Institute for Economic Studies, Keio University
Keio-IES Discussion Paper Series

The effect of staying at home on suicide
during the COVID-19 pandemic
井深陽子、筧悠夫、小林流基、中嶋亮
2024 年 2 月 29 日

DP2024-004
https://ies.keio.ac.jp/publications/23461/

Institute for Economic Studies, Keio University
2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan
ies-office@adst.keio.ac.jp
29 February, 2024

The effect of staying at home on suicide during the COVID-19 pandemic
井深陽子、筧悠夫、小林流基、中嶋亮

IES Keio DP2024-004
2024 年 2 月 29 日

JEL Classification: H12, I12, I18
キーワード: COVID-19 pandemic, Mental health, Lockdown, Shift-share IV,
Specification curve analysis【要旨】
Studies have reported a strong association between policy stringency and mental health problems
during the COVID-19 pandemic, primarily among females, but the causal effect of people's stay-at-homebehavior on mental health is not yet known. This study evaluated how pandemic-related confinement athome affected the incidence of suicide among Japanese females. We employ a shift-share IV design,assessing whether differential exposure to the pandemic shock led to changes in the outcome variable.
We found that suicide increased among females under 20 years old as more people stayed at home. The
results are robust across different model specifications. Counterfactual analyses show that at least 37%
of suicides in the demographic group can be attributed to home confinement. Our results suggest that asubstantial part of the observed increase in suicide rates among female children and adolescents was
driven by lifestyle changes during the pandemic.

井深陽子
慶應経済大学経済学部
東京都港区三田二丁目１５−４５
ibuka@econ.keio.ac.jp
筧悠夫
慶應義塾大学経済学部
東京都港区三田二丁目１５−４５
haruokakehi@keio.jp
小林流基
慶應義塾大学経済学部
東京都港区三田二丁目１５−４５
rkobayashi@keio.jp
中嶋亮
慶應義塾大学経済学部
東京都港区三田二丁目１５−４５
nakajima@econ.keio.ac.jp
謝辞：This research is supported by JSPS Kakenhi 20K01731 and 21H00672. We
appreciate Yusuke Inami’s efforts and excellent assistance. We are grateful for comments fromseminar participants at Hitotsubashi University, Keio University, Kobe University, Osaka University,
Otaru University of Commerce, Sophia University, and the University of Hawai‘i at Manoa.

The effect of staying at home on suicide
during the COVID-19 pandemic
Yoko Ibuka ∗Haruo Kakehi †Ryuki Kobayashi ‡Ryo Nakajima §February 29, 2024

Abstract
Studies have reported a strong association between policy stringency and mental health problems during the COVID-19 pandemic, primarily among females, but the causal effect of people’s stay-at-home behavior on mental health is not yet known. This study evaluated how
pandemic-related confinement at home affected the incidence of suicide among Japanese females. We employ a shift-share IV design, assessing whether differential exposure to thepandemic shock led to changes in the outcome variable. We found that suicide increased
among females under 20 years old as more people stayed at home. The results are robust
across different model specifications. Counterfactual analyses show that at least 37% of suicides in the demographic group can be attributed to home confinement. Our results suggestthat a substantial part of the observed increase in suicide rates among female children andadolescents was driven by lifestyle changes during the pandemic.

Keywords: Mental health; Lockdown; Shift-share IV; Specification curve analysis
JEL classification : H12, I12, I18∗

Faculty of Economics, Keio University & Department of Economics, University of Hawai‘i at Mānoa. 2-15-45
Mita, Minato-ku, Tokyo 108-8345, Japan. Email: ibuka@econ.keio.ac.jp†
Graduate School of Economics, Keio University. 2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan. Email:haruokakehi@keio.jp‡
Faculty of Economics, Keio University.
2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan.
Email:
rkobayashi@keio.jp§
Corresponding author. Faculty of Economics, Keio University. 2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan.
Email: nakajima@econ.keio.ac.jp1

1

Introduction

Suicide is a serious public health problem with detrimental effects on individuals and society. Morethan 700,000 people die by suicide every year (WHO, 2021), and suicide is the tenth and second
leading cause of death among the population at large and young adults specifically in the UnitedStates (CDC, 2022). In some countries, the problem has become more serious in recent years. In
the United States, for example, the suicide rate increased by 30% between 2000 and 2018. Suicideand suicide attempts in the United States are estimated to cost approximately 60 billion US dollars
in medical and labor loss costs alone (Shepard et al., 2016). In Japan, the country under scrutiny inthis study, the female suicide rate in 2019 was the second highest among OECD member countries(OECD, 2022). Suicide prevention is, therefore, one of the most important public health concerns
worldwide.
The global COVID-19 pandemic that started in 2020 further increased the risk of mental
health problems and suicidal behavior. To date, a number of economic and public health studies
have shown an elevated risk of the symptoms of depression, an increase in calls to mental healthhelplines, suicide attempts, and self-harm (Brodeur et al., 2021; Brülhart et al., 2021; Dubé et al.,2021; Vahratian et al., 2021; John et al., 2020a). A slight decrease in the incidence of suicide wasreported in the early stage of the pandemic, followed by a gradual increase in suicides and suicidal
behavior (John et al., 2020b). Understanding the cause of this increase during the pandemic is
an important scientific and policy concern. Specifically, this study sheds light on lifestyle changes
caused by countermeasures to the pandemic and examines the impact of staying at home on suicide
during the first year of the pandemic.
Suicide is rarely driven by a single cause, and suicide risk is associated with a combination of
personal, relational, community, and social factors. Nonetheless, some evident risk factors could
be considered related to suicide during the COVID-19 pandemic. One such factor is economic
problems, together with financial instability. Previous studies indicate that working-age males
are most vulnerable to macroeconomic shocks such as financial crises (Chen et al., 2009, 2015).
Consistent with this finding, economic disturbances induced by the COVID-19 pandemic and social
distancing policies were hazardous for individuals (Ando and Furuichi, 2022; Matsubayashi et al.,2022; Elbogen et al., 2020).
What is puzzling, however, is that existing studies show a clear and consistent pattern in which
increases in suicide under the pandemic are more pronounced among young people and females
2Figure 1: Suicide Trends in Japan: Impact of COVID-19(a) Yearly Trends in Suicide Incidents by Gender(b) Age-specific Percentage Changes in Female Suicide
Incidents

Notes: Panel (a) displays the long-term yearly trend in the number of suicide incidents among men and womenin Japan from 2010 to 2022, with 2019 as the base year. Panel (b) shows the percentage change in the numberof suicide incidents among women after COVID-19, compared with the incidence before COVID-19, broken down
by age group. The sample period spans from April 2019 to March 2021. The term “before COVID-19” refers to
the period between April 2019 and March 2020, while “after COVID-19” pertains to the period from April 2020
onwards.

in Japan (Tanaka and Okamoto, 2021; Sakamoto et al., 2021; Ueda et al., 2021). Tanaka andOkamoto (2021) argues that the increase in suicides in the summer of 2020 was primarily driven
by females and young people, with increases in fatality of 37% and 49% for females and for children
and adolescents, respectively. This trend are confirmed by the data shown in Figure 1, where theincrease in suicide is greater among females and, in particular, among those under age 30. Thesesegments of the population have been thought to be less susceptible to economic problems. The
difference between the affected population in the pandemic condition and the population affectedin past macroeconomic shocks is likely to suggest the involvement of another factor distinct fromtraditional economic problems.
One major risk factor in relation to the pandemic is disconnection from society. Early publichealth responses against the COVID-19 pandemic included strong social distancing measures suchas lockdowns and stay-at-home orders. Although the intensity of such policies varied from country
to country, most developed countries, including Japan, kept large segments of society at homein the early stages of the pandemic1 . The WHO warned that social disconnection during the
pandemic could increase the risk of mental illness. One study validated this finding by showing
a positive association between the stringency of social distancing policies and the prevalence ofmental health problems (Aknin et al., 2022).
1

The COVID-19 policy details are found in the Appendix A.1.

3

In this paper, we examine the causal impact and the degree of influence of staying at homeon female suicide using Japanese data to understand the mechanism underlying the increase insuicide among young females. Previous studies have examined the impact of social distancing on
various outcomes, including mental health, leveraging regional differences in the degree and/or
timing of stay-at-home policies (Baek et al., 2021; Altindag et al., 2022; Serrano-Alarcón et al.,2022). In this paper, we assess the impact using a direct measure of staying at home to capture theactual behavior of individuals. This is important because voluntary social distancing, as opposed
to policy-induced social distancing, has been shown to represent a substantial part of observed
behavioral responses (Yan et al., 2021). This matters even more in the context of Japan, where
curfews imposed under the state of emergency had no legal binding force through fines or arrests,
and as a result, the declaration of the state of emergency did not necessarily serve as a good proxyof the degree to which people stayed at home.
An empirical challenge in testing the effect of stay-at-home behavior on suicide is that homeconfinement is potentially endogenous. For example, mental health status could be an unobserved
confounding factor that could affect home confinement as well as suicide. To address this issue,
we employ a shift-share instrumental variable (IV) design. Specifically, our analysis exploits geographical variation in exposure to the pandemic and tests whether differential exposure to theshock led to differential changes in suicide incidence. We employ an IV strategy where a change
in individuals’ stay-at-home behavior is predicted by prepandemic commuting behavior. In thisstudy, we focused on females because the increase during the pandemic was more pronounced forfemales, as shown in Figure 1a.
This study contributes to the literature in three ways. First, we add to the economics literature
on mental health by examining physical disconnection from society as a risk factor for suicide.
To date, the economic literature on psychological well-being and suicide has focused primarilyon economic factors (Hamermesh and Soss, 1974; Darity and Goldsmith, 1996; Ruhm, 2000).
Second, we shed light on individual patterns of home confinement as the mechanism underlying
the increase in suicide rates during the COVID-19 pandemic. Understanding the pathway throughwhich suicide rates increase has important implications for public policies regarding both infectious
disease control and suicide prevention. Additionally, it is essential to quantify the social costs ofpublic health responses during the pandemic in terms of human lives. Third, we employ the shiftshare IV design to obtain a causal impact. Mobility tends to be endogenous in terms of outcome

4variables related to individual decisions, and addressing it is an important empirical consideration.

2

Empirical model

2.1

Setup

To estimate the effect of staying at home on the incidence of suicide, we consider the following
structural equation:ln(1 + Suicidemt ) = αm + δ × t + βStayHomemt + εmt ,(1)

where m indexes the municipalities and t indexes the time period. The outcome variable of interestis the natural logarithm of one plus the number of female suicide cases, Suicidemt , in municipality
m in period t 2 . We consider two time periods, that is, a pre-COVID-19 period for t = 0 (from
April 2019 to March 2020) and a post-COVID-19 period for t = 1 (from April 2020 to March 2021)
3.
The explanatory variable of our focus is the degree of staying at home, StayHomemt , of res-idents of municipality m in period t. It is potentially endogenous and influenced by unobserved
confounding factors such as area-specific psychological traits and environmental conditions. These
factors may simultaneously affect both suicidal and stay-at-home behaviors. The OLS estimatesof β could be biased due to these confounding factors. We control for municipality-specific fixed
effects αm and common time trends δ × t.
In practical applications, when addressing municipality-specific fixed effects within the panel
data structure, one common approach to employ is a first-difference specification designed toeliminate the influence of the municipality fixed effect, denoted as αm . This approach can be
expressed as follows:∆ ln(1 + Suicidem ) = δ + β∆StayHomem + ∆εm ,2(2)

In the following tables and figures, we report the semielasticity of suicide with respect to mobility, calculated fromthe estimated coefficient β̂ based on the following equation: β̂ · (1 + Suicide)/Suicide. Thus, we could multiple
the numbers in the tables by 100 to obtain a percentage change in suicide.
3
We adopt this time frame because the number of COVID-19 cases increased rapidly at the end of March, and thegovernment declared the first state of emergency on April 7th, as discussed in Section A.1. We do not consider
the period after April 2021, when vaccines became available, and people subsequently started returning to normal
life. The sensitivity of our estimation results to the choice of periods is examined through a specification curve
analysis conducted in Section 4.3

5

where ∆ represents the first-differencing operator.
To mitigate endogeneity concerns regarding the variable StayHomemt in Equation (1) or∆StayHomem in Equation (2), we employ a shift-share instrument. This method introduces
an additional dimension, distinct from both the cross-sectional dimension and the time dimension,
aimed at reducing the influence of potential confounders on our causal estimates. To achieve this,we express the variable StayHomemt as the average residents’ proportion of time spent at homein municipality m during period t as:X 1 

StayHomemt =

i∈Nm

nm

sit .

In the equation, i represents residents, Nm denotes the set of municipality m residents, nm
indicates the total resident count, and sit signifies the proportion of time resident i spends athome during period t. Importantly, sit is influenced by municipality-specific psychological andenvironmental factors, making StayHomemt an endogenous variable within the structural equation.
In the context of the shift-share instrumental variable approach, 1/nm embodies the share-related
factor, whereas sit corresponds to the shift-related element. Consequently, the endogenous variable
is articulated as the inner product of these share and shift components.
Utilizing the inner-product structure of the endogenous variable, we construct a shift-share
instrument, categorizing residents into K types based on stay-at-home behavior. Assuming similar
stay-at-home propensities among residents of each type k, we define sikt as sikt = gkt + ξikt . Here,gkt indicates the average proportion of time that type k residents spend at home in period t, whileξikt captures the idiosyncratic deviation from a typical stay-at-home propensity for each resident
i of type k in period t. It is important to note that the individual-specific term ξikt might becorrelated with various confounding factors in municipality m.
Accordingly, we reformulate the stay-at-home variable specific to each municipality as follows:
StayHomemt =X  nmk 
k∈K

nm

gkt +

X X  1 k∈K i∈Nmk

nm

ξikt ,(3)

where Nmk is the cohort of type-k residents in municipality m, and nmk is their number. The
first term, representing the shift-share instrument Shif tSharemt , isolates the exogenous variationin StayHomemt from the endogenous individual component related to ξikt . With zmk = nmk /nm
denoting the proportion of type-k residents in m, the shift-share instrument, Shif tSharemt =6

Pk zmk gkt , is composed of two elements: the share component (zmk ), which is predetermined andgoverning exposure to shocks, and the shift component (gkt ), which is a common trend among
type-k residents across municipalities.

2.2

Canonical Case: Two Types

To illustrate the shift-share instrumental variable estimation, let us use a simplified scenario tofocus on the differential impact of COVID-19 on two types of residents: commuters and noncommuters. Suppose that noncommuters (represented as k = 0) predominantly stayed home,while commuters (represented as k = 1) were mostly away before the pandemic started. Emergency declarations in the post-COVID-19 period led both groups to largely remain at home. Thischange is captured by setting the following average stay-at-home time proportions: g10 = 0 and
g00 = g01 = g11 = 1. Thus, for any resident i of type k in municipality m, Shif tSharemt =Pk zmk gkt = (t − 1)zm1 + 1, where zm1 is the proportion of commuters in municipality m in the
pre-COVID-19 period. In a time-difference form, given that ∆g1 = 1 and ∆g0 = 0, we have asimple expression as ∆Shif tSharem = zm1 .
This approach, often referred to as a differential exposure design, exploits the stark contrast
in stay-at-home duration triggered by COVID-194 . Noncommuters maintain their longer stay-athome tendency, whereas commuters newly experience working from home under the new stateof emergency order. Therefore, “treatment” municipalities with more commuters thus experiencegreater COVID-19 exposure than “control” municipalities with fewer commuters. The effect isgauged by the commuter population’s relative change, aligning with the difference-in-differences(DiD) methodology in a regression framework, albeit with a continuous treatment variable.
Two key assumptions must be satisfied to estimate an unbiased treatment effect. First, theshift-share instrumental variable must accurately represent the stay-at-home variable, a necessity
known as the relevance condition, to prevent bias due to weak instrumental variables. This entails a significant correlation between ∆StayHomem and ∆Shif tSharem , or equivalently, between∆StayHomem and the commuting ratio z1m . Figure 2a displays scatter plots that link stay-athome rates to commuting ratios, illustrating that municipalities with higher commuting ratios
typically have lower stay-at-home rates both before and after the outbreak of COVID-19. Notably,
4

See Nunn and Qian (2014) and Kearney and Levine (2015) for similar identification strategies under differential
exposure design.

7

Figure 2: Relevance and Pre-trend: Two-Type Case(a) Relevance: Commuting Ratio vs. Stay-at-Home(b) Pre-trend: Suicide Trends by Commuting Patterns

Notes: Panel (a) depicts the relationship between the commuting ratio zm1 and the stay-at-home variable
StayHomemt at two distinct time points, t = 0 and t = 1. Panel (b) compares the average number of suicide
incidents between the first and fourth quartiles of the commuting ratio zm1 , covering the sample period from April
2019 to March 2021. A vertical dotted line marks April 2020, dividing the analysis into two periods: Pre-COVID19 (prior to April 2020) and Post-COVID-19 (from April 2020 onwards). The commuting ratio’s definition can befound in Appendix C.

the variation in stay-at-home rates across the periods (depicted by the vertical distance between thedark and light blue dots in the figure) is more pronounced in municipalities with higher commuting
ratios. This pattern suggests a positive correlation between the change in stay-at-home rates over
the two periods and the commuting ratio, reinforcing the validity of the relevance assumption with
empirical evidence, despite the graphical nature of the depiction.
Second, consistent with the DiD methodology, the treatment and control groups must exhibit
parallel trends under a counterfactual scenario. This parallel trend assumption, vital for validating
the instrumental variable in a differential exposure design, is challenged if regions with varied
commuting habits display different suicide trends in the pre-COVID-19 period. However, Figure
2b, which compares the average number of female suicide incidents across municipalities withdifferent commuting ratios, shows nearly parallel prepandemic trends but a noticeable increase insuicides in areas with higher commuting ratios in the postpandemic period, resulting in a trend
divergence. While the initial parallel trends are highlighted, careful interpretation is necessary,as differentiating true deviations from noise can be challenging. Further analysis of the parallel
pre-trends is detailed in Section 4.2.

8

2.3

Shift-Share IV: Multiple Types

In the canonical case, a single municipal characteristic, the commuting ratio, defines the shift-share
instrument. The two-type example provides a useful, intuitive illustration of the exposure design.
However, as highlighted in the studies by Christian (2017) and Jaeger (2020), relying exclusively
on variation among a limited number of types may undermine the assumption of parallel trends
between the treatment and control groups in a differential exposure design. To mitigate theseissues, our analysis employs an expanded-type shift-share instrument that exploits the variation in a
municipal attribute across different types. This enhancement allows for a more robust identificationof variations. Furthermore, the introduction of this instrument facilitates the utilization of thediagnostic tools proposed by Goldsmith-Pinkham et al. (2020).
In our expanded analysis, we categorize residents by their commuting times, assigning a commuting time of zero to those who do not commute, referred to as noncommuters. As detailed in
Section 3, our empirical analysis identifies six specific commuting time brackets. Each bracket,
denoted as k, is assumed to exhibit a uniform propensity for staying at home. It is expected that
noncommuters with zero commuting time are more likely to stay at home in the prepandemic
period. In contrast, individuals with longer commute times are likely to spend more time away
due to the distance of their workplaces; thus, they are presumed to have a lower propensity tostay at home. This analytical approach enables us to discern variations in stay-at-home behavior
across different commuter groups, moving beyond the binary distinction between commuters andnoncommuters. Specifically, for noncommuters (k = 0), we set g0t = 1 for both pre- and postCOVID-19 (t = 0 and t = 1, respectively) in the same way as in the two-type case. Conversely, forcommuters (k = 1..., 5), the pre-COVID-19 stay-at-home rates differ across commuting categories.
In the post-COVID-19 period, influenced by the state of emergency orders, there was an increasein the average work-from-home duration. This results in gk0 ≤ gk1 across all commuting types k.
The impact of emergency declarations and restrictions on commuting affected commuter types
to varying degrees. For instance, medium- and long-distance commuters were likely to experiencea more significant shift toward staying at home than noncommuters and those with shorter commutes. Consequently, the time change in stay-at-home propensity, represented by ∆gkt = gk1 −gk0 ,shows distinct patterns across different commuter types k. This variation underscores the diverse
effects of COVID-19-related measures on individuals’ commuting habits and work-from-home dynamics.
9

Expanding on the analysis that includes multiple commuter types, our empirical model consistsof the following equations. The structural equation is given by:\ m + ∆εm .
∆ ln(1 + Suicidem ) = δ + β∆StayHome(4)\ m , is derived from the following first-stage regression:
The estimated variable, ∆StayHome∆StayHomem = δ f + π∆Shif tSharem + ∆εfm .

(5)Here, the first-time differenced shift-share instrument is defined as∆Shif tSharem =KX

zmk × ∆gk ,

(6)

k=1

where zmk represents the proportion of commuters of type k in municipality m before COVID19, and ∆gk indicates the first time-difference stay-at-home propensity for type k commuters, a
type-specific common trend applicable to all municipalities. Finally, the reduced-form regression
is presented as follows:r∆ ln(1 + Suicidem ) = δ +KXγ(zmk × ∆gk ) + ∆εrm .

(7)

k=1

In this case, γ = β × π.
Our identification strategy relies on the assumption of share exogeneity, leading to the following
exclusion restriction:
E (zmk ∆εm ) = 0 for k = 1, · · · , K.

(8)This condition implies that zmk , as a share, serves as a single instrument. The shift-share instrumental variable can be seen as a linear combination of shares, each weighted by a shift. The
shift-share estimator β̂ obtained in this system is a weighted sum of just-identified instrumental variable estimators β̂k , each using a separate share as an instrument (Goldsmith-Pinkham
et al., 2020). These weights, known as Rotemberg weights, are particularly useful when multiple
instruments are employed. The key idea is that Rotemberg weights quantify how much the mis-10

specification of a particular instrument contributes to the overall bias of the estimator. A highRotemberg weight of a specific instrument implies that any bias in this instrument has a larger
impact on the overall bias in the shift-share estimator. Therefore, the Rotemberg weights enable
researchers to refine their instrument selection, potentially improving estimate credibility.

3

Data

Suicide: The monthly suicide statistics, sourced from the National Police Agency (NPA) andprovided by the Ministry of Health, Labour and Welfare (MHLW) (MHLW, 2021), are a product ofcollaborative efforts between these two organizations. The NPA is responsible for gathering data,
which the MHLW then publishes5 . The data collected incidents at the municipal level based on thelocation of residence and were aggregated every month. As mentioned above, we categorized themonthly data into two periods: pre- and post-COVID-19. The data include detailed information
on biological sex and age groups, in ten-year age increments up to 79 and aggregated data forthose 80 years or older6 .
Our analysis focuses specifically on female suicides. As illustrated in Figure 1a, the suicide
rate among women post-COVID-19 notably increased by more than 15% between 2019 and 2020.
However, this trend was not uniform across all age groups. Figure 1b shows the percentage change
in national suicide incidents by age group, with a more pronounced increase particularly observed
among young women. The data indicate an approximate 42.7% increase in this demographic after
the outbreak of COVID-19.
Stay-at-Home variable: We measure the extent of stay-at-home behavior in each municipality
during the pre-COVID-19 and post-COVID-19 periods using mobile phone location data. The
data, provided by Agoop, a subsidiary of one of Japan’s major mobile phone companies, offers
hourly population counts in each municipality. These counts are derived from users’ time spentin each municipality and are adjusted for demographic characteristics7 . It should be noted that
5

In Japan, only doctors can issue death certificates. The Medical Practitioners Law requires them to report unusual
deaths to the NPA within 24 hours. The NPA then conducts comprehensive investigations, including physiologicaltests, reviews of suicide notes and emails, family interviews, and document assessments to determine the cause ofdeath.
6
For other demographic characteristics, if there are two or fewer suicides in a municipality, the number is masked
for confidentiality reasons, and we are not able to use the data for the analysis.
7
Agoop is funded by SoftBank, one of Japan’s largest cell phone companies, and possesses the GPS information of
SoftBank cell phone users.

11

Agoop’s data are available for the period from January 2019 onward and cover individuals aged
15 and above.
The stay-at-home variable for a municipality m in period t is defined as the ratio of the municipality’s daytime population to its nighttime population during that period. For this calculation,
the daytime population is averaged using data from 11 am to 2 pm, and the nighttime population
is calculated based on data from 1 am to 4 am. This variable measures daily shifts in the resident population, highlighting the contrast between daytime and nighttime numbers. As residents
typically stay at home at night, the nighttime population tends to be smaller than its daytime
counterpart. Consequently, the values of the stay-at-home variable range from 0 to 1, where ahigher value indicates less outflow from the municipality and a greater inclination toward staying
at home.
The Agoop data provide valuable insights into residents’ movements between municipalities8 .
Although these data are aggregated at the municipal level and the analysis does not include thedetailed time spent at home found in studies based on SafeGraph data, the data still serve as areliable indicator of stay-at-home behavior9 . In Appendix Section A.2, we establish the reliabilityof our stay-at-home variable, computed from the Agoop data, by demonstrating its correlation with
the Google mobility index, which is designed to measure time spent at home10 . This correlation
reinforces the effectiveness of our stay-at-home measure as a valuable proxy for assessing stay-athome behaviors.
The stay-at-home variable varies significantly across different types of municipalities, reflecting
distinct patterns of resident mobility. For instance, business districts experience a large influx ofnonresidents and some resident outflow during the day, resulting in lower values, while suburban
8

Population count data provided by Agoop’s Papilio service are structured to provide data by prefecture, bymunicipality, and at specific locations. At the prefecture level, the data enable the identification of the prefectural
origin of individuals. For municipalities and specific locations, the data can distinguish whether individuals livein the same municipality or come from different municipalities. To define our municipality-specific stay-at-homemeasure, we use nighttime and daytime population counts of those who live in the same municipality, excluding
individuals coming from other municipalities.
9
SafeGraph is a major source of foot traffic data for US studies to quantify the mobility of individuals. The data
are often used to measure the degree to which people stay at home in a certain geographical area. The data
provide information on time spent at home in each area. For example, Yan et al. (2021) used information on the
median time spent at home to measure the degree to which people stayed at home at the county level. Hsu andHenke (2021) measure the degree to which people stayed at home by dividing the number of individuals (withmobile devices) who stayed home all day by the total number of individuals at the county level. Unfortunately,
information on time spent at home is not available in the Agoop data.
10
Google’s Mobility Index provides information on how the time people spend at home has changed since the startof the pandemic compared to prepandemic levels. While the Google mobility index captures changes in timespent at home, it is available only at the prefecture level, with only 47 variations.

12areas show a significant daytime reduction in the resident population due to commuting, leading
to higher values. Detailed explanations and observable fluctuations in the stay-at-home variable
can be found in Appendix Figure B1, where the distinctions between a business district and a
suburban residential area are depicted.
The outbreak of COVID-19 and the subsequent declaration of a state of emergency significantly
impacted the stay-at-home behavior of people across Japan. Figure 3a illustrates the national average change in the stay-at-home variable over time. There was a notable increase in the index
in April 2020, immediately following the outbreak of the pandemic. The index remained above
prepandemic levels throughout 2020 and 2021, with some observable seasonal variations. Furthermore, the extent of the shift to stay-at-home behavior varied across regions after the COVID-19
outbreak. Figure 3b shows the distribution of the stay-at-home variable before and during the pandemic. After the outbreak of the pandemic, the distribution underwent two significant changes:a shift to the right, indicating an increase in stay-at-home behavior nationwide, and a narrowingof the distribution, which points to less variability among municipalities. Although stay-at-homebehavior generally increased after the outbreak of the pandemic, the degree of this increase was
not uniformly distributed across municipalities.
Figure 3: Stay-at-Home Trends and Distribution Before and After COVID-19(a) Trend(b) Distribution

Notes: Panel (a) shows the trend of the stay-at-home variable between April 2019 and March 2021. The lines
indicate the average for the stay-at-home variable in Japan. Panel (b) shows the distribution of the stay-at-homevariable. Before COVID-19 refers to the period between April 2019 and March 2020, and after COVID-19 refers to
the period between April 2020 and March 2021. The stay-at-home variable is defined by Equation 3.

Commuting time and work from home: Our shift-share instrumental variable comprises
two components. First, individuals are classified into K categories based on commuting time.
13

The proportion of residents in different commuting time brackets within a municipality during the
pre-COVID-19 period forms the share (zmk ) of the IV. Second, the nationwide average tendency
of individuals who worked from home in each commuting time bracket, both before and after
COVID-19, indicates the shift (gkt ).
To determine the share component, we divide residents into six categories based on commuting
duration. Specifically, k = 0 corresponds to noncommuters with zero-minute commuting time;k = 1 represents commuters traveling up to only 30 minutes; k = 2, k = 3, and k = 4 represent
mid-distance commuters with commute times of more than 30 and equal to or less than 60 minutes(30-60 minutes), more than 60 and equal to or less than 90 minutes (60-90 minutes), and morethan 90 and equal to or less than 120 minutes (60-120 minutes), respectively; and k = 5 represents
long-distance commuters who travel more than 120 minutes.
We base this classification on data from the 2018 Housing and Land Survey of Japan conductedby the Statistics Bureau. The survey collected information on the commuting habits of household
heads. The survey sample comprises individuals who live in cities and wards and in counties withpopulations of 15,000 or more11 . Our analysis incorporates 1,241 out of 1,891 municipalities forwhich commuting time data are available. In the first column of the Appendix, Table B2 provides
the average share of commuters in each category.
Regarding the shift components gkt for each k commuter type, we use the propensity to workfrom home as a proxy for the inclination to stay at home, reflecting the significant adoption ofwork-from-home practices during the COVID-19 pandemic, as documented by Okubo (2021, 2022).
In Japan, the state of emergency issued in response to COVID-19 did not enforce compliance withstaying at home through legal penalties. Instead, companies made voluntary requests of theirworkers to work from home. Consequently, some workers were allowed to continue commuting
to their workplace during the pandemic, indicating that not everyone shifted uniformly to workfrom home even amidst an increase in COVID-19 infections in late 2020. We can, therefore, inferthat the changes in stay-at-home tendencies largely paralleled the adoption of work-from-homepractices. Importantly, the adoption rate of work-from-home practices varied across industries,
occupations, regions and commuting times. Our paper’s identification strategy leverages thesevariations, specifically analyzing differences in work-from-home practices based on commuting
times across municipalities.
11

The dataset made available to the public aggregates statistics at the municipality level.

14

The work-from-home data are derived from a survey conducted by Okubo (2021)1213 . The
prepandemic data represent the work-from-home proportion in January 2020, and the postpandemic data reflect the proportion in April or May 2020. These values are presented in the second
and third columns of Appendix Table B2. In the pre- and post-COVID-19 periods, the proportionof individuals working from home varied substantially across commuting time categories. Generally, the longer the commuting time is, the greater the tendency to work from home. Moreover, theoutbreak of COVID-19 led to an increase in the proportion of people working from home across all
commuting time categories. The category with the most significant increase in work-from-homerates is those with 90-120 minutes of commuting time, with a 21 percentage point increase inindividuals opting to work from home. This trend reflects the substantial impact of the pandemic
on work and commuting behaviors.

4

Results

4.1

Main results

The baseline results for females are shown in Table 1. The first column presents the OLS estimates, indicating that a 1% point increase in the stay-at-home variable is associated with a 1.11%
reduction in suicide. However, the OLS estimate is likely to be overestimated because of possible confounding factors such as psychological and environmental traits. To address this issue,
we employ the shift-share IV. The first stage by Equation (5) and the IV results by Equation(4) are presented in Columns (2) and (3), respectively. The first-stage result shows a clear andstrong association between the difference in the stay-at-home variable and the difference in theshift-share IV. In addition, the effective F statistic exceeds the threshold value at the conventional
level, rejecting the null hypothesis of the weak IV. The IV estimate again shows a positive sign.
However, the effect size decreases with increasing standard error, and thus, the estimates are nolonger statistically significant. Column (4) shows the reduced-form results indicated by Equation(7). The estimate of the shift-share IV is positive but small in magnitude, indicating that municipalities that were more exposed to the COVID-19 shock tended to have a greater increase in
12

Okubo (2021) surveyed nationwide workers in December 2020 to understand changes in work style, lifestyle, andattitudes caused by the COVID-19 pandemic. The total number of responses was 10,523. The work-from-homedata for each commuting time used in our analysis are based on Table 1-17-1 of Okubo (2021), aggregated from
more detailed time brackets.
13
As illustrated in Section 2.2, noncommuting residents are assumed to always work from home (g00 = g01 = 1).

15

Table 1: Effect of Staying at Home on Suicide: Females∆StayHomeOLS

1st Stage

IV

Reduced form

(1)
1.109
(0.432)[0.010]

(2)

(3)
0.827
(0.554)[0.136](4)∆ShiftShare

1.620(0.037)[0.000]Effective F statistic
τ =10%
Observation

1964.183
23.109
1241

1241

1.339
(0.901)[0.137]

1241

1241

Notes: We estimate the first-order time difference as in Equation (4). In the regression for Columns (1), (3), and(4), the outcome variable is defined as the logarithm of one plus the number of female suicide cases and we convert
the coefficient to the semi-elasticity of suicide by using the equation specified in footnote 2. In the regression forColumn (2), the outcome variable is the stay-at-home variable. Cluster-robust standard errors are in parentheses.
P values are in brackets. The effective F statistics are those developed by Montiel Olea and Pflueger (2013).

suicide rates, although the estimate is not statistically significant.
Importantly, the data during the pandemic have heterogeneity in the incidence of suicide. As
discussed in Section 1, an increase in suicide during COVID-19 was not uniform but was more
pronounced in some demographic groups, especially young people. This suggests that the detrimental effect of a change in stay-at-home behavior may also occur within a certain demographic
group. To examine the heterogeneity in the effect, we conduct a subsample analysis by age group.
We consider the following age groups: those under 20; those in their 20s; those in their 30s;thosein their 40s, those in their 50s, and those aged 60 years or older.
The results are shown in Table 2. Panel A shows the IV results, and Panel B shows the reducedform results. In all age groups, the sign of the coefficient is positive, indicating that staying at
home tended to increase the likelihood of suicide. Furthermore, the effect of staying at home is far
greater for females under 20 years old than for other age groups, and it is statistically significant
at the conventional level. The magnitude of the increase is approximately 5%, associated witha 1 percentage point increase in the staying-at-home variable for females under 20, whereas thecorresponding value is 1 to 2% for all the other age groups, with the oldest group showing theleast pronounced increase. The prepandemic average number of suicide cases for females under 20
years of age is 0.176 per municipality, as shown in Appendix Table B1, and the increase before
and after the pandemic in StayHome is 6.1%, resulting in an increase in suicide due to staying at
home that corresponds to an increase of 0.228 per municipality. We will more rigorously analyze
16Table 2: IV Estimates by Age Group(1)
Under 20

(2)
20-29

(3)
30-39

(4)
40-49

(5)
50-59

(6)
Over 60

4.823(2.026)[0.017]1.492(1.189)[0.210]1.760(1.291)[0.173]1.338(0.989)[0.176]1.743(1.048)[0.096]

1.213(0.772)[0.116]

7.811(3.288)[0.018]1241

2.416(1.930)[0.211]1241

2.851(2.096)[0.174]1241

2.167(1.603)[0.176]1241

2.822(1.701)[0.097]1241

1.965(1.253)[0.117]1241

Panel A: IV∆StayHome

Panel B: Reduced form
∆ShiftShare

Observation

Notes: Cluster-robust standard errors are in parentheses. P values are in brackets. The outcome variable is definedas the logarithm of one plus the number of female suicide cases. We convert the coefficient to the semielasticity ofsuicide by using the equation specified in footnote 2. In panel A, we estimate the first-order time difference as inEquation (4) in different age female groups. In panel B, we report the estimation result in Equation (7).

the magnitude of this effect to discuss the degree of attribution of staying at home to the increasein suicide incidence by conducting a counterfactual analysis in Section 4.4. The results from thereduced-form analysis are qualitatively consistent with the IV results.

4.2

Estimation Validity

Our analysis revealed empirical evidence indicating a causal link between the stay-at-home variable
and a significant increase in suicide rates among young women, particularly those under the age of20, during the COVID-19 period in Japan. To confirm the validity of these findings, we conduct robustness checks in this section. By following the framework set forth by Goldsmith-Pinkham et al.
(2020), we scrutinize the necessary conditions required for the shift-share instrumental variable
estimator to be unbiased.
Rotemberg Decomposition: We utilize Rotemberg decomposition, as introduced in Section 2.3,
to evaluate the robustness of our estimated outcomes. This technique breaks down the estimatedvalue of the structural equation, β̂, which relies on the shift-share instrument Shif tSharemt , intoa weighted sum. In particular, β̂ consolidates individual estimates β̂k , each originating from aPdistinct instrumental variable zmk for k = 0, · · · , 5, resulting in β̂ = k α̂k β̂k . The Rotemberg
weights α̂k play a pivotal role in pinpointing which instruments might be prone to misspecification.
In the case of shift-share instruments, these weights identify which shares potentially introduce

17Table 3: Rotemberg Diagnosis
α̂k

β̂k

F̂k

95 perc. C.I.

0.659
0.551
0.185
0.003
-0.397

0.188
2.414
4.451
4.013
4.619

1681.095
722.220439.269
26.636
1807.293[2.448, 11.213][-2.618, 7.456][-1.651, 10.416][-12.010, 20.775][0.626, 8.595]

60-90 min30-60 min60-90 min30-60 minAll

∆StayHome

6.820(2.246)[0.002]2.415(2.570)[0.347]5.173(2.040)[0.011]

4.505(2.002)[0.024]

Effective F statistics
τ =10%
Over ID test

1964.183
23.109722.220
23.1091241

1241

881.52111.6182.964[0.085]1241

11.02622.8547.131[0.129]1241

Panel A: Shares
Commuting time (60,90] mins
Commuting time (30,60] mins
Commuting time (90,120] mins
Commuting time > 120 mins
Commuting time (0,30] mins

Panel B: IV estimates

Observations

Notes: Cluster-robust standard errors are in parentheses. P values are in brackets. Panel A presents the shift-sharediagnostics as recommended by Goldsmith-Pinkham et al. (2020). The computation is based on a Stata package
developed by the authors. Panel A reports four key metrics for Rotemberg decomposition – Rotemberg weights
α̂k , the just-identified coefficients β̂k , and the first-stage F-statistics for the just-identified instruments F̂k for each
commuter type k. The 95% confidence intervals are robust to weak instruments, as calculated using the method
proposed by Chernozhukov and Hansen (2008), spanning from −10.00 to 5.00 in increments of 0.01. Panel B presents
the estimated coefficients for the impact of stay-at-home on suicide among females under 20 years old, associatedwith the highest and second-highest Rotemberg weight shares (60-90 minutes and 30-60 minutes), employing a single
instrument approach (as depicted in the first and second columns). It further presents the coefficients estimatedusing these two shares as instruments (in the third column) and the coefficients obtained by employing all commuter
shares as multiple instruments (in the fourth column). Additionally, we include the first-stage F statistic and, whereapplicable, the p value for the Sargan overidentification test to assess the validity of the instrumental variables used.

bias, particularly when the exclusion restriction, as detailed in Equation (8), might be invalid.
The results of the Rotemberg decomposition are presented in Table 3 Panel A. A full rangeof diagnostic statistics are reported in Table B4 in Appendix B. Using the shares of individuals
with varying commuting times in municipalities during the pre-COVID-19 period as instrumental
variables, we find that the instrument with the highest Rotemberg weight is associated with theshare of individuals with 60-90 minutes of commuting time. This is followed by the share ofindividuals commuting for 30-60 minutes14 .
We employ the shares with the highest and second-highest Rotemberg weights as individual
14

According to the shares perspective by Goldsmith-Pinkham et al. (2020), our shift-share instrument is equivalent
to using shares of individuals with varying commuting times as instruments. This implies that the exogeneity
assumption should be interpreted within the context of these shares.

18and combined instruments to investigate the impact of a stay-at-home variable on suicide amongfemales under 20 years of age. The findings, as outlined in Table 3 Panel B, demonstrate that theestimated effects are in line with the findings obtained from the shift-share instrument. The resultsof the Montiel Olea and Pflueger tests (Montiel Olea and Pflueger, 2013) show that the hypothesisof weak instrumental variables is rejected when we use the shares of individuals with 60-90-minuteand 30-60-minute commutes, both as a single instrument and in combination. Conversely, when
all commuting time brackets are employed separately as multiple instruments, they are found tobe weak. Additionally, to address potential misspecifications when multiple instrumental variables
are used, we conduct Hansen’s J-test for overidentification. According to the F statistic and pvalue provided in Table 3, the exogeneity of the share instrument zmk is not rejected at the 5
percent significance level.
Parallel Pre-trend:

A potential threat to our identification strategy is the presence of pre-trends. Although we have demonstrated visual evidence of parallel pre-trends in Section 2.2, relying
solely on this evidence is inadequate for rigorous analysis. Therefore, we implemented additional
checks for parallel pre-trends, as suggested by Goldsmith-Pinkham et al. (2020).
To assess the assumption of parallel pre-trends, we performed a regression analysis in which our
primary focus was on examining the predictive power of these instruments for changes in suicidal
behavior among young females in the pre-COVID-19 period. Figure 4 plots the coefficient of the
instrumental variable for the following reduced-form regression:∆ ln(1 + Suicidemℓ ) = δ r + γℓ Zm + ∆εrm ,(9)where ∆ ln(1 + Suicidemℓ ) ≡ ln(1 + Suicidemℓ+1 ) − ln(1 + Suicidemℓ ) represents the l-th orderdifference in the logarithm of the number of suicides of females under 20 in the lead direction, andthe instrumental variable Zm can take one of two forms, ∆Shif tSharem or the highest Rotemberg
weight share zm2 , which is the share of commuters with a 60-90-minute commute. To facilitatecomparison, we plotted baseline estimates at ℓ = 0, representing the coefficients from the baseline
reduced-form regression as outlined in Equation (7). Here, ℓ = 0 denotes the year 2020 as thereference point, and we extend our analysis back to ℓ = −5, which corresponds to 2015. The
findings consistently indicate no systematic positive impact of commute time on pre-COVID-19
suicide rates among females under 20. This lack of a significant trend in the pre-COVID-19 period19supports our presumption that the share-based instrumental variables are uncorrelated with preCOVID-19 trends in the suicidal behavior of females under 20.
Figure 4: Pre-trend Test(a) Shift-Share IV(b) Single IV with Commute Time of 60-90 Minutes

Notes: The dot shows the estimated semielasticities of suicide with respect to the exposure variable (a) commutetime IV and (b) commute time 60-90 minutes IV for females under 20 years of age with their 95% confidence intervals
for the post-COVID-19 period (t = 0) and pre-COVID-19 period (t < 0). The semielasticity is calculated based on
the estimated coefficient γℓ of Equation (9). We use the stay-at-home variable and the baseline specification, where
we do not include time-varying control variables.

Group-specific Time Trend: Our approach to identifying changes in suicide rates hinges on
a comparative analysis among municipalities before and after the COVID-19 pandemic. Given
the perspective of the share proposed by Goldsmith-Pinkham et al. (2020), our analysis uses
commuter share variation as the instrumental variable. Specifically, commuters traveling 60-90
minutes, distinguished by the most-significant Rotemberg weight, are essential for elucidating
causal relationships. Therefore, our analysis contrasts municipalities with a significant proportionof 60-90-minute commuters against those with a smaller proportion.
In the differential exposure design, a principal concern is the presence of unobserved variations
in characteristics among municipalities that were classified into the treatment and control categories. The observed changes in suicide rates among young females may not exclusively stem fromdifferences in commuting time distributions. Other factors, such as the degree of urbanization andthe nature of local industries, might also play a significant role. If these elements drive the suicide
trend after the pandemic started, the observed correlation between post-COVID-19 stay-at-homeincreases and suicide rates, as indicated by regression analyses, may not necessarily reflect a causal
relationship. The observed effects might stem from distinct municipal attribute trends over timerather than exclusively from changes in stay-at-home behaviors.
To establish a causal relationship, controlling for time trends specific to municipal attributes,
20

Table 4: The Effect of Stay-at-Home on Suicides of Females Under 20: Robustness Check∆StayHome

(1)
4.823
(2.026)[0.017](2)
4.532(2.012)[0.024](3)
4.192(2.750)[0.127]

Group specific trends
Cityx
Industryx
OthersLocal labor market
Unemployment rate
Active job opening ratio
Functional form
log-linear log-linear log-linear
Effective F statistics
1964.183 1990.049 1164.216
τ = 10%23.10923.10923.109Observations
1241
1241
1241

(4)
6.333
(3.189)[0.047](5)
4.760(2.126)[0.025](6)
5.163(2.224)[0.020](7)
5.029(2.302)[0.029](8)
5.319(2.206)[0.016]xlog-linear log-linear log-linear
836.7201677.856 1654.951
23.10923.10923.109
1241
1241
1241

x
x
log-linear
1654.951
23.1091241

ihs-linear
1964.183
23.109
1241

x
x
x
x

Notes: Cluster-robust standard errors are in parentheses. P values are in brackets. In Columns (1)-(7), theoutcome variable is defined as the logarithm of one plus the number of female suicide cases. The outcome variable
is transformed in Column (8) with the inverse hyperbolic sine function. In group-specific trends, City is a dummy
variable indicating municipal classification as a city; Industry represents the shares of the primary, secondary,and tertiary sectors; and Others encompass variables such as the youth rate, self-employment rate, labor force
participation rate, and single-person household rate, all as outlined in Table B3.

termed group-specific trends, is essential. The structural models incorporating these differential
trends can be formulated as follows:∆ log(1 + Suicidem ) = δ + β∆StayHomem +SX

ϕs Wsm + ∆νm

(10)

s=1

In this model, Wm = (W1m , · · · , WSm ) is a set of municipal characteristics, and ϕs represents acommon time trend for groups sharing the characteristic s, which serves as factor loading in a linear
factor model for time series analysis. The baseline Equation (4) incorporates an unobserved error
P
component, expressed as ∆εm = Ss=1 ϕs Wsm + ∆νm . If specific time trends tied to each groupinfluence suicide incidence within municipalities, this could compromise the exclusion restriction
given by Equation (8), as Zkm and Wsm might be correlated for some k and s. Such a scenario
would result in omitted variable bias when estimating the causal effects of home confinement on
suicide using the baseline model.
Acknowledging the potential influence of group-specific time trends, Table 4 presents additional
regression analyses incorporating urban-specific and industry-specific trends as factor loadings inestimating Equation (10). The results in Column (1) serve as a baseline in Table 2, showing
estimates without considering group-specific time trends for reference. The findings in Columns (2)21and (3) indicate that including these trends does not significantly alter the estimated impact of stayat-home orders on suicide among young women. Specifically, when controlling for urbanization witha dummy variable denoting whether a municipality is a city or not, the effect of home confinement
on young women’s suicide incidence remained statistically significant at the 5% level. Conversely,
when categorizing industries into primary, secondary, and tertiary sectors and accounting for thevarying ratios of industrial composition within municipalities, the estimate of the impact of homeconfinement on young women’s suicide is positive but not statistically significant. Nonetheless, asmall change in these estimates from the baseline, when incorporating a range of controls, indicatesthat group-specific trends do not drive our previous findings.
In addition to differences in urbanization and industry type, variations in other municipality attributes may also affect the prevalence of suicide among young women, influenced by group-specificcommuting patterns. However, accounting for factor loadings across all municipality attributes andcontrolling for them in regression analyses is a daunting task because numerous potential attributes
could be driving these group-specific trends. Instead, we could focus on the factors that strongly
correlate with the share type with the highest Rotemberg weight, the segment of 60-90-minutecommuters. Table B3 details the regression analysis of the share of 60-90-minute commutersagainst community characteristics in the pre-COVID-19 era. It reveals significant correlations between this commuter share and key municipality attributes such as age composition, employment
patterns, and the prevalence of single-person households15 . Given that Rotemberg weights emphasize the instruments most susceptible to endogeneity in the estimated parameters, as exploredin Goldsmith-Pinkham et al. (2020), these attributes could introduce notable estimation bias via
group-specific trends, which requires thorough investigation.
A factor-loading model, represented by Equation (10), was estimated to control for time trends
in the municipality characteristics highly correlated with the share of 60-90-minute commutersand city and industry dummies. The results, displayed in Column (4) of Table 4, reveal that theestimates remain positive and statistically distinct from zero. Therefore, even with adjustments
for group-specific trends, there is a positive causal effect of home confinement on the suicide ofyoung women.
Time-Varying Labor Market Conditions and Other Functional Forms:

A remaining

concern is the unobservable changes in labor-market conditions related to the pandemic. In Japan,
15

Employment patterns include overall employment rates and distinctions between employees and the self-employed.

22

previous research indicates a correlation between worsening labor-market conditions, particularly
unemployment, and an increase in suicide rates (Chen et al., 2009, 2015). There may be a causal
relationship linking stay-at-home behaviors, which could exacerbate downturns in the regional
economy, to worsened local labor-demand conditions and, consequently, an increase in suicide
among young women. To check whether this path exists, we include the unemployment rate andthe job opening-to-applicant ratio for each municipality in our analysis. The results displayed inColumns (6)-(7) of Table 4 indicate that the estimated parameter of the stay-at-home variable remains positive and statistically significant even after controlling for those labor-market conditions.
Notably, when compared to baseline estimates without controls for labor-market conditions, themagnitude of this effect diminishes slightly. This suggests, prima facie, that the impact of homeconfinement on the suicide of women under 20 is a direct effect rather than one mediated throughdeclining labor-market conditions. However, interpreting these regression results notably requires
caution. The local job opening-to-applicant ratio, potentially influenced by our treatment variable,
staying at home, presents a “bad control” problem, as described by Angrist and Pischke (2008).
This issue could introduce bias into our estimates. Appendix D contains a detailed discussion on
potential bias, explained using a Directed Acyclic Graph (DAG).
Finally, to enhance the robustness of our results, we conducted further analysis on the functional form of our model. Initially, we replaced the log(1 + Suicidemt ) transformation with aninverse hyperbolic sine transformation for the dependent variable, defined as arcsin(Suicidemt ) =plog(Suicidemt + 1 + Suicide2mt ). The outcomes, presented in Column (8) of Table 4, continue todemonstrate a statistically significant effect of the stay-at-home variable on suicide among young
women.

4.3

Specification Curve Analysis

A growing body of literature discusses the credibility and reproducibility of research in economics
and other fields (Christensen and Miguel, 2018). One of the issues is specification searching, wheremodel specifications are determined based on results. To demonstrate how sensitive the results are
to model specifications and other decisions about the research design, we conduct a specification
curve analysis (Simonsohn et al., 2020). This approach visualizes the magnitude of the estimatedeffect across a range of specifications, presenting the effects sorted by their magnitude. Specification
curve analysis thus acts as both an extension and a formalization of traditional robustness checks.
23

Figure 5: Specification Curve Analysis

Notes: The figure shows the specification curve analysis for females under age 20 using the stay-at-home variable.
Each dot shows the semielasticity of suicide with respect to the stay-at-home variable, with different sets of covariates, IVs, and sample periods. The total number of specifications is 3,072, and the first-stage F value exceeds 100
in all specifications. Specifically, 3,072 is the product of 2 specifications of the outcome variable × 2 IV choices ×
26 (with and without 6 factors for time trend) × 22 (with and without 2 control variables for economic conditions)× 3 period choices (including March 2020 and 2021; including March 2021 but not March 2020; excluding March
2020 and 2021). The red dot is the estimate in our baseline specification.

In our study, we explored different model specifications considering various factors. These factors include the presence or absence of group-specific trends and local labor-market conditions thatmight correlate with commuter share, as shown in Table B3. In addition, we considered the choiceof instrumental variables (utilizing a shift-share variable as one IV or the segment with the highest
Rotemberg weight as a single IV), functional form (log-linear or inverse hyperbolic sine transformations), time periods (adjusting the boundary between the pre-COVID-19 and post-COVID-19
periods by approximately one month), and the treatment of ambiguous months (whether to include
or exclude March 2020). The total number of specifications amounts to 3,072, and the first-stage
regression’s F value exceeds 100 in all formulations.
Figure 5 displays the estimated parameters β̂ pertinent to these models, arranged in order ofmagnitude. The red dot corresponds to our baseline estimate in Table 2; the smallest estimate is2.857 (at the left end of the graph), and the largest is 16.170 (at the right end). This indicates24

that in approximately 77.6% of specifications, the coefficient is significantly different from zero atthe 5% level, which reinforces the robust causal relationship between stay-at-home behavior andsuicide rates in females under 20.

4.4

Counterfactual Analysis

Given these results, we conduct a counterfactual analysis to quantify how much of the increase insuicide in females under 20 years of age can be attributed to stay-at-home behavior. Following
the estimated empirical model, we calculate a counterfactual number of suicide cases at the preCOVID-19 stay-at-home level and compare it with a simulated number of suicides with the actual
post-COVID-19 stay-at-home level. To be precise, we estimate our model in Equation (1) andpredict the counterfactual/actual suicide incidence using the stay-at-home variable in the pre- andpost-COVID-19 periods as follows:bb\ m,counterf actual ) = αcln(1 + Suicidem + δ × 1 + βStayHomem,0 + εm,1 ,(Counterfactual)bb\ m,actual ) = αcln(1 + Suicidem + δ × 1 + βStayHomem,1 + εm,1 .

(Actual)Then, we reported the aggregated change in suicide incidence based on the pre-COVID-19 period.
PM\ =∆Suicidem=1 Suicidem,counterf actual − Suicidem,actual.
PM\ m,actual
Suicide
m=1

\

\

(11)

where M is the number of municipalities.
In this simulation, we select two model specifications that correspond to the smallest and largest
point estimates β̂ in the specification curve analysis 4.3. Therefore, we can consider the calculated
numbers as lower and upper bounds of the contribution of stay-at-home behavior. Our simulation
revealed that approximately 37.4-42.2% of national suicides were caused by home confinement,
and these estimates suggest that the effect of stay-at-home behavior was substantial16 .
16

According to our data, the actual number of suicides among females under 20 years of age during the preCOVID-19 period was 218. The smallest estimated model specification showed that the simulated number ofsuicides was 171.929, and the counterfactual number of suicides was 236.252. According to the largest estimatedmodel specification, the simulated number of suicides was 192.084, whereas the counterfactual number of suicides
was 273.100.

25

5Discussion

One of the greatest social problems that occurred during the pandemic was the deterioration inmental health. The most serious consequences were increased suicides. Our IV estimates indicatedthat the increase was attributable to home confinement. The effect was heterogeneous by age group,and the most affected population was females under 20 years of age. The result was robust acrossdifferent model specifications, with choices of the IVs, as well as with alternative model types.
Studies on the negative effects of COVID-19 show that the pandemic has had a heavy burden
on young females, and our results align with these studies. Review papers on the effect of thepandemic on mental health have identified risk factors for distress as including female gender
identity, adolescent age, young adult age, and student status (Xiong et al., 2020; Penninx et al.,2022). Furthermore, using a large-scale, nationally representative survey in the UK, a study
reported that the prevalence of psychiatric disorders and loneliness is greater among females andyoung people than among the rest of the population, suggesting that young females are more
vulnerable to the pandemic than others are (Li and Wang, 2020). Moreover, studies from Norway,
the United States, and Canada reported an increase in cases of eating disorders, which are highly
related to mental health disorders, in children and adolescents compared with incidence rates during
the prepandemic period (Otto et al., 2021; Toulany et al., 2022; Surén et al., 2022). Althoughwhich aspect of the pandemic affects the mental health of females and young people has notbeen examined, these studies argue that heightened trends of mental disorders are associated withsocial changes induced by restrictions placed on this population’s lives and activities and thatthe disruption of social lives during the pandemic is likely to be greater among young people(Li and Wang, 2020). The COVID-19 pandemic affected almost all the population, but thesestudies suggest that the mental effects of lifestyle changes induced by the pandemic were especiallydetrimental to that particular population. Our results are in line with these findings, indicatingthat physical disconnection from society was one of the key pathways for such adverse effects on
mental health among young females.
Our results are not necessarily inconsistent with U.S. research showing that increased suicide isassociated with a return to in-class schooling (as opposed to school closure) for the following two
reasons (Hansen et al., 2022). First, the backgrounds of Japan and the USA during the pandemic
differed. Japan showed a consistently high rate of female suicides during the COVID-19 pandemic,
which was not observed in other countries, including the US (Pirkis et al., 2022). Second, our26study does not aim to examine the effect of schooling mode on mental health. Rather, we focus
on the effect of home confinement, which includes a wider aspect of lifestyle changes, including
relationships with family members and the opportunity to meet friends outside of school. To thisend, the variation we exploit to identify the impact of home confinement differs from the variation
used to identify the impact of a change in the school mode. Specifically, since we aim to examine
the effects of home confinement during the pandemic compared with patterns in the prepandemic
period, the time-series variations we use are those before and after the pandemic started, where
the postpandemic period is aggregated as a single period. The postpandemic periods include not
only the initiation but also the termination of school closures. This means that the period of theanalysis that we focused on is not aligned with the study that aimed to identify the effect of school
openings.
Using a regression discontinuity design based on an age cutoff, a previous study demonstrated
that lockdown deteriorated the mental health of elderly people (Altindag et al., 2022). Although
the RD design sharply identifies the effect of lockdown policies, the nature of the design does not
permit analysis of how the effect differs by age. Our shift-share IV estimate indicates that, in thecontext of Japan, heterogeneity is important, and the effect on disconnection from society is more
serious among young people in terms of suicide, the worst consequence of mental disorders.
There are several possible mechanisms through which home confinement affects mental health.
Social disconnection through home confinement itself elevates the risk of mental illness morbidity
through the feelings of isolation and loneliness (Loades et al., 2020). In addition, in the context of
COVID-19, people tended to stay at home across the population, including adults, and therefore,
some of the observed effects among young people could be related to family-related issues such as
domestic violence or abuse, as reported in other countries (Leslie and Wilson, 2020; Arenas-Arroyo
et al., 2021; Berniell and Facchini, 2021; Hsu and Henke, 2021). Furthermore, staying at home mayhinder access to treatment and care in mental health services. All of these factors could be related
to the increased risk of suicide that we observed. Indeed, an analysis of the cause of Japaneseyouth suicides during the pandemic using time-series data revealed periodic increases attributed
to family-related and social concerns, whereas there was a notable increase in suicides linked tomental illness as the pandemic progressed into the later months of 2020 (Goto et al., 2022).
Early public health responses against the COVID-19 pandemic include social distancing measures such as lockdowns and stay-at-home orders, and these measures could be policy options for27

future infectious-disease pandemics, especially before pharmaceutical interventions become available. The adverse effects of these pandemic responses on the mental health of children and adolescents were reported from the experiences of H1N1 influenza and SARS (Sprang and Silman,2013). Our results show that the effect was devastating in the case of the COVID-19 pandemic,
demonstrating that collective social distancing policies impose high social costs on both individuals and society in the worst form as lives are lost. Public policy-making should be built upon theawareness of such risks and implement countermeasures for high-risk populations as a part of aneffort to enhance pandemic preparedness.

Acknowledgments: This research is supported by JSPS Kakenhi 20K01731 and 21H00672. We
appreciate Yusuke Inami’s efforts and excellent assistance. We are grateful for comments fromseminar participants at Hitotsubashi University, Keio University, Kobe University, Osaka University, Otaru University of Commerce, Sophia University, and the University of Hawai‘i at Manoa.

Declaration of Competing Interests: The authors declare that they have no competing financial interests that could have appeared to influence the work reported in this study.

28

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32

Appendix

1

A

Additional information

A.1

Background

Policies on COVID-19: The first COVID-19 cases in Japan were reported on January 16, 2020,
with little increase in the number of cases until the end of March. The earliest measure taken was
to order the closure of schools at the end of February, and in response, most schools decided toclose until the semester was over at the end of March.
The first action taken by the government and affecting all of society was the declaration of thestate of emergency. On April 7, 2020, the first state of emergency was declared in seven prefectures,
including Tokyo17 . Nine days later, the state of emergency was extended to all prefectures inJapan. The first declaration of the state of emergency decreased the number of people leaving
their homes by 8.5% (Watanabe and Yabu, 2021). The state of emergency was gradually lifted
depending on the pandemic situation18 . Even after the state of emergency was lifted, mobility
did not substantially recover because of various government interventions for social distancing.
These interventions included encouraging working or studying from home and the early closureof restaurants and bars. In addition to the effect of government interventions, people’s behavior
was affected by new social norms (Takahashi and Tanaka, 2021). Consequently, staying at home
continued until March 2021, as shown in Section 3.
Trend in suicide: In Japan, suicide cases reached their highest record in 1998 after the economiccrisis and remained high until 2009. However, because of the government’s effort to prevent suicide,the number of cases decreased until the pandemic started. In 2020, the suicide rate increased forthe first time since 2010. Figure A1 shows the suicide rate between April 2019 and March 2021.
For females, the suicide rate was approximately 8 per 10,000 people as of June 2020; however, thetrend then began to increase, reaching the highest level of more than 13 per 10,000 people, andremained at a higher level in the first half of 2021. For males, the relationship with COVID-19 isless clear than it is for females.
17
18

These prefectures include Tokyo, Kanagawa, Chiba, and Saitama, as well as Osaka, Hyogo, and Fukuoka.
Decisions on declaring and terminating the state of emergency were made mainly with the following three factors:
the number of cases per population, medical surges, and PCR testing volume for COVID-19.

2

Figure A1: Suicide incidence per 100,000 population, April 2019 - March 2021

Notes: Data shows suicide incidence per 100,000 people in Japan between April 2019 and March 2021. A verticaldotted line marks April 2020, dividing the analysis into two periods: Pre-COVID-19 (prior to April 2020) and
Post-COVID-19 (from April 2020 onwards).

3

A.2

Correlation with the Google mobility index

To confirm the reliability of our stay-at-home variable, we obtained a correlation with the Google
mobility index. Google provided the mobility index to offer timely information on individual travel
during the COVID-19 pandemic. The daily mobility data are publicly available as “COVID-19:
Community Mobility Report.” The Japanese data are available at the prefecture level starting in
February 2020. The data are available as a change compared to the median value of a reference
day that is the same day of a week during the prepandemic period, where the prepandemic period
is designated from January 3rd to February 6th. Among the six location categories, we focus
on residence as our measure of stay-at-home behavior. The Google mobility index shows thepercentage change in time spent in the residential area from the prepandemic period. We take themonthly average of the daily data of the prefecture-level Google mobility index between February2020 and December 2021 and obtain the correlation coefficient with the difference in the monthly
stay-at-home variable between January 2020 and each month at the prefecture level using a total of1081 (=23 months47 prefectures) data points. The correlation between our stay-at-home variable
and the Google mobility index is 0.74.
Next, we closely examine the correlation in the time series direction. Figure A2 in the Appendix
shows the time series plot of the stay-at-home variable and the Google mobility index for residences
between February 2020 and December 2021. Our stay-at-home variable closely follows the Google
mobility index of residence throughout the period of our analysis.
Furthermore, we explored the association in the regional direction. Figure A3 in the Appendix
shows the association between the stay-at-home variable and the Google mobility index of residence in each month from February 2020 to March 2021. In the first two panels that show theassociation in the prepandemic period, there was almost no regional variation in either of the variables. However, starting in April 2020, diversity emerged. In all the sample periods, we see a clear
positive correlation, confirming that our measure shows stay-at-home behavior at the individuallevel.

4

Figure A2: Time Series Association between the Stay-at-Home Variable and the Google Mobility
Index

Notes: The figure shows the plot of the national average of ∆StayHome (blue) and the Google mobility index(red). ∆StayHome shows the change from January 2020 to be consistent with the definition of the Google mobility
index.

5

Figure A3: Regional Association between the Stay-at-Home Variable and the Google Mobility
Index

Notes: Each panel shows the scatterplot of the prefecture average of ∆StayHome and its linear fit. StayHome
shows the weighted average of the municipality-level data to obtain the prefecture average, and ∆StayHome shows
the change from January 2020 to be consistent with the definition of the Google mobility index.

6

B

Additional figures and tables
Figure B1: Foot Traffic Data: Examples

Notes: Light green shows resident population counts, and orange shows nonresident population counts for eachhour. The data shows the average population in each hour in April 2019. Minato Ward, Tokyo, is an example of abusiness district, and Kokubunji City, Tokyo, is an example of a residential district. The stay-at-home rate is 0.67
for Minato Ward and 0.45 for Kokubunji City. A greater stay-at-home value indicates a higher tendency to stay inthe municipality.

7

Table B1: Descriptive Statistics
Total

Before

After

Mean

Std. Dev.

Min

Max

Mean

Std. Dev. Mean

Std. Dev.

5.175
0.213
0.612
0.550
0.828
0.796
2.175
0.670

6.266
0.532
1.245
1.037
1.325
1.314
2.575
0.123

0
0
0
0
0
0
0
0.409

48
5
17
10
11
10
20
0.998

4.633
0.176
0.507
0.469
0.719
0.716
2.044
0.640

5.534
0.475
1.041
0.941
1.171
1.204
2.416
0.132

5.717
0.251
0.716
0.630
0.938
0.876
2.305
0.701

6.880
0.581
1.412
1.119
1.455
1.413
2.719
0.106

0.464
0.017
0.589
0.270
0.090
0.025
0.009
1.000
0.055
0.153
0.200
0.214
0.223
0.116

0.194
0.009
0.175
0.100
0.084
0.029
0.008
0.000
0.025
0.078
0.094
0.106
0.021
0.053

0.009
0.000
0.142
0.033
0.000
0.000
0.000
1.000
0.030
0.075
0.106
0.108
0.202
0.040

0.787
0.114
0.929
0.610
0.431
0.213
0.086
1.000
0.080
0.230
0.294
0.320
0.244
0.261

0.464
0.017
0.589
0.270
0.090
0.025
0.009
1.000
0.030
0.075
0.106
0.108
0.202
0.069

0.195
0.009
0.175
0.100
0.084
0.029
0.008
0.000
0.000
0.000
0.000
0.000
0.000
0.013

0.464
0.017
0.589
0.270
0.090
0.025
0.009
1.000
0.080
0.230
0.294
0.320
0.244
0.163

0.195
0.009
0.175
0.100
0.084
0.029
0.008
0.000
0.000
0.000
0.000
0.000
0.000
0.032

0.060
0.259
0.681
0.782
0.124
0.583
0.285
0.569
0.100
0.139

0.063
0.075
0.096
0.413
0.019
0.043
0.057
0.039
0.032
0.028

0.000
0.081
0.430
0.000
0.057
0.413
0.149
0.387
0.042
0.032

0.395
0.512
0.919
1.000
0.207
0.748
0.518
0.699
0.246
0.231

Active opening ratio
Unemployment rate

1.361
2.436

0.644
0.493

0.000 8.935
1.250 3.600

1.586
2.195

0.723
0.386

1.136
2.677

0.453
0.471

Observations

2482

Panel A: Suicide and stay-at-home index
Female suicide (cases)
Female suicide under 20
Female suicide 20-29
Female suicide 30-39
Female suicide 40-49
Female suicide 50-59
Female suicide 60s or over
StayHome
Panel B: Shares, shifts, and IV
Commuting ratio
Commuting time 0 mins
Commuting time (0,30] mins
Commuting time (30,60] mins
Commuting time (60,90] mins
Commuting time (90,120] mins
Commuting time > 120 mins
Work from work ratio (commuting time 0 mis)
Work from work ratio (commuting time (0,30] mins)
Work from work ratio (commuting time (30,60] mins)
Work from work ratio (commuting time (60,90] mins)
Work from work ratio (commuting time (90,120] mins)
Work from work ratio (commuting time > 120 mins)
Shift-Share IV
Panel C: Time-invariant covariates
Primary industry ratio
Secondary industry ratio
Tertiary industry ratio
City dummy
Young rate
Middle rate
Elderly rate
Labor force rate
Self-employment rate
Single person household rate
Panel D: Time-varying covariates

1241

1241

Notes: The sample period is between April 2019 and March 2021. Before COVID-19 indicates the period between
April 2019 and March 2020, and after COVID-19 indicates the period between April 2020 and March 2021. The
definitions of the variables are provided in the main text or in Appendix C.

8Table B2: Descriptive Statistics for Commuter Type
Commuting time k
0 mins0(0, 30] mins
1(30, 60] mins
2(60, 90] mins
3
(90, 120] mins
4
> 120 mins
5

z̄k
0.017
0.589
0.270
0.090
0.025
0.009

gk0
1.000
0.030
0.075
0.106
0.108
0.202

gk1
1.000
0.080
0.230
0.294
0.320
0.244

Notes: Commuter type is represented by k = 0, 1, 2, 3, 4, 5. z̄k represents the average share of each commuter typek across municipalities. In each municipality m, the share of each commuter type is calculated based on the 2018
Housing and Land Survey of Japan. gk0 and gk1 represent the proportion of those who worked from home before
and after the pandemic, respectively. The data are obtained from Table 1-17-1 of Okubo (2021)

9

Table B3: Correlation between the Share of 60-90-Minute Commuters and Municipality Characteristics
City

(1)
0.096
(0.003)[0.000]Primary industry share(2)-0.398(0.031)[0.000]-0.114(0.020)[0.000]0.211(0.008)[0.000]

Secondary industry share

Tertiary industry share

(3)
0.000
(0.005)[0.938]-0.398(0.031)[0.000]-0.114(0.020)[0.000]0.211(0.009)[0.000]Young rate (under 15)

Middle rate (between 15-64)

Elderly rate (above 64)

Employment rate

Employee rate

Self-employment rate

Labor force rate

Single person household rate

Adj. R-Square
Observations

0.470
1241

0.666
1241

0.666
1241

(4)
0.016
(0.004)[0.000]0.293(0.241)[0.225]-0.037(0.236)[0.875]0.434(0.226)[0.055]-1.786(0.211)[0.000]0.125(0.187)[0.503]-0.267(0.181)[0.139]0.103(0.127)[0.418]-0.010(0.098)[0.919]-0.388(0.197)[0.049]-0.618(0.077)[0.000]2.190(0.099)[0.000]
0.784
1241

(5)
0.016
(0.004)[0.000]0.357(0.206)[0.083]0.024(0.189)[0.900]0.490(0.180)[0.007]-1.774(0.203)[0.000]0.144(0.185)[0.435]-0.243(0.173)[0.160]-0.380(0.115)[0.001]-0.598(0.068)[0.000]2.154(0.091)[0.000]0.784
1241

Notes: Cluster-robust standard errors are in parentheses. P values are in brackets. The dependent variable is theshare of commuters whose commuting time ranges from 60 to 90 minutes (the highest Rotemberg weight share).
All variables are derived from data in the census in 2015. The definitions of the variables are provided in the main
text or in Appendix C.

10Table B4: Rotemberg Decomposition Diagnostics
Sum
Mean
Panel A. Negative and positive weightsα̂k ≤ 0-0.397-0.397α̂k > 0
1.397
0.349
α̂k
∆gk
Panel B. Correlations
α̂k
1.000
∆gk
0.702
1.000
β̂k
0.230
0.509
F̂k
0.057
0.311
Var(zk )
0.765
0.608
α̂k
∆gk
Panel C. Share Statistics
Commuting time (0,30] mins
-0.3970.050
Commuting time (30,60] mins
0.551
0.155
Commuting time (60,90] mins
0.659
0.188
Commuting time (90,120] mins
0.185
0.212
Commuting time > 120 mins
0.003
0.042
α weighted share ofsumoverall βPanel D: β̂k for positive and negative weights
Negative-1.609-0.381
Positve
5.838
1.381

Share
0.221
0.779
β̂k

F̂k

Var(zk )

1.000
0.680
0.370
β̂k

1.000
0.609
F̂k

1.000
95 % C.I.

1807.293
722.220
1681.096
439.269
26.637[0.549, 7.535][-2.296, 6.537][2.146, 9.831][-1.447, 9.132][-10.530, 18.215]

4.050
2.117
5.980
3.902
3.519
Mean

4.050
3.880

Notes: The table presents the shift-share diagnostics as recommended by Goldsmith-Pinkham, Sorkin, and Swift(2020). The statistics are computed using a Stata package developed by the authors. Panel A delineates theaggregate, average, and distribution of negative and positive Rotemberg weights, α̂k , across different commuter
types k. Panel B elucidates the pairwise correlations among four key metrics for Rotemberg decomposition –Rotemberg weights α̂k , the first difference in telework ratio ∆gk , the just-identified coefficients β̂k , and the firststage F statistics for the just-identified instruments F̂k – and the variance across municipalities for each commuter
type k, denoted as V ar(zk ). Panel C shows the key statistics for each commuting time ratio, including the 95%
confidence intervals that are robust to weak instruments, as calculated using the method proposed by Chernozhukov
and Hansen (2008), spanning from −10 to 5 in increments of 0.01. Lastly, Panel D provides insights into thevariability of the just identified coefficients β̂k in relation to the positive and negative Rotemberg weights for each
commuter type k.

11

CControl Variables

The data sources for the control variables used in the robustness check are explained below.
Local labor market: We used two variables, the active job-opening ratio at the municipality
level and the prefecture-level unemployment rate, to control for local labor-market conditions.
The active job-opening ratio is frequently used to measure macroeconomic conditions at the local
level, and it is defined as the ratio of active job openings to the number of active applicants. The
Ministry of Health, Labour, and Welfare provides monthly data specific to the level of the public
employment security office on request. On average, one public employment security office covers
a few municipalities. We used the same number of active job-opening ratios for municipalities
covered by the same public employment security office. Some municipalities are divided into several
districts, which are covered by different public employment security offices. In that case, we used
the municipality’s average active job-opening ratio of the public employment security offices. The
active job-opening ratio was calculated by dividing the number of openings by the number of job
seekers in each public employment security office. We used the average active job-opening ratio
for the before- and during-COVID-19 periods.
We also included unemployment rates on a prefecture basis. Based on the Labor Force Survey
conducted and reported quarterly by the Ministry of Internal Affairs and Communications at thenational level in Japan, estimates of the prefecture–quarterly unemployment rate can be obtainedusing a time-series model. Since only quarterly–prefecture data were available, the same numbers
were used for the same quarter for all the municipalities in the same prefecture. We included theaverage unemployment rate in the quarterly data before and during the COVID-19 pandemic.
Industry composition and city dummies: To control for time trends, we included a set ofvariables to indicate the industry composition of the municipality and a city dummy.
The variables to indicate the industry composition show the share of workers in the 20 industries
based on the Japan Standard Industry Classification (https://www.soumu.go.jp/english/dgpp_
ss/seido/sangyo/san13-3.htm), which includes the following industry categories: agriculture
and forestry; fisheries; mining and quarrying of stone and gravel; construction; manufacturing;
electricity, gas, heat, and water; information and communications; transport and postal activities;
wholesale and retail trade; finance and insurance; real estate and goods rental and leasing; scientific12research, professional and technical services; accommodations, eating and drinking services; livingrelated and personal services and amusement services; education, learning support; medical, healthcare and welfare; compound services; services, N.E.C.; government, except elsewhere classified;industries unable to classify. We used the 2015 census data for the prepandemic period and the2020 data for the pandemic period, as the census survey was conducted in October of that year.
For a city dummy to control for urban areas, we create binary variables to take unity if amunicipality is a city and zero otherwise.
Other variables: Some of the specifications included socioeconomic and demographic information at the municipality level. The demographic information included the age composition of three
age groups: under 15, 15-64, and 65 years or older. We also include the labor force participation
rate, self-employment rate, and single-person household rate. The labor force participation rate isdefined as the sum of the number of people who are working and who are unemployed but seeking
jobs divided by the total population aged 15 years or older. The self-employment rate is defined
as follows: The numerator is the sum of those who own their business, including homeworkers andthose with and without employees, and the denominator is the number of workers. The singleperson household rate is the share of the households that consist of a single person to the total
number of households. These variables are taken from the 2015 Census Survey by the Statistical
Bureau.
Commuting ratio: In the empirical model section, we established a canonical model with two
types as an illustration. The commuting ratio for each municipality is obtained by the 2015 Census
Survey by the Statistics Bureau and defined as the sum of the number of residents working at home
and the number of residents working or studying within their own municipality divided by the total
number of students and workers aged 15 years or older.

D

DAG: Bad Control

Angrist and Pischke (2008) describe variables that could be impacted (postdetermined) by thetreatment as “bad controls,” advising against their inclusion in a regression model, even if their
inclusion might alter the regression coefficients. Conversely, they define “good controls” as variables
thought to have been established at the point when the regressor of interest was determined13

(predetermined).
In the context of this paper, the treatment variable, stay-at-home behavior, is likely to affectlocal labor-market dynamics, including unemployment rates. This aspect classifies the local labormarket condition as a postdetermined variable. This characteristic suggests that it might serve asa “bad control” in the regression analysis estimating the causal effect of stay-at-home behavior on
suicide rates among young women.
We employ causal diagrams to distinguish when postdetermined variables affected by the treatment can be considered “good” or “bad.” Using directed acyclic graphs (DAGs), we provide visualsummaries of causal relations in various typical scenarios. When including labor market–related
variables in the regression, these summaries could provide a biased estimate of the causal effect ofstaying at home on young women’s suicidal behaviors.
Graphically, the potential causal paths under various assumptions are illustrated through diagrams. We represent the outcome of interest, the number of female suicides, by Y . The endogenous
stay-at-home variable is denoted by S, and the shift-share instrumental variable is denoted by Z.
If we represent labor-market conditions by M , it is influenced by stay-at-home S and, in turn,affects the outcome Y . This influence is depicted through the path S → M → Y so that M serves
as a mediator. In Figure D1a, a confounder U is assumed to affect both the outcome Y and theendogenous variable S. The instrumental variable Z is assumed to only affect S (i.e., exclusion
restriction), and S influences Y through M . The direct effect of S on Y , when M is controlled,
is the average change in Y when S is altered exogenously by one unit, known as the controlled
direct effect as per Pearl (2009). To estimate the controlled direct effect using the instrumental
variable S, it is necessary to control M to close the backdoor path Z → S → M → Y . Hence, inthis scenario, M is a “good control” and should be included in the regression analysis to estimate
the direct effect of S on Y .
Conversely, Figure D1b shows a situation where the confounder U affects not only Y and S but
also M . In this case, controlling M would open a colliding path Z → S → M ← U → Y , making it
impossible to identify the direct effect of S on Y using Z as an instrumental variable. Therefore, M
is a “bad control”. Additionally, Figures D1c and D1d illustrate cases where another confounder
V affects both the mediator M and the outcome Y . In Figure D1c, even though the confounder U
does not affect the mediator M , controlling M opens the colliding path Z → S → M ← V → Y ,making M a “bad control”. Similarly, in Figure D1d, where the confounder U affects the mediator

14M , controlling M opens both paths Z → S → M ← U → Y and Z → S → M ← V → Y ,reinforcing M as a “bad control.” Therefore, in these scenarios, M should not be included in the
regression analysis using Z as an instrumental variable.
In summary, in the situation depicted in Figure D1a, the local labor-market condition M is a“good control” and helps unbiasedly estimate the direct effect in the regression analysis. However,in the other scenarios shown from Figure D1b to Figure D1d, M becomes a “bad control” andshould not be included in the regression analysis.
Figure D1: DAG: Good or Bad Controls(a) One confounder with M unconfounded by U(b) One confounder with M confounded by U(c) Two confounders with M unconfounded by U(d) Two confounders with M confounded by U

15