K ILLER A LERTS ? P UBLIC H EALTH WARNINGS AND H EATS TROKE IN JAPAN*
Lester Lusher,a Tim Rubergb†aUniversity of Pittsburgh and IZAb

University of Hohenheim
October 18, 2023

PRELIMINARY DRAFT, PLEASE DO NOT CITE/CIRCULATE
Abstract
In 2020, Japan introduced a comprehensive heat-health warning system where daily alerts were issued
by region when forecasted wet bulb globe temperature (WBGT) exceeded a threshold (33◦ C). Utilizing plausibly exogenous region-day variation in the difference between actual and forecasted WBGT
(i.e. forecasting errors), we find that the alerts led to a large and precisely estimated increase in heatstroke counts. Paired with data from Google Trends, Google Mobility Reports, and the population of
ambulance records, we identify potential mechanisms, including increased reporting of heat stroke cases
and “adverse” behavioral responses (e.g. people spending more time outdoors) when alerts were issued,
while ruling out potential substitution in health diagnoses away from other sudden illnesses.

Keywords: heat stroke, climate change, warning effectiveness, avoidance behavior
JEL codes: D90, I12, I18, Q54* We thank Mika Akesaka, Scott Carrell, Timothy Halliday, Shotaro Nakamura, Matt Naven, Yuki Nohara, Akira Sasahara,
Hitoshi Shigeoka, Jeffrey Shrader, Manasvini Singh, Mirka Zvedelikova, and seminar participants at the University of Hohenheim,
University of Tokyo, Osaka University, and Keio University for helpful comments. We thank the Fire and Disaster Management
Agency (FDMA) for providing micro-level data. The views expressed in this paper are those of the authors and do not necessarilyreflect the positions or policies of the organizations mentioned above. Any remaining errors are our own. Declarations of interest:
none.
†
Lester Lusher, Department of Economics, Pittsburgh, 230 South Bouquet Street, PA 15260, United States; IZA; Email: lesterlusher@pitt.edu. Tim Ruberg, University of Hohenheim, Department of Economics, Schloss Hohenheim, 70593 Stuttgart, Germany; Email: tim.ruberg@uni-hohenheim.de.

1Introduction
In 2023, the world experienced one of the hottest summers ever recorded in human history. July 3,2023, was the hottest day ever recorded, a record broken the very next day (and again on July 5 and July 6).1
Extreme heat is the leading cause of weather-related death in the United States (more so than hurricanes,
floods, and tornadoes, for example),2 with the Los Angeles Times recently calling extreme heat the “quiet”
health crisis.3 Worldwide, extreme heat is a significant, and likely under-counted, cause of overall death(Ebi et al., 2021). Climate change has led to a drastic increase in average global warming, which has caused
an increase in the incidence of record high temperatures and extreme heat events measured across weather
stations (Robinson et al., 2021). Historically, single extreme heatwaves have claimed thousands of lives,
like in the United States in 1995 (Palecki et al., 2001), Europe in 2003 and 2022 (D’Ippoliti et al., 2010;
Ballester et al., 2023), and India in 2015 (Dodla et al., 2017), raising great concern for the implications ofclimate change in the present and immediate future.4
In this study, we investigate the impacts of a public warning system for extreme heat events on health andbehavioral outcomes. Despite their potential importance and growing usage, relatively little is known about
the efficacy of heat warning systems.5 The context of our study is Japan. To our knowledge, Japan is oneof only several high-income countries to have adopted a heat-health warning system.6 Experts worldwide
agree that there is a global need for heat-health warning systems, yet are severely lacking, even among
high-income countries (Li et al., 2022). In the United States, a recent 2022 National Public Radio article
dissected the insufficiencies of the current heatwave warning system.7 In-progress examples of heat warning
systems at the state level include the HeatRisk program covering a handful of western U.S. states, currently
in a piloting phase,8 and an impending program in California, which approved a bill in September 2022
1
Source: PBS News Hour.
Website: https://www.pbs.org/newshour/science/summer-of-recordbreaking-heat-paints-story-of-a-warming-world-scientists-say, retrieved July 25, 2023.
2
U.S. National Weather Service. Website: https://www.weather.gov/hazstat/, retrieved February 28, 2023.
3
Los Angeles Times. Website: https://centerforhealthjournalism.org/2021/11/04/extreme-heatoverlooked-health-crisis-worth-covering-la-times-investigation-shows, retrieved March 1, 2023.
4
See Martiello and Giacchi (2010) for a review of the relationship between high temperatures and mortality and morbidity
outcomes.
5
Small-scale and/or correlational studies from epidemiology assessing the potential health impacts from heat warnings include
Weinberger et al. (2018), Benmarhnia et al. (2019), Vaidyanathan et al. (2019), and Weinberger et al. (2021).
6
Other high-income countries to have adopted a heat-health warning system as of 2022 include France, the UK, and Spain(Kotharkar and Ghosh, 2022).
7
For example, the primary variable for the U.S. warning system is the National Weather Service’s “heat index,” which underestimates the effect of extreme temperatures on the human body, thus underselling the true hazard from heat. Furthermore, only vague
information is given regarding health hazards in response to heat from the U.S. warning system. National Public Radio, “Why heatwave warnings are falling short in the U.S.” Website: https://www.npr.org/2022/09/13/1122491546/why-heatwave-warnings-are-falling-short-in-the-u-s, retrieved March 1, 2023.
8
National Weather Service, “NWS HeatRisk Prototype.” Website: https://www.wrh.noaa.gov/wrh/heatrisk/,
retrieved March 1, 2023.

1

requiring the state to develop a heat wave ranking system.9 Japan piloted a heat-health warning system in
2020, then fully implemented the program nationally in 2021, granting us three years of observations witha warning system in place (2020–2022).
Additional advantages from the Japanese context include comprehensive hospitalization data and features of the warning system, which allow us to plausibly identify the causal impacts of the warning systemon health outcomes. First, Japan provides extensive information for heat stroke outcomes by severity, demographic, and location, among other variables, whereas in the United States, hospitals and health careproviders are not required to report heat-related illnesses to public health agencies.10 Furthermore, in Japan,
micro-level data exist for the population of ambulance dispatches, including various information on patient
health outcomes and location.
For plausibly exogenous variation, Japan issues warnings across spatial areas (n = 44 regions) basedon whether the forecasted wet bulb globe temperature (WBGT) exceeds a certain threshold (33.0◦ C). Combined with data on whether a warning was issued for a region and actual WBGT, our econometric model
effectively allows us to estimate the effect of a heat warning while flexibly controlling for local daily WBGT.
In other words, our data include region-dates where an alert was issued but the actual WBGT was below
33.0◦ C (i.e. a false positive) and region-dates where an alert was not issued but the actual WBGT exceeded33.0◦ C (i.e. a false negative). Thus, our econometric model separately disentangles the effects of a highheat warning from high heat itself using exogenous variation in forecasting errors.
We precisely estimate a substantial increase in heat stroke cases in response to a heat-health warning
being issued. For our fully specified model, which flexibly controls for same-day and prior-day local WBGT
(i.e. dummies for each WBGT level, rounded to the nearest tenth) as well as prefecture and date fixed effects, a heat-health warning increases the incidence of heat stroke hospitalizations by nearly 17%. The
effects exist across the spectrum of severity, including “severe” cases which require multiple weeks of hospitalization. We further show the robustness of these results to various considerations, including additional
controls for region-specific time trends and COVID-19 policies, alternative difference-in-differences estimators, estimating OLS vs. log-linear vs. negative binomial regression models, different sample restrictions,randomization inference, and intertemporal models. The result still appears even in a simple descriptive
figure, where on average, for the exact same WBGT, region-days with a heat alert experience more heatstrokes than region-days without a heat alert.
We then consider several potential mechanisms to explain these seemingly counter-intuitive results.
910

California Legislative Information, “AB-2238 Extreme heat: statewide extreme heat ranking system.” retrieved March 1, 2023.
Centers for Disease Control and Prevention, “Picture of America Heat-Related Illness Fact Sheet,” retrieved March 1, 2023

2

First, it could be that the health issues associated with extreme heat often go unidentified and/or unreported,and by raising the awareness of the hazards induced by high heat, the heat-health alert effectively encouraged
people to report what were otherwise unidentified cases of heat stroke (i.e. an “increased reporting” effect).
Second, it may be that cases of heat stroke are sometimes conflated with other sudden illnesses (such as
actual stroke, heart attacks, and other cardiovascular issues), and when an alert is issued, there’s effectively
a “substitution” in health diagnoses away from other sudden illnesses and into heat stroke. Note that in bothof these instances, the “actual” incidence of heat stroke is unknown (and possibly decreasing), it’s just that
“increased reporting” and/or “substituting” lead to increased heat stroke counts. Third and finally, it may
be the case that the alerts induced changes in behavior that led to “real” increases in actual heat stroke. For
example, it could be that on days when a warning was issued, various locations (e.g. workplaces, offices,
schools) closed more, and perhaps individuals are better protected from heat stroke at these locations vs. attheir own home (e.g. due to better air conditioning). More generally, it may be that other factors associatedwith the alerts encourage individuals to engage in an “adverse” behavioral response, thus exposing them togreater risk for heat stroke in response to an alert.
In order to shed some light onto these potential mechanisms, we first utilize micro-data from the population of ambulance records in Japan.11 We first find that total ambulance calls increase in response to aheat alert, even for cases where the patient was “severely injured.” Thus, the main effects cannot be solely
attributed to health diagnoses “substituting” away from other sudden illnesses and into heat stroke (since
substitution would imply no change in ambulance pickups). Additionally, we estimate a small but statistically insignificant increase in overall mortality (that involved an ambulance) in region-days with a heat
alert. Combined with a statistically significant positive effect for severe cases (in both heat strokes and overall ambulance records), we further believe that the overall heat stroke effect cannot be solely attributed to“increased reporting” (since severe cases, and cases which lead to death, would presumably have utilized an
ambulance irrespective of an alert being issued).
Thus, to consider the possibility of an “adverse” behavioral response to the alerts, we next make use ofdata from Google Trends and Google Community Mobility Reports, which tracked daily foot traffic acrosslocations by several broad categories from 2020 to 2022, while also considering the home residence andworkplace of the tracked individual. From Google Trends, we first find that the heat alert raised generalawareness, as reflected by increases in searches for the terms “heat stroke,” “heat stroke alert,” and “temperature.” Searches for “air conditioning” also significantly increased. Searches for the general term “indoor”
11
Discussed in further detail later, and as described in Akesaka and Shigeoka (2023), ambulance services are provided to the
public for free in Japan, and thus constitute a significant share of hospital transport in Japan.

3increase as well, though searches for specific popular indoor activities such as “cinema” and “karaoke” were
unaffected. These Google Search results may reflect some evidence of “avoidance” behaviors, the specific
aim of the heat-health alert in Japan.
The remainder of the evidence, however, suggests a potentially adverse response to the heat alert. First,
in the Google Mobility Report data, people were more likely to leave their home on alert days. This behavior
stands in direct contrast to the government guideline to stay home and use air conditioner. Although thismay simply reflect the “increased reporting” channel (where people leave their home via ambulance andreport cases that were otherwise unidentified), we also estimate a large increase in visitations to parks,
the sole outdoor category provided in the Google Community Mobility Reports, when an alert was issued.
We also find increased searches on Google for outdoor activities such as “sea bathing” and “park.” It isalso possible that these behaviors still constitute “avoidance” behaviors, where perhaps people are better
protected from high heat outside their own homes. However, additional explorations fail to uncover any
evidence of avoidance behaviors leading to improved outcomes. For example, we find that the alerts haveno effect on heat strokes on days t + 1 or t + 2, where perhaps avoidance behaviors today would capitalize
into reduced heat strokes tomorrow or the day after tomorrow.12
To further contextualize our results, we consider several related studies from the Japanese context which
uncovered similarly concerning results. First, a recent study from He and Tanaka (2023) found increased
mortality and reduced air conditioning usage, particularly on extremely hot days, in response to large-scaleenergy-saving campaigns in Japan. Related work from Neidell et al. (2021) found that households reduced
their electricity consumption when local prices rose (after the Fukushima accident), causing an increase inmortality. These studies suggest that in our context, the observed behavioral responses may come froma lack of (good) air conditioning at home and/or a desire to save energy on (what households perceive tobe) especially hot days. For example, individuals may have been more electricity-price conscious when awarning was issued. We also find that people are more likely to leave their home and more likely to go to thepark in response to actual WBGT - these behaviors are consistent with households saving energy on highheat days, and thus households may be using the heat alert as an indicator for whether they should leave
their home in order to save energy.13
This paper provides several important contributions to the literature. To our knowledge, it is the firstlarge-scale study to examine the causal effects of a heat warning system. It is also one of only two studies
12
We further find no change in the likelihood an individual went into work. We also note that the effect of a heat alert on heatstroke is slightly weaker on weekdays than on weekends, suggesting that changes in workplace location on heat alert days cannot
explain the results.
13
Other related work from Akesaka and Shigeoka (2023) also utilizes geolocation data in Japan to find that despite causing
significant mortality risks, people do not curtail outdoor activities in response to seasonal allergies.

4

to examine the effects of any public alert system on direct health outcomes, with nearly the entirety of theprevious literature focusing on how air quality alerts impact avoidance behavior.14 Early work from Neidell(2009) and Zivin and Neidell (2009) examine avoidance behavior in California in response to smog alerts,
finding reduced outdoor activity when alerts are issued, with reduced efficacy for alerts made on consecutive
days. Noonan (2014) finds that elderly users and exercisers reduce their use of a major park in response toa smog alert, with no effect on driving behavior. Ward and Beatty (2016) find similar behavioral effects ofreduced outdoor activity, particularly among the elderly. Saberian et al. (2017) find reductions in cycling
in Sydney when an air quality alert is issued. Beyond air quality alerts, Gutteling et al. (2018) investigate
behavioral responses from 643 survey participants to a Dutch civil defense warning system, carried via
cell phone messages, which warned citizens of various local natural and man-made threats (e.g. toxic fumes
released with fire). Ferris and Newburn (2017) find that mobile phone alerts for flash flood events in Virginia
led to reductions in traffic volume and car accidents.15
Our study also relates to the strand of literature identifying factors that influence the climate-health relationship. As mentioned above, perhaps the two most directly related studies to ours come from Neidell et al.
(2021) and He and Tanaka (2023), who study electricity prices and energy-saving campaigns, respectively,in the Japanese context.16 Other prominent studies identifying adaptive behaviors to climate include Deschenes and Moretti (2009) (migration), Zivin and Neidell (2014) (staying indoors), and Barreca et al. (2016)(air conditioning).17 Finally, Mullins and White (2020) show that better access to health care can mitigate
the negative impacts of heat. Our study contributes to this literature by being the first to look at how publicalerts affect the climate-health relationship, and along with He and Tanaka (2023), is one of the first studies
to evaluate any government policy specifically targeting behavioral responses to higher temperatures.18
The remainder of this paper proceeds as follows. In Section 2, we describe Japan’s heat warning system
14

To our knowledge, the only study examining how a public alert impacted direct health outcomes comes from Mullins andBharadwaj (2015), who find that an “Environmental Episode” policy from Chile reduced air pollution by 20% and improved day-ofelderly mortality.
15
A separate strand of related research investigates how private health alerts affect health outcomes (Kim et al., 2019; Iizuka
et al., 2021), finding that alerts overall have little effect on behavior, but can have substantial positive effects for those who are athigh risk (for diabetes).
16
Our study also differs from these two in several noticeable ways. First, we study different treatment variables (heat-healthwarnings vs. energy-saving campaigns vs. electricity prices). Further, whereas the previous two studies utilize monthly-regionalvariation driven by the Fukushima accident, our data are more granular (daily and smaller spatial units) while our identifying
variation utilize both space-time variation and variation in whether forecasted WBGT exceeded a specific threshold. Our data also
include health outcomes beyond mortality, including severity of heat stroke. Lastly, given the recent timeframe of our study, we areable to utilize Google Mobility Reports data to measure behavioral outcomes such as staying indoors.
17
Additional studies broadly discuss the role of adaption on the climate-health relationship, including Deschênes and Greenstone(2011), Geruso and Spears (2018), Mullins and White (2019), and Heutel et al. (2021).
18
A relevant working paper from Shrader et al. (2023) estimates the value of accurate weather forecasts in the United States.
The authors estimate that a 50% increase in forecast accuracy would save approximately 2,200 lives a year. Our work compliments
Shrader et al. (2023) by highlighting “false” weather warnings due to inaccurate forecasts could lead to further health losses.

5and the criteria a warning is based on. In Section 3, we describe the various data used and present descriptive
statistics. In Section 4, we introduce our estimation strategy. In Section 5, we present our results. We first
discuss graphical findings before turning to our main results. We further discuss potential mechanisms. In
Section 6, we show robustness of our results to a series of considerations. Finally, in Section 7, we conclude.

2

BackgroundIn 2020, Japan introduced a comprehensive heat warning system to raise awareness of heat-related

illnesses and promote heat stroke prevention measures. In the first year of implementation, the program
was tested in nine prefectures (Chiba, Gunma, Ibaraki, Kanagawa, Nagano, Saitama, Tochigi, Tokyo, andYamanashi), and then the warning system was rolled out to all regions of Japan starting in 2021. Alerts
are communicated via the Ministry of Environment’s homepage, weather news broadcasting stations, radio,
public institutions, social networking services (i.e. LINE), and smartphone applications. The way thesealerts are communicated (e.g. through newspapers, TV, public billboards and loudspeakers, or TV screensin trains) makes it likely that most people receive this information.19 The alerts encourage people to stayat home, drink a lot, take salt, avoid exercise, use air-conditioning, and report to others if they are in poor
physical condition (i.e. children, elderly, and disabled). As shown in Figure 1, Google Searches for the term“heat stroke alert” in Japan began when the warning was introduced and were especially high in the summer,
and as shown later econometrically, daily searches in a region increased significantly when a warning wasissued.
Warnings are issued based on whether the region-specific forecasted heat index “Wet Bulb Globe Temperature” (WBGT) (Yaglou et al., 1957) exceeds a threshold of 33.0◦ C.20 This index serves as a morecomprehensive indicator of heat stress and its impact on the human body than temperature alone, because it
also incorporates humidity, radiant heat, and wind speed, which in combination with temperature influence
the risk of heat stroke. In order to calculate forecasted WBGT for a specific region, the Japan Meteorological Agency (JMA) collects information on temperature, humidity, amount of rain, wind strength, and solar
radiation across 1,300 weather stations. All of these stations collect information on rain, while 840 stations
collect further information about temperature, humidity, and wind strength/direction. Among all weather
stations, only 11 stations collect information about wet bulb temperature, wbt, black bulb temperature, bbt,
19
20

In the online appendix, we present examples of those public announcements.
Website: https://www.env.go.jp/press/109467.html, retrieved October 10, 2022.

6

100
Relative search frequency
20
40
60
80
0
May 2017

May 2018

May 2019

Heat stroke

May 2020

May 2021

May 2022

Heat stroke alert

Figure 1: Google Searches for Heat Strokes and Heat Stroke Alerts
Source: Authors’ presentation using data from Google Trends. Note: This graph shows Google Trends Searches for 熱中症 (heatstroke) and 熱中症警戒アラート (heat stroke alert), respectively. The dashed line indicates the introduction of the Heat Stroke
Alert System in August 2020.

and dry bulb temperature, dbt, and calculate WBGT according to
WBGT = 0.7 · wbt + 0.2 · bbt + 0.1 · dbt.

(1)

44 stations collect information about global solar radiation in order to estimate WBGT following Ono andTonouchi (2014):
WBGT = 0.735 · T a + 0.0374 · RH + 0.00292 · T a · RH + 7.619 · SR(2)2

− 4.557 · SR − 0.0572 · W S − 4.064,
with T a being the temperature in ◦ C, RH being the relative humidity in %, SR being global solar radiationin kW/m2 , and W S being the average wind speed in m/s. 98 stations do not collect information about
global solar radiation, but estimate it using the relationship between the previous 10 minutes of sunshine
hours (min) and the relationship between clear-sky irradiance and total solar irradiance based on historicalobservations. 687 stations further do not collect information about humidity. This information is estimatedusing numerical forecast data or predicted values from the JMA that is reanalyzed with observed values

7from surrounding observations. The remaining 460 stations only collect information about precipitation andtherefore cannot estimate WBGT.
With a formula for WBGT for each of 840 weather stations, an alert is issued for a region based onwhether any of the stations within a specific region have a forecasted WBGT greater than 33.0◦ C, i.e.
whether the maximum forecasted WBGT across all stations within a region exceeds 33.0◦ C. Furthermore,
the decision rule is based on two forecasts: 5pm of the previous day or 5am of the same day. For example,
if the 5pm forecast for tomorrow’s WBGT is 33.1◦ C for a single station in the Osaka region, then an alert
is issued for Osaka (and will be broadcast across Osaka as a warning for tomorrow). If tomorrow, the sameday 5am forecast is at most 32.9◦ C across all stations in Osaka, then the alert still applies. In some cases,
the 5pm forecast is below 33.0◦ C while the 5am forecast is above 33.0◦ C, in which case an alert is issued
in the morning, even though no warning was issued the evening before. In our main analysis, for simplicity,
we consider whether a warning was ever issued, regardless of the consistency between 5pm the previous day
and 5am the same day, while later, for robustness, we separately consider the warnings issued based on thetwo forecasts.

3

Data and Summary Statistics

3.1

Heat Alerts
The Ministry of Environment provides information about the issuance of heat alerts for each of 58

regions on a given day since the implementation of the program in 2020.21 As described in the previous
section, these alerts are either issued at 5pm on the previous day or 5am on the same day. Appendix Figure
A1 shows the fraction of dates between May 1 and September 30, 2022, that had a heat stroke warning byregion. There exists substantial variation across regions in how often alerts were issued, with some regions
issuing zero alerts (Aomori, Akita, Iwate, and Miyagi) and others issuing alerts for as many as 26% (Oita)of the summer days in 2022.

3.2

Wet Bulb Globe Temperature (WBGT)
Although heat alerts are issued on the regional level based on the maximum forecasted WBGT among all

weather stations within a region, data from the Ministry of Environment only includes daily measurements
for the maximum realized WBGT for each of 840 weather stations, rounded to the first decimal.22 The
21
22

Website: https://www.wbgt.env.go.jp/alert_record.php, retrieved October 10, 2022.
Website: https://www.wbgt.env.go.jp/doc_trendcal.php, retrieved October 10, 2022.

8

data cover the dates between May 1 and September 30 for the years 2017 to 2022.23 To reflect the decisionvariable, we calculate the maximum realized WBGT across all stations within a region on a specific dateas our primary control variable. Appendix Figure A2 plots the full distribution of observed daily WBGT
across region-days for the full sample.
Importantly, because we do not have information about the 5pm and 5am forecasted WBGTs, i.e. the“running variable” used to determine whether an alert was issued for a specific region, our identification
strategy will effectively leverage variation in whether an alert was issued controlling for actual WBGT (described further in Section 4). In particular, alerts do not perfectly match actual WBGT because of forecastingerrors. Thus, although we do not observe the actual decision variable for determining an alert, we are still
able to flexibly control for the sole concerning confounder of the heat alert: actual WBGT.

3.3

Health Outcomes

3.3.1

Heat Stroke

The primary outcome variable for our study is heat stroke. Heat stroke is the most serious heat-related
illness (vs. heat exhaustion, rhabdomyolysis, heat syncope, heat cramps, and heat rash).24 With a heat stroke,
the body typically loses the ability to control its own temperature, which can lead to permanent disability ordeath without emergency treatment. Symptoms include very high body temperature, potential loss of consciousness, seizures, dry skin, or profuse sweating. Treatment typically includes cooled IV fluids, a coolingblanket, an ice bath, medication for seizures, or supplemental oxygen. Broadly speaking, there are two
types of heat strokes: “exertional” heat stroke results from physical overexertion in hot, humid conditions,and develops over a few hours, while non-exertional, or “classic,” heat stroke is the more common type, andis due to age or underlying health conditions, and tends to develop over several days.
The Fire and Disaster Management Agency (FDMA) provides administrative data on the number ofheat strokes by age group, place of incidence, and severity, for each of 47 prefectures on a daily basis in thesummer months since 2008.25 These data include all individuals who were transported to a hospital via anambulance. As Akesaka and Shigeoka (2023) describe, ambulance services are provided to the public for
free, and thus, utilized similarly by individuals across socioeconomic status. Ambulance use is extremely
prevalent among patients in Japan, and in general, Japanese policymakers express significant concern of23

Detailed data can be downloaded following the description in the manual: https://www.wbgt.env.go.jp/man15NH/
R04_wbgt_data_service_manual.pdf, retrieved October 10, 2022.
24
Source: Center for Disease Control and Prevention, 2023. Website: https://www.cdc.gov/niosh/topics/
heatstress/heatrelillness.html, retrieved July 13, 2023.
25
Website: https://www.fdma.go.jp/disaster/, retrieved October 10, 2022.

9

1000

1000

# of heat strokes
400
600
800

# of heat strokes
400
600
800

r
Se

pt
e

m
be

us
t
Au
g

ly
Ju

ne

M

Se

pt
e

Ju

ay

r
m
be

us
t
Au
g

ly
Ju

Ju

ne

0

200

200
0

ay
M

Children
AdultsAdolescents
Elderly

Severly injured
Mildly injured(a) Age

Injured(b) Severity

Figure 2: Heat Strokes by Age and Severity
Source: Authors’ presentation using data from Fire and Disaster Management Agency (FDMA). Note: This graph shows the numberof heat strokes by age group (a) and severity (b) in 2022 for Japan. “Mildly injured” are any heat stroke incidents that did not require
hospitalization. “Injured” are incidents that required no more than three weeks of hospitalization, while “severely injured” are thosethat required at least three weeks of hospitalization.

overuse of ambulance services. For example, Kadooka et al. (2017) estimate that nearly 50% of ambulancerides were for minor conditions where patients could have taken a taxi instead of an ambulance. In responseto the summer heat of 2023, Tokyo is on pace to set a new record number for ambulance dispatches.26 Thus,though we do not observe visits made to hospitals via methods other than an ambulance, the data likely
cover a significant share of all visits to hospitals.
In Figure 2, we plot counts of daily heat strokes by age group and severity for the entirety of Japan in
2022. Consistent with the prior health literature, elderly are at the most risk for heat stroke, and heat stroke
incidence is strongest in July and August. Although most cases are mild, there are a few severely injured
cases (which require multiple weeks of hospitalization).
We match these prefecture-daily data to the Ministry of Environment’s region-daily data. Three prefectures (Hokkaido, Kagoshima, and Okinawa) are distinguished into 8, 2, and 4 regions, respectively, whereas
the remaining 44 prefectures perfectly map into the remaining 44 regions. For simplicity, we drop thesethree prefectures. Since the Ministry of Environment’s data date from 2017, our matched sample begins in
2017, granting us an additional three years of observations pre-warning system. In Appendix Figure A3, weoverlap counts of heat strokes with heat alerts for all of Japan for 2021 and 2022. Unsurprisingly, a positive
correlation arises since heat alerts are issued on hotter days, and heat stroke incidence increases on hotter
26

Source: The Japan Times. Website: https://www.japantimes.co.jp/news/2023/07/01/national/tokyoambulance-calls-rise/, retrieved September 7, 2023.

10

days.
3.3.2

Ambulance Transports

The FDMA also provides administrative data on the population of ambulance transportations to hospitals in Japan. The data cover all transports from 2017 to 2021 for each of the 47 prefectures on a daily basis.
Appendix Table A1 provides summary statistics for the number of transports by severity. There are on average 371 ambulance transports per day and region. The majority of these transports are for mild cases (92%),
while 7% are for severe cases, and 1% result in death. Similar to the heat stroke severity categorization,“severe” cases require multiple nights of hospitalization, while “mild” cases require no hospitalization.
We utilize these data for multiple purposes. First, assuming the majority of cases that result in death inJapan utilize an ambulance, these records allow us to observe the effect of the heat alerts on region-day-levelmortality. Second, discussed in further detail later, these data allow us to investigate potential substitution
between heat stroke cases and other sudden illnesses. In particular, we can distinguish between injuries
resulting from natural disasters, fire, or water (0.1%), traffic accidents (6.9%), work- or sports-related accidents (2.0%), sudden and general illnesses (81.7%), injuries due to violence and self-harm (1.0%), andtransfers from one hospital to another (8.2%). Since heat stroke falls under the “sudden and general illnesses” category, the other categories also grant us useful placebo tests.

3.4

Behavioral Outcomes

3.4.1

Google Trends

We further utilize data from Google Trends to examine how searches for various terms on Google’s
search engine changed in response to heat alerts. Google Trends provides measurements of the relative
search frequency of a queried search item (e.g. heat stroke alert) on Google Search for a selected geography(e.g. Tokyo) and time period (e.g. May 1 to September 30, 2021), indexed to a range of 0 and 100, where
100 represents the date within the specified geography that had the greatest search volume for the queried
search item. Importantly, these indices are generated from relative search volume, not total searches on
Google. Google Trends takes a random sample of total searches for the queried geography and time frame,then generates the indices based on the share of total searches that the queried item constituted. We consider
searches for the terms heat strokes, heat stroke alert, weather, temperature, air conditioner, outdoor, sea
bathing, park, indoor, cinema, and karaoke for each prefecture for May 1 to September 30 for 2020 to 2022,11

respectively. Appendix Table A2 provides summary statistics for these terms.27
3.4.2

Google Community Mobility Reports

In response to the COVID-19 pandemic, Google provided aggregated, anonymized “reports” which
tracked daily movement by location. The data used to generate the reports are the same used in Google
Maps, where users can track how busy a specific location is. In particular, the Community Mobility Reports include information on shares of people who left their residence, and which locations individuals
visited across different categories: retail and recreation, groceries and pharmacies, parks, transit stations,and workplaces. These reports were provided from January 2020, with daily updates ceasing on October 15,
2022. Visitations are reported as percentage changes in daily visits relative to visitations for that weekday
pre-Covid. Naturally, these data have been used to investigate questions related to traveling behaviors in response to COVID-19 and corresponding policies (e.g. Chetty et al., 2020; Fernández-Villaverde and Jones,
2020; Sulyok and Walker, 2020; Karaivanov et al., 2021; Mendolia et al., 2021). For our study, we utilize mobility reports at the region-day level in order to investigate behavioral responses to the heat alerts.28
Since these data only start from 2020, analyses using the Community Mobility Reports do not include any
pre-treatment observations.29 Appendix Table A3 provides summary statistics for these reports.

3.5

Sample Restrictions
Our heat stroke data consist of 44 regions with information about maximum realized WBGT and heat

alerts for each day between May 1st and September 30 for 2017 to 2022. However, because there arealmost no heat alerts for region-days with a low WBGT, our primary analyses restrict the data to days witha WBGT of at least 28◦ C. Further, there are a few region-days with an extremely high number of heatstrokes; to lessen the effect that potential outlier events drive our results, we drop region-days with heatstrokes above the 99th percentile. Robustness exercises test the sensitivity of our main results to these two
sample selections. Our final primary sample consists of 20,612 observations.
27

We downloaded the data for each prefecture and year (May 1 to September 30) separately, such that numbers are relative to thesearches within a specific prefecture in a year. Alternatively, one could download all regions on a given date and repeat this for all
required dates. Since the data are indices related to a specific geographic and temporal area, the numbers can change. Nevertheless,our results are robust to downloading the data for all regions of Japan at different points in time (not reported).
28
To our knowledge, our study is the first to use these data to investigate mobility responses outside of Covid-related policies.
29
More information on these reports can be found at https://www.google.com/covid19/mobility/.

12

3.6

Summary Statistics
Table 1 provides summary statistics for the months of May through September for the years 2017 to 2022

at the region-day level. There are approximately 15 heat strokes every day in any given region. The majorityof heat strokes result in mild injury, which are cases that do not require overnight hospitalization. Only a
few cases require hospitalization of up to three weeks. While only very few heat strokes are experienced bychildren, over half of all heat strokes are experienced by those aged 65 years and older. Interestingly, themost common location for a heat stroke to occur is in one’s home. This, coupled with the fact that elderly
are at greater risk of heat stroke, highlights how the majority of heat strokes occur due to age and underlying
health, and are not necessarily due to one overexerting themselves outdoors. Heat alerts were issued on
approximately six percent of region-days. This number increases to almost 17% when only considering theyears 2021 and 2022, the time the alert system was fully implemented. Finally, the average WBGT in thesummer months was around 31◦ C.

4

Empirical Strategy
A heat stroke warning was issued for region r on day t when the maximum forecasted WBGT fromeither the 5pm forecast on day t − 1 or the 5am forecast on day t exceeded 33.0◦ C at any weather station
within region r. Conversely, a region issued no warning if WBGTrt < 33 for both the evening-before(5pm) and the morning-of (5am) forecasts across all stations within region r.
If one observed the forecasted WBGT, one could compare region-days with a forecasted WBGT justmarginally below the 33.0◦ C threshold against region-days with a forecasted WBGT just marginally above
the threshold in a Regression Discontinuity Design (RDD). This identification strategy would then compare
region-days with very similar forecasts that differed within some tight range of forecasted WBGTs, andassume that region-days on opposite sides of the cutoff are otherwise similar to each other. However, as
discussed in the previous section, our data do not include station-level forecasts of WBGT, and thus, we do
not observe the “running variable” necessary for an RDD.30
Still, our data include realized WBGT and indicators for whether a heat stroke warning was issued
due to the evening-before or morning-of forecasts, or both, i.e. whether the forecasted WBGT crossed the30

Moreover, the most concerning confounder for identifying the effect of an alert on heat stroke would be actual WBGT, notforecasted WBGT, since actual weather is what directly impacts individuals. Still, it is possible that individuals make decisions(e.g. plans for tomorrow) based on forecasted WBGT, independent of the alert, and fail to adjust their plans when actual WBGT
differs from forecasted WBGT. So long as the forecasting errors are distributed around zero, any such possibility should merely
attenuate estimated treatment effects of an alert on outcomes. Also note that forecasted WBGT should theoretically be perfectly
collinear with the alert, and thus it is impossible to disentangle the effect of the alert from the effect of forecasted WBGT.

13

Table 1: Summary Statistics
(1)
Obs.

(2)
Mean

(3)
SD

(4)
Min

(5)
Max

Heat strokes: total

20612

14.890

19.95

0.00

141.00Heat stroke result: mild injury
Heat stroke result: injury
Heat stroke result: severe injury

20612
20612
20612

9.443
5.020
0.340

13.17
7.21
0.84

0.00
0.00
0.00

115.0071.00
15.00

Heat strokes: 28 days–6 years
Heat strokes: 7–17 years
Heat strokes: 18–64 years
Heat strokes: ≥65 years

20612
20612
20612
20612

0.112
1.690
5.196
7.891

0.38
3.07
7.89
10.39

0.00
0.00
0.00
0.00

6.00
54.00
79.00
95.00

Heat stroke loc.: homeHeat stroke loc.: workplace (construction)
Heat stroke loc.: workplace (field)
Heat stroke loc.: education
Heat stroke loc.: public (inside)
Heat stroke loc.: public (outside)
Heat stroke loc.: street
Heat stroke loc.: other

20612
20612
20612
20612
20612
20612
20612
20612

5.999
1.669
0.364
0.825
1.132
1.803
2.272
0.826

8.26
3.04
0.75
1.83
2.20
3.08
4.16
1.37

0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

86.00
43.00
10.00
42.00
35.00
56.00
68.00
16.00

20612

0.063

0.24

0.00

1.00

20612

31.212

1.86

28.00

37.60

Panel A: Outcome variables

Panel B: Treatment variable= 1 if heat alert
Panel C: Control variable
WBGT index

Source: Authors’ calculations using data from Ministry of Environment and Fire and Disaster Management Agency (FDMA).
Note: This table presents descriptive statistics of all key variables used in the analysis. Mild injuries do not require overnighthospitalization. Severe injuries require up to three weeks of inpatient care. Home: all locations on the property. Workplace(construction): road construction sites, factories, manufacturing, etc. Workplace (field): fields, forests, oceans, rivers, etc.
(only if agricultural, livestock, or fishery operations are conducted). Education: kindergartens, nursery schools, elementary
schools, junior high schools, high schools, vocational schools, universities, etc. Public (inside): indoor areas where people
enter and exit (theaters, concert halls, restaurants, department stores, hospitals, public bathhouses, train stations (undergroundplatforms), etc.). Public (outside): outdoor portions of places where people enter and exit (stadiums, outdoor parking lots,
outdoor concert venues, train stations (outdoor platforms), etc.). Street: general roads, sidewalks, toll roads, highways, etc.
WBGT ≥ 28.

threshold or not. With these data, we are able to regress our outcome variables yrt on a dummy variable

14

Alertrt , indicating a heat stroke warning in region r at time t, while flexibly controlling for actual WBGT:

yrt = α + βAlertrt +KX

γk 1 (WBGTrt = k) + δr + λt + ζr year + ϵrt ,(3)

k=28.0◦ C

where 1(·) is an indicator function, δr are region fixed effects, λt are day fixed effects, and ζr are regionspecific linear time trends. To control for WBGT, we follow the decision rule for the alerts and assign
each region-day rt the maximum observed WBGT across stations within that region-day (calculated as
WBGTrt ), and include dummies for each possible WBGTrt , rounded to the nearest tenth.31
We are able to separately estimate β from γk since Alertrt does not perfectly correlate with WBGTrt .
In other words, we are able to separately disentangle the effects of the heat warning from heat itself on
outcomes since (unobserved) forecasted WBGTrt does not perfectly predict actual (observed) WBGTrt . In
essence, our identification strategy relies on variations in forecasted and actual WBGTrt (i.e. “forecastingerrors”), and whether forecasted WBGTrt and actual WBGTrt laid on the opposite sides of the 33.0◦ C
threshold. Because of these forecast errors, we observe region-days where an alert was issued but actual
WBGTrt was below 33.0◦ C (i.e. false positive), and region-days where no alert was issued but actual
WBGTrt exceeded 33.0◦ C (i.e. false negative). This identification strategy relies on the assumption that“forecasting errors” are random and therefore independent from unobservable features of region-days that
could potentially affect heat stroke. Region fixed effects further control for all time-invariant differences
across regions, such as average temperature and average heat stroke incidence. Day fixed effects control for
all unobserved nationwide trends, including potential changes in average weather or heat stroke incidence.
Since the heat stroke warnings are only based on forecasted WBGTrt and no other potentially confounding
factors, controlling for actual WBGTrt in this panel regression specification allows us to identify the causal
effect of the heat warning on our outcomes yrt .
Our identification strategy relies on the fact that regions issue alerts based on the forecasted WBGT
across all stations, yet due to forecasting errors, the same region may experience different days with identical
WBGTs (e.g. 33.1◦ C) but some with a warning issued and others without. To illustrate the relationshipbetween WGBT and alerts, in panel (a) of Figure 3, we plot the fraction of region-days that issued an alertby the WBGT for that region-day. We see that around the 33.0◦ C cutoff, the probability an alert is issued
increases from around 45% to around 55%, and slowly increases in WBGT. Still, due to forecasting errors,
substantial variation remains: some region-days with maximum WBGT as low as 30.0◦ C issued alerts,
while some region-days with maximum WBGT above 34.0◦ C failed to issue an alert. If forecasting errors
31

Unreported in equation (3) for simplicity, we similarly flexibly control for WBGTrt−1 , the region’s previous day’s WBGT.

15

.8

1
0

0

.2

.2

Density.4

Fraction of days
.4
.6

.6

.8

Mccrary disc. estimate: -.013 (.07)
Bias-robust density test: p-value = .858

30

32
WBGT

Cutoff343630(a) Fraction of region-days with alert

32
Cutoff
WBGT

34

36

(b) Distribution of false alerts

Figure 3: Alerts and WBGT
Source: Authors’ presentation using data from Ministry of Environment. Note: Figure (a) shows the share of region-days with analert among all region-days for all values of WBGT. Figure (b) shows the distribution of false alerts for different values of WBGT.
Local polynomial approximations on both sides of the cutoff are based on the triangular kernel and local quadratic regressions.
Bandwidths for the local polynomial regressions on both sides are 2.35. 95%-confidence intervals are shown as shaded area.
N = 1125. Data for 2020–2022. In 2020, the sample is restricted to the nine regions that had the heat warning system implemented.
WBGT = [28, 36]. Results are obtained from the Stata command rddensity of Cattaneo et al. (2018).

did not exist, then the entire mass of observations would lie above 33.0◦ C.
The core assumption is that regions do not systematically issue “false” alerts with respect to the 33.0◦ C
threshold. More specifically, we assume that regions are not more or less likely to issue an alert when
WBGTrt is below 33.0◦ C relative to failing to issue an alert when WBGTrt exceeds 33.0◦ C. This isequivalent to assuming that forecasting errors are random. In panel (b) of Figure 3, we present the distribution of false alerts across actual WBGT.32 If forecasting errors are independent from actual WBGT, thedistribution of false alerts should be smooth around the threshold. Conversely, if regions manipulate theforecasts or the decision rule such that more alerts are issued on either side of the 33.0◦ C threshold, thenwe would expect to see a higher share of false alerts just above or below the threshold. To formally test forsuch a discontinuous jump in the distribution, we conduct the manipulation test of Cattaneo et al. (2020)and fail to reject the null hypothesis of no manipulation. Moreover, unsurprisingly, there is a greater massof false alerts around the 33.0◦ C cutoff, since this is where even a small forecasting error can result in a
false alert. Thus, though we do not observe forecasted WBGT, this evidence suggests that is highly unlikelythat forecasting errors are systematically biased away from zero, or that regions implementing the alerts
systematically deviated from the 33.0◦ C decision rule.
A region-day issued a “false” alert if either (a) an alert was issued but actual WBGT was below 33.0◦ C, or (b) an alert was notissued but actual WBGT was greater than 33.0◦ C.
32

16

5
Log(# of heat strokes + 1)
2
3
4

50
40
# of heat strokes
20
30

1

10
0
20

25

30
WBGT

35

40

28

(a) WBGT and heat stroke

30

32

WBGT

34

36

38

(b) WBGT and heat stroke by heat alert days

Figure 4: Relationship Between Heat Stroke and Wet Bulb Globe Temperature
Source: Authors’ presentation using data from Ministry of Environment and Fire and Disaster Management Agency (FDMA).
Note: These graphs show the relationship between the WBGT index and heat stroke. Panel (b) separates the relationship by regiondays with a heat alert (in red) vs. without a heat alert (in blue). Local linear regressions are based on a triangular kernel and therule-of-thumb bandwidth.

5

Results

5.1

Descriptive Figures – Increased Heat Strokes on Alert Days Conditional on WBGT
We begin by investigating the graphical relationship between temperature, heat strokes, and heat alerts.

In panel (a) of Figure 4, we plot the relationship between WBGT and the number of heat strokes acrossthe full bandwidth of possible WBGTs. The size of the circles indicate the number of region-days thatexperienced that specific WBGT; for example, WBGTs above 35◦ C were relatively rare. Unsurprisingly,
the relationship between WBGT and heat stroke is positive, and interestingly, appears to be nonlinear, where
the number of heat strokes increases at an increasing rate for higher WBGT.
Then, in panel (b), we plot the relationship between WBGT and heat stroke separately for region-days
without an alert (in blue) and with an alert (in red). Immediately, even in just the “raw” data, we see apreview of the main results: region-days when a heat alert was issued systematically have more heat stroke
hospitalizations across the full range of WBGTs. Although this difference is largest for smaller values
of WBGT, it remains positive for higher values with more region-days experiencing an alert. In the next
section, we will test whether this descriptive result still manifests in our econometric models.

17

Table 2: Effect of Heat Alerts on Heat Strokes
Log (# of heat strokes +1)# of heat strokes

OLS

Neg. binomial regression

(1)

(2)

(3)

(4)

(5)

(6)

= 1 if heat alert

0.199∗∗∗(0.018)0.197∗∗∗(0.018)3.876∗∗∗(0.977)

4.108∗∗∗(1.021)0.174∗∗∗(0.016)0.172∗∗∗(0.015)

WBGT
Lagged WBGTRegion FE
Day FE
Region-spec. trend

✓
✓
✓
✓

✓
✓
✓
✓
✓

✓
✓
✓
✓

✓
✓
✓
✓
✓

✓
✓
✓
✓

✓
✓
✓
✓
✓

Mean heat strokes
Observations

14.10020612

14.10020612

14.10020612

14.10020612

14.10020612

14.10020612

Source: Authors’ calculations using data from Ministry of Environment and Fire and Disaster Management Agency (FDMA).
Note: This table shows results from OLS and negative binomial regressions of the number of heat strokes in a region on thepresence of a heat alert and a set of control variables. The mean number of heat strokes is for regions without heat alerts(control group). WBGT ≥ 28. Robust standard errors clustered at the regional level are given in parentheses. ∗ , ∗∗ , and ∗∗∗denote significance at the 10%-, 5%-, and 1%-level, respectively.

5.2

Main Econometric Results
We begin by estimating Equation (3) for our full sample in Table 2, where we regress the incidenceof heat strokes across region-days in response to a heat alert being issued, also controlling nonparametrically for the maximum region-day WBGT and region-previous-day WBGT. Across columns, we consider
combinations of measurements of the outcome variable, regression models, and the sensitivity of the results
to region-specific time trends. We cluster standard errors at the region level. Starting with the first twocolumns, which take the log of region-day heat strokes (plus one), we estimate that the incidence of heatstrokes increased by nearly 20% for region-days that issued a heat alert. In columns (3) and (4), we estimate
the count of heat strokes as the outcome variable to find similarly large effects. Since our primary outcome
variable is count data (and includes zero), perhaps the most appropriate regression model is the negative
binomial regression model, which we consider in columns (5) and (6).33 For our fully specified model incolumn (6), the expected log count of heat strokes on heat alert days is 0.172 higher than the expected log
count for region-days without an alert. Across all specifications, our estimates are statistically significant
at the 1%-level. In the remainder of our analyses, we will estimate our full model using negative binomial
regressions.
33

Results do not change if we use a Poisson regression model instead.

18

To present the heat-health relationship, in Appendix Figure A4, we plot the coefficients from the sameday and lagged WBGT dummy variables. Unsurprisingly, higher WBGTs produce more heat strokes (conditional on the previous day’s WBGT and on whether a heat alert was issued). We also see some evidence
that lagged WBGT impacts heat stroke, which is consistent with “classical” heat stroke, where the consequences of heat exposure accumulate over several days. Still, the same-day impact of WBGT on heat stroke
is substantially stronger.

5.3

Potential Mechanisms
In this section, we consider several exercises in order to unpack the potential mechanisms of our main

results. Before moving to the analyses, we first hypothesize several channels of potential responses resulting from the heat warning. First, as is the primary intention of the program, we may expect some typeof “avoidance” behavior - in response to an alert, individuals engage in behaviors to avoid or mitigate theconsequences of exposure to high heat. Generally speaking, this would involve the movement of individuals
from hotter to cooler areas, or greater usage of appliances to reduce ones temperature (e.g. air conditioning). Of course, this channel cannot explain our main results in Table 2, since such avoidance behavior
should mitigate exposure to heat and thus reduce heat stroke. Still, avoidance behavior could exist, yet beoutweighed by other opposing factors.
Thus, we further posit a secondary set of behavioral responses that may emerge due to a heat alert andcould plausibly explain our findings. The first we call an “increased reporting” effect. Given the alert is
explicitly about heat stroke health, it may be that the alert raises one’s awareness of the potential presence ofheat stroke. That is, in the absence of an alert, some people who experience a heat stroke may not realize it or
may fail to consider the seriousness of their symptoms - subsequently, their heat stroke case goes unreported
and uncounted. This would be consistent with a health literature that suggests that the damages from highheat are significantly under-counted and unreported (Ebi et al., 2021). Then, in the presence of the alert,some share of these individuals may now visit the hospital and report their heat stroke case.
The second channel we hypothesize is a “substitution” effect in how health cases are diagnosed. Heat
stroke is generally brought upon by high heat, while high heat also substantially increases the risk of othersudden respiratory and cardiovascular diseases (e.g. Michelozzi et al., 2009). In the US, the EPA estimatesthat approximately one quarter of heat-related deaths are tied to cardiovascular disease interacting withhigh heat.34 Given these important interactions, we posit that heat stroke diagnoses can potentially be
34
Source: United States Environmental Protection Agency, https://www.epa.gov/climate-indicators/
climate-change-indicators-heat-related-deaths, retrieved October 16, 2023.

19

conflated with other sudden illnesses when patients are transported to a hospital. Then, in the presence ofthe heat alert, the identification of heat-related symptoms by doctors and hospitals lead to “substitutions”
away from other related sudden illnesses and into heat stroke. Furthermore, because doctors fill out a form
providing information about the ambulance transport (i.e. cause or reason for the transport, severity, locationof incidence, ...) upon arrival at the hospital, these on-the-spot decisions could also lead to substution awayfrom other categories to sudden illnesses.35
Note that with both of these channels (“increased reporting” and “substitution”), the actual incidenceof heat stroke is unchanged (all else equal), while reported heat stroke increases. Thus, it is possible thateven in the presence of avoidance behavior (which subsequently reduces actual heat stroke), the total countof reported heat strokes increases in response to the alert (if the “increased reporting” and “substitution”
effects outweigh reductions in actual heat stroke from any “avoidance” behaviors).
Lastly, we consider the possibility of an “adverse” behavioral response - the heat alert may be associatedwith other changing factors which then induce individuals to engage in behaviors that place them at a greaterrisk of heat stroke. For example, it may be that on days when a warning was issued, workplaces closed more,
and perhaps individuals are better protected from high heat at their workplace relative to their home (perhaps
due to better air conditioning).36 If people were less likely to go into work, then they may have engaged
in other adverse behaviors with their newfound free time as well. Moreover, He and Tanaka (2023) foundincreased mortality in high heat months after the Fukushima accident, which they attribute to a behavioral
response from households saving energy (where electricity prices increased). In our context, it may be thathouseholds associate energy savings and electricity prices with the heat alert, and thus perhaps individuals
engage in adverse behaviors in order to save energy on (what they perceive to be) a high heat day. In general,
forecasting errors in our setting - when actual WBGT does not coincide with forecasted WBGT (and thus,
the heat warning) - may lead people to engage in behaviors placing them at a greater risk of heat stroke (andas highlighted by Shrader et al. (2023), inaccurate forecasts contribute to a significant share of annual deathsin the United States).
In an effort to identify these potential mechanisms, our next analyses consider (a) various heterogeneities
in the heat stroke results, (b) intertemporal models of alerts on future heat strokes, (c) other health andmortality outcomes using the population of ambulance records, (d) searches on Google Trends, and (e) foot35
Anecdotal evidence shows that these decisions may also reflect the attitudes of doctors. For example, in the case of work-related
accidents, managers and companies are held responsible for the victim’s compensation, while in the case of a sudden illness, themedical expenses need to be covered by the victim itself.
36
Other interactive effects could occur too: for example, an alert could induce people to go into work to receive the air conditioning (“avoidance” behavior), but then perhaps an increased exposure to peers increases the likelihood that one reports another assuffering from a heat stroke (“increased reporting”).

20

Table 3: Effect of Heat Alerts on Heat Strokes by Severity# of heat strokes(1)
Mild injury(2)
Injury(3)
Severe injury= 1 if heat alert

0.167∗∗∗(0.016)0.161∗∗∗(0.023)0.166∗∗∗(0.059)WBGT
Lagged WBGTRegion FE
Day FE
Region-spec. trend

✓
✓
✓
✓
✓

✓
✓
✓
✓
✓

✓
✓
✓
✓
✓

Mean heat strokes
Observations

8.984
20612

4.723
20612

.310
20612

Source: Authors’ calculations using data from Ministry of Environment and Fire and Disaster Management Agency (FDMA).
Note: This table shows results from negative binomial regressions of the number of heat strokes by severity in a region on
the presence of a heat alert and a set of control variables. The mean number of heat strokes is for regions without heat alerts(control group). WBGT ≥ 28. Robust standard errors clustered at the regional level are given in parentheses. ∗ , ∗∗ , and ∗∗∗denote significance at the 10%-, 5%-, and 1%-level, respectively.

traffic outcomes as measured through Google Community Mobility Reports.
5.3.1

Heterogeneity in Heat Strokes

In Table 3, we consider our full specification while splitting heat stroke counts by the severity of theheat stroke. We estimate positive coefficients across the spectrum of severity. Strong positive effects arefound for mild injuries that do not require hospitalizations and injuries that require some hospitalization.
Interestingly, we precisely estimate a large effect for “severe injury” as well, which requires at least three
weeks of hospitalization. Assuming that severe heat stroke would occur irrespective of awareness of analert, this suggests that the main effects cannot be solely attributed to an “increased reporting” effect.
In Table 4, we estimate our full specification while splitting the counts of heat strokes across four age
groups: children (aged less than seven years), adolescents (aged seven to 17), adults (aged 18 to 64), andelderly (aged 65 and above). We observe large and statistically significant effects for all age groups except
for children. Although adolescents are the most resilient to heat, an alert increases heat strokes for this age
group as well. However, the largest increase in heat stroke incidence is experienced by adults and the elderlyin particular. In Japan, workers are strongly encouraged to retire by the age of 60, and in the majority ofsettings, are forced to retire by the age of 65. Given these strong results for elderly, this suggests that theoverall increase in heat stroke incidence cannot be solely attributed to changes in behavior involving the21Table 4: Effect of Heat Alerts on Heat Strokes by Age Group# of heat strokes
(1)
Child

(2)
Adolesc.

(3)
Adult

(4)
Elderly= 1 if heat alert

0.001(0.097)0.121∗∗∗(0.033)0.166∗∗∗(0.020)0.181∗∗∗(0.016)

WBGT
Lagged WBGTRegion FE
Day FE
Region-spec. trend

✓
✓
✓
✓
✓

✓
✓
✓
✓
✓

✓
✓
✓
✓
✓

✓
✓
✓
✓
✓

Mean heat strokes
Observations

.109
20612

1.649
20612

4.908
20612

7.431
20612

Source: Authors’ calculations using data from Ministry of Environment and Fire and Disaster Management Agency (FDMA).
Note: This table shows results from negative binomial regressions of the number of heat strokes for different age groups in aregion on the presence of a heat alert and a set of control variables. The mean number of heat strokes is for regions without
heat alerts (control group). WBGT ≥ 28. Robust standard errors clustered at the regional level are given in parentheses. ∗ , ∗∗ ,and ∗∗∗ denote significance at the 10%-, 5%-, and 1%-level, respectively.

workplace.37
In Appendix Table A4, we consider heterogeneity by the location of the heat stroke incident. We findthat the positive effects are consistent across all locations, both indoors and outdoors. Though one may
expect stronger effects in outdoor locations, these location outcomes do not trace movement (e.g. goingfrom outdoors to indoors) and are net of any potential avoidance or adverse behavioral effects (i.e. people
with varying degrees of risk of heat stroke changing where they spend their day in response to the alert).
5.3.2

Intertemporal Effects of Heat Alerts

Next, we consider several models to test for potential intertemporal effects of the heat alerts. First,
given heat strokes can develop over several days, it may be that any gains from avoidance behaviors are notrealized until several days in the future. That is, if an alert encouraged avoidance behavior on day t, thenperhaps we would see a reduction in heat strokes on day t + 1 or t + 2. In these same models, we also
consider whether future heat alerts (on day t + 1 and t + 2) had an effect on heat strokes on day t. Thislargely serves as a placebo exercise, though given an alert for t + 1 could be generated on day t based on
the 5pm forecast, it is possible that an alert made for t + 1 could lead to an “increased reporting” effecton day t. The results from this exercise are presented in Table 5. By and large, we find no evidence of an
37

In the online appendix, we present various robustness checks for levels of severity and age groups.

22

Table 5: Intertemporal Effect of Heat Alerts on Heat Strokes# of heat strokes
(1)

(2)

(3)

(4)

0.014
(0.016)
0.163∗∗∗(0.015)0.011(0.017)0.014(0.015)0.162∗∗∗(0.014)0.011(0.017)−0.007(0.022)0.017(0.015)0.159∗∗∗(0.015)0.003(0.017)0.028∗(0.016)−0.007(0.021)0.018(0.015)0.159∗∗∗(0.015)0.004(0.017)0.029∗(0.016)

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14.10920600

14.10920600

14.10920600

14.10920600= 1 if heat alertt−2= 1 if heat alertt−1= 1 if heat alertt= 1 if heat alertt+1= 1 if heat alertt+2

WBGTt−2
WBGTt−1 , WBGTt , WBGTt+1
WBGTt+2
Region FE
Day FE
Region-spec. trend
Mean heat strokes
Observations

Source: Authors’ calculations using data from Ministry of Environment and Fire and Disaster Management Agency (FDMA).
Note: This table shows results from negative binomial regressions of the number of heat strokes in a region on the presence ofa heat alert and a set of control variables. The mean number of heat strokes is for regions without heat alerts (control group).
WBGT ≥ 28. Robust standard errors clustered at the regional level are given in parentheses. ∗ , ∗∗ , and ∗∗∗ denote significanceat the 10%-, 5%-, and 1%-level, respectively.

intertemporal effect of alerts on heat stroke: both past and future heat alerts have almost no effect on heat
alerts on day t, with the entirety of the main effects still occurring in response to an alert on day t. These
results suggest that any increases in avoidance behavior did not capitalize into a reduction of heat strokes in
the future.
We next consider whether there were any dynamic effects based on the issuance of alerts across consecutive days. Zivin and Neidell (2009) evaluate avoidance behavior in response to smog alerts, and findthat there was little effect of a smog alert being issued on the second day of consecutive smog alert days.
In Appendix Table A5, in column (1) we first consider our baseline model with a regressor for an alert ondays t and t − 1 separately. In column (2), we then interact the dummies for the two alert days to find no
additional interactive effect for alerts being issued on consecutive days. For completeness, the remainingcolumns consider further interactions for consecutive-day alerts, including two day gaps, and still we findno evidence of intertemporal effects.
23

Table 6: Effect of Heat Alerts on Ambulance Transports# of heat strokes
All(1)= 1 if heat alert

Mild
(2)

(3)

Severe(4)(5)

Deaths

(6)

(7)

(8)

0.015∗∗∗ 0.014∗∗∗ 0.015∗∗∗ 0.013∗∗∗ 0.019∗ 0.023∗∗ 0.005
0.015(0.004) (0.004) (0.004) (0.005) (0.011) (0.012) (0.033) (0.030)

WBGT
Lagged WBGTRegion FE
Day FE
Region-spec. trend

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Mean # of transports
Observations

368.58416879

368.58416879

338.89816879

338.89816879

26.10316879

26.103
16879

3.584
16879

3.584
16879

Source: Authors’ calculations using data from Ministry of Environment and Fire and Disaster Management Agency (FDMA).
Note: This table shows results from negative binomial regressions of the number of ambulance transports by severity in aregion on the presence of a heat alert and a set of control variables. The mean number of transports is for regions without heat
alerts (control group). WBGT ≥ 28. Data for 2017–2021. Robust standard errors clustered at the regional level are given inparentheses. ∗ , ∗∗ , and ∗∗∗ denote significance at the 10%-, 5%-, and 1%-level, respectively.

5.3.3

Ambulance Records – Mortality and Substitution

We next turn to the ambulance transports data, which include information on the population of ambulance transportations made in Japan from 2017 to 2021. In Table 6 we estimate our Equation (3) for all
ambulance transportations using negative binomial regressions (in columns (1) and (2)), then split by severity in the remaining columns: mild (columns (3) and (4)), severe (columns (5) and (6)), and death (columns(7) and (8)). Similar to our main results, we estimate that heat alerts led to increases in ambulance usage,
primarily due to mild and severe cases. These results suggest that the main effects cannot be driven solely
by a “substitution” effect, where the increase in heat strokes comes from a reduction in ambulance usage
stemming from other sudden illnesses.
In the final two columns of Table 6, we find that the heat alert did not have a statistically significant
effect on mortality. Again this suggests that if there were any avoidance behaviors, the benefits were notcapitalized into reduced mortality. They also show that the increase in heat strokes did not result in astatistically significant increase in mortality - concern remains however on the size of the effect, which is
positive and comparable in magnitude to the effect sizes for mild and severe cases. We conclude that at aminimum, this is no evidence that the heat alerts led to reductions in mortality.
We can further utilize the ambulance records to identify the effect of heat alerts on patient outcomes24

Hospital transfer

Hospital transfer

Violence & Self-harmViolence & Self-harm

Sudden & general illness(incl. heat stroke)

Sudden & general illness(incl. heat stroke)

Work & Sports

Work & Sports

Traffic accident

Traffic accident

Fire & Water & Natural disaster

Fire & Water & Natural disaster

-.5

0
.5
Effect of heat alert
All

Mild

Severe1-2Deaths(a) Percentage change

024Effect of heat alert

All

Mild

Severe68Deaths(b) Absolute change

Figure 5: Effect of Heat Alerts on Ambulance Transports
Source: Authors’ presentation using data from Ministry of Environment and Fire and Disaster Management Agency (FDMA).
Note: Panel (a) shows results from negative binomial regressions of the number of ambulance transports by severity in a region on
the presence of a heat alert and a set of control variables. Panel (b) shows these results multiplied by the daily mean number oftransports for regions without heat alerts (control group). WBGT ≥ 28. Data for 2017–2021. 95%-confidence intervals are basedon robust standard errors clustered at the regional level.

by the “cause” or reason for the ambulance transport. We consider six categories: hospital transfer (anambulance transferring a patient from one hospital to another), violence and self-harm, sudden and general
illness (which includes heat strokes), work and sports, traffic accidents, and fire and water and natural
disasters.38 In Figure 5, we plot the coefficients from estimating our full specification for each of thesecategories, while also considering subgroups by severity (mild, severe, death). In panel (a), we plot thecoefficients and their 95%-confidence intervals directly from our main equation, while panel (b) scales thecoefficients by the daily mean number of ambulance transports. Reassuringly, we find that the positive effectof heat alerts on ambulance usage is driven solely by sudden and general illnesses, whereas the remaining
five categories (which effectively act as placebo tests) all display statistically insignificant effects. When
split by the three categories of severity, only one of the 15 placebo estimates (mortality due to fire and waterand natural disasters) displays statistical significance, which can likely be attributed to general randomness.
This implies that there is no substitution within the sudden illnesses category that could explain the results.
We also do not find reductions in other accidents and illnesses that could be substituted for sudden illnesses.

38

The raw FDMA data provide finer categories than these six groupings. For example, sudden illness is separately reported fromgeneral illness, and accidents due to fire, water, and natural disasters are separated from each other. We group sudden illnesses
with general illnesses since heat strokes could fall into either of these categories, and in these data we otherwise cannot distinguishwhether a specific ambulance transport was directly related to a heat stroke. We group fire, water, and natural disasters together due
to the nature of their causes and to increase their counts.

25

Table 7: Google Trends – Effect of Heat Alerts on Searches Related to Awareness(1)“Heat stroke”(2)“Heat stroke alert”(3)“Weather”(4)“Temperature”(5)“Air conditioner”= 1 if heat alert

5.248∗∗∗(1.182)

3.182∗∗∗(0.976)−5.548∗∗∗(0.638)4.575∗∗∗(0.724)2.925∗∗∗(0.711)

WBGT
Lagged WBGTRegion FE
Day FE
Region-spec. trend

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18.538
10647

8.104
10647

52.96610647

21.11410647

34.85310647

Mean outcome
Observations

Source: Authors’ calculations using data from Google Trends and Ministry of Environment. Note: This table shows resultsfrom OLS regressions of Google Trends Searches for 熱中症 (heat stroke), 熱中症警戒アラート (heat stroke alert), 天気(weather), 気温 (atmospheric temperature), and エアコン (air conditioner), respectively, in a region on the presence of a heat
alert and a set of control variables. The mean outcome is for regions without heat alerts (control group). WBGT ≥ 28. Data for
2020-–2022. Robust standard errors clustered at the regional level are given in parentheses. ∗ , ∗∗ , and ∗∗∗ denote significanceat the 10%-, 5%-, and 1%-level, respectively.

5.3.4

Google Trends

We now turn to Google Trends to see whether region-day level searches for specific terms responded toan alert. In Table 7, using the 2020 to 2022 sample, we consider searches for the terms (Japanese equivalent)“heat stroke,” “heat stroke alert,” “weather,” “temperature,” and “air conditioner.” We find large increases
in searches for “heat stroke” and “heat stroke alert,” highlighting people’s awareness of the warning andfor the primary outcome of interest. Interestingly, we see a significant increase in searches for the word
“temperature” and a significant decrease in searches for “weather,” suggesting perhaps a substitution awayfrom a generic term (“weather”) in favor of becoming more temperature-conscious on days when an alert
was issued.39 Further, we observe a strong increase in searches for the term “air conditioner,” highlighting
again people’s awareness of heat on a day with an alert.
Next, in Table 8, we consider whether searches for activities related to potential avoidance behavior
responded to a heat alert, since searches could be indicative of intentions for that day’s activities (e.g. a
reduction in searches for outdoor activities could indicate intentions to stay at home more). We first see anincrease in searches for outdoor activities such as “sea bathing” and “park” in response to an alert being
issued, suggesting perhaps people were more likely to go outside on (what they perceive to be) a high heat
39
Anecdotal evidence also suggests that it is common for people to search for “weather” when they are interested in precipitation.
This could further explain the strong decrease in search frequency if an alert has been issued and rain is unlikely.

26

Table 8: Google Trends – Effect of Heat Alerts on Searches Related to Avoidance Behavior
OutdoorIndoor(1)“Outdoor”(2)“Sea bathing”(3)“Park”(4)“Indoor”(5)“Cinema”(6)“Karaoke”= 1 if heat alert

0.553(0.801)2.084∗(1.127)1.208∗∗(0.465)

1.577∗(0.934)0.015(0.920)−0.563(0.773)

WBGT
Lagged WBGTRegion FE
Day FE
Region-spec. trend

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22.09010647

11.58310647

30.55910647

13.77910647

28.65310647

39.57310647

Mean outcome
Observations

Source: Authors’ calculations using data from Google Trends and Ministry of Environment. Note: This table shows resultsfrom OLS regressions of Google Trends Searches for 屋外 (outdoor), 海水浴 (sea bathing), 公園 (park), 屋内 (indoor), 映
画館 (cinema), and カラオケ (karaoke), respectively, in a region on the presence of a heat alert and a set of control variables.
The mean outcome is for regions without heat alerts (control group). WBGT ≥ 28. Data for 2020-–2022. Robust standard
errors clustered at the regional level are given in parentheses. ∗ , ∗∗ , and ∗∗∗ denote significance at the 10%-, 5%-, and 1%-level,
respectively.

day. Still, these results should be interpreted cautiously since an alert could just be reflective of a change inthe composition of Google Search users for that region-day, and searches do not necessarily reflect actual
actions (e.g. an alert piques an individual’s curiosity as to whether a nearby park is closed for the day).
These actions may also constitute “avoidance behavior” in that options such as “sea bathing” and “park”
may better protect some individuals from heat relative to their own homes. Finally, we estimate a positive
but statistically insignificant coefficient for the search term “outdoor” in response to a heat alert.
Similarly, searches for the general term “indoor” increase, which suggests that people search for ways
to combat heat with indoor activities. In contrast, searches for popular indoor activities such as “cinema” or“karaoke,” which provide protection from heat due to air conditioning, seem unaffected by alerts. People are
not more or less interested in those indoor activities if an alert was issued. However, we should be cautious
again because these results only reflect search behavior and not actual visits.40
5.3.5

Google Community Mobility Reports

Finally, we turn to the Google Community Mobility Reports data to investigate whether foot traffic
responded to heat alerts. The results are presented in Table 9. Recall that the outcome is measured as
40

In Appendix Figure A5, we plot the coefficients on the dummies for maximum daily WBGT for each Google Search term. The
relationship is positive for heat stroke, temperature, and air conditioner, suggesting that these searches are responsive to heat itself.

27

Table 9: Google Mobility Reports – Effect of Heat Alerts on Mobility
Percentage change in visits(1)
Retail &recreation(2)
Grocery &pharmacy

(3)
Parks

(4)
Transitstations(5)
Workplaces(6)
Residential= 1 if heat alert

0.083(0.224)0.323∗∗(0.146)1.713∗∗∗(0.613)0.945∗∗(0.400)−0.029(0.142)−0.115∗∗(0.056)

WBGT
Lagged WBGTRegion FE
Day FE
Region-spec. trend

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4.511
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2.640
10293

−15.94410636−11.262
10647

5.780
10647

Mean outcome
Observations

Source: Authors’ calculations using data from Google COVID-19 Community Mobility Reports and Ministry of Environment.
Note: This table shows results from OLS regressions of the percentage changes in number of visits to specific places in a region
on the presence of a heat alert and a set of control variables. The mean number of the outcomes is for regions without heat
alerts (control group). WBGT ≥ 28. Data for 2020-–2022. Robust standard errors clustered at the regional level are given inparentheses. ∗ , ∗∗ , and ∗∗∗ denote significance at the 10%-, 5%-, and 1%-level, respectively.

the percentage change in visits to a specific place (e.g. “Retail & recreation”) relative to pre-Covid;41 forexample, looking at the means of the outcome variables, people spent more time at home and at parks andless time at transit stations and workplaces relative to pre-Covid.
We find that people spent less time at home in response to an alert. This may be reflective of people visiting the hospital for heat stroke (i.e. “increased reporting”). But, we also find that people spent significantly
more time at parks, which aligns with the Google Trends results from Table 8. This could be indicative of
avoidance behavior, or it may be that people engaged in more (outdoor) activities, placing themselves atgreater risk for heat stroke. In Appendix Figure A6, we plot the coefficients from the dummies for daily
WBGT for each location category - interestingly, we also find that people are more likely to leave their homeresidence and more likely to go to the park in response to a higher WBGT. Importantly, we do not see anychange in visits to workplaces in response to a heat alert. Thus, it does not seem to be the case that people
are skipping their workplace more in response to an alert in favor of, for instance, going to the park.42
41
In particular, the reports show percentage changes relative to a reference day, which is the median of the corresponding weekday
in the five weeks preceding the COVID-19 pandemic (i.e. January 3 to February 6).
42
In line with these null workplace results, in Appendix Table A6, we separate the main effects by weekdays and weekends andonly find a slightly smaller effect of heat alert on weekdays.

28

5.4

Overall Mortality
In Appendix Figure A7, we provide further analyses utilizing additional data from the Vital Statistics

reported by the Ministry of Health, Labor, and Welfare (MHLW) on the population of deaths in Japan, which
are only available at the region-month level by cause, gender, and age at death.43 In contrast to the mortality
data from the ambulance records, these data include all deaths across Japan independent of ambulance usage.
Since the data do not allow us to estimate our preferable model at the region-day level, we closely follow
He and Tanaka (2023) to estimate monthly-level effects of heat alerts on mortality. In short, we find someevidence of increased mortality that is mostly driven by the elderly, particularly due to “external causes” andthe “circulatory system,” in months that had more heat alerts (controlling for monthly WBGT). Althoughdeath by heat stroke does not have a separate category, it is most likely categorized as a problem with therespiratory or circulatory system. This evidence once again suggests there was little avoidance behavior
responses, and if anything, potentially adverse responses among the elderly.

6

Robustness Checks and Placebo Exercises
In this section, we consider a series of robustness checks and placebo exercises. First, due to the scarcity

of alerts being issued on days with low temperatures, we restricted our main sample to region-days with a
WBGT above 28.0◦ C. We test the robustness of our results in Appendix Table A7 by estimating our main
specification across different sample cutoffs in WBGT. Across all sample considerations, the estimatedcoefficients remain consistent and statistically significant at the 1%-level. Furthermore, results remain unchanged if we do not restrict the sample to region-days with heat stroke counts below the 99th percentile(see Appendix Table A8).
As an alternative econometric robustness check, particularly given a relatively low number of region
clusters (n = 44), we conduct a randomization inference test (Bertrand et al., 2004). To do so, we randomly
reassign each region’s treatment pattern (heat alerts) across regions, and estimate our main specification. We
repeat this procedure 1,000 times, then plot the resulting placebo distribution of treatment effects alongside
the actual treatment effect estimated in column (6) from Table 2. The results from this exercise are displayed in Appendix Figure A8, and once again we see that the primary treatment effect is highly statisticallysignificant.
Next, we consider whether the source of the alert (previous-day 5pm forecast vs. same-day 5am forecast)
had any differential bearing on heat strokes. In Appendix Table A9, we estimate our main specification
43

For a detailed description of the data, estimation strategy, validation exercise, and results by age group, see the online appendix.

29

separately for alerts issued on the same day (5am) and the previous day (5pm). Note that an alert from 5pm
the previous day was never “retracted” if the 5am same day WBGT forecast was below 33.0◦ C. Therefore,
the model for the same-day alert corresponds to our main model with an alert at any time. Meanwhile, only
4.6% of all region-days had an alert issued at 5am even though there was no alert from 5pm the previous
day. In columns (1) to (4), we find that there is virtually no difference if we code the alert as stemming from
5am or 5pm. If we allow for interactions of both alerts, we find that around two thirds of the effect is driven
by the same-day alert and only one third stems from the previous-day’s alert that is confirmed on the same
day.
Furthermore, in Appendix Table A10, we estimate our main specification for each year separately to test
whether our results are driven by a specific year. Although the effect is strongest for 2020, it is very similar
for the years 2021 to 2022, suggesting similar responses to heat alerts over time since the full roll out of thepolicy, and therefore similar effectiveness of the policy.
Because the introduction of the heat warning system coincided with the COVID-19 pandemic, it ispossible that some of our results are due to policies affecting people’s daily lives that coincided with heat
alerts (e.g. movement restrictions). As a measure to prevent the spread of the virus, Japan’s Prime Minister
Shinzo Abe declared a state of emergency for Tokyo and six other regions on April 7, 2020. Although thismeasure was regional and temporary, it was extended to the entire country on April 16 and lasted until May
25. In the following two years, the Japanese government proclaimed four more states of emergency forvarious regions.44 Although this measure did not restrict people’s movement directly, people were asked
to stay at home, and businesses as well as public facilities such as schools reduced operating hours or shut
down completely. To test whether those measures coincided with heat alerts and thus drive our results, inAppendix Table A11, we estimate model (3) and include a dummy of whether a region-day was in a stateof emergency. The results show that this policy did neither affect heat stroke nor the effect of a heat alert onheat stroke.45
In essence, our main specification corresponds to a two-way fixed effects regression in which units
switch on treatment at different points in time for exactly one period. A recent literature has shown thatin such staggered Difference-in-Differences (DiD) settings standard two-way fixed effects regressions areproblematic in presence of treatment effect heterogeneity over time (Borusyak and Jaravel, 2018; De Chaisemartin and d’Haultfoeuille, 2020; Goodman-Bacon, 2021). To address the issues arising from the negative
weighting problem, in Appendix Table A12, we estimate our main specification using the alternative esti44

In the online appendix, we present the timeline of these policies across prefectures.
In the online appendix, we also show that the alerts did not have any effect on the number of Covid infections and that theeffect of an alert on heat stroke is not driven by the number of Covid infections.
45

30

mator proposed by De Chaisemartin and d’Haultfoeuille (2020), which is robust to heterogeneous treatment
effects. Coefficients are remarkably similar to our main results (Table 2, columns (1) to (4)) and highly statistically significant, leaving us confident that treatment effect heterogeneity is not a concern in our setting.
Finally, in Appendix Figure A9, we allow the effect to vary with subsamples of different WBGT cutoffs
and for subsamples centered around specific WBGT values, respectively. Neither of these figures exhibit
any heterogeneity. The results are rather stable around 0.15 to 0.18 and highly statistically significant. In
contrast to the descriptive evidence from Figure 4, this suggests that the effect of a heat alert is the same fordifferent temperatures. In particular, there is no differential effect for region-days with actual temperatures
below and above the threshold of 33◦ C.

7Conclusion and Discussion
This study investigates the impacts of heat alerts on health and behavioral outcomes. The context of ourstudy is Japan, which in 2020, introduced a comprehensive heat warning system to raise awareness of heatrelated illnesses and promote heat stroke prevention measures. Our identification strategy utilizes variation
across region-days in whether an alert was issued while flexibly controlling for same-day and previous-day
wet bulb globe temperature (WBGT). We document substantial variation, with no manipulation, in whether
an alert was issued for a region-day, controlling for observed WBGT - the lack of perfect collinearity comesfrom unobserved “forecasting errors,” where alerts are determined solely by (inaccurate) forecasts.
Our findings suggest an increase in hospitalizations for heat stroke in response to a heat alert being
issued. The effect exists across the spectrum of severity, including severe cases which require multiple daysof hospitalization, and is most prevalent for adults and elderly. The effects are large, precisely estimated,
and robust to a vast array of model considerations. Unsurprisingly, we further document that same-day andprevious-day WGBT adversely impact heat stroke hospitalizations as well.
To unpack the potential mechanisms driving these results, we hypothesize that heat alerts can havevarious impacts on people’s behavior. Primarily, we may expect some type of avoidance behavior, as oneof the primary intentions of the program was to encourage people to engage in behaviors that mitigate
the risk of heat - such a possible channel cannot solely explain our results however, given we observe a
net increase in heat stroke hospitalizations. Thus, we consider an alternate set of potential mechanisms to
explain the increase in heat stroke. First, alerts may raise one’s awareness of the potential presence and riskof heat stroke (possibly without changing the actual incidence of heat stroke), leading to increased reporting.
Second, we hypothesize that heat strokes may conflate with other sudden illnesses, such as cardiovascular31

disease and stroke, and when an alert is issued, there’s effectively a “substitution” in how diseases arerecorded, away from other causes and into heat stroke. Finally, an alert may be associated with otherchanging factors which then induce people to engage in behaviors that place them at a greater risk of heatstroke (e.g. leaving their home). In line with these potentially “adverse” responses, a previous literature fromJapan has documented energy price sensitivity leading people to engage in some (unobserved) behaviors that
increased overall mortality (Neidell et al., 2021; He and Tanaka, 2023).
To identify these potential behavioral responses, we utilize additional data from the population of ambulance records, Google Trends, and Google Community Mobility Reports. In total, we do not find any
clear evidence for avoidance behavior except for an increase in the search term “air conditioner,” which
points toward people trying to combat high heat. Instead, we find that searches for various outdoor activities increased, and people were more likely to leave their home residence on heat alert days in favor ofvisiting both indoor and outdoor locations (i.e. parks). This evidence may still be consistent with avoidance
behavior, particularly if individuals believe they are better protected from high heat away from home. Thisevidence may also partially reflect people leaving their residence to visit the hospital. We note, however,that the overall increase in leaving home residences (of over 11%) goes in direct contrast with the country’s
messaging encouraging people to stay at home. We still estimate an effect on overall ambulance transports
for sudden and general illnesses, suggesting the increases in heat stroke are not driven by a substitution
away from other diagnoses. We also estimate a significant increase in severe cases of heat stroke, whichsuggests the effects cannot be solely attributed to increased reporting of what were otherwise unidentified
cases of heat stroke (in the absence of an alert). Finally, several explorations into mortality fail to uncoverany evidence that the heat alerts reduced mortality.
Identifying the net welfare impacts of the heat alert system is challenging. For instance, suppose oureffects were solely driven by increased reporting (with no effect on the actual incidence of heat stroke):on one hand, people would now receive proper medical treatment to prevent potential long-term negative
impacts from heat stroke; on the other hand, due to hospital capacity constraints (particularly during apandemic), medical resources devoted to a heat stroke could potentially have been “better” served to someother medical condition. Additionally, it is possible that the behaviors we observe (leaving home residence
for both indoor and outdoor locations) constitute avoidance behavior, and that “actual” heat stroke incidence
is decreasing, while observed heat stroke hospitalization is increasing solely due to the increased awarenesseffect. In this case, one could see welfare benefits from the warning system.
Still, significant concern remains on whether the behaviors we observe truly constitute heat-mitigating,welfare-improving responses from Japanese households. First, people leaving their households directly
32contrasts the government recommendation to stay at home. Second, one would expect that any gains fromavoidance behavior would manifest in several potential ways, which we fail to see in the data, includingintertemporal effects (we see no effect of heat alerts on future heat stroke) and overall mortality. Finally,
as documented in Neidell et al. (2021) and He and Tanaka (2023), Japanese households were sufficiently
energy-price sensitive to curb their behaviors in a matter that led to increased mortality. In our context,
it may be that the heat alerts also trigger a desire to save energy among Japanese households.46 Though
merely speculation, we posit that our observed behavioral effects are driven by both an “avoidance” effect(i.e. people are better protected from high heat outside their homes) and a desire to save energy on (whatthey perceive to be) high heat days.
Ultimately, more research is needed to understand how people react to alerts, whether such responses
are welfare-improving, and how to better improve alert systems. Concerns over “adverse” responses to alert
systems are not unique to this context as well. Perhaps most infamously, in the summer of 2023, Maui failed
to activate their audible warning system during wildfires that killed over 100 people, with county officials
citing a fear that people would respond inappropriately to the alert by moving toward the (inland) fires.47
In the UK, an alert system carried through an audible 10-second mobile phone alert has critics concernedthat it puts people’s safety at risk, including by potentially distracting drivers.48 A separate literature has
investigated the effects of warning labels regarding the credibility of specific news articles (i.e. “fake news”);
in one prominent study, Pennycook et al. (2020) find that such labels improved the perceived credibility ofother “false” articles that failed to receive a label. Though these contexts differ in many ways, they highlight
how behavioral responses may conflict with the intended effects of an alert to generate adverse results.
46

So far as we are aware, data do not exist on daily-level energy consumption and saving behavior. Still, energy costs are atan all time high in Japan, and overall, local governments have been encouraging households to conserve energy. Source: The
Japan Times, https://www.japantimes.co.jp/news/2022/09/15/business/economy-business/japanenergy-request-winter/, retrieved September 13, 2023.
47
Source: The New York Times, https://www.nytimes.com/2023/09/03/us/maui-wildfires-emergencyalerts.html, retrieved September 13, 2023.
48
Source:Sky News, https://news.sky.com/story/emergency-alert-test-millions-of-phoneusers-in-uk-receive-message-and-loud-alarm-12864193, retrieved September 13, 2023.

33

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37

A

Appendix Tables and Figures
Table A1: FDMA Data – Summary Statistics
(1)
Obs.

(2)
Mean

(3)
SD

(4)
Min

(5)
Max

16879
16879
16879
16879

370.778
340.873
26.2963.610

435.00411.06
24.36
3.80

45.00
36.00
0.00
0.00

2985.00
2779.00
193.00
30.00

16879
16879
16879
16879
16879
16879
16879
16879
16879
16879
16879
16879
16879
16879
16879
16879
16879
16879
16879
16879
16879
16879
16879
16879

0.526
0.384
0.087
0.055
25.46124.410
0.960
0.091
7.301
6.931
0.349
0.022
302.861280.895
18.848
3.118
3.874
3.221
0.416
0.237
30.50724.858
5.593
0.057

3.29
3.11
0.37
0.25
28.38
27.70
1.24
0.31
8.41
8.20
0.64
0.15
370.20351.71
18.27
3.42
5.72
4.97
0.88
0.53
28.67
24.59
5.50
0.24

0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
30.00
27.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

362.00349.0020.00
4.00
185.00182.00
11.00
4.00
83.00
80.00
7.00
3.00
2644.00
2474.00
157.00
28.00
45.00
40.00
9.00
5.00
195.00154.0046.00
3.00

Panel A: Overall# of transports# of mild cases# of severe cases# of deaths
Panel B: Cause for ambulance transport# of transports (Fire & Water & Natural disaster)# of mild cases (Fire & Water & Natural disaster)# of severe cases (Fire & Water & Natural disaster)# of deaths (Fire & Water & Natural disaster)# of transports (Traffic accident)# of mild cases (Traffic accident)# of severe cases (Traffic accident)# of deaths (Traffic accident)# of transports (Work & Sports)# of mild cases (Work & Sports)# of severe cases (Work & Sports)# of deaths (Work & Sports)# of transports (Sudden & General illness)# of mild cases (Sudden & General illness)# of severe cases (Sudden & General illness)# of deaths (Sudden & General illness)# of transports (Violence & Self-harm)# of mild cases (Violence & Self-harm)# of severe cases (Violence & Self-harm)# of deaths (Violence & Self-harm)# of transports (Hospital transfer)# of mild cases (Hospital transfer)# of severe cases (Hospital transfer)# of deaths (Hospital transfer)

Source: Authors’ calculations using data from Fire and Disaster Management Agency (FDMA). Note: This table presents
descriptive statistics of all key variables used in the analysis. WBGT ≥ 28. Data for 2017-–2021.

38

Table A2: Google Trends – Summary Statistics

Heat strokes
Heat stroke alert
WeatherTemperature
Air conditioner
OutdoorSea bathing
Park
IndoorCinemaKaraoke

(1)
Obs.

(2)
Mean

(3)
SD

(4)
Min

(5)
Max

10647
10647
10647
10647
10647
10647
10647
10647
10647
10647
10647

20.6708.813
51.95222.99835.99022.02012.09230.64314.08529.41739.863

23.04
19.35
19.25
21.20
22.02
25.03
21.35
15.39
21.47
25.29
22.14

0.00
0.00
11.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00

100.00100.00100.00100.00100.00100.00100.00100.00100.00100.00100.00Source: Authors’ calculations using data from Google Trends. Note: This table presents descriptive statistics of all key
variables used in the analysis. Google Trends Searches for 熱中症 (heat stroke), 熱中症警戒アラート (heat stroke alert),
天気 (weather), 気温 (atmospheric temperature), エアコン (air conditioner), 屋外 (outdoor), 海水浴 (sea bathing), 公園(park), 屋内 (indoor), 映画館 (cinema), and カラオケ (karaoke). WBGT ≥ 28. Data for 2020-–2022.

Table A3: Google Community Mobility Reports – Summary Statistics

Retail & recreation
Grocery & pharmacy
Parks
Transit stations
Workplaces
Residential

(1)
Obs.

(2)
Mean

(3)
SD

(4)
Min

(5)
Max

10647
10647
10293
10636
10647
10647

−6.5694.847
3.067−15.544−11.612
5.876

9.59
7.06
27.74
15.29
12.97
3.80

−89.00−82.00−80.00−85.00−78.00
−2.00

56.00
45.00
280.00105.008.00
34.00

Source: Authors’ calculations using data from Google COVID-19 Community Mobility Reports. Note: This table presents
descriptive statistics of all key variables used in the analysis. WBGT ≥ 28. Data for 2020-–2022.

39

Table A4: Effect of Heat Alerts on Heat Strokes by Place of Incidence# of heat strokes(1)Home= 1 if heat alert(2)(3)
Workpl. Workpl.
(constr.) (field)

0.154∗∗∗ 0.204∗∗∗(0.020) (0.034)

0.145∗∗(0.066)

(4)
Educ.

(5)
Public(ins.)(6)
Public(outs.)(7)
Street(8)
Other0.208∗∗∗ 0.127∗∗∗ 0.195∗∗∗ 0.164∗∗∗ 0.167∗∗∗(0.051) (0.046) (0.033) (0.030) (0.045)

WBGT
Lagged WBGTRegion FE
Day FE
Region-spec. trend

✓
✓
✓
✓
✓

✓
✓
✓
✓
✓

✓
✓
✓
✓
✓

✓
✓
✓
✓
✓

✓
✓
✓
✓
✓

✓
✓
✓
✓
✓

✓
✓
✓
✓
✓

✓
✓
✓
✓
✓

Mean heat strokes
Observations

5.672
20612

1.544
20612

.347
20612

.809
20612

1.090
20612

1.722
20612

2.128
20612

.787
20612

Source: Authors’ calculations using data from Ministry of Environment and Fire and Disaster Management Agency (FDMA).
Note: This table shows results from negative binomial regressions of the number of heat strokes by place of incidence in aregion on the presence of a heat alert and a set of control variables. The mean number of heat strokes is for regions without
heat alerts (control group). WBGT ≥ 28. Robust standard errors clustered at the regional level are given in parentheses. ∗ , ∗∗ ,and ∗∗∗ denote significance at the 10%-, 5%-, and 1%-level, respectively.

40

Table A5: Effect of Consecutive Heat Alerts on Heat Strokes# of heat strokes
(1)

(2)

(3)

(4)

0.027
(0.033)−0.004(0.030)0.005(0.021)0.158∗∗∗(0.019)−0.006(0.041)0.030(0.033)−0.004(0.030)0.005(0.021)0.166∗∗∗(0.021)−0.051(0.057)0.009(0.037)0.064(0.051)Heat strokes: total= 1 if heat alertt−2= 1 if heat alertt−1= 1 if heat alertt

0.016(0.015)0.166∗∗∗(0.015)0.004(0.021)0.156∗∗∗(0.018)

Alertt × alertt−2
Alertt × alertt−1
Alertt × alertt−1 × alertt−2

WBGT
Lagged WBGTRegion FE
Day FE
Region-spec. trend

✓
✓
✓
✓
✓

✓
✓
✓
✓
✓

✓
✓
✓
✓
✓

✓
✓
✓
✓
✓

Mean heat strokes
Observations

14.10920600

14.10920600

14.10920600

14.10920600

Source: Authors’ calculations using data from Ministry of Environment and Fire and Disaster Management Agency (FDMA).
Note: This table shows results from negative binomial regressions of the number of heat strokes in a region on the presence ofa heat alert and a set of control variables. The mean number of heat strokes is for regions without heat alerts (control group).
WBGT ≥ 28. Robust standard errors clustered at the regional level are given in parentheses. ∗ , ∗∗ , and ∗∗∗ denote significanceat the 10%-, 5%-, and 1%-level, respectively.

41

Table A6: Effect of Heat Alerts on Heat Strokes on Weekdays vs. Weekends# of heat strokes
Weekdays= 1 if heat alert

(1)

(2)

(3)

(4)

0.171∗∗∗(0.018)0.156∗∗∗(0.018)0.180∗∗∗(0.029)0.212∗∗∗(0.030)

✓
✓
✓
✓

✓
✓
✓
✓
✓

✓
✓
✓
✓

✓
✓
✓
✓
✓

13.80514791

13.80514791

14.8455821

14.8455821

Mean WBGT
Lagged Mean WBGTRegion FE
Day FE
Region-spec. trend
Mean heat strokes
Observations

Weekends

Source: Authors’ calculations using data from Ministry of Environment and Fire and Disaster Management Agency (FDMA).
Note: This table shows results from negative binomial regressions of the number of heat strokes in a region on the presence ofa heat alert and a set of control variables. The mean number of heat strokes is for regions without heat alerts (control group).
WBGT ≥ 28. Robust standard errors clustered at the regional level are given in parentheses. ∗ , ∗∗ , and ∗∗∗ denote significanceat the 10%-, 5%-, and 1%-level, respectively.

Table A7: Robustness Check – Different Cutoffs for WBGT
# of heat strokes(1)
WBGT =[20, max](2)
WBGT =[25, max](3)
WBGT =[28, max](4)
WBGT =[20, 35](5)
WBGT =[25, 35](6)
WBGT =[28, 35]= 1 if heat alert

0.164∗∗∗(0.016)0.164∗∗∗(0.015)0.172∗∗∗(0.015)0.165∗∗∗(0.015)0.165∗∗∗(0.015)0.173∗∗∗(0.015)

WBGT
Lagged WBGTRegion FE
Day FE
Region-spec. trend

✓
✓
✓
✓
✓

✓
✓
✓
✓
✓

✓
✓
✓
✓
✓

✓
✓
✓
✓
✓

✓
✓
✓
✓
✓

✓
✓
✓
✓
✓

Mean heat strokes
Observations

8.432
37212

10.36929708

14.100
20612

8.227
36919

10.12629415

13.78820319

Source: Authors’ calculations using data from Ministry of Environment and Fire and Disaster Management Agency (FDMA).
Note: This table shows results from negative binomial regressions of the number of heat strokes in a region on the presence ofa heat alert and a set of control variables. The mean number of heat strokes is for regions without heat alerts (control group).
Robust standard errors clustered at the regional level are given in parentheses. ∗ , ∗∗ , and ∗∗∗ denote significance at the 10%-,5%-, and 1%-level, respectively.

42

Table A8: Robustness Check – Effect of Heat Alerts on Heat Strokes, Full Sample
Log (# of heat strokes +1)# of heat strokes

OLS

Neg. binomial regression

(1)

(2)

(3)

(4)

(5)

(6)

0.213∗∗∗

0.213∗∗∗

6.415∗∗

6.702∗∗

0.191∗∗∗(0.021)(0.020)(2.641)(2.769)(0.021)0.190∗∗∗(0.020)WBGT
Lagged WBGTRegion FE
Day FE
Region-spec. trend

✓
✓
✓
✓

✓
✓
✓
✓
✓

✓
✓
✓
✓

✓
✓
✓
✓
✓

✓
✓
✓
✓

✓
✓
✓
✓
✓

Mean heat strokes
Observations

15.77220816

15.77220816

15.77220816

15.77220816

15.77220816

15.77220816= 1 if heat alert

Source: Authors’ calculations using data from Ministry of Environment and Fire and Disaster Management Agency (FDMA).
Note: This table shows results from OLS and negative binomial regressions of the number of heat strokes in a region on thepresence of a heat alert and a set of control variables. Data is not restricted to the 99th percentile of the number of heat strokes.
The mean number of heat strokes is for regions without heat alerts (control group). WBGT ≥ 28. Robust standard errors
clustered at the regional level are given in parentheses. ∗ , ∗∗ , and ∗∗∗ denote significance at the 10%-, 5%-, and 1%-level,
respectively.

Table A9: Robustness Check – Alert Due to Same-Day 5am vs. Previous-Day 5pm Forecasts# of heat strokes
Morning
(1)

Night

(2)

= 1 if heat alert at 5pm

Morning & night

(3)

(4)

(5)

(6)

0.171∗∗∗(0.017)0.167∗∗∗(0.015)

0.061∗∗∗(0.020)0.132∗∗∗(0.020)0.061∗∗∗(0.018)0.130∗∗∗(0.020)0.174∗∗∗(0.016)0.172∗∗∗(0.015)

WBGT
Lagged WBGTRegion FE
Day FE
Region-spec. trend

✓
✓
✓
✓

✓
✓
✓
✓
✓

✓
✓
✓
✓

✓
✓
✓
✓
✓

✓
✓
✓
✓

✓
✓
✓
✓
✓

Mean heat strokes
Observations

14.10020612

14.10020612

14.32620612

14.32620612

14.32620612

14.32620612= 1 if heat alert at 5am

Source: Authors’ calculations using data from Ministry of Environment and Fire and Disaster Management Agency (FDMA).
Note: This table shows results from negative binomial regressions of the number of heat strokes in a region on the presence ofa heat alert and a set of control variables. The mean number of heat strokes is for regions without heat alerts (control group).
WBGT ≥ 28. Robust standard errors clustered at the regional level are given in parentheses. ∗ , ∗∗ , and ∗∗∗ denote significanceat the 10%-, 5%-, and 1%-level, respectively.

43

Table A10: Robustness Check – Effects by Year# of heat strokes
2020

2021

2022

(1)

(2)

(3)

0.239∗∗∗

0.162∗∗∗(0.085)(0.032)0.179∗∗∗(0.021)

WBGT
Lagged WBGTRegion FE
Day FE

✓
✓
✓
✓

✓
✓
✓
✓

✓
✓
✓
✓

Mean heat strokes
Observations

13.487
3500

9.782
3241

11.9493906= 1 if heat alert

Source: Authors’ calculations using data from Ministry of Environment and Fire and Disaster Management Agency (FDMA).
Note: This table shows results from negative binomial regressions of the number of heat strokes in a region on the presence ofa heat alert and a set of control variables. The mean number of heat strokes is for regions without heat alerts (control group).
WBGT ≥ 28. Robust standard errors clustered at the regional level are given in parentheses. ∗ , ∗∗ , and ∗∗∗ denote significanceat the 10%-, 5%-, and 1%-level, respectively.

Table A11: Robustness Check – State of Emergency (COVID-19)# of heat strokes
(1)

(2)

(3)

(4)

= 1 if heat alert

0.175∗∗∗

0.173∗∗∗

0.177∗∗∗= 1 if state of emerg.

(0.016)0.018(0.049)(0.015)0.041(0.056)(0.017)0.023(0.050)−0.025(0.043)0.176∗∗∗(0.016)0.046(0.056)−0.027(0.044)

WBGT
Lagged WBGTRegion FE
Day FE
Region-spec. trend

✓
✓
✓
✓

✓
✓
✓
✓
✓

✓
✓
✓
✓

✓
✓
✓
✓
✓

Mean heat strokes
Observations

14.10020612

14.10020612

14.10020612

14.10020612

Alert × state of emerg.

Source: Authors’ calculations using data from Ministry of Environment and Fire and Disaster Management Agency (FDMA).
Note: This table shows results from negative binomial regressions of the number of heat strokes in a region on the presence of aheat alert, presence of state of emergency, and a set of control variables. The mean number of heat strokes is for regions without
heat alerts (control group). WBGT ≥ 28. Robust standard errors clustered at the regional level are given in parentheses. ∗ , ∗∗ ,and ∗∗∗ denote significance at the 10%-, 5%-, and 1%-level, respectively.

44

Table A12: Robustness Check – Estimator of De Chaisemartin and d’Haultfoeuille (2020)
Log(# of heat strokes +1)# of heat strokes

(1)

(2)

(3)

(4)

0.160∗∗∗

0.160∗∗∗

3.799∗∗∗(0.028)(0.028)(0.935)3.798∗∗∗(0.935)

WBGT
Lagged WBGTRegion FE
Day FE
Region-spec. trend

✓
✓
✓
✓

✓
✓
✓
✓
✓

✓
✓
✓
✓

✓
✓
✓
✓
✓

Mean heat strokes
Observations

14.10020612

14.10020612

14.10020612

14.10020612= 1 if heat alert

Source: Authors’ calculations using data from Ministry of Environment and Fire and Disaster Management Agency (FDMA).
Note: This table shows results from OLS regressions of the number of heat strokes in a region on the presence of a heat alert
and a set of control variables, using the estimator of De Chaisemartin and d’Haultfoeuille (2020). WBGT and lagged WBGT
is controlled for linearly. The mean number of heat strokes is for regions without heat alerts (control group). WBGT ≥ 28.
Robust standard errors clustered at the regional level are obtained from 199 bootstrap replications and are given in parentheses.
∗ ∗∗, , and ∗∗∗ denote significance at the 10%-, 5%-, and 1%-level, respectively. Results are obtained from the Stata command
did multiplegt of De Chaisemartin et al. (2019).

45

Figure A1: Share of Dates with Heat Alert in 2022
Source: Authors’ presentation using data from Ministry of Environment. Note: This graph shows the share of dates with a heat
alert between May 1 and September 30, 2022, across Japan. Regions in white are dropped due to imperfect mapping of regions toprefectures (described further in text). Regions in bright blue had zero alerts. Darker color indicates a higher share.

46

.1
.08
Density
.06
.04
.02
0
20

25

30
WBGT

35

40

Figure A2: Distribution of Realized WBGTHeat strokes

40
10
20
30
# of heat stroke alerts
0

be
r
Se

Au

pt
em

gu
st

ly
Ju

ne

ay
M

Ju

# of heat strokes
500
1000
0

1500

10
20
30
# of heat stroke alerts
0

be
r
Se

Au

pt
em

gu
st

ly
Ju

ne
Ju

M

ay

0

# of heat strokes
500
1000

40

1500

Source: Authors’ presentation using data from Ministry of Environment. Note: This graph shows the distribution of maximum
realized WBGT in a region for the dates between May 1 and September 30 for the years 2017 to 2022. A kernel density estimatebased on the epanechnikov kernel and the rule-of-thumb bandwidth is shown in red.

Heat alerts

Heat strokes(a) 2021

Heat alerts(b) 2022

Figure A3: Heat Strokes and Heat Alerts Over Time
Source: Authors’ presentation using data from Ministry of Environment and Fire and Disaster Management Agency (FDMA). Note:
These graphs show the distribution of the number of heat strokes and heat alerts for 2021 (a) and 2022 (b).

47

2.5
2

2.5

0

Effect on # of heat strokes
.5
1
1.5

Effect on # of heat strokes
.5
1
1.5
2
0
28

30

32
WBGT

34

36

28

(a) Actual WBGT

30

32
WBGT

34

36

(b) Lagged WBGT

Figure A4: Relationship between WBGT and Heat Strokes
Source: Authors’ presentation using data from Ministry of Environment and Fire and Disaster Management Agency (FDMA). Note:
These graphs show the relationship between the WBGT index (a) and the lagged WBGT index (b), respectively, and heat strokes
according to our baseline specification presented in column (6) of Table 2. The estimated coefficients are presented for WBGT= [28, 35], whereas WBGT = 28.0 serves as baseline. The grey area denotes 95%-confidence intervals based on robust standard
errors clustered at the regional level.

48

40

100
30

32
WBGT

34

36

-40

-50

Effect on searches for weather
-20
0
20

Effect on searches for heat stroke alert
050

50
Effect on searches for heat stroke
-50
0
-100
28

28

32
WBGT

34

36

28

30

34

36

34

36

34

36

(c) “Weather”

60
-40

-50

-40

Effect on searches for outdoor
-20
0
20

Effect on searches for air conditioner
-20
0
20
40

Effect on searches for temperature
0
50

32
WBGT

40

(b) “Heat stroke alert”

100(a) “Heat stroke”

30

28

30

32
WBGT

34

36

28

32
WBGT

34

36

28

30

(e) “Air conditioner”

32
WBGT

(f) “Outdoor”

28

30

32
WBGT

34

36

-40

-40

-20

Effect on searches for indoor
-20
0
20
40

Effect on searches for park0
20
40

Effect on searches for sea bathing
-20
0
20

60

40

60

(d) “Temperature”

30

28

30

34

36

28

30

32
WBGT

(i) “Indoor”

-40

-40

Effect on searches for cinema
-20
0
20

Effect on searches for karaoke
-20
0
20
40

60

(h) “Park”

40(g) “Sea bathing”

32
WBGT

28

30

32
WBGT

34

36

(j) “Cinema”

28

30

32
WBGT

34

36

(k) “Karaoke”

Figure A5: Relationship between WBGT and Google Trend Searches
Source: Authors’ presentation using data from Google Trends and Ministry of Environment. Note: These graphs show the relationship between the WBGT index and Google Trend Searches for 熱中症 (heat stroke), 熱中症警戒アラート (heat stroke alert), 天
気 (weather), 気温 (atmospheric temperature), エアコン (air conditioner), 屋外 (outdoor), 海水浴 (sea bathing), 公園 (park),
屋内 (indoor), 映画館 (cinema), and カラオケ (karaoke), respectively, according to our baseline specification presented in Table
7 and 8. The effects are presented for WBGT = [28, 35], whereas WBGT = 28.0 serves as baseline. Data for 2020–2022. The
grey area denotes 95%-confidence intervals based on robust standard errors clustered at the regional level.

49

-5

-20

-10

Effect on mobility
010Effect on mobility
0
5

20

30

10

10
Effect on mobility
5
0
-5
28

30

32
WBGT

34

36

28

32
WBGT

34

36

28

30

32
WBGT

34

36

34

36

(c) Parks

Effect on mobility
-4
-2
-6

-20

-10

Effect on mobility
020

Effect on mobility
0
10

0

20

(b) Grocery & pharmacy

40(a) Retail & recreation

30

28

30

32
WBGT

34

(d) Transit stations

36

28

30

32
WBGT

34

36

28

(e) Workplaces

30

32
WBGT

(f) Residential

Figure A6: Relationship between WBGT and Mobility
Source: Authors’ presentation using data from Google COVID-19 Community Mobility Reports and Ministry of Environment.
Note: These graphs show the relationship between the WBGT index and the percentage changes in number of visits to specificplaces in a region, according to our baseline specification presented in Table 9. The effects are presented for WBGT = [28, 35],
whereas WBGT = 28.0 serves as baseline. Data for 2020–2022. The grey area denotes 95%-confidence intervals based on robust
standard errors clustered at the regional level.

50External causes
Digestive system
Respiratory system
Circulatory system
Nervous system
Endocrine, nutrit., and metabolic diseases
Blood and blood-forming organs
Neoplasms
Infectious diseases

-.015

-.01

-.005
0
.005
Effect of heat alert.01

Figure A7: Effect of Heat Alerts on Cause-Specific Mortality
Source: Authors’ presentation using data from Ministry of Environment, Ministry of Health, Labor, and Welfare, and Fire andDisaster Management Agency (FDMA). Note: This graph shows the effects of the number of deaths on the number of heat alerts
in a region and a set of control variables using negative binomial regressions. Observations are at the region-month level and weestimate the following model:
Deathscrym = α + βAlertrym + Share T empkrym + δry + λrm + θym + ϵcrym ,where Deathscrym is the number of deaths in category c in region r in year y in month m. Alertrym is the number of heat alerts,
Share T empkrym is the share of days in a month with WBGT in the interval k (i.e. [20; 21), ..., [34; max]), and δry , λrm , andθym are region-by-year fixed effects, region-by-month fixed effects, and year-by-month fixed effects, respectively. ϵcrym is an error
term. In the online appendix, we present results by age group. N = 1276. 95%-confidence intervals are based on robust standard
errors clustered at the regional level.

51

20
15
Density
10
5
0
-.2

-.1

0
Estimated coefficient.1

Actual .2

Figure A8: Placebo Distribution of Heat Alert Effects
Source: Authors’ presentation using data from Ministry of Environment and Fire and Disaster Management Agency (FDMA). Note:
This graph shows the distribution of placebo negative binomial regressions of the number of heat strokes in a region on the presenceof a heat alert and a set of control variables. The actual estimate based on column (6) of Table 3 is shown as solid line. Heat alert
patterns over time are randomly assigned to regions (without replacement) and the negative binomial regression as described before
is run. This procedure is repeated 1,000 times. This approach is based on Bertrand et al. (2004). Dashed lines indicate empirical
95%-confidence intervals.

52

.3
-.1

Effect on # of heat strokes
0
.1
.2

.3
Effect on # of heat strokes
0
.1
.2
-.1
31

32

33
WBGT

34

35

31

(a) Increasing sample size

32

33
WBGT

34

35

(b) Moving average

Figure A9: Effect of Heat Stroke Alerts on Heat Strokes by WBGT Intervals
Source: Authors’ presentation using data from Ministry of Environment and Fire and Disaster Management Agency (FDMA).
Note: These graphs show the effect of heat stroke alerts on heat strokes according to our baseline specification, while increasing
the sample size in 0.5-intervals of the WBGT index (a) and using subsamples for specific WBGT intervals, respectively. In panel(a), presented estimates are the effect of an alert for the sample with temperatures just including the respective WBGT. In panel (b),presented estimates are the effect of an alert for a sample with temperatures centered around the respective WBGT ± 1. WBGT
≥ 28. 95%-confidence intervals are based on robust standard errors clustered at the regional level.

53