclusions
This paper finds that 1) Japan was hit twice by the pandemic, once in the winter of 19181919 and again in winter-spring 1919-1920, that the urban population was more exposed to
the virus and faced a greater risk to life, while the rural population, once infected, was more
likely to succumb to the disease, 2) the pandemic seemed to have a noticeable effect onsocioeconomic activities in the short and medium terms, suggesting a trigger of population
outflows and substitution of labor by capital without any adverse effect on income, and 3) the
government response included medical and public health measures but not economic
measures.
Though the GIP was similar to COVID-19 in terms of epidemiological patterns, it
was very different in terms of human agency and socioeconomic consequences. Quantitative
analysis indicates that a) non-pharmaceutical interventions were minimal at best, and b) the
pandemic had a negative impact on population growth through the direct effect of excess
mortality and the indirect effect of reduced chances of pregnancy.
These results are largely in line with the findings of Velde (2020) and Chapelle(2020) for the United States, namely that, unlike in the case of COVID-19, the people andthe government responded calmly to the GIP and incorporated very few behavioral changes,
and that the socioeconomic effects of the GIP were temporal and small. While some studies
attempt to derive direct socioeconomic implications, including the effect of nonpharmaceutical intervention during the GIP (e.g., Correia et al. 2020), one must exercise
caution in extrapolating such direct implications from GIP to COVID-19.17
At the same time, there are indications of capital-labor substitution triggered by the
pandemic. Also, one must not dismiss the negative demographic impacts of the GIP.
Ogasawara (2017) finds that children born during 1919-1920 were shorter than those in16

In panel regressions here, time dummies are statistically insignificant in the cases ofdeaths/population and cases/population ratios. In the case of deaths/cases ratio, time
dummies are significant at the 10 percent level, and the coefficient on income growth in
1919-1920 and in 1921-1922 are insignificant.
17

According to Markel et al. (2007), non-pharmaceutical interventions during the GIP wereshort and temporal compared to those during COVID-19.
27

surrounding cohorts in Japan. As for other parts of the world, Almond (2006) finds that in the
United States, cohorts in utero during the pandemic displayed lower profiles of human capitaldevelopment with reduced educational attainment, increased rates of physical disability, lower
income, lower socioeconomic status, and higher transfer payments compared with other birthcohorts. Aassve at al. (2020) finds that lower social trust was passed on to the descendants of
GIP survivors who migrated to the United States after the pandemic. In the case of Brazil,
Guimbeau et al. (2020) finds that the GIP had significant negative impacts on infant mortality
and sex ratios at birth in the short-run and persistent effects on health, educational attainment,
and productivity more than twenty years later. Although the impact on demographics andlong-term effects on human capital are beyond the scope of its study, the present paper
suggests the possibility of a rise in case fatality serving as a trigger of population outflows anda change in the capital/labor ratio. The topic is a potential area for future research, given the
protracted and extensive non-pharmaceutical interventions underway in COVID-19.
Another potential extension of historical research with relevance to COVID-19 would beon other major external socioeconomic shocks. Arthi and Parman (2021) focuses on the GIP
and the Great Depression of the 1930s, arguing that these two global crises were comparable
to COVID-19 in scale and scope. For Japan, Shizume (2021) puts forward post-WWII reforms
in the late 1940s and the two oil crises of the 1970s as appropriate cases to compare with thecurrent situation in terms of a massive qualitative shift in demand structure and social norms.
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