DP2023-17“Invisible Killer”:
Seasonal Allergies and Accidents
Mika AKESAKA
Hitoshi SHIGEOKA

August 21, 2023

“Invisible Killer”: Seasonal Allergies and Accidents *Mika Akesaka †Hitoshi Shigeoka ‡Kobe University

Simon Fraser University,
University of Tokyo,
IZA, and NBER

First Draft: August 2023
Abstract
Although at least 400 million people suffer from seasonal allergies
worldwide, the adverse effects of pollen on “non-health” outcomes,
such as cognition and productivity, are relatively understudied.
Using ambulance archives from Japan, we demonstrate that highpollen days are associated with increased accidents and injuries—
one of the most extreme consequences of cognitive impairment.
We find some evidence of avoidance behavior in buying allergy
products but limited evidence in curtailing outdoor activity,
implying that the cognitive risk of pollen exposure is discounted.
Our results call for governmental efforts to raise public awarenessof the risks and promote widespread behavioral change.
Keywords: Seasonal allergies, Pollen, Accidents, Cognition, Avoidance behaviors, Adaptation,
Climate change
JEL code: I12, J24, Q51, Q53, Q54* Jack Madison provided superb research assistance. The authors thank Panle Jia Barwick, Robert

Kaestner, Hiroyuki Kasahara, Munechika Katayama, Yoshifumi Konishi, Yuta Kuroda, Young Lee,Michelle Marcus, David Molitor, Claudia Persico, Julian Reif, Yutaro Sakai, Edson Severnini, Shinsuke
Tanaka, Rusty Tchernis, Erdal Tekin, Shinsuke Uchida, Hendrik Wolff, and the seminar participants at
Advances in Utilizing Mobile Location Data (Waseda University), AASLE 2022, AMES China 2022,
AMES Tokyo 2022, AsianWEHE 2022, Japan Economic Association 2022, Hanyang-Kobe-Nanyang
Conference, J-TREE, Kobe University, LACEA/LAMES 2022, NBER Health Economics Fall Meeting
2022, Osaka Metropolitan University, and University of Bologna, for their comments and suggestions. We
thank the Fire and Disaster Management Agency of the Ministry of Internal Affairs and Communications
and the National Policy Agency for providing us with ambulance records and police records. Akesaka
acknowledges financial support from JSPS KAKENHI (23K12492, 22K01534, 21H04397, 20K13511) andInamori Foundation. Shigeoka acknowledges financial support from JSPS KAKENHI (23H00828,
22H00057, 22H00847, 22H05009) and Tokyo Center for Economic Research. Any errors are our own.
† Kobe University, Research Institute for Economics and Business Administration, 2-1, Rokkodai, Nada,
Kobe, Hyogo, 657-8501, JAPAN. Email: mika_akesaka@rieb.kobe-u.ac.jp‡ Corresponding author: Simon Fraser University, Department of Economics, 8888 University Drive,
Burnaby, BC V5A1S6, CANADA, University of Tokyo, IZA, and NBER. Email: hitoshi_shigeoka@sfu.ca
11. Introduction“Hay fever,” or seasonal allergic rhinitis (SAR) as it is medically known, is a common
chronic condition induced by exposure to airborne allergens such as pollen and dust. SAR affects
up to 30% of the population in developed countries, with an estimated 400 million sufferers
worldwide (Greiner et al. 2011). Despite its widespread global prevalence, the adverse impact ofpollen has, to date, received surprisingly little attention from economists. First, the symptoms of
SAR tend to be chronic and relatively mild; however, the non-acute physiological effects ofpollen exposure can be potentially problematic as people may not engage in avoidance behaviors
to limit their exposure.
Second, in sharp contrast with man-made air pollution, the scope of governmental policy
intervention for damage caused by natural sources, like pollen, is seemingly limited. 1 However,a recent study projects that global warming will accelerate pollen production and thus increase
pollen concentrations and prolong the duration of pollen seasons (Zhang and Steiner 2022).
Figure 1 demonstrates the strong positive association between pollen counts and maximum
temperature (Panel A) and the number of hot days above 30℃ (Panel B) in the previous summer
in Japan—our study setting. The figure content highlights that climate change caused by humanactivities can exacerbate the potential damage arising from naturally occurring pollen production.
In addition to the apparent negative health consequences, clinical studies have identified the
detrimental effects pollen exposure can have on cognitive performance, in the form of decreased
attention span and increased reaction time (Wilken et al. 2002). However, existing studies
examining the effects of seasonal allergies on “non-health” outcomes, such as cognition andproductivity, in the field setting are limited to its influence on the test performance of children(Bensnes 2016; Marcotte 2015, 2017). We hypothesize that exposure to seasonal pollen has
much broader consequences—beyond children’s test scores. If pollen exposure severely inhibits
cognitive functioning, any daily activity that requires continuous attention and performance, suchas driving, can be affected.
We conduct the first investigation of the effects of acute and short-term pollen exposure on
the incidence of accidents. These include traffic accidents and work-related injuries, which arearguably some of the most extreme consequences of cognitive impairment. Traffic accidents are
the leading cause of accidental deaths globally, and hence, any factor that influences the risk of
traffic accidents is of great relevance to social welfare. 2 Work-related injuries are also worth
1 Economists gather knowledge on the impact of exposure to air pollution on a variety of outcomes ranging

from health, human capital, labor productivity, and crime. See Graff Zivin and Neidell (2013) and AguilarGomez et al. (2022) for summaries of relevant literature.
2 Traffic accidents are the second leading cause of accidental deaths (after asphyxia), with a recorded averageof over 4,000 and 700,000 annual fatalities and injuries, respectively, for the period 2008 to 2019 (MHLW
2009; NPA 2022), in a total population of 127 million in Japan.
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investigating as they translate to substantial productivity losses in the labor market. 3
Furthermore, these accidents can cause negative externalities for individuals who do not sufferfrom seasonal allergies but are involved in the accidents.
We combine the data on pollen counts with the newly available administrative ambulance
records covering all accidents that occurred from 2008 to 2019 in Japan. These accidents were
especially severe as they required ambulance transportation to hospitals. Japan is the ideal setting
to conduct such research as the pollen monitoring stations are densely located across the country
at a rate 42 times higher than that of the United States. The intensity of pollen concentration
varies widely across space and time, allowing us to accurately measure pollen exposure andexamine the potential non-linear dose responses. Furthermore, we are able to provide nationally
representative estimates, unlike the samples in previous studies that are limited to only a handfulof schools or districts near pollen stations. This assists in mitigating concerns about thegeneralizability of our findings. We leverage daily levels of spatial and temporal variation inpollen counts of differing magnitudes to identify the effect of pollen exposure on accidents.
There are four primary findings. First, we find that high daily pollen counts are associatedwith increased accidents of all types, including traffic accidents, work-related injuries, sports
injuries, and fire accidents. The relationship between pollen counts and the number of accidents
is clearly concave, suggesting that even low concentrations of pollen—which are more frequent
than higher concentrations—can have a significant adverse impact on cognitive performance andhence the incidence of accidents. While the effects are more pronounced for less severe
accidents, elevated pollen exposure also increases accidents that lead to death.
These findings suggest that pollen exposure not only affects the school performance ofchildren but also has a wider impact on cognition, productivity, and daily activities. For example,
an increase in workplace injuries implies a detrimental effect on labor productivity. Therefore,
the current estimated cost of exposure to airborne allergens, which is based mainly on
straightforward health outcomes, school days missed, and work absenteeism, may severely
underestimate the true cost to society.
Second, we find mixed evidence of short-term avoidance behaviors to reduce the risk ofpollen-induced accidents. On the one hand, using in-home scanner data, we show that people
increase spending on products that protect against seasonal allergies, such as medications, eyedrops, and masks. On the other hand, we find limited evidence of individuals taking more serious
measures to alleviate the risk of accidents, such as reducing outdoor activities; using geolocation
data, we discover a very small reduction in the number of people outdoors in bustling areas onhigh pollen days.
3 Broten et al. (2019) find that workers who are injured on the job face a subsequent earnings penalty of 8% on
average, and this increases to 30% for those who are permanently disabled.

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These results indicate that people may lack awareness of or underestimate the cognitive riskof pollen exposure. Thus, the status quo that relies on individuals’ self-protection to mitigate
pollen exposure may only lead to limited benefits. A government intervention that raises
awareness regarding the risk that pollen exposure poses to health and cognition might benecessary, in addition to the provision of real-time pollen information. For example, public
information campaigns, such as a “pollen alert”—recommending that allergy sufferers curtail
outdoor activities and refrain from driving or operating machines on high pollen days—could bea cost-effective tool to promote widespread behavioral change and reduce pollen-induced
accidents.
Third, we fail to find strong evidence of medium-term adaptation to the adverse impact ofpollen exposure. The fundamental question relating to environmental hazards is whether suchhazards are mostly unavoidable or whether individuals can adapt by utilizing current
technologies. 4 In the context of this study, newer medications for seasonal allergies have fewerside effects, including lower chances of drowsiness, which might reduce the risk of accidents
caused by medication-induced drowsiness. In addition, one may have more time to invest in
defensive strategies such as the purchase of an air purifier. We find that the detrimental effects ofpollen exposure have not declined significantly in recent years, at least not within the past 12
years of our sample period. Furthermore, individuals who live in the high-average pollen regions
have similar pollen-induced accident rates as those who live in the low-average pollen regions,corroborating previous findings.
Finally, we combine our estimates of the effect of pollen on accidents with projections of
the future climate, and also the relationship between the temperature and pollen counts shown in
Figure 1 to illustrate the magnitude of the social cost of anthropogenic climate change. The“business as usual” scenario from the Intergovernmental Panel on Climate Change (IPCC)—
which predicts an increase of 4.1℃ in the summer temperature in the years 2076–2095 in
Japan—would lead to 1,823 additional pollen-induced accidents annually. Multiplying theresulting accident counts with the average accident costs, the expected annual social cost ofpollen-induced accidents is approximately $236 million. This estimated social cost is very likely
to be a lower bound since we fail to consider the accidents at both sides of the severity
distribution (i.e., minor cases as well as immediate deaths) that do not require ambulancetransportation to hospital.
While much of the literature on climate change has focused on the impact of risingtemperature on direct outcomes such as aggregate incomes, mobility, mortality, and agricultural
4 The current evidence presents the significant adaptation potential in the context of mortality to heat exposure(Barreca et al. 2016; Heutel et al. 2021), infant mortality to dust (Adhvaryu et al. 2022), with mixed evidence
for workplace injuries to heat (Dillender 2021; Park et al. 2021).

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outcomes (Carleton and Hsiang 2016; Dell et al. 2014), the increase in the number of seasonalallergy sufferers and the associated performance impairment could be the indirect andundiscovered cost of anthropogenic climate change. Consequently, any measure to mitigate therisk of a warming climate could have substantial social benefits by preventing temperaturedriven increases in airborne pollen.
The rest of the paper is organized as follows. Section 2 briefly describes the background,
Section 3 describes the data, Section 4 presents the econometric model, and Section 5 reports themain findings of this study. Section 6 examines the short-term avoidance behaviors, and Section
7 conducts additional analysis. Section 8 offers conclusions.

2. Background2.1. Pollen and seasonal allergies
SAR is a common chronic condition that occurs when an individual’s immune system reacts
to allergens in the air, such as pollen and dust, causing the immune system to produce antibodies(such as histamines and cytokines) to fight the perceived threat from the pollen grains. The
antibodies, in turn, cause inflammation in the airways, inducing various allergic symptoms, suchas a runny nose, nasal congestion, sneezing, and itchy eyes (Greiner et al. 2011).
SAR is a global health problem as otherwise seemingly healthy individuals can be affected.
The prevalence rates vary by country but are generally between 10% and 30% in developed
countries (Greiner et al. 2011; Schmidt 2016). This number is likely to underestimate the true
prevalence rate as some individuals do not seek medical assistance for the condition.
Furthermore, numerous studies document increasing prevalence due to various factors such asurbanization, westernization of lifestyles, and climate change (Schmidt 2016).
Japan is no exception. An extensive epidemiological survey guided by the Japan Society of
Immunology and Allergology in Otolaryngology for otolaryngologists and their families has
been conducted every ten years (1998, 2008, and 2019). According to this study, the prevalence
rate of SAR has steadily increased by roughly 10 percentage points every ten years: from 19.6%
in 1998 to 29.8% in 2008 and further to 42.5% in 2019. The prevalence rate has been found topeak around middle age, but a substantial number of young and older individuals also sufferfrom SAR (Matsubara et al. 2020). 5
Because SAR has relatively mild and chronic symptoms, its economic cost has been
overlooked. In addition to direct medical costs such as medications and emergency department
5 The prevalence rates of SAR in 2019 are 30.1% (ages 5–9), 49.5% (10–19), 47.5% (20–29), 46.8% (30–39),

47.5% (40–49), 45.7% (50–59), 36.9% (60–69), and 20.5% (70–) (Matsubara et al. 2020). In addition, theprevalence rate is generally high in urban areas, and in Tokyo, it increased from 19.4% in 1996 toapproximately 48.8% in 2016 (Tokyo Metropolitan Institute of Public Health 2017).
5

visits (Xing et al. 2023), past studies suggest that pollen allergies are a major source ofabsenteeism from work and school (Hellgren et al. 2010; Lamb et al. 2006).
Most relevant to this study, clinical studies have identified the negative effects of SAR on
cognitive performance. This often occurs indirectly as a result of deteriorated sleep quality(Craig et al. 2004; Santos et al. 2006) and directly by antibodies themselves via brain function(McAfoose and Baune 2009). For example, Wilken et al. (2002) find that allergic adults who are
randomly exposed to pollen perform worse on a broad range of cognitive measures than those
who are not exposed; these measures include longer response times, reduced working memory,
divided attention, and slower computation. Unfortunately, medical studies have shown thatallergy medications can induce similar or even worse effects on cognitive functioning due tovarious side effects, such as drowsiness, dry mouth, and lethargy (Jáuregui et al. 2009; Kay
2000).
To date, existing studies of the effect of seasonal allergies on cognition and productivity inthe field setting have been limited to their effect on the test performance of children (Bensnes
2016; Marcotte 2015, 2017). However, as pollen exposure impairs cognitive performance, almost
any daily activity can be severely affected. In this paper, we focus on accidents, including trafficcollisions and work-related injuries, as they involve the most extreme forms of performance
impairment. For example, it is well established that cognitive function is negatively correlatedwith the likelihood of motor vehicle accidents (Anstey et al. 2005, 2012). 6 Similarly, workplace
injuries are most commonly caused by distractions (European Commission 2009).
In summary, while scientific knowledge concerning the impact of other environmental
stressors (such as pollution and temperature) on cognitive performance and productivity has been
gained 7, the impact of airborne pollen and associated seasonal allergies on these outcomes has
yet to be sufficiently examined.
2.2. Warming climate and pollen
Because higher temperatures and carbon dioxide (CO2) concentrations have been found toincrease pollen production, climate change is expected to impact pollen concentrations and theduration of pollen seasons significantly. Anderegg et al. (2021) track pollen trends across 60
pollen stations in the United States from 1990 to 2018 and find increases of 20.9% and 21.5% in

6 Vuurman et al. (2014) argue that the magnitude of the impairing effects of allergic rhinitis on driving iscomparable to the effects of blood alcohol content (BAC) of 0.05%, which is the legal limit in many countries.
Smith (2016) also demonstrates that a one-hour sleep loss increases the probability of being in a drowsinessrelated fatal motor vehicle accident by 46%.
7 See for example: pollution (e.g., Chang et al. 2016, 2019; Ebenstein et al. 2016; Graff Zivin and Neidell
2012; He et al. 2019; La Nauze and Severnini 2021; Sager 2019; Zhang et al. 2018) and temperature (e.g.,
Adhvaryu et al. 2020; Lavy et al. 2022; Park et al. 2021; Somanathan et al. 2021).
6

the annual and spring (February–May) pollen concentrations, respectively; the pollen season startdate occurred 20 days sooner, and the pollen season duration increased by eight days. They
further conduct a model selection analysis to identify the main drivers of pollen proliferation andfind that mean annual temperature is the strongest predictor of the above-mentioned pollen
metrics among eight climate variables (including temperature, precipitation, frost days, and CO2
concentrations). Increased pollen quantities, an earlier pollen season start date, and prolonged
duration of the pollen season have similarly been observed in Europe (D’Amato et al. 2007;
Ziello et al. 2012; Hamaoui-Laguel et al. 2015).
We confirm that such a relationship is also observed in the data we collected and analyzed
in Japan. Figure A1 displays the time series of the average daily pollen counts for the period
from February to May and the average maximum temperature (Panel A), as well as the numberof days where the temperature exceeded 30℃ (Panel B) in July and August in the previous
summer, using station-year panel data from all the pollen monitoring stations (N= 120) during
the period from 2008 to 2019. Pollen counts are expressed in (24-hour cumulative) particles per
cubic meter of air (grains/m3). Both graphs show a positive relationship between the temperaturein summer and the pollen counts the following spring. For example, a cold summer was
experienced in 2009, and the pollen concentration in the spring of 2010 was relatively low. By
contrast, a hot summer was experienced in 2010, and high pollen counts were observed in thespring of 2011.
Figure 1, mentioned in the introduction, presents the associations between pollen counts andtemperatures in the previous summer using the same data. The binscatter plot exploits thevariation in pollen counts within the same pollen monitoring station over time by controlling forstation fixed effects (FEs). The linear slope of 167.4 (t-stats= 11.2) in Panel A indicates that anincrease of 1℃ in the maximum temperature in the previous summer is associated with anaverage additional 167 grains/m3 daily pollen count the following spring. As the mean andmedian daily pollen counts of 120 stations in the same period are 955 and 712 grains/m3,
respectively, such an increase may be sizable. Similarly, the slope in Panel B is 23.7 (t-stats=11.2), indicating that ten more hot days above 30℃ in the previous summer could increase thedaily pollen count by 237 grains/m3 the following spring.
To summarize, the evidence thus far indicates that climate change caused by humanactivities has increased the intensity of pollen seasons in different parts of the world. The
expected temperature rise due to global warming is likely to intensify and accelerate this trend in
the coming decades (Ziska et al. 2019). For example, Zhang and Steiner (2022), project thatclimate change will further hasten the arrival of the pollen season (by up to 40 days), prolong theduration of the pollen season (by approximately 19 days), and, as a result, increase the annual
total pollen emission (from 24% to 40%) in the United States.
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3. Data
This study has compiled a comprehensive dataset to study the impact of pollen exposure on
accidents. For our primary analysis, we combine the daily airborne pollen counts with newly
available ambulance records that report accidents from 2008 to 2019 in Japan. To ourknowledge, this is the first study to use this dataset for economic research. To avoid confusion,
supplementary data used to examine symptoms (Google Trends and Twitter data, Section 5.1)
and avoidance behaviors (in-home scanner data and geolocation data, Sections 6.2 and 6.3) aredescribed later. The details of the data sources are provided in Appendix H.
3.1. Airborne pollen
We obtain airborne pollen data from the Japanese Ministry of the Environment’s pollen
monitoring system (designated “Hanako-san”), which provides hourly readings of pollen counts(grains/m3) for Japanese cedar and hinoki cypress (Yamada et al. 2014). Figure A2 displays thepollen season calendar for typical plants, showing that these two types of plants are the primarysource of pollen production in Japan, mainly for February to May. Comprehensive data on pollencounts are available beginning from 2008. This information is automatically published on the
Ministry of the Environment’s website. In addition, during the pollen season, the pollen level iscommonly reported in the weather forecast, along with the usual temperature and precipitation.
See Figure 2 for real-time and forecasted pollen levels reported on television on a typical day
during the pollen season in Japan. Thus, the cost of acquiring such information is nearly zero.
A total of 120 pollen monitoring stations are located across Japan. Panel A of Figure 3
displays the locations of all monitoring stations as of 2019. 8 Each of the 46 prefectures has, on
average, two to three monitoring stations, both in urban areas with high population densities andin mountainous regions, which are the major sources of pollen production (Wakamiya et al.
2019). 9 The number of pollen monitoring stations is extremely high, given the size of thecountry. For example, the United States—which is 26 times larger than Japan—has only 74
pollen monitoring stations nationwide. This suggests that the density of pollen stations is 42
times higher in Japan than it is in the United States.
Since typical pollen in Japan can be distributed more than 100 km (160 miles) away andremain in the air for more than 12 hours, almost entire areas of Japan, even in sparsely forested
cities, can be contaminated by airborne pollen (Yamada et al. 2014). Panel B of Figure 3 plots
8 The number of pollen stations has remained at 120 since 2008; thus, our estimates are not affected by theincrease or decrease in the number of monitoring stations. The movement of monitoring stations is limited to ahandful of stations, and the distance of movement is very small.
9 Okinawa prefecture, which is the southernmost remote island in Japan, with a different climate from the restof Japan, has no pollen station, as little pollen is observed. We exclude Okinawa from the entire analysis.
8the cumulative distribution of the distance from the nearest pollen station to the centroid of each
emergency response unit (our regional unit of analysis, as explained in Section 3.2). The mean
and median distances from pollen stations are 25.4 and 17.5 km, respectively. Even with aconservative threshold of 48 km (30 miles) for pollen measurements to be valid (Chalfin et al.
2019), 90.2% of all units are within this threshold. 10
The high density of stations across the country enables us to (i) accurately measure theexposure to pollen, (ii) provide nationally representative estimates of pollen exposure (unlike
previous studies that are limited to a handful of schools or districts near pollen stations), and (iii)
include the continuous pollen exposure variable with differing magnitudes as the regressor toinvestigate the potential non-linearity in dose-response (unlike past studies that only included adichotomous variable that defined high pollen days).
As the pollen emission from the Japanese cedar (the main pollen-producing plant) starts in
February and peaks from March to April (as shown in Figure A2), the pollen count is monitoredfrom February to May every year. 11 We aggregate the monitor readings to obtain the daily levelby adding the hourly observations to calculate the accumulated number of pollen grains counted
in 24 hours. In addition, weather covariates from nearby weather stations are included in thesame data. In particular, hourly temperature, precipitation, and wind speed are recorded.
Similarly, we aggregate these variables to obtain the daily level.
3.2. Ambulance records
Our data relating to accidents and injuries are derived from the Fire and Disaster
Management Agency (FDMA) of the Ministry of Internal Affairs and Communications, Japan.
The data cover all ambulance calls, except for those in metropolitan Tokyo, 12 for the period 2008
to 2019 that required ambulance transportation to hospitals. The registration of all ambulance
records in the online system of FDMA became mandatory in 2008. As ambulance service isprovided to the public free of charge in Japan, there is no differential selection of the sample bysocioeconomic status, unlike in countries like the United States, where ambulance use varies byhealth insurance status (Meisel et al. 2011).
A total of 14.7 million accidents are recorded for the period 2008 to 2019, with a yearly
10 Chalfin et al. (2019), who examine the effect of pollen on crime in cities in the United States, use criminal

records from stations within 30 miles (48 km) of the city center, while the National Allergy Bureau suggeststhat pollen measurements are valid within a 20-mile (32 km) radius of each station.
11 The exception is Hokkaido prefecture, which is located in the far north (with four stations), and thus themonitoring period is delayed by one month (March to June).
12 Tokyo is excluded from the sample because (i) metropolitan Tokyo (23 wards of central Tokyo and mostcities in Tokyo) falls under one ambulance operating system (Tokyo Fire Department), which provides noregional variation in pollen counts, and (ii) data from metropolitan Tokyo are publicly available only from2016 onwards.
9average of 1.2 million accidents. 13 The data provide detailed information for individual
accidents, including the location of the accidents, date and time of the ambulance calls, type of
accidents, severity of injuries, and age and gender of those involved in the accidents.
Two features of this dataset are particularly beneficial for our study. The first feature is that
we have knowledge of the type of accidents that occurred, including traffic accidents, workrelated injuries, sports injuries, and fire accidents. The other distinctive feature is that it includes
information on the severity of injuries sustained. This measure is expected to be accurate as it isbased on the initial clinical assessment by the doctors who first attended to the injured person at
hospital admission. The severity measure ranges from death/fatal, serious, moderate, to light,
where “serious” accidents require more than three weeks of hospitalization and treatment;“moderate” accidents require hospitalization, but for less than three weeks; and “light” accidents
do not require hospital admission. Ambulance records have two key advantages over vitalstatistics: They can capture non-fatal but severe injuries that would be missed in vital statistics,
and they can capture the accident data on the day of occurrence without measurement errors,
unlike vital statistics, which may record some accidental deaths with delays.
The geographical unit in ambulance records is an emergency response unit (hereinafterreferred to as “unit”), which is the level of ambulance service in Japan. While many units aremunicipalities themselves, some small municipalities form a unit to increase the efficiency of theambulance service. As of 2019, 1,700 municipalities (equivalent to counties in the United States)
across 46 prefectures (equivalent to states in the United States) form 705 units. We aggregate theaccident records to the unit-day level by adding the hourly observations within the units.14
3.3. Sample construction and summary statistics
To construct our primary sample, we merge unit-day level ambulance records with thesame-day daily level pollen counts from the nearby monitoring stations. Thus, the primary
sample comprises records from February to May (i.e., high pollen seasons only) for all
prefectures except for Hokkaido, which records from March to June, for 2008 to 2019.
Table 1 presents summary statistics for our primary estimation sample, which consists of
970,309 unit-day observations. On average, there are 33 daily accidents per million people.
13 The ambulance records also include emergency cases due to illness (72.3% of all records). Since our focus is

accidents, we extract data on five types of accidents from the ambulance archives: traffic accidents, workrelated injuries, sports injuries, fire accidents, and other accidents, which add up to 25.9% of all records. The
remaining records relate to self-injurious activities, assault, drowning, natural disasters, and other categories(1.8%).
14 The timestamp of each accident reflects the time at which the accident is reported to emergency responseunits rather than the actual time that the accident occurred. This might result in some degree of measurement
error in terms of the hour (more likely) than the date. As a result, we aggregate accidents to the daily level,
following the existing literature (e.g., Park et al. 2021).
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Traffic accidents are the leading type of accident and account for 37.6% of all accidents. This isfollowed by work-related injuries (3.5%), sports injuries (2.6%), and fire accidents (0.5%). The
other accidents that do not belong to any of these categories accounted for over half of allaccidents (55.8%). 15
The mean daily concentration of airborne pollen is 984 grains/m3 (with a standard deviationof 2,135). 16 Figure 4 plots the daily pollen counts for five selected locations from the north to thesouth of Japan, demonstrating substantial variations in pollen concentration across space andtime. As pollen counts are rightly skewed, we take the natural log of pollen counts as the main
regressor throughout the study unless otherwise mentioned. Panel A of Figure A3 displays thehistogram of the logged daily pollen counts during the period 2008 to 2019, while Panel B
displays the same counts after residualization by the unit, month-by-year, month-by-prefecture,and day-of-the-week FEs. This figure demonstrates substantial residual variation in pollen
concentrations, indicating ample variation for obtaining precise estimates.

4. Econometric model
We estimate the effect of short-run exposure to pollen on the number of accidents, net ofany potentially confounding factors:′+ 𝛼𝛼𝑖𝑖 + 𝛼𝛼𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 + 𝜀𝜀𝑑𝑑𝑑𝑑𝑑𝑑 , [1]𝑌𝑌𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = 𝛽𝛽log (𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 ) + 𝛾𝛾𝑋𝑋𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖

where the dependent variable 𝑌𝑌𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 is the number of accidents per million people in unit 𝑖𝑖 on day
𝑑𝑑 in month 𝑚𝑚 and year 𝑦𝑦. The logged form of the main regressor (pollen counts) is consistent

with the non-linearity in dose-response in clinical studies (Erbas et al. 2007). 17 We later show theresults from the alternative specifications (e.g., level-level or “dose-response”) or Poisson modelto account for the count nature of the accidents and assess the sensitivity of our results to zero
observations. The parameter of interest is 𝛽𝛽, which measures the change in the outcome
associated with a 100% increase in pollen counts. Unit FE (𝛼𝛼𝑖𝑖 ) controls for geographic
differences in health and pollen concentrations.

The high granularity of our data allows us to include multiple sets of high-dimensional timeFEs (𝛼𝛼𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 ). The baseline specification includes prefecture-by-month (𝛼𝛼𝑝𝑝𝑝𝑝 ), month-by-year(𝛼𝛼𝑚𝑚𝑚𝑚 ), and day-of-the-week FEs (Deryugina et al. 2019). We later replace these with more or
15 For example, these accidents range from minor to major ones and include: (i) slipping on a step on the roadand falling down, (ii) slipping on a snowy road and falling down, (iii) spilling a pot and getting burned, and(iv) slamming a finger in a screen door.
16 We have truncated pollen counts at the 99.9 percentile (55,104 grains/m3) to account for outliers.
17 We add one to account for zero pollen counts (0.83%) before taking the log. Later, we show that our results
are robust to dropping these observations and taking logs without adding 1 in Table 3. Bensnes (2016) andMarcotte (2017) also take the log form of pollen counts.
11less stringent ones as a robustness check. Prefecture-by-month FE controls any seasonalcorrelation between pollen counts and accidents, allowing this correlation to vary by prefecture.
The month-by-year FE controls flexibly for nationwide time-varying shocks during our sample
period. Finally, day-of-the-week FE accounts for within-week variation in accidents. In this way,
we compare days in the same month in the same unit that happen to differ in pollen concentration
across years, alleviating concerns that other seasonal trends in accidents could affect the results.
′
The 𝑋𝑋𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖
flexibly controls for daily variation in weather covariates. We include sevenindicators for 5℃ intervals of daily average temperatures, ranging from 0℃ or less to 25℃ or
more. For daily precipitation, we include four indicators (no rain, less than 1 mm of rain, 1 mm
to 2 mm of rain, and more than 2 mm of rain). We also control for the average wind speed andduration of darkness—the time between dusk and dawn, which is an important unobserved factor
for traffic accidents (Bünnings and Schiele 2021). Finally, we control the logged population,
which is related to population density and congestion (once with unit FE included), potentiallyaffecting the risk of accidents (Abouk and Adams 2013).
The underlying assumption for 𝛽𝛽 to reflect the causal impact of pollen is that once wecontrol for the series of location and time FE as well as weather covariates, the temporal,

seasonal, and geographic variations in daily pollen counts would be considered as good as
random. This assumption is arguably plausible with the very granular sets of FE and daily
variation in pollen counts from naturally occurring processes.

We cluster all standard errors at the pollen monitoring station (N= 120)—the level ofunderlying variation in our treatment variable (Abadie et al. 2017)—to account for possible serial
correlation, and weight all estimates by the relevant population in cases where the dependent
variable is in per capita terms.

5. Main results
5.1. “First stage”—Symptoms of seasonal allergies
Before revealing our main findings, we present evidence of a “first stage”—whether people
are more likely to suffer from seasonal allergy symptoms on high pollen days than low pollen
days—using data on both internet search activities (Google Trends) and social media posts(Twitter data).
We use publicly available Google Trends data on two broad categories: (1) pollen-related
keywords, and (2) symptoms-related keywords during the period 2016 to 2019 at the prefectureday level (N= 21,551). See Table B1 for the complete list of search keywords used. The Google
search index reflects search term popularity and takes a value of 0 to 100 in a given prefecture

12and on a given day in proportion to total searches within the search period. 18
Figure 5 displays the results of symptom-related keywords (“runny nose,” “nasal
congestion,” “sneeze,” and “itchy eyes” from Category 2 in Table B1). Panel A shows the timeseries of daily pollen counts (grains/m3) and the Google search index for these keywords in 2018as an example. The variables move closely over time. Panel B displays the binscatter plots of therelationship between the logged daily pollen counts and search index after controlling for themonth-by-prefecture, month-by-year, and day-of-the-week FEs. The linear lines fit the data well.
A 100% increase in daily pollen counts leads to increases in the search index by 3.6 on a 0–100
scale, with a mean of 30.4 (p-values<0.01). Similar patterns are observed for pollen-related
keywords in Figure B1.
We replicate the same relationship between pollen counts and keywords using public
Twitter data for the period 2016 to 2019 at the prefecture-day level. 19 The only difference fromthe above analysis is that the dependent variable is now the number of tweets that contain thesame two sets of keywords. Figure B2 demonstrates that people similarly tend to tweet these
keywords more on high pollen days.
One advantage of Twitter data over Google Trends data is that while the sample is moreskewed to younger cohorts, people tend to express the status of their “feelings” in tweets (Baylis
2020; Burk et al. 2022). Thus, in addition, we collect data pertaining to the number of tweets
related to sleep, namely, “having a hard time falling asleep” and “feeling sleepy,” as clinicalstudies suggest that decreased sleep quality is one way in which pollen exposure worsens
cognitive functioning (Craig et al. 2004; Santos et al. 2006). Figure B3 shows that individuals
seem to have more sleep-related issues as the pollen level increases. This “first stage” evidenceshows that individuals do suffer from typical seasonal allergy symptoms and that some of them
are aware of being exposed.
5.2. Basic results
Figure 6 displays the binscatter plots of the relationship between the logged daily pollencounts (grains/m3) and the number of accidents per million people: all accidents (Panel A),
followed by each type of accident by size (except for “other” accidents), namely, traffic
accidents (Panel B), work-related injuries (Panel C), sports injuries (Panel D), fire accidents(Panel E), and other accidents (Panel F), after controlling for the unit, month-by-year, month-byprefecture, and day-of-the-week FEs. All figures display a relatively linear relationship with the
18 The cutoff of 2016 is motivated by data completeness as well as the fact that Google made a change to its

data collection system on January 1, 2016. We follow Brodeur et al. (2021) to construct daily-level Google
Trends data across multiple years using overlapping periods of daily and weekly data.
19 We assign prefecture based on the location at the time of the tweet.
13

logged pollen counts, slightly flattening at a very high pollen concentration level. This simple
plot of raw data (net of location and time FEs) reveals a strong relationship between the pollen
concentration and the number of accidents. We formally test this relationship below.
Table 2 reports the main estimates from Equation [1]. Column (1) indicates that a 10%
increase in pollen count leads to an increase in the number of accidents by 0.0231 (0.231×0.1)
per million people, which is precise and highly statistically significant (p-value<0.001, t-stats=
14.0). Columns (2) to (5) show that, while the magnitude varies by accident type, the elevatedpollen concentration increases all types of accidents. In particular, while the share is small(3.5%), the increased incidence of work-related injuries indicates that labor productivity, in
general, is likely to be affected by pollen exposure. Owing to space considerations, we focus on
the categories “all accidents” and “traffic accidents” from here on. We choose traffic accidents asthey make up 37.6% of accidents that occur. We discuss the monetary values of pollen-induced
accidents in Section 7.2, where we project the potential damages from climate change.
Importantly, our estimates provide a lower bound on the effect of pollen on accidents as theambulance records analyzed do not include accidents on the extremes of the severity distribution(i.e., minor cases and immediate deaths, which do not require ambulance transportation tohospitals). In addition, the severity of injuries is assessed at the time of hospital admission, andthis may have led to an underestimation of eventual deaths. 20
Dose-response—. Figure C1 plots the estimates from a non-parametric binned regression to
examine dose responses more flexibly. Specifically, the logged daily pollen counts in Equation[1] is replaced by dummies for each decile of daily pollen in levels. Panel A for all accidents
reveals a clear concave relationship, suggesting that even low concentrations of pollen—which
are more frequent than higher concentrations—may have a meaningful impact on cognitive
performance and hence the incidence of accidents. Notably, this concave dose-response function
also seems to indicate the benefits of reducing pollen concentrations even for countries withlower pollen levels than those in our setting. In addition, the shape of the function explains why
the level-log specification in Equation [1] fits the data well.
5.3. Robustness
Our results on the effect of pollen on accidents are robust to a battery of specification
checks, including different types of location and time FEs, different ways of constructing
20 For example, the number of work-related injuries leading to death that are recorded in our ambulance

records for 2019 is 392, while the corresponding number reported to the Ministry of Health, Labour andWelfare (MHLW) is 845 (MHLW 2020). Similarly, the number of work-related injuries, including injuries ofall severity levels in our ambulance records in 2019, is 50,578, while the total number of work-related injuries
that result in either death or at least four days of work absenteeism reported to the MHLW is 125,611 (MHLW
2020).
14regressors, as well as outcomes, alternative specifications, and placebo exercises.
Robustness—. Table 3 provides the results of robustness checks and extensions. Our results
are robust to different ways of constructing the pollen concentration, including an inverse
distance weighted average of three nearby stations (Columns (2) and (3)), adding pollution
covariates 21 and the full interaction of temperature and rain dummies (Columns (4) and (5)),
limiting the sample to units within 48 km from pollen monitoring stations to reduce measurement
errors (Column (6)), and an unweighted ordinary least square (Column (7)).
To address the possibility that the effect of pollen can potentially appear with lags, we
extend the outcome window to the following day and then to the following two days (i.e., 𝑡𝑡 and𝑡𝑡+1, or 𝑡𝑡, 𝑡𝑡+1, and 𝑡𝑡+2) in Columns (8) and (9) of Table 3 (Deryugina et al. 2019). Theseestimates are slightly larger than the contemporaneous effect in Column (1), implying that we

may even have underestimated the effect of pollen. The short-lived effect is not surprising; whilepollen triggers an allergic reaction within minutes after SAR sufferers are exposed, its effects
generally do not last more than four to eight hours (Skoner 2001). 22 Our results are also robust to
aggregating data at the weekly level to net out any temporal displacement and capture dynamic
effects that persist over the week (e.g., through deteriorated sleep quality) in Column (10).
Table C1 illustrates that our estimates are robust to more or less stringent FEs, assuring that
our results cannot be explained by specific unobserved seasonal or regional patterns. Table C2
also indicates that our results are robust to different clustering choices, including two-way
clustering by both monitoring stations and dates, to additionally account for possible spatial
correlation or choosing broader geographical areas (46 prefectures) than simply pollen
monitoring stations (120 stations). In addition, spatially clustered standard errors, as in Conley(1999), do not inflate our standard errors significantly (last three rows). Table C3 shows that our
results are robust to the log-log specification or Poisson pseudo-maximum likelihood model toaccount for the count nature of the accidents.
Placebo—. Table C4 falsely assigns the pollen levels of the exact day from the previous
year or the next year in the same unit, finding much smaller and statistically insignificant results.
Furthermore, Figure C2 displays the binscatter plots of the relationship between the logged dailypollen counts and the number of daily emergency ambulance transportation trips due to cancer. 23
We find no discernable pattern as expected, which reassures us that our results are not driven by
specific unobserved seasonal or regional patterns.
21 The correlation between pollen counts and other pollutants is as low as 0.02 (CO)-0.12 (PM10), allowing

pollen to have an independent impact on the number of accidents that occur.

22 The short-lived effect of pollen exposure is consistent with previous literature documenting the short-lived

impact of pollution exposure on health (e.g., Schlenker and Walker 2016), and productivity (e.g., Graff Zivin
and Neidell 2012; Chang et al. 2016, 2019).
23 The ambulance records include detailed diagnostic information (equivalent to ICD10) from 2015 onward.
15

Replication—. Traffic accidents are also separately recorded in police records, which
include traffic accidents that result in personal injury and that are reported to the National Police
Agency for the period 2019 to 2020 (see Appendix D for details on data). Table D1 compares themortality estimates from traffic accidents using ambulance (our primary sample) and police
records. While the estimate of the effect of pollen on mortality from traffic accidents using police
records (p-value<0.01) is slightly larger than that using ambulance records, they are notstatistically distinguishable from each other. 24 We are reassured that the effect of pollen is robust
to different samples collected by different government bureaus with potentially different
definitions of traffic accidents, which strengthens the internal validity of our estimates.
5.4. Heterogeneity
Severity—. Figure 7 plots the estimates along with a 95% confidence interval for eachseverity level, separately for all accidents (Panel A) and for traffic accidents (Panel B). Panel A
suggests that while the estimates become smaller as the severity level increases, all the estimates,
including death/fatal, are positive and statistically significant. Panel B shows similar patterns for
traffic accidents. Interestingly, the estimate relative to the mean is larger in death/fatal than any
other severity level in the case of traffic accidents, suggesting that pollen exposure may lead toserious consequences.
Other heterogeneity—. The ambulance records also include other characteristics of theaccidents and people involved. Using all accident samples, Figure 8 explores the heterogeneous
treatment effect other than severity. 25 Panels A and B explore the heterogeneity bydemographics, namely, age and gender. Overall, we find statistically significant effects in all age
groups and both genders, with relatively similar magnitudes relative to the means, reported on
the far right in the figure. The only exception is a slightly larger effect for the elderly (>65), evenrelative to the high baseline mean. This finding is in line with the elderly’s higher vulnerability toenvironmental externalities such as heat-mortality (Carleton and Hsiang 2016), cold temperaturemortality (Cohen and Dechezleprêtre 2022), and pollution-mortality (Jia and Ku 2019; Barwick
et al. 2020) relationships.
Panel C examines the heterogeneity by location of accident occurrence. The increase in theoccurrence of accidents even takes place when individuals are at home, indicating how difficult it
is for individuals to completely avoid outdoor pollen as the grains can remain on clothing (e.g.,woolen coats) and are easily brought indoors. This may also reflect the lingering effect of
24 One potential reason for this observation is that the police records include all the deaths within 24 hours due

to traffic accidents, unlike ambulance records, which only include deaths that occur at the time of hospital
admission.
25 For completeness, the heterogeneity analysis for the traffic accidents is displayed in Figure C3.
16

outdoor pollen exposure. Finally, Panel D shows that the effect of pollen is larger on weekends
than on weekdays. On the one hand, most individuals should have more discretion to remainindoors and avoid exposure on weekends, as non-mandatory trips can be canceled or
rescheduled. On the other hand, those who go out on weekends might be less experienced drivers
who do not usually drive for work during weekdays, or they travel to unfamiliar locations forleisure, making them potentially more subject to the elevated risk of pollen exposure. Our results
suggest that the latter seems to dominate the former in this setting.

6. Short-term avoidance behaviors
Since both forecasts and real-time pollen information are commonly reported via television,
newspapers, and various mobile phone apps during our sample period, people have ample
information and time to adopt avoidance behaviors if they take the cognitive risk of pollen
exposure seriously. A few low-cost and effective ways to mitigate or avoid the risk of temporalexposure to pollen are often mentioned on television and other media. These include wearing
particulate-filtering masks and glasses, washing hands, avoiding wearing clothes that easily
attract pollen, taking medications, and avoiding going out (Japan Society of Immunology andAllergology in Otolaryngology 2021).
6.1. Conceptual framework
Here, we provide a simple framework to consider the role of avoidance behaviors. Let us
assume that 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 = 𝑓𝑓(𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆, 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴), where the number of accidents is the function of thesickness level (𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆) and avoidance behaviors (𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴). Since sickness level is influenced by theambient pollen concentration (𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃) and avoidance behaviors, that is, 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 =𝑔𝑔(𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃, 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴), 26 substituting it yields the following equation.
𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 = 𝑓𝑓(𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃, 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴)

Then, the total derivative can be written as follows (Moretti and Neidell 2011; Neidell 2009):
𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕
=
+
, [2]
𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑
𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕
𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕 𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕

“behavioral” effect“biological” effecteffect of avoidance behaviors

where the “behavioral” effect (what we estimate so far) of pollen on accidents consists of the“biological” effect of pollen (the first component of the right-hand side (RHS) variable) and the
26 More precisely, the sickness level is the function of the dose of pollen one is exposed to, 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 = 𝑔𝑔(𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷),

and the dose is determined by ambient pollen concentration (𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃) and avoidance behaviors (𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴), that is,
𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 = ℎ(𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃, 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴). Substituting it in the first equation yields 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 = 𝑔𝑔(𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃, 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴), as shown inthe main text.
17

effect of avoidance behaviors (the second component of the RHS variable). The latter is the𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕

product of the marginal return from avoidance behaviors � 𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕 < 0�, and the magnitude of𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕

avoidance behaviors in response to the level of pollen �𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕 > 0�. As the second componentof the RHS variable is supposed to be negative, the total derivative that already incorporates

avoidance behaviors is smaller (i.e., underestimated) than the desired partial derivative. 27
Among the many avoidance behaviors, we specifically examine two types that can beobserved through existing data, as complete data on these behaviors are usually unavailable(Deschênes et al. 2017). First, using in-home scanner data, we examine the purchase of productsthat protect against seasonal allergies, such as medications and masks. Second, using geolocation
data, we examine whether people, including allergy sufferers, curtail outdoor activities, whichmainly reduces the risk of outdoor accidents. Further, staying indoors limits possible pollen
infiltration and, hence, simply averts the appearance of symptoms; as pollen grains are relatively
large (≈30 𝜇𝜇𝜇𝜇) compared to much smaller particles, such as PM2.5, they are less likely to beable to enter houses if windows and doors are properly closed. Below, we investigate the extent𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕

of these two types of selective avoidance behaviors �𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕� in our context. 28
6.2. Purchase of allergy products
Data—. We use in-home scanner data (called “Quick Purchase Report” (QPR)) provided byMacromill, Inc, a marketing company that owns one of the largest research panels on consumerpurchase behaviors in Japan (Kuroda 2022). QPR collects data from roughly 30,000 monitors toconstruct a nationally representative panel dataset. 29 The monitors scan all barcoded items theypurchase every day. The data report the name and code, price, and quantity of the products
purchased. They also contain some demographic information about the monitors, such as ZIP
codes, age, gender, family structure, and income category, which are annually updated.
We extract the purchase records on three items, namely, allergy-related medications,
27 For example, Neidell (2009) finds that the “reduced-form” effect of ozone is 40% and 160% smaller for theelderly and children, respectively, than the pure “biological” effect.

28 Checking against the conceptual framework, each type of avoidance behavior is considered to correspond todifferent pathways to reduce the risk of accidents. The purchase of allergy products corresponds to the 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴
term in 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 = 𝑔𝑔(𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃, 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴), which lowers the sickness level and, hence, indirectly reduces theaccidental risk. By contrast, curtailing outdoor activities mainly corresponds to the 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 term in
𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 = 𝑓𝑓(𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆, 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴), which directly reduces the likelihood of outdoor accidents. Of course, this also
contains the first type of avoidance effects, as staying indoors limits pollen exposure, thus lowering the
sickness level.
29 The QPR monitors are selected in each region to represent the gender, age group, and family structure(marital status and existence of a housemate) of the country. To maintain data quality, the monitor is replacedby another one with similar characteristics when unusual scans are detected, or scans are not observed forseveral weeks. Thus, the number of active QPR monitors at any given time is maintained at approximately
30,000, and the total number of unique QPR monitors during our sample period is 70,795.
18

allergy-related eye drops, and masks. 30 The data include over 28 million individual-day
observations, including days with no purchases, from February to May for the period 2012 to2019. To account for the fact that these products can be easily stockpiled, we collapse data at anindividual-week level. 31 See Table E1 for summary statistics for the sample.
A few limitations of this dataset should be noted. We observe the purchases of over-thecounter (OCT) medications, but not prescription medications. Second, some people maypurchase these goods in advance before the pollen season starts or use leftover medications fromprevious seasons. Therefore, the consumption expenditure measured here provides the lower
bound of the actual expenditure on allergy products. Finally, we have no information on actual
usage, which is a common issue for any in-home scanner data.
Results—. Figure E1 displays the binscatter plots of the relationship between the logged
average daily pollen counts and the weekly spending per person (in 10-3 $) for all allergy
products in Panel A and for individual products in Panels B to D: medications (Panel B), eyedrops (Panel C), and masks (Panel D). All figures display a linear relationship with the logged
pollen counts, suggesting that people are more likely to purchase defensive products as the pollen
concentration exacerbates.
Table 4 reports the estimate from the variant of Equation [1], modified to the weekly level
data. Column (1) shows that a 10% increase in pollen counts leads to 4.40 (in 10-3 $) additional
weekly spending on allergy products. On the national level, this translates to $9.6 million (=4.40×10-3×120/7×127.4 million) per season, where 120 days comprise a typical pollen season,and 127.4 million is the total population of Japan. Columns (2)–(4), which describe individual
products, show that the largest increase (relative to mean) comes from medication purchases. 32
Supplementary analysis—. We also complement the above analysis by using the Google
Trends/Twitter data that include the keywords “mask,” “air purifier,” and leading brand names ofallergy medications in Japan, to study whether people seek information on specific protective
products. Previous studies document that these searches are closely linked to actual purchases(Goel et al. 2010). Compared to the purchase data, these measures are more likely to capture
contemporaneous behavior that should be more directly affected by the daily fluctuation ofpollen counts. We are reassured by the fact that Figure F1 shows a strong relationship between
pollen counts and both Google Trends data and the number of tweets on these keywords.
30 While medications are intended to mitigate the symptoms (e.g., stop a runny nose), they may not always𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕

reduce the risk of accidents because they induce drowsiness for some individuals (i.e., the sign of 𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕 isunclear for them).
31 Indeed, the weekly analysis provides larger estimates than would be implied by a linear scale-up of the daily
estimate (not shown).
32 These results are consistent with those of previous studies, which document a similar relationship between
pollen counts and sales of OCT allergy medication in New York City in the United States (Ito et al. 2015), andJapan (Kuroda 2022).
19

6.3. Avoidance of going out
Data—. We use geolocation data (called “Mobile Spatial Statistics” (MSS)) provided by
NTT DOCOMO, Inc., Japan’s largest mobile phone carrier. MSS provides the population
estimate at a 500×500-meter mesh at an hourly level across Japan, based on the location
information of 85 million users of NTT DOCOMO (as of March 2022) from a total population of
127 million for Japan (Terada et al. 2013). 33 While physical mobility data has received
significant attention in the social sciences since the onset of the COVID-19 pandemic (e.g.,
Google’s COVID-19 Community Mobility Reports), such data are not yet widely used to
examine avoidance behaviors or environmental stressors (see Burk et al. 2022).
Our mobility measures capture the estimated population of a mesh with the largest numberof establishments in the customer service industry in the municipality to capture bustling areas(e.g., business districts and shopping and dining areas), which are more likely to represent the
population engaging in outdoor activities. We use the estimated population at 2 pm, because thedaily population in commercial areas tends to peak around this time (Seike et al. 2015). We
collapse the estimated population at the unit level by taking the average of all municipalities in
the unit for the period from February 2014 to May 2019. We treat this measure as a proxy forengaging in outdoor activities (“outdoor population,” hereafter). See Appendix G for details of
the data construction of our mobility measures. 34
Before examining the relationship between pollen load and mobility measures, we attempt
to verify that curtailing outdoor activities is effective in reducing the risk of accidents. 35 We
regress the number of accidents (our main outcome) on our mobility measure with the same setsof FEs and controls as Equation [1] (excluding a logged number of pollen counts). Table E1
shows that outdoor population is highly positively correlated with the number of accidents that
occur, reassuring us that simply reducing outdoor activities can be a meaningful means to
mitigate the risk of accidents, that is, �𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕
𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕

< 0�.

Results—. Figure G1 displays the binscatter plots of the relationship between the logged

pollen counts and the logged outdoor population. Panel A shows no clear relationship between
pollen counts and daytime outdoor population on average. Because most people should havemore discretion to stay indoors on weekends than on weekdays, Panel B plots the same

33 Owing to its representativeness and the long time span of the sample, this dataset has been widely used,especially for measuring people’s mobility during the COVID-19 pandemic (e.g., Kondo 2021; Kuroda et al.
2022).
34 As in the other studies on physical movement, we cannot distinguish two possibilities for staying indoors:people may be extremely sick and have to stay home or they may display some form of avoidance.
35 We do not similarly verify whether the purchase of allergy products effectively reduces accidents because
the regional sampling units in in-home scanner data are only ten divisions (albeit nationally representative),and they do not correspond to the detailed regional units of the ambulance records (N= 705).
20

relationship for weekends only. Here, we find some negative relationships, suggesting that
individuals seem to engage in some avoidance behaviors on weekends. However, as discussed
below, the magnitude of avoidance is quite small.
Table 5 reports the estimate from Equation [1], where the outcome is the logged outdoor
population. Columns (1) and (2) show that the estimates are negligible and far from statisticallysignificant for all days and weekdays. Column (3) for weekends shows the elasticity of outdoor
population with respect to pollen counts is as low as –0.0021 (p-value<0.01), implying that a
10% increase in pollen concentration leads to only a 0.021% reduction in the outdoor population.
Table 5 also reports the estimates on four indicators for rainfall (base: no rainfall) forcomparison. The outdoor population decreases as rain intensity increases, confirming that ourmobility measure adequately captures individuals’ decision-making processes in relation to
engaging in outdoor activities. 36 The estimates from Column (3) indicate that rainfall between
1mm and 2mm reduces the outdoor population by 9.9 times more than a 1% increase in thepollen count.
6.4. Policy implications
Our analysis reveals that people are engaging in some avoidance behaviors. Hence, one
might argue that the present status of protective behavior reflects people’s revealed preferencesthat already incorporate the potential risk (Leard and Roth 2019) and, thus, there is no room forpolicy intervention. However, it is more natural to think that the current status that relies on
individuals’ self-protection to mitigate pollen exposure and prevent accidents would beinsufficient for the following reasons. First, to our knowledge, this is the first study to document
the causal link between pollen exposure and the risk of accidents in a field setting. Hence, it ishard to believe that most people do fully understand such risk and consider it when making
decisions. Second, the negative externality of accidents is unlikely to be incorporated into
individual behaviors.
Since ample information on pollen is already provided in various forms in Japan, it is
crucial to draw more attention to such information. 37 For example, public information campaigns
such as the “pollen alert”—which encourage decreasing outdoor activities, or recommend using
public transportation on high pollen days due to the heightened risk—can be a low-cost and
36 See Table G2 for the estimates of other weather covariates with expected signs: While the outdoor

population increases as the temperature rises, it decreases as the average wind speed and duration of darkness
increases.
37 This distinction is important for policy considerations since if there is a problem with access to information,
we would suggest that the government should attempt to increase the dissemination of pollen information more
widely (Barwick et al. 2020; Jha and Nauze 2022). However, if access to information is of a sufficient level, as
in this case, but attention is lacking, we would have to propose more effective ways to raise awareness of therisks and encourage further behavioral change.
21effective tool to reduce pollen-induced accidents. In fact, a few studies demonstrate that alerts for
smog and ozone successfully induce people to take precautionary actions to reduce exposure(e.g., Anderson et al. 2022; Cutter and Neidell 2009). Considering the negative externality ofaccidents, the government can mandate firms to allow sick leave for seasonal allergies, or at least
allow remote work for patients with severe seasonal allergies. Providing a subsidy for protective
products or technology (e.g., allergen immunotherapy) is another option.
Finally, unlike “ex-post” government interventions, such as pollen alerts, “ex-ante”
interventions, such as trimming pollen-emitting plants and replanting newly developed trees that
emit less pollen (e.g., the Japanese cedar) to remove the source of pollen “production,” could be
more cost-effective in the long-run. However, as implementing this process may take time, expost interventions, like the pollen alert, and aids for engaging in avoidance behavior, can be aneffective temporary remedy.

7. Additional analysis
7.1. Medium-term adaptation
While our focus so far is limited to short-term outcomes, longer-term adaptation (either
technological, behavioral, or biological) may take time. Recent medications for seasonal allergies
cause less drowsiness and reduce the risk of unsafe driving. Table A1 lists medications forseasonal allergies released during the period 1994 to 2017 in Japan and shows that it is now lesscommon for manufacturers to mention the medication’s effect on driving ability, throughcautionary text, such as “Driving not allowed.” In fact, using in-home scanner data used above,
Figure A4 shows that the share of spending on allergy medications that do not prohibit driving
among total medication spending increased from 25.2% in 2012 to 45.6% in 2019. 38 Individuals
may also engage in defensive spending (e.g., buying air purifiers or receiving allergen
immunotherapy) over a longer time span. Furthermore, unlike short-run physiologicalacclimatization, behavioral acclimatization may require more time to take effect (Graff Zivin and
Neidell 2014).
Thus, we utilize data that spans a 12-year period from 2008 to 2019 to examine how the
impact of pollen on accidents has changed over time. Figure 9 presents estimates of the effects ofpollen separately for each year. While pollen-accident sensitivity becomes marginally smaller inthe later periods compared to that in the earlier periods, the magnitude of the decline is slight,
and none of the estimates in the later periods are statistically distinguishable from those in theearlier periods.
38 We classify both nasal and oral medications for seasonal allergies into two types based on any particularmention of whether driving is not allowed after taking the medication, including the ones listed in Table A1.
22

We also divide 705 units into quantiles by the mean of pollen counts during the period 2008
to 2019 to investigate whether people who live in regions with a high pollen concentration, on
average, experience smaller impacts of pollen concentrations than those who live in regions witha low pollen concentration through undertaking a series of longer-term adaptations. 39 Figure C4
indicates that the estimates are reasonably similar across quantiles, again demonstrating limited
longer-term adaptive potential. Overall, we fail to find compelling evidence of medium-term
adaptation to the destructive impact of pollen exposure.
7.2. Projecting damages due to climate changeThis section combines the estimates of the effect of pollen on accidents documented thus far
with projections of future climate, as well as the relationship between the temperature and pollencounts obtained in Figure 1, to measure the magnitude of expected changes in the number ofpollen-induced accidents. 40
Table 6 summarizes the projected damages based on the “business as usual” scenario (RCP
8.5), which predicts that the summer temperature in Japan will increase by 4.1℃ in the years
2076 to 2095 (MEXT and JMA 2020). We calculate the number of additional pollen-induced
accidents associated with this temperature change as follows: Based on the relationship between
the summer temperature and pollen counts (Panel A of Figure 1), this increase in temperature
could lead to additional daily pollen counts of 686 (= 167.4×4.1) grains/m3, which would
correspond to a 0.529 increase in the logged pollen counts from the mean of 984 grains/m3. We
then multiply the estimates from Panel A of Figure 6 for each severity level by 0.529, and thenby 120 (days per pollen season), and then further by 127.4 (the total population) to calculate theadditional annual accidents as is reported in Row (1). Row (1) indicates that a 4.1℃ increase in
the average summer season temperature is expected to increase the pollen-induced death/fatal,
serious, moderate, and light accidents by 30, 216, 541, and 1036, respectively, bringing the total
number of additional annual accidents to 1,823.
We then convert these additional accidents into monetary terms by multiplying the resulting
accident counts in Row (1) by the average accident costs from Bünnings and Schiele (2021) in
the United Kingdom, as is reported in Row (2). 41 Row (3) demonstrates that pollen-induced

39 See Figure A5, which plots the spatial variation in pollen counts across the country.

40 We acknowledge that we make two strong assumptions here: (i) the level of protective technologies stay thesame; (ii) the marginal treatment effect of an unanticipated “weather” shock documented so far is identical tothe marginal effect of an anticipated “climate” shift. Past studies have adopted a similar approach when
projecting the impact of these gradual changes on income (Deryugina and Hsiang 2014), mortality (Deschênes
and Greenstone 2011), amenity values (Baylis 2020), and other outcomes.
41 We determine the prospective additional cost to be $3.2 million for fatal accidents (those resulting in death),$365,000 for accidents resulting in serious injuries, and $38,000 for those resulting in both moderate and light23

accidents resulting in death/fatal, serious, moderate, and light injuries correspond to actual
monetary costs of $96.3 million, $79.1 million, $20.6 million, and $39.6 million, respectively.
This equates to a total annual social cost of $236 million, which can be seen in Row (4).
Interestingly, this figure far exceeds the budget for the Japanese Forestry Agency’s pollen
reduction program, which is currently $1.1 million (Forest Agency 2021). 42
We note that our simple damage projection calculation probably yields a conservative
estimate because (i) we do not taken into account any extension of the pollen season due to theincreasingly hotter summer seasons, (ii) we only consider approximately four months of highpollen season, (iii) we are unable to take into account the accidents that occur at both sides of theseverity distribution spectrum (i.e., minor cases that do not require ambulance transportation tohospitals and immediate deaths), and (iv) we make use of the relatively conservative value of astatistical life (Bünnings and Schiele 2021) rather than a more commonly utilized value. 43
Finally, to the extent that spending on allergy products can be considered defensive
spending rather than just another health expense (i.e., transfer from individuals to firms), suchspending should be included in the social cost (Deschênes et al. 2017). Based on the computationin Section 6.2, a 0.529 (instead of 0.1) increase in the logged pollen counts due to elevatedtemperature leads to $50.8 million (= $9.6 million×0.529/0.1) additional spending on allergy
products. This figure is not trivial compared to the $236 million associated with pollen-induced
accidents calculated above, suggesting the empirical importance of defensive spending. Noteagain that the cost of allergy products includes that of OCT drugs only.

8. Conclusion
This study is the first assessment of the impact of pollen exposure on the likelihood ofaccidents using Japanese archived ambulance records for the period 2008 to 2019. We find that
exposure to elevated pollen levels increases the occurrence of all types of accidents. Our results
are consistent with those of established, clinically based studies that have documented theadverse cognitive effects of pollen exposure, the long-term implications of which have notpreviously been assessed in the real-world, except for children’s academic performance. In
addition, as we find the effects of pollen exposure to be broadly uniform across a range of

injuries (Bünnings and Schiele 2021). For simplicity, an exchange rate of 1.5 $/£ is used. Unfortunately, to ourknowledge, there are no appropriate estimates of accident costs at each severity level in Japan.
42 Panel B of Table 5, using the number of days where the temperature increased above 30℃ in the previous
year (Panel B of Figure 1), gives roughly 40% larger estimates of social cost than those in Panel A of Table 6,
which uses the maximum temperature.
43 For example, the central estimate for the value of statistical life used by the Environmental Protection
Agency in the United States is $9.8 million in 2021, and Smith (2016) similarly uses $4 million to $10 million
per fatality, all of which are larger than the figure of $3.2 million that we utilize.
24

observed demographics, locations, and calendar months, and find limited evidence of changes inthe treatment effects over time, the underlying mechanism linking pollen exposure to accidents
may be generalizable across different contexts.
Our analysis of internet search activities and social media posts for pollen-related topics
suggests that individuals seem to have a good awareness of daily pollen levels. However, whilesome people obviously take measures to alleviate their allergy symptoms by purchasing available
technologies, the very limited avoidance behaviors, determined from geolocation data, imply thatthey lack awareness of or underestimate the cognitive risk of pollen exposure. Thus, the current
policy that relies on individuals’ self-protection to mitigate pollen exposure may only have
limited benefits. Governmental efforts to raise public awareness of the risks of pollen exposure
and promote widespread behavioral change might be necessary.
Finally, it is imperative to reiterate that the estimated social cost we establish might onlyreflect the tip of the iceberg of the potentially extensive social cost associated with increasing
pollen counts. While we remain agnostic about the underlying mechanisms, to the extent that
exposure to pollen impairs cognitive function, any daily human activity that requires normal
cognitive performance and decision-making abilities can be affected. Thus, it is vital to quantify
the potential damage in order to acquire a better understanding of the full costs of pollen
concentration.

25

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Schlenker, W., W.R. Walker. 2016. “Airports, Air Pollution, and Contemporaneous Health.”
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Schmidt, C.W. 2016. “Pollen Overload: Seasonal Allergies in a Changing Climate.”
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29

Figure 1—Pollen and temperature in the previous summer
A. Maximum temperature in the previous year

B. Number of days above 30 ℃ in the previous year

1400

1400

1200

Daily pollen counts

Daily pollen counts

1200

1000

1000

800

800

600

600
29

30

31

32

20

33

Maximum temperature (previous summer)

30

40

50

60

Number of days above 30 °C (previous summer)

Notes: The graphs display the binscatter plots of the relationship between the average (24-hour cumulative) daily pollen counts (grains/m3) for the period fromFebruary to May and the average maximum temperature (in ℃) in Panel A and the number of days the temperature exceeded 30℃ in Panel B in July and Augustin the previous summer, after controlling for pollen monitoring station fixed effects. The sample consists of station-years (N= 1440) from all the pollen monitoring
stations (N= 120) during the period 2008 to 2019 (12 years). The slope in Panel A is 167.4 (t-stats= 11.16), indicating that a one degree increase in the maximum
temperature in the previous summer increases the daily pollen counts by 167.4 grains/m3. Similarly, the slope in Panel B is 23.7 (t-stats= 11.18), indicating thatten additional hot days above 30℃ in the previous summer increases the daily pollen counts by 237 grains/m3. The mean and median of daily pollen counts fromFebruary to May during the period 2008 to 2019 at 120 monitoring stations are 955.6 and 712.5 grains/m3, respectively.

30

Figure 2—Real-time and forecasted pollen levels reported on television
A. Real-time pollen forecast

B. This week’s forecast

Notes: The figures show the pollen level reported on television on a typical day during the pollen season in Japan. Panel A displays real-time pollen levels at
various locations, and Panel B displays the forecast of the week’s pollen level (for March 1 to March 6 as of February 28, 2021) of differing magnitudes at various
locations from the south to the north of Japan.
Sources: Japan Weather Association (2022) https://tenki.jp/ (in Japanese)31

Figure 3—Pollen monitoring stations
A. Location of pollen monitoring stations

B. Distance to the pollen monitoring stations
1.0

Cumulative Distribution

0.8

0.6

0.4

0.2

0.0
0

50

100

150

Distance from pollen montoring stations (km)Notes: Panel A displays the locations of all pollen monitoring stations as of 2019. There are a total of 120 pollen stationsin Japan. On average, each of the 47 prefectures in Japan has 2 to 3 stations, except for Okinawa. Okinawa, which islocated the furthest south, has a different climate from the rest of the country, and has no stations as pollen is not observed
there. Panel B plots the cumulative distribution of distance from the centroid of units (N= 705) to the nearest pollen
monitoring station (N= 120). The vertical dotted line corresponds to 48 km (30 miles), which is used by Chalfin et al.
(2019). A total of 90.2% of stations (636 out of 705) are within this threshold.
32

Figure 4—Daily pollen counts for selected locations
SapporoDaily pollen counts

10000

5000

0
Feb01

Mar01

Apr01

May01

Jun01

Jul01

Jun01

Jul01

Jun01

Jul01

Jun01

Jul01

Jun01

Jul01

Tokyo
Daily pollen counts

10000

5000

0
Feb01

Mar01

Apr01

May01

NagoyaDaily pollen counts

10000

5000

0
Feb01

Mar01

Apr01

May01

Osaka
Daily pollen counts

10000

5000

0
Feb01

Mar01

Apr01

May01

Fukuoka
Daily pollen counts

10000

5000

0
Feb01

Mar01

Apr01

May01

Notes: The figure displays day-to-day variations in the daily pollen counts (grains/m3) across five different locations

from the north to the south of Japan in 2019. Sapporo, Tokyo, Nagoya, Osaka, and Fukuoka are located in the
Hokkaido, Tokyo, Aichi, Osaka, and Fukuoka prefectures, respectively.
33

Figure 5—Pollen and symptom-related Google search index
A. Time series

B. Binscatter plot
100

8000

4000
60
2000

Google search index

Daily pollen counts

80

Google search index

30

6000

40

20

0
Feb01

Mar01

Apr01

Daily pollen counts

May01

25

Jun01

1

Google search index

2

3

4

5

ln(daily pollen counts)

Notes: Panel A shows the time series patterns of the average daily pollen counts (grains/m3) and Google search index for symptom-related keywords in 2018 at
the country level. The symptom-related keywords are “runny nose,” “nasal congestion,” “sneeze,” and “itchy eyes.” June is omitted as only four stations in
Hokkaido (the northernmost island of Japan) were still operating in June. Panel B displays the binscatter plots of the relationship between the logged daily pollencounts (grains/m3) and Google search index for the same keywords, after controlling for the month-by-year, month-by-prefecture, and day-of-week FEs. See
Figures B1 and B2 for similar plots for pollen allergy-related and medication-related keywords, which show similar patterns.

34

Figure 6—Pollen and the number of accidents

B. Traffic accidents

A. All accidents

13.0

34

Daily number of cases per million

Daily number of cases per million

35

33

32

31

30

12.5

12.0

11.5
3

4

5

6

7

8

3

4

ln(daily pollen counts)

5

6

7

8

7

8

ln(daily pollen counts)

D. Sports injuries

C. Work-related injuries
1.3

Daily number of cases per million

Daily number of cases per million

0.9
1.2

1.1

1.0

0.9

0.8

0.7

0.6
3

4

5

6

7

8

3

ln(daily pollen counts)

4

5

6

ln(daily pollen counts)

35

F. Other accidents

E. Fire accidents
19.5

Daily number of cases per million

Daily number of cases per million

0.25

0.20

0.15

19.0
18.5
18.0
17.5
17.0
16.5

0.10
3

4

5

6

7

3

8

4

5

6

7

8

ln(daily pollen counts)ln(daily pollen counts)

Notes: The sample is derived from the ambulance records for the period 2008 to 2019, and the level of observation is a unit per day (N= 970,309). There are 705
units in total. The graphs display the binscatter plots of the relationship between the logged daily pollen counts (grains/m3) and the number of daily cases per
million people for all accidents in Panel A and for each type of accident in Panels B to F: traffic accidents (Panel B), work-related injuries (Panel C), sports injuries(Panel D), fire accidents (Panel E), and other accidents (Panel F), after controlling for the unit, month-by-year, month-by-prefecture, and day-of-week FEs. The
shares of traffic accidents (Panel B), work-related injuries (Panel C), sports injuries (Panel D), fire accidents (Panel E), and other accidents (Panel F) are 37.6%,
3.5%, 2.6%, 0.5%, and 55.8%, respectively. Estimates are weighted by the population in each unit.

36Figure 7—Treatment effects by severity
A. All accidents

Estimate (95%CI)

Mean Rel. to mean

Death/Fatal

0.004 (0.001, 0.007)

0.30

1.27 %

Serious0.027 (0.017, 0.037)

2.05

1.31 %

Moderate

0.068 (0.044, 0.093)

9.05

0.75 %

Light

0.130 (0.097, 0.162)

21.58

0.60 %

Death/Fatal

0.003 (0.001, 0.004)

0.08

3.32 %

Serious0.007 (0.003, 0.011)

0.49

1.44 %

Moderate

0.027 (0.016, 0.038)

2.29

1.19 %

Light

0.041 (0.021, 0.061)

9.53

0.43 %

B. Traffic accidents

0

0.05
0.1
Estimate (95%CI)

0.15

Notes: The sample is derived from the ambulance records for the period 2008 to 2019, and the level of observation is aunit per day (N= 970,309). There are 705 units in total. The graphs display the estimates and 95% confidence interval of
the treatment effects of the logged daily pollen counts from Equation [1]. The standard errors are clustered at pollen
monitoring station levels. The dependent variable is the number of daily cases per million people for each severity level.
The severity is based on the doctor’s judgment at the time of hospital admission. “Serious” accidents are defined as suchwhen the level of injuries requires more than three weeks of hospitalization and treatment; “moderate” requires hospital
admission, but hospitalization of less than three weeks, and “light” does not require hospital admission. The share oftraffic accidents (Panel B) among all accidents (Panel A) is 37.6%. The mean is the number of daily cases per million
people. The relative to the mean is the estimate divided by the mean.

37

Figure 8—Other heterogeneous treatment effects (All accidents)
A. AgeEstimate (95%CI)

Mean

Rel. to mean

0-24

0.068 (0.030, 0.106)

6.34

1.07 %

25-44

0.030 (0.003, 0.057)

4.61

0.65 %

45-64

0.062 (0.026, 0.097)

5.81

1.06 %

65-

0.179 (0.108, 0.251)

14.09

1.27 %

Male

0.269 (0.184, 0.354)

15.88

1.69 %

Female0.170 (0.104, 0.237)

13.91

1.23 %

Roads

0.099 (0.059, 0.139)

8.73

1.13 %

Home

0.049 (0.019, 0.079)

6.61

0.74 %

Public space

0.011 (-0.013, 0.035)

3.51

0.31 %

Workplace

0.008 (0.003, 0.013)

0.58

1.39 %

Weekdays

0.185 (0.143, 0.227)

32.10

0.58 %

Weekends

0.310 (0.236, 0.383)

35.39

0.87 %

B. Gender

C. Location

D. Weekdays/Weekends

0

0.1

0.2
Estimate (95%CI)

0.3

0.4

Notes: The sample is derived from the ambulance records for the period 2008 to 2019, and the level of observation is aunit per day (N= 970,309). There are 705 units in total. The graphs display the estimates and 95% confidence interval ofheterogeneous treatment effects of the logged daily pollen counts from Equation [1]. The standard errors are clustered atpollen monitoring station levels. The dependent variable is the number of daily cases of accidents per million peopleusing all accident data. The mean is the number of daily cases per million people. The relative to the mean is the estimate
divided by the mean.

38

Figure 9—Effects of pollen over time
0.8

Estimates

0.6

0.4

0.2

19
20

18
20

17
20

16
20

15
20

14
20

13
20

12
20

11
20

10
20

09
20

20

08

0.0

Year
Notes: The sample is derived from the ambulance records for the period 2008 to 2019, and the level of observation is aunit per day (N= 970,309). There are 705 units in total. The graphs display the estimates and 95% confidence interval of
the treatment effects of the logged daily pollen counts from Equation [1], generated separately for each year. The standard
errors are clustered at pollen monitoring station levels. The dependent variable is the number of daily cases of accidents
per million people using all accident data.

39

Table 1—Summary statistics
Variables
A. Outcomes (per 1,000,000 per day)All accidents
Type: Traffic accidents
Type: Work-related injuries
Type: Sports injuries
Type: Fire accidents
Type: Other accidents
Severity: Death/Fatal
Severity: Serious
Severity: Moderate
Severity: Light
Ages: 0-24 years
Ages: 25-44 years
Ages: 45-64 years
Ages: 65 years and older
Gender: Male
Gender: Female
Location: Roads
Location: Home
Location: Public space
Location: Workplace
B. Regressors (per day)Pollen counts (grains/m3)
Logged (Pollen counts)
Precipitation (mm)Average temperature (℃)
Average wind speed (m/s)
Darkness (hours)
SO2 (ppb)
NO2 (ppb)
CO (0.1ppm)
OX (ppb)
PM10 (μg/m3)

Sharewithincategory

37.6%
3.5%
2.6%
0.5%
55.8%
0.9%
6.2%
27.4%
65.4%
20.5%
15.0%
18.8%
45.7%
53.3%
46.7%
44.9%
34.0%
18.1%
3.0%

Obs

Mean

Std. dev.

Min

Max

970,309970,309970,309970,309970,309970,309970,309970,309970,309970,309970,309970,309970,309970,309970,309970,309970,309970,309970,309970,309

33.03
12.41
1.15
0.86
0.17
18.44
0.30
2.05
9.05
21.58
6.34
4.61
5.81
14.09
15.88
13.91
8.73
6.61
3.51
0.58

28.23
16.09
3.87
3.24
2.10
17.77
2.11
6.07
11.90
20.65
9.44
7.64
8.90
15.98
17.55
15.82
12.66
9.90
5.73
1.14

0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

5,091
5,091
1,367
1,195
2,612
2,447
943
1,572
1,958
4,570
2,112
2,186
2,637
2,695
4,769
2,366
198
82
51
58

970,309970,309970,309970,309970,309852,948846,719
846,659
848,798
850,847
850,577984.34
0.16
11.90
2.93
10.48
2.20
6.14
15.11
4.02
36.76
20.04

2135.26
0.43
6.14
1.39
1.28
2.16
10.07
9.60
1.84
11.51
11.14

0
0
0
0
7
0
0
0
0
0
0

55,104
10
28
16
13
521
307
88
61
120
299

Notes: The sample is derived from the ambulance records for the period 2008 to 2019, and the level of observation is aunit per day. There are 705 units in total. The population in each unit is used as weights. The share within the category
should be summed to 100%. The pollution data are available from 2009 onwards.

40

Table 2—Main results: Accidents
B. By type
A. All accidents

Traffic
accidents

(1)

(2)

Workrelated
injuries(3)ln(pollen counts)

0.231***(0.020)0.079***(0.012)R-squaredN
N of units
N of clusters
Mean of dep. Var
Share
Unit FE
Day-of-week FE
Month-year FE
Month-prefecture FE

0.46
970,309
705
120
33.03
100%
X
X
X
X

0.24
970,309
705
120
12.41
37.6%
X
X
X
X

SportsinjuriesFire
accidents

Other
accidents

(4)

(5)

(6)

0.012***(0.002)0.007**(0.003)0.006***(0.002)0.127***(0.016)0.06
970,309
705
120
1.15
3.5%
X
X
X
X

0.08
970,309
705
120
0.86
2.6%
X
X
X
X

0.00
970,309
705
120
0.17
0.5%
X
X
X
X

0.37
970,309
705
120
18.44
55.8%
X
X
X
X

Notes: The sample is derived from the ambulance records for the period 2008 to 2019, and the level of observation is aunit per day (N= 970,309). There are 705 units in total. The dependent variable is the number of daily cases per million
people for each accident type. The estimates from Equation [1] are reported along with the standard errors clustered atpollen monitoring station levels in parentheses. In addition to the FEs in the table, we include weather covariates(precipitation, temperature, wind speed), darkness, and the logged population. Estimates are weighted by the populationin each unit. Significance levels: *** p<0.01, ** p<0.05, * p<0.10.

41

Table 3—Robustness: Accidents
(1)

(2)

(3)

(4)

(5)

Construction ofpollen measures

(6)

(7)

(8)

(9)

Expanding windowsof outcome

Other

(10)

Baseline

Weighted
average ofnearby threestations

Drop zeropollenobservations(1)+Add
pollution(1)+Add
temperature& raininteractions

Units
within 48
km fromstations

Unweighted

Add thefollowing
one day

Add thefollowing
two days

Collapse
data attheweeklylevelln(pollen counts)

0.231***(0.020)0.234***(0.021)0.249***(0.021)0.220***(0.022)0.205***(0.020)0.229***(0.020)0.402***(0.066)

0.236***(0.037)0.265***(0.052)0.213***(0.036)R-squaredN

0.46
970,3090.46
970,3090.46
962,255

0.47
814,5780.46
970,3090.48
872,2270.33
970,309

0.62
961,901

0.71
953,5220.85
147,066ln(pollen counts)

0.079***(0.012)0.082***(0.012)0.090***(0.013)0.073***(0.013)0.063***(0.012)0.078***(0.012)0.137***(0.037)0.086***(0.019)0.110***(0.027)0.124***(0.020)R-squaredN

0.24
970,3090.24
970,3090.24
962,255

0.24
814,5780.24
970,3090.26
872,2270.12
970,309

0.38
961,901

0.48
953,5220.70
147,066

A. All accidents

B. Traffic accidents

Notes: The sample is derived from the ambulance records for the period 2008 to 2019, and the level of observation is a unit per day. There are 705 units in total.
The estimates from variants of Equation [1] are reported along with the standard errors clustered at pollen monitoring station levels in parentheses. The share oftraffic accidents (Panel B) among all accidents (Panel A) is 37.6%. Column (1) replicates the results from Table 2 (baseline) for ease of comparison. Column (2)uses the daily pollen counts constructed by the inversely weighted average of three nearby stations as the main regressor. Column (3) drops zero pollen counts(0.83%) and takes a log without adding 1. Column (4) adds air pollution covariates (SO2, NO2, CO, OX, PM10) for the period April 2009 to April 2019 whensuch data are available, and Column (5) adds the full interaction of temperature and precipitation. Column (6) restricts the sample to the units within 48 km ofmonitoring stations. Column (7) is an unweighted ordinary least square (OLS). Columns (8) and (9) add the number of accidents on the following day and thenumber of accidents on the following two days to the outcome, respectively. Column (10) collapses the data at the weekly level. All specifications include unit,
month-by-year, month-by-prefecture, and day-of-week FEs except for Column (10). Column (10) includes year-by-prefecture and week-by-unit FEs. Estimates
are weighted by the population in each unit. Significance levels: *** p<0.01, ** p<0.05, * p<0.10
42

Table 4—Avoidance behaviors: Purchase of allergy products
B. By category

A. Total

Medications

Eye drops

Masks

(1)

(2)

(3)

(4)

ln(pollen counts)

44.002***(2.545)24.521***(1.428)13.143***(0.969)6.429***(0.824)R-squaredN
N of individuals
Mean of dep. var (in 10-3 $)Share
Municipality FE
Year-prefecture FE
Week FE

0.006
4,303,417
70,795295.78
100%
X
X
X

0.004
4,303,417
70,795109.94
37%
X
X
X

0.003
4,303,417
70,795103.41
35%
X
X
X

0.002
4,303,417
70,795
82.52
28%
X
X
X

Notes: The sample is derived from the in-home scanner data for the period from February to May for the years 2012 to 2019, andthe level of observation is an individual per week (N= 4,303,417). The dependent variable is the weekly spending (in 10-3 $) for
each allergy product. The estimates from the variant of Equation [1] are reported along with the standard errors clustered at pollen
monitoring station levels in parentheses. In addition to the FEs in the table, we include weather covariates (precipitation,
temperature, wind speed) and darkness. Significance levels: *** p<0.01, ** p<0.05, * p<0.10.

43

Table 5—Avoidance behaviors: Refrain from going out
Outcome: logged outdoor population

A. AllB. By type of day
Weekdays

Weekends

(1)

(2)

(3)

-0.0005(0.0006)

0.0000(0.0006)-0.0021***<1 mm-0.0061***-0.0059***-0.0047**

1 mm≤ & < 2 mm

-0.0167***-0.0144***-0.0208***≥ 2 mm-0.0167***-0.0150***-0.0249***0.98
478,853
X
X
X
X

0.99
343,454
X
X
X
X

0.99
135,399
X
X
X
X

ln(pollen counts)(0.0007)

Rainfall (base: no rainfall)R-squaredN
Unit FE
Day-of-week FE
Month-year FE
Month-prefecture FE(0.0013)(0.0035)(0.0045)(0.0011)(0.0024)(0.0041)(0.0019)(0.0080)(0.0082)

Notes: The sample is derived from cellphone location data for the period from February to May during the years 2014 to 2019,
and the level of observation is a unit per day. See Appendix F for the data construction. There are 705 units in total. The estimatesfrom Equation [1] are reported along with the standard errors clustered at pollen monitoring station levels in parentheses. The
outcome is the logged daily outdoor population at 2 pm. In addition to the FEs and weather covariates in the table, we include
average wind speed, darkness, and the logged population. Estimates are weighted by the population in each unit. See Appendix
Table F2 for the estimates of all other weather covariates. Significance levels: *** p<0.01, ** p<0.05, * p<0.10

44

Table 6—The impact of climate change from the “business as usual” scenario
A. Maximum temperature(+4.1℃)
Severity level

B. Number of days above 30℃(+48.6 days)

Death/fatal

SeriousModerate

Light

Death/fatal

SeriousModerate

Light

(1)

Increase in accidents per year

30.1

216.3

540.5

1,036.5

44.1

316.8

791.6

1,518.1(2)

Social cost per case (USD)

3,196,383

365,453

38,177

38,177

3,196,383

365,453

38,177

38,177(3)

Social cost per year (million USD)

96.34

79.06

20.63

39.57

141.11115.79

30.22

57.95

(4)

Total social cost per year (million USD)

235.6

345.1

Notes: Panel A uses the relationship between pollen counts and maximum temperature (Panel A of Figure 1), and Panel B uses the relationship between pollencounts and the number of days the temperature is above 30℃ (Panel B of Figure 1). The total social cost per year in Row (4) for Panel A is calculated as follows:
The increase in the average temperature by 4.1℃ from the “business as usual” scenario (RCP 8.5) leads to an increase in the daily pollen counts by 686.3 (= 167.4
× 4.1), where 167.4 comes from Panel A of Figure 1. This leads to 0.529 increases in the logged pollen counts from the mean (= log(984.34 +686.3)-log(984.34)).
Row (1) is the product of each severity level estimate from Figure 6 and 0.529, then by 120 days (Feb to May) of the typical pollen season in Japan, and finallyby 127.4, which is the average population (in millions) in Japan during the period 2008 to 2019. The figures in Row (2) are obtained from Bünnings and Schiele(2021) in the United Kingdom, and an exchange rate of 1.5 USD/£ is used. Row (3) is the product of Row (1) and Row (2). Row (4) is the sum of Row (3) for all
severity levels. Similarly, for Panel B, the increase in the number of days where the temperature increases above 30℃ by 48.6 days leads to an increase in thedaily pollen counts by 1151.8 (= 23.7 × 48.6), where 23.7 comes from Panel B of Figure 1. This leads to a 0.775 increase in the logged pollen counts (=log(984.34+1151.8)-log(984.34)). The calculations for Rows (3) and (4) are identical.

45

Online Appendix(Not for Publication)“Invisible Killer”: Seasonal allergies and accidents
By Mika Akesaka and Hitoshi Shigeoka
Table of Contents
Section A

Additional figures and tables

Section B

First stage—Symptoms of seasonal allergies

Section C

Ambulance records

Section D

Police records

Section E

Avoidance behaviors from in-home scanner data

Section F

Avoidance behaviors from Google Trends/tweets

Section G

Avoidance behaviors from geolocation data

Section H

Data Appendix

46

Appendix A: Additional Figures and Tables
Figure A1—Times series of pollen counts and maximum temperature

500

30

20

08

10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19

09

34

0

20

08

20

20

1000
500

29

0

38

10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19

30

1500

09

1000

42

20

1500

46

2000

Daily pollen counts

31

Max temperature (previous summer)

2000

Daily pollen counts

2500

32

20

2500

B. Number of days above 30 ℃ in the previous year

Year

Year
Daily pollen counts# of days above 30 °C (previous summer)

A. Maximum temperature in the previous year

Daily pollen counts

Max temperature (previous summer)# of days above 30 °C (previous summer)

Notes: The figure shows the relationship between the average daily pollen counts (grains/m3) for the period from February to May and the average maximum
temperature (in ℃) in Panel A and the number of days the temperature rose above 30℃ in Panel B during July and August in the previous summer from 120
pollen monitoring stations during the period 2008 to 2019.

47

Figure A2—Calendar of pollen season in Japan
Types of Pollen

Japanesecedar

Region●Hokkaido

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec●Tohoku●Kanto●Tokai●Kansai●Kyushu

Cypress

White
birch

PoaceaeRagweed

Hokkaido
TohokuKanto
Tokai
KansaiKyushuHokkaido
TohokuKanto
Tokai
KansaiKyushuHokkaido
TohokuKanto
Tokai
KansaiKyushuHokkaido
TohokuKanto
Tokai
KansaiKyushu~5.0 grains/cm2

5.1-50.0 grains/cm2

50.1- grains/cm2

Notes: The figure shows the pollen dispersal season in Japan for five selected pollen types: Japanese cedar, cypress,
white birch, poaceae, and ragweed for six regions in Japan (Hokkaido, Tohoku, Kanto, Tokai, Kansai, and Kyushu). The
pollen monitoring system called “Hanaok-san,” operated by the Ministry of the Environment, monitors the hourly pollen
levels of Japanese cedar and cypress from February to May, except for Hokkaido, which is covered from March to June.
The height indicates the average amount of pollen.
Source: Kishikawa et al. (2020)

48

Figure A3—Variation in pollen counts
A. Distribution of the logged daily pollen counts

B. Variation for identification

0.4

0.3

0.3

DensityDensity

0.2
0.2

0.1
0.1

0.0

0.0
0

2

4

6

8

-10

10

-5

0

5

ln(daily pollen counts) (residualized)ln(daily pollen counts)

Notes: Panel A displays the histogram of the logged daily pollen counts (grains/m3) for the period 2008 to 2019. Panel B displays the histogram of the logged
daily pollen counts after residualization by the unit, month-by-year, month-by-prefecture, and day-of-week FEs.

References
Kishikawa, R. et al. 2020. “Pollen Calendar of Important Allergenic Airborne Pollen in Japan.” (in Japanese) Japanese Journal of Palynology,
65(2): 55–66.

49

35.4

Weekly spending-3
per person (10 $)

100

37.0

37.1

2014

2015

44.1

43.9

2017

2018

45.6

35.3

25.2

45
40
35
30
25

Share (%)

Figure A4—Time series of spending on medications for seasonal allergies (by type)

80
60
40
20
2012

2013

2016

Year

2019

(1) No specific mention of driving(2) Driving not allowed
Share of (1)Notes: The sample is derived from the in-home scanner data for the period from February to May for the years 2012 to2019. The graphs plot the weekly spending (in 10-3 $) per person for two types of medications for seasonal allergies:“No specific mention of driving” and “Driving not allowed” on the left y-axis, and the share of the former on the righty-axis during the period 2012 to 2019. See Table A1 for the subsets of the two types of medications.

Figure A5—Spatial variation in pollen counts≥ 1867< 1867 (90%)< 1589 (80%)< 1250 (70%)< 1011 (60%)< 881 (50%)< 827 (40%)< 766 (30%)< 667 (20%)< 545 (10%)
No data

Notes: This figure shows the mean pollen counts (grains/m3) for the period 2008 to 2019, by municipality. Okinawa
prefecture, which is the southernmost island in Japan and has a different climate from the rest of the country, has nopollen station, as little pollen is observed.
50

Table A1—List of medications for seasonal allergies
Brand namein Japanese

Brand namein English

Year ofreleaseMention of driving

アレジオン

Alesion1994

Driving not allowed

エバステル
ジルテック
タリオン
アレグラ
アレロック
クラリチン
ザイザル
ディレグラ
ビラノア
デザレックスルパフィン

EvastelZyrtecTalionAllegraAllelock
Claritin
Xyzal
Dellegra
Bilanoa
Desalex
Rupafin

1996
1998
2000
2001
2001
2002
2010
2013
2016
2016
2017

Driving not allowed
Driving not allowed
Driving not allowed
No specific mention of driving
Driving not allowed
No specific mention of driving
Driving not allowed
No specific mention of driving
No specific mention of driving
No specific mention of driving
Driving not allowed

Notes: The table lists the brand names of allergy medications used to treat seasonal allergies together with the year of
release of the medication as well as any particular mention of whether you are allowed to drive after taking the medication.
The medications highlighted in gray do not include any specific mention of driving after taking same. Note that our
primary data on accidents span 2008 to 2019.

51

Appendix B: “First stage”—Symptoms of Seasonal Allergies
Figure B1—Daily pollen counts and Google Trend
A. Time series
Pollen allergy
100

8000

2000

0
Mar01

Apr01

Daily pollen counts

May01

Google search index

40

25

Google search index

60
4000

Feb01

30

80

6000

Daily pollen counts

B. Binscatter plot
Pollen allergy

20

15

20

10

0

5

Jun01

5.716***(0.233)
Mean: 16.37
1

Google search index

2

Symptoms

5

100

4000
60
2000

Google search index

80

Google search index

30

6000

25

3.545***(0.229)
Mean: 30.40

40
20

0
Feb01

4

Symptoms

8000

Daily pollen counts3ln(daily pollen counts)

Mar01

Apr01

Daily pollen counts

May01

Jun01

1

Google search index

2

3

4

5

ln(daily pollen counts)

Notes: The sample is derived from Google Trends data, and the level of observation is prefecture per day. Panel A shows
the time series patterns of the average daily pollen counts (grains/m3) and Google search index for pollen allergy-related
and symptom-related keywords in 2018 at the country level. See Table B1 for the list of search keywords within eachcategory. June is omitted as only four stations in Hokkaido (the northernmost island of Japan) were still operating in June.
Panel B displays the binscatter plots of the relationship between the logged daily pollen counts (grains/m3) and Google
search index for the same keywords, after controlling for the month-by-year, month-by-prefecture, and day-of-week FEs,for the period 2016 to 2019 (N= 21,551). The estimates from the variants of Equation [1], where unit FE is replaced by
prefecture FE, are reported in the box. The standard errors clustered at prefecture levels are reported in parentheses.
Estimates are weighted by the population in each prefecture per year. Significance levels: *** p<0.01, ** p<0.05, * p<0.10

52

Figure B2—Daily pollen counts and tweets
A. Time series
Pollen allergy
8000

B. Binscatter plot
Pollen allergy
3000

10

4000
1000

Number of tweets

2000

Number of tweets

Daily pollen counts

6000

2000

0

0
Feb01

Mar01

Apr01

Daily pollen counts

May01

5

2.282***(0.671)
Mean: 5.32

0

Jun01

1

Number of tweets

2

600

4000

400

2000

200

0

2.5

Daily pollen counts

May01

2

0.489***(0.112)
Mean: 1.95

1.5

0
Apr01

5

3

Number of tweets

6000

Mar01

4

Symptoms
800

Number of tweets

Daily pollen counts

Symptoms
8000

Feb01

3

ln(daily pollen counts)

1

Jun01

1

Number of tweets

2

3

4

5

ln(daily pollen counts)

Notes: The sample is derived from Twitter data, and the level of observation is prefecture per day. Panel A shows thetime series patterns of the average daily pollen counts (grains/m3) and the number of tweets for pollen allergy-related
and symptom-related keywords in 2018 at the country level. See Table B1 for the list of search keywords within eachcategory. June is omitted as only four stations in Hokkaido (the northernmost island of Japan) were still operating in June.
Panel B displays the binscatter plots of the relationship between the logged daily pollen counts (grains/m3) and thenumber of tweets for the same keywords, after controlling for the month-by-year, month-by-prefecture, and day-of-week
FEs, for the period 2016 to 2019 (N= 21,551). The estimates from the variants of Equation [1], where unit FE is replacedby prefecture FE, are reported in the box. The standard errors clustered at prefecture levels are reported in parentheses.
Estimates are weighted by the population in each prefecture per year. Significance levels: *** p<0.01, ** p<0.05, * p<0.10

53

Figure B3—Binscatter plot of pollen counts and tweets: Sleep-related
Sleeplessness(“Having a hard time falling asleep”)
3.6

Number of tweets

3.4

3.2

3

0.138**(0.057)
Mean: 3.18

2.8
1

3

2

4

5

ln(daily pollen counts)

Sleepiness(“Feeling sleepy”)

Number of tweets

7.0

6.5

0.171*(0.089)
Mean: 6.49

6.0
1

2

3

4

5

ln(daily pollen counts)

Notes: The sample is derived from Twitter data for the period 2016 to 2019, and the level of observation is prefecture
per day (N= 21,551). The graphs display the binscatter plots of the relationship between the logged daily pollen counts(grains/m3) and the number of tweets that include the terms “Hard time falling asleep” and “Feeling sleepy” after
controlling for the month-by-year, month-by-prefecture, and day-of-week FEs. See Table B1 for the list of search
keywords within each category. The estimates from the variants of Equation [1], where unit FE is replaced by prefecture
FE, are reported in the box. The standard errors clustered at prefecture levels are reported in parentheses. Estimates areweighted by the population in each prefecture per year. Significance levels: *** p<0.01, ** p<0.05, * p<0.10

54

Table B1—List of search keywords for symptoms
Category #Categories

Japanese

Common for Google Trends and Tweets
1
Pollen allergy
花粉

2Symptoms

Only for Tweets (sleep-related)
3
Sleeplessness

EnglishPollen

花粉症

Pollen allergy

スギ花粉

Pollen of Japanese cedar

鼻水

Runny nose

鼻づまり

Nasal congestion

くしゃみ

Sneeze目のかゆみ

Itchy eyes

寝付けない, ねつけない

Having a hard time
falling asleep

寝れない, ねれない
眠れない, ねむれない
4

Sleepiness

眠い, ねむいFeeling sleepy

眠たい, ねむたい
眠すぎる, ねむすぎる
Notes: The table lists the keywords for each category in Japanese as well as English (for reference).

55

Appendix C: Ambulance Records
Figure C1—Dose responses

0

Density.002 01

Estimates

2

3

A. All accidents

0

1000

2000

3000

4000

5000

6000

5000

6000

Daily pollen counts

0

Density.002

0.0

Estimates
0.5

1.0

1.5

B. Traffic accidents

0

1000

2000

3000

4000

Daily pollen counts

Notes: The sample is derived from the ambulance records for the period 2008 to 2019, and the level of observation is aunit per day (N= 970,309). There are 705 units in total. The graphs display the estimates and 95% confidence interval of
the treatment effects of daily pollen counts (in levels) from the variant of Equation [1], where the logged daily pollen is
replaced by the dummies for each decile of daily pollen in levels (grains/m3). The dependent variable is the number ofdaily cases per million people for each accident type. The standard errors are clustered at pollen monitoring station levels.
The share of traffic accidents (Panel B) among all accidents (Panel A) is 37.6%. All specifications include unit, monthby-year, month-by-prefecture, and day-of-week FEs, as well as weather covariates (precipitation, temperature, windspeed), darkness, and the logged population. Estimates are weighted by the population in each unit. The histogram at thebottom shows the distribution of daily pollen counts (grains/m3).
56

Figure C2—Pollen and emergency ambulance transportation due to cancer

Daily number of cases per million

0.22

0.21

0.20

0.19

0.18
3

4

5

6

7

8

ln(daily pollen counts)

Notes: The sample is derived from the ambulance records for the period 2015 to 2019, and the level of observation is aunit per day (N= 407,463) since the ambulance records include detailed diagnosis information (equivalent to ICD10)
from 2015 onwards. There are 705 units in total. The graphs display the binscatter plots of the relationship between thelogged daily pollen counts (grains/m3) and the number of daily emergency ambulance transportations due to cancer per
million people, after controlling for the unit, month-by-year, month-by-prefecture, and day-of-week FEs.

57

Figure C3—Other heterogeneous treatment effects (Traffic accidents)
A. AgeEstimate (95%CI)

Mean

Rel. to mean

0-24

0.024 (0.003, 0.046)

3.12

0.78 %

25-44

0.016 (0.000, 0.033)

2.91

0.56 %

45-64

0.029 (0.011, 0.046)

2.83

1.01 %

65-

0.055 (0.038, 0.071)

2.65

2.06 %

Male

0.109 (0.069, 0.149)

6.36

1.72 %

Female0.059 (0.030, 0.087)

4.70

1.25 %

Roads

0.071 (0.038, 0.104)

6.54

1.09 %

Home-0.001 (-0.004, 0.002)

0.07-1.58 %

Public space

0.000 (-0.008, 0.007)

0.16-0.12 %

Workplace

0.001 (0.000, 0.002)

0.01

6.30 %

Weekdays

0.059 (0.030, 0.087)

12.46

0.47 %

Weekends

0.117 (0.075, 0.158)

12.29

0.95 %

B. Gender

C. Location

D. Weekdays/Weekends

00.1
Estimate (95%CI)

0.2

Notes: The sample is derived from the ambulance records for the period 2008 to 2019, and the level of observation is aunit per day (N= 970,309). There are 705 units in total. The graphs display the estimates and 95% confidence interval ofheterogeneous treatment effects of the logged daily pollen counts from Equation [1]. The standard errors are clustered atpollen monitoring station levels. The dependent variable is the number of daily cases of accidents per million peopleusing all accident data. The share of traffic accidents among all accidents is 37.6%. The mean is the number of dailycases per million people. The relative to the mean is the estimate divided by the mean.

58

Figure C4—Treatment effects by regions with a high pollen concentration
vs. regions with a low pollen concentration
A. All accidents

0.4

Estimates

0.3

0.2

0.1
1

2

3

4

Quantile of units by the averge pollen counts

B. Traffic accidents
0.20

Estimates

0.15

0.10

0.05

0.00
1

2

3

4

Quantile of units by the averge pollen counts

Notes: The sample is derived from the ambulance records for the period 2008 to 2019, and the level of observation is aunit per day (N= 970,309). There are 705 units in total. The graphs display the estimates and 95% confidence interval ofheterogeneous treatment effects of the logged daily pollen counts from Equation [1]. The dependent variable is thenumber of daily cases per million people for each accident type. The standard errors are clustered at pollen monitoring
station levels. We calculate the average pollen counts during the period 2008 to 2019 for each unit and divide the units
into quantiles. A lower quantile indicates that the units have low average pollen concentrations, while a higher quantile
indicates that the units have high average pollen concentrations. The share of traffic accidents (Panel B) among all
accidents (Panel A) is 37.6%. All specifications include unit, month-by-year, month-by-prefecture, and day-of-week FEs,
as well as weather covariates (precipitation, temperature, wind speed), darkness, and the logged population. Estimates
are weighted by the population in each unit. The mean (median) pollen counts (grains/m3) at each quantile are 66(68),
238(232), 587(559), and 3260(1871), respectively.
59

Table C1—Different time FE
(1)

(2)

(3)

(4)

(5)

(6)

ln(pollen counts)

0.231***(0.020)0.217***(0.019)0.235***(0.020)0.241***(0.020)0.280***(0.022)0.331***(0.024)R-squaredN
Mean of dep. Var

0.46970,30933.03

0.46
970,30933.03

0.46
970,30933.03

0.47
970,30933.03

0.46
970,309
33.03

0.49
969,62333.03

ln(pollen counts)

0.079***(0.012)0.079***(0.012)

0.091***(0.013)0.084***(0.013)0.118***(0.014)0.140***(0.015)R-squaredN
Mean of dep. Var

0.24
970,30912.41

0.24
970,30912.41

0.24
970,30912.41

0.24
970,30912.41

0.24
970,30912.41

0.27
970,306
12.41

X
X
X
X

X
X

X
X
X

X
X

X

A. All accidents

B. Traffic accidents

Unit FE
Day-of-week FE
Month-year FE
Month-prefecture FE
Month & Year FE
Month-unit FE
Month-year-prefecture FE
Month-year-unit FE

X
X

X
X

X

X

X

Notes: The sample is derived from the ambulance records for the period 2008 to 2019, and the level of observation is aunit per day (N= 970,309). There are 705 units in total. The dependent variable is the number of daily cases per million
people for each accident type. The estimates from Equation [1] are reported along with the standard errors clustered atpollen monitoring station levels in parentheses. In addition to the FEs in the table, we include weather covariates(precipitation, temperature, wind speed), darkness, and the logged population. Column (1) replicates the results from
Table 2 (baseline) for ease of comparison. The share of traffic accidents (Panel B) among all accidents (Panel A) is 37.6%.
Estimates are weighted by the population in each unit. Significance levels: *** p<0.01, ** p<0.05, * p<0.10

60

Table C2—Different levels of clustering
Clustering variables

N of clusters

A. Allaccidents

B. Traffic
accidents
0.079(0.012) ***Monitoring stations (baseline)

120

0.231
(0.020) ***Monitoring stations and date

120 + 1796(0.028) ***(0.016) ***Monitoring stations and month-year 120 + 60

(0.030) ***(0.019) ***

Unit

705

(0.019) ***(0.012) ***Unit and date

705 + 1796(0.028) ***(0.016) ***Unit and month-year

705 + 60

(0.031) ***(0.019) ***Prefecture

47(0.016) ***(0.012) ***Prefecture and date

47 + 1796(0.025) ***(0.015) ***Prefecture and month-year

47 + 60(0.028) ***(0.018) ***Conley (50 km)-(0.018) ***(0.012) ***Conley (100 km)-(0.019) ***(0.012) ***Conley (150 km)-(0.020) ***(0.013) ***Notes: The sample is derived from the ambulance records for the period 2008 to 2019, and the level of observation is aunit per day (N= 970,309). The dependent variable is the number of daily cases per million people for each accident type.
The estimates from Equation [1] are reported. All specifications include unit, month-by-year, month-by-prefecture, andday-of-week FEs, as well as weather covariates (precipitation, temperature, wind speed), darkness, and the logged
population. The number of pollen monitoring stations, units, and prefectures are 120, 705, and 46, respectively. The last
three rows report the spatially clustered standard errors as in Conley (1999) with 50, 100, and 150 km distance cutoffs,
respectively. The share of traffic accidents (Panel B) among all accidents (Panel A) is 37.6%. Estimates are weighted by
the population in each unit. Significance levels: *** p<0.01, ** p<0.05, * p<0.10

61

Table C3—Alternative specifications
(1)

(2)

(3)
Poissonpseudomaximum
likelihood(PPML)

level-log
OLS(Baseline)

log-logOLS

ln(pollen counts)

0.231***(0.020)0.0054***(0.0006)

0.030***(0.003)R-squaredN

0.46
970,3090.90
970,309970,309ln(pollen counts)

0.079***(0.012)0.0056***(0.0010)0.013***(0.002)R-squaredN

0.24
970,3090.80
970,309970,309

X
X
X
X

X
X
X
X

X
X
X
X

A. All accidents

B. Traffic accidents

Unit FE
Day-of-week FE
Month-year FE
Month-prefecture FE

Notes: The sample is derived from the ambulance records for the period 2008 to 2019, and the level of observation is aunit per day (N= 970,309). There are 705 units in total. The share of traffic accidents (Panel B) among all accidents(Panel A) is 37.6%. Column (1) replicates the results from Table 2 (baseline) for ease of comparison. Column (2) reports
the estimates from variants of Equation [1], where the dependent variable takes the log of the number of daily cases per
million people. Estimates are weighted by the population in each unit in Columns (1) and (2). Column (3) reports themarginal effect of the Poisson pseudo-maximum likelihood (PPML) (dydx command in Stata). The standard errors
clustered at pollen monitoring station levels are reported in parentheses. Significance levels: *** p<0.01, ** p<0.05, *p<0.1062

Table C4—Placebos
(1)

(2)

(3)

Baseline

Assigning lastyear’s pollencountsAssigning next
year’s pollencountsln(pollen counts)

0.231***(0.020)0.008(0.022)0.021(0.022)R-squaredN
Mean of dep. var

0.46
970,309
33.03

0.47
879,77733.21

0.46
881,22632.89

ln(pollen counts)

0.079***(0.012)-0.018(0.012)-0.002(0.011)R-squaredN
Mean of dep. var

0.24
970,30912.41

0.24
879,77712.27

0.24
881,226
12.59

X
X
X
X

X
X
X
X

X
X
X
X

A. All accidents

B. Traffic accidents

Unit FE
Day-of-week FE
Month-year FE
Month-prefecture FE

Notes: The sample is derived from the ambulance records for the period 2008 to 2019, and the level of observation is aunit per day. There are 705 units in total. The dependent variable is the number of daily cases per million people for eachaccident type. The estimates from Equation [1] are reported. All specifications include unit, month-by-year, month-byprefecture, and day-of-week FEs, as well as weather covariates (precipitation, temperature, wind speed), darkness, andthe logged population. Column (1) replicates the results from Table 2 (baseline) for ease of comparison. Columns (2) and(3) assign pollen levels of the same day in a previous year and the following year (e.g., for March 3, 2018, in unit X,
Columns (2) and (3) assign the pollen levels on March 3, 2017 and March 3, 2019, in the same unit X.) The share oftraffic accidents (Panel B) among all accidents (Panel A) is 37.6%. Estimates are weighted by the population in each unit.
Significance levels: *** p<0.01, ** p<0.05, * p<0.10

63Appendix D: Police Records
Police records contain all 690,415 (or 106,533 in pollen season) traffic accidents for the period 2019 to2020. The data are at the individual level of accidents and include information on the location, date, and time
the accident occurred. Unlike the ambulance service, where the unit of service is a unit (N= 705), the police
service is administered at the municipality level (N= 1,700). Thus, we aggregate the number of casualties tothe municipality-day level by adding the hourly observations within the municipalities.
Figure D1 displays the binscatter plots of the relationship between the logged average daily pollencounts (grains/m3) and the number of deaths from traffic accidents per million people after controlling formunicipality, month-by-year, month-by-prefecture, and day-of-the-week FEs. The figure shows the positive
relationship between the two variables.
Table D1 reports the estimates from Equation [1], where unit FE is replaced by municipality FE. Forease of comparison, Column (1) replicates the death/fatal estimates for traffic accidents from the 2008 to2019 ambulance records (from the first row of Panel B in Figure 4). Column (2) reports the mortality
estimates from the 2019 to 2020 police records. The estimate of 0.0040 (p-value<0.01) in Column (2) islarger than the estimate of 0.0026 from Column (1), indicating that we may have slightly underestimated theeffect of pollen exposure on the number of deaths as a result of traffic accidents. However, the two estimates
are not statistically distinguishable at the conventional level.

64Figure D1—Pollen and mortality from traffic accidents (police records)

Daily number of deaths per million

0.20

0.15

0.10

0.05

0.00
3

4

5

6

7

8

ln(daily pollen counts)

Notes: The sample is derived from police records for the period 2019 to 2020, and the level of observation is municipality
per day (N= 399,749). There are 1,700 municipalities in total. The graph displays the binscatter plots of the relationshipbetween the logged daily pollen counts (grains/m3) and the number of deaths per million people within 24 hours as aresult of traffic accidents after controlling for municipality, month-by-year, month-by-prefecture, and day-of-week FEs.

Table D1—Mortality as a result of traffic accidents(ambulance records vs. police records)
Ambulance
Record(1)

PoliceRecord(2)ln(pollen counts)

0.0026***(0.0007)0.0040***(0.0015)

R-squaredN
N of unit/municipality
N of clusters
Mean of dep. var
Unit/municipality FE
Day-of-week FE
Month-year FE
Month-prefecture FE

0.01
970,309
705
120
0.16
X
X
X
X

0.01
399,749
1,700
120
0.12
X
X
X
X

Notes: The sample for Column (1) is derived from the ambulance records for the period 2008 to 2019, while the sample
for Column (2) is derived from police records for the period 2019 to 2020. The level of observation is unit per day for
Column (1) and municipality per day for Column (2). There are 705 units and 1,700 municipalities in total. The dependent
variable is the number of deaths as a result of traffic accidents per million people. The estimates from Equation [1] arereported along with the standard errors clustered at pollen monitoring station levels in parentheses. In addition to the FEs
in the table, we include weather covariates (precipitation, temperature, wind speed), darkness, and the logged population.
Column (1) is identical to the first row of Panel B in Figure 4. Estimates are weighted by the population in each unit in
Column (1) and by the population in each municipality in Column (2). Significance levels: *** p<0.01, ** p<0.05, *p<0.1065

Appendix E: Avoidance behaviors from in-home scanner data
Figure E1—Pollen counts and weekly spending on allergy products
A. Total

B. Medication
160

Weekly spending (in 10^-3 USD)

Weekly spending (in 10^-3 USD)

400

350

300

250

200

140

120

100

80

60
4

5

6

7

8

4

5

C. Eye drops

7

8

7

8

D. Masks
95

Weekly spending (in 10^-3 USD)

140

Weekly spending (in 10^-3 USD)

6ln(daily average pollen counts)ln(daily average pollen counts)

120

100

90

85

80

75

70

80
4

5

6

7

8

4

ln(daily average pollen counts)

5

6ln(daily average pollen counts)

Notes: The sample is derived from the in-home scanner data for the period from February to May during 2012 to 2019,
and the level of observation is an individual per week (N= 4,303,417). The graphs display the binscatter plots of therelationship between the logged daily average pollen counts (grains/m3) and the weekly spending per person (in 10-3 $)for all allergy products in Panel A and for each product in Panels B to D: medications (Panel B), eye drops (Panel C),
and masks (Panel D), after controlling for the municipality, month-by-year, month-by-prefecture, and day-of-week FEs.
The shares of medications (Panel B), eye drops (Panel C), and masks (Panel F) are 35%, 37%, and 28%, respectively.

66

Table E1—Summary statistics of in-home scanner data
Variables

Obs

Mean

A. Outcomes (weekly per person in 10-3 $)Spending: Total
4,303,417
Spending: Medications
4,303,417
Spending: Eye drops
4,303,417
Spending: Masks
4,303,417

Std. dev.

Min

Max

295.78109.94103.4182.52

2343.95
1528.34
1018.201324.5600002,141,620
227,200
104,000
2,141,620

4,303,417
4,303,417
4,303,417
4,303,417
4,303,417
4,303,417
4,303,417
4,303,417
4,303,417
4,303,417

0.13
0.38
0.38
0.11
0.50
0.11
0.46
0.55
0.58
2.61

0.33
0.48
0.49
0.32
0.50
0.31
0.50
0.50
0.49
2.22

0
0
0
0
0
0
0
0
0
1

1
1
1
1
1
1
1
1
1
13

C. Regressors (daily average in a week)
Pollen counts (grains/m3)
4,303,417
Logged (Pollen counts)
4,303,417
Precipitation (mm)4,303,417
Average temperature (℃)
4,303,417
Average wind speed (m/s)
4,303,417

843.2
0.1
12.0
2.8
10.5

1337.4
0.2
6.0
0.9
1.3

0
0
-8
0
7

22,812
3.9
26
11.6
13

B. Individual characteristics
Ages: 0–24
Ages: 25–44
Ages: 45–64
Ages: 65 years and older
FemaleStudent
Household head
Have kids
Own house
Annual salary category

Notes: The sample is derived from the in-home scanner data for the period from February to May during 2012 to 2019,
and the level of observation is an individual per week. The spending is the weekly aggregate per person (in 10-3 $).

67

Appendix F: Avoidance Behaviors from Google Trends/tweets
Figure F1—Pollen and Google Trends/tweets

A. Google Trends
Medications

B. Tweets
Medications

0.20

Number of tweets

Google search index

15

10

3.763***(0.294)
Mean: 12.32

5
1

2

3

4

0.15

0.10

0.038***(0.013)
Mean: 0.11

0.05
1

5

2

3

4

5

ln(daily pollen counts)ln(daily pollen counts)

Mask

Mask
1.6

17

Number of tweets

Google search index

1.5

16

0.601***(0.222)
Mean: 27.70

15
1

2

3

4

1.4

1.3

0.106***(0.038)
Mean: 1.35

1.2

1.1

5

2

1

ln(daily pollen counts)

3

4

5

ln(daily pollen counts)

Air purifier

Air purifier
0.12

7

Number of tweets

Google search index

0.10

6

0.06

0.794***(0.177)
Mean: 11.57

5
1

2

3

4

0.08

0.012**(0.004)
Mean: 0.08

0.04

5

1

ln(daily pollen counts)

2

3

4

5

ln(daily pollen counts)

Notes: The sample is derived from Google Trends data for Panel A and Twitter data for Panel B for the period 2016 to 2019, and thelevel of observation is prefecture per day. The graphs display the binscatter plots of the relationship between the logged daily pollencounts (grains/m3) and Google search index in Panel A and the number of tweets in Panel B for medication, mask, and air purifierrelated keywords, after controlling for the month-by-year, month-by-prefecture, and day-of-week FEs. See Table F1 for the list of
search keywords within each category. The estimates from the variants of Equation [1], where unit FE is replaced by prefecture FE,
are reported in the box. The standard errors clustered at prefecture levels are reported in parentheses. Estimates are weighted by the
population in each prefecture per year. Significance levels: *** p<0.01, ** p<0.05, * p<0.10

68

Table F1—List of search keywords
Category #1Categories
Medications(product name of
popular allergy
medications)Japanese

English

アレジオン

Alesionアレグラ

Allegra

クラリチン

Claritin

2

Mask

マスク

Mask

3

Air purifier

空気清浄機

Air purifier

Notes: The table lists the keywords for each category in Japanese as well as English (for reference).

69

Appendix G: Avoidance Behaviors from Geolocation Data
Our geolocation data are called “Mobile Spatial Statistics” (MSS), provided by NTT DOCOMO,
Inc., Japan’s largest mobile phone carrier. Based on the location information of 85 million users of
NTT DOCOMO (as of March 2022), MSS provides the population estimate at a 500×500-meter meshat an hourly level across Japan. See Terada et al. (2013) for detailed procedures for constructing
population estimates.
Our dataset on mobility measures is constructed as follows. First, for each municipality, wechoose a 500×500-meter mesh with the largest number of establishments in the customer service
industry (e.g., accommodation, restaurants, and entertainment) based on information obtained from the2016 Economic Census (MIC 2019). 1 We choose the service industry to capture bustling areas (e.g.,business districts and shopping and dining areas), which are more likely to represent the population
engaging in outdoor activities. Second, we provide the list of the meshes to NTT DOCOMO, Inc,
which returns the estimated population at each mesh for the period February 2014 to May 2019. Third,
we collapse the estimated population at the unit level by taking the average of all municipalities in theunit. We use the estimated population at 2 pm, as the daily population in commercial areas tends topeak around that time (Seike et al. 2015). 2 We treat this measure as a proxy for engaging in outdoor
activities (“outdoor population,” hereafter) to examine avoidance behaviors.
Finally, we briefly discuss the advantages and disadvantages of this dataset. There are two typesof geolocation data in Japan: the first come from the leading smartphone mapping application in Japan(“Docomo Chizu NAVI”), which collects GPS coordinates of each smartphone device whenever thedevice is turned on. The most attractive feature of this application is that it effectively follows eachindividual over time (but only in very recent years); researchers can identify “home” locations as themost frequent locations of geographically contiguous stays (Miyauchi et al. 2021), and measure
whether the individuals leave their home. The drawback is that the sample is limited to individuals who
gave permission to share location information, causing selection issues to the users of the particularapplication as well as those who gave permission, and also resulting in a small sample size (545,000
users as of 2019). Meanwhile, the second type of data, like ours, based on information transmission
from each mobile terminal (of 85 million users in our case) to base stations as long as mobile devices
are turned on, are more nationally representative with wide spatial coverage of the entire country, while
they only provide the hourly estimated population in a given area.
As the main objective of this study is to provide nationally representative estimates of the effects
of pollen exposure on accidents and corresponding avoidance behaviors over an extended period oftime, we chose the latter dataset for analysis in this study. Owing to its representativeness and longtime span of the sample, this dataset has been widely used, especially in measuring people’s mobility
Owing to budgetary reasons, our data consist of one mesh per municipality. However, we confirm that our mobility
measure captures overall daytime outdoor activities adequately, using the 2019 data for which we have an outdoor
population for all meshes. Indeed, our mobility measure from the representative mesh correlates as high as 0.889 with thesummary measure that uses the average of all the meshes with at least one establishment in the service industry.
2
We also examine alternative methods of constructing outdoor mobility measures, specifically, the difference between
daytime (2pm) and nighttime (4am) populations and the ratio of daytime to nighttime populations. The results are
qualitatively similar (not shown) mainly due to the fact that the nighttime population is relatively stable over time, and thus
it does not provide much additional information after controlling unit FE.
170

during the COVID-19 pandemic (e.g., Kondo 2021; Kuroda et al. 2022).

References

Kondo, K. 2021. “Simulating the Impacts of Interregional Mobility Restriction on the Spatial Spreadof COVID-19 in Japan.” Scientific Reports, 11: 18951.
Kuroda, Y., T. Sato, Y. Matsuda. 2022. “How Long do Voluntary Lockdowns Keep People at Home?
The Role of Social Capital during the COVID-19 Pandemic.” Data Science and Service Research
Discussion Paper No. 125.
MIC (Ministry of Internal Affairs and Communications). 2019. “Economic Census.” (in Japanese)
https://www.e-stat.go.jp/gis/statmapsearch?page=1&type=1&toukeiCode=00200553&toukeiYear=2016&aggregateUnit=H&serveyId=
H002005112016&statsId=T000918 (accessed March 23, 2022).
Miyauchi, Y., K. Nakajima, S.J. Redding. 2021. “The Economics of Spatial Mobility: Theory andEvidence Using Smartphone Data.” NBER Working Paper No. 28497.
Seike, T., H. Mimaki, S. Morita. 2015. “Study on the Population Characteristics in a City Center
District Utilizing Mobile Spatial Statistics” (in Japanese). Journal of Architecture and Planning,
80(713): 1625–1633.
Terada, M., T. Nagata, M. Kobayashi. 2013. “Mobile Spatial Statistics’ Supporting Development ofSociety and Industry—Population Estimation Technology for Mobile Spatial Statistics.” NTT
DOCOMO Technical Journal, 14: 16–23.

71

Figure G1—Pollen and avoidance of going out
A. All days

B. Weekends

7.640

ln(population outside)ln(population outside)

7.610

7.635

7.605

7.600

7.630
3

4

5

6

7

3

8

4

5

6

7

8

ln(daily pollen counts)ln(daily pollen counts)

Notes: The sample is derived from cellphone location data for the period 2014 to 2019, and the level of observation is a unit per day (N= 478,853 for Panel A and
135,399 for Panel B). There are 705 units in total. The graphs display the binscatter plots of the relationship between the logged daily pollen counts (grains/m3),
and the logged daily number of people outdoors at 2 pm for all days in Panel A and only for weekends in Panel B, controlling for the month-by-year, month-byprefecture, and day-of-week FEs, weather covariates (precipitation, temperature, wind speed), darkness, and the logged population. Estimates are weighted by the
population in each unit.

72

Table G1—Avoidance of going out and accidents
A. All accidentsln(outdoor population)

B. Traffic accidents

All

Weekdays

Weekends

All

Weekdays

Weekends

(1)

(2)

(3)

(4)

(5)

(6)

0.960**(0.418)0.465(0.441)2.007**(0.775)0.484**(0.224)

0.553**(0.254)1.024***(0.341)R-squared
0.49
0.48
0.51
0.24
0.24
0.23
N
478,853
343,454
135,399
478,853
343,454
135,399
Unit FE
X
X
X
X
X
X
Day-of-week FE
X
X
X
X
X
X
Month-year FE
X
X
X
X
X
X
Month-prefecture FE
X
X
X
X
X
X
Notes: The sample is derived from the ambulance records for the period 2014 to 2019 that are matched to “Mobile Spatial
Statistics” data provided by NTT DOCOMO, Inc at the unit-day level (N= 478,853). There are 705 units in total. The
share of traffic accidents (Panel B) among all accidents (Panel A) is 37.6%. The estimates from regressing the numberof daily accidents per million people (our main outcome) on the logged outdoor population, with the same sets of FEs
and controls as an Equation [1] (excluding the logged number of pollen counts), are reported. The standard errors
clustered at pollen monitoring station levels are reported in parentheses. Columns (2) and (5) restrict the samples to
weekdays, and Columns (3) and (6) restrict the samples to weekends. Estimates are weighted by the population in each
unit. Significance levels: *** p<0.01, ** p<0.05, * p<0.1073

Table G2—Avoidance of going out with full covariates
Outcome: logged outdoor population
A. AllB. By type of day(1)

Weekdays(2)

Weekends(3)-0.0005(0.0006)

0.0000(0.0006)-0.0021***(0.0007)<1 mm-0.0061***(0.0013)-0.0059***(0.0011)-0.0047**(0.0019)

1 mm≤ & <2 mm-0.0167***(0.0035)-0.0144***(0.0024)-0.0208***(0.0080)-0.0167***(0.0045)
Mean temperature (base: <0 ℃)-0.0150***(0.0041)-0.0249***(0.0082)ln(pollen counts)
Rainfall (base: no rainfall)≥2 mm[0, 5) ℃

0.0018(0.0033)

0.0068**(0.0034)

0.0096(0.0064)[5, 10) ℃

0.0111***(0.0036)0.0139***(0.0037)

0.0189***(0.0067)[10, 15) ℃

0.0089**(0.0043)0.0120***(0.0043)0.0160**(0.0070)[15, 20) ℃

0.0061(0.0045)0.0094*(0.0049)

0.0172**(0.0068)[20, 25) ℃

0.0120***(0.0043)0.0165***(0.0046)0.0200***(0.0069)≥ 25 ℃

0.0273***(0.0057)

0.0264***(0.0066)

0.0319***(0.0079)

Mean wind speed-0.0022***(0.0005)-0.0021***(0.0005)-0.0023***(0.0007)

Darkness-0.0084***(0.0014)-0.0090***(0.0012)-0.0073***(0.0023)ln(population)

1.3108***1.2630***1.4497***(0.1975)(0.1997)(0.2074)
R-squared
0.98
0.99
0.99
N
478,853
343,454
135,399
Pref FE
X
X
X
Day-of-week FE
X
XX
Month-year FE
X
X
X
Month-prefecture FE
X
X
X
Notes: The sample is derived from cellphone location data for the period from February to May for the years 2014 to
2019, and the level of observation is a unit per day. There are 705 units in total. The estimates from variants of Equation[1] are reported along with the standard errors clustered at pollen monitoring station levels in parentheses. The outcome
is the logged daily outdoor population at 2 pm. Estimates are weighted by the population in each unit. Significance levels:*** p<0.01, ** p<0.05, * p<0.1074Appendix H: Data Appendix
Data
Ambulance
records

Policerecords
PollenTemperature

Pollution

Googletrends data

SourceYears: 2008–2019 (detailed diagnosis information is available from 2015 onwards)
Data description: ambulance records archive
Source: Fire and Disaster Management Agency (FDMA) of the Ministry of Internal Affairs
and Communications
https://www.fdma.go.jp/en/post1.html
Years: 2019–2020
Data description: traffic accident records of accidents that involve injuries or deaths
Source: National Policy Agency (NPA)
https://www.npa.go.jp/publications/statistics/koutsuu/opendata/index_opendata.html
Years: 2008–2019
Data description: Hourly pollen counts from 120 stations, as well as hourly rainfall,temperature, wind speed, and wind direction from nearby weather stations during thepollen season (February to May except for Hokkaido, where the pollen season is March toJune).
Source: Ministry of the Environment (MOE), Pollen Monitoring System “Hanako-san”
https://tenki.jp/pollen/
Note: MOE terminated data collection of pollen counts in 2021.
Years: 2008–2019
Data description: Hourly temperature (outside of the pollen season)
Source: Japan Automated Meteorological Data Acquisition System (AMeDAS)
operated by the Japan Meteorological Agency (JMA)
https://www.data.jma.go.jp/obd/stats/etrn/
Years: 2009 April–2019 March
Data description: Hourly SO2, NO, NO2, CO, OX, PM10
Source: National Institute for Environmental Studies
https://www.nies.go.jp/igreen/index.html
Years: 2016–2019
Data description: The Google search index which reflects search term popularity for theselected keywords and takes a value of 0 to 100 in a given prefecture and on a given day inproportion to total searches within the search period

Twitter data

Years: 2016–2019
Data description: The number of tweets that contain the selected keywords

In-homescanner data

Years: 2012–2019
Data description: called “Quick Purchase Report,” which is the daily panel of purchaserecords from roughly 30,000 monitors
Source: Macromill, Inc
https://www.macromill.com/service/digital-data/consumer-purchase-history-data/ (inJapanese)Years: 2014–2019
Data description: called “Mobile Spatial Statistics” data, which are estimates based on thelocation information of 85 million NTT DOCOMO cellphone users (as of March 2022)
Source: NTT DOCOMO, Inc
https://mobaku.jp/ (in Japanese)

Geolocationdata

75