Discussion Paper No. 114
The effect of pollen exposure on economic activity:
Evidence from home scanner data
Yuta Kuroda

May, 2020

Data Science and Service Research
Discussion Paper

Center for Data Science and Service Research
Graduate School of Economic and Management
Tohoku University27-1 Kawauchi, Aobaku
Sendai 980-8576, JAPANThe effect of pollen exposure on economic activity:
Evidence from home scanner data
Yuta KurodaaaGraduate School of Economics and Management, Tohoku University
Sendai-Shi, Aoba-Ku, Kawauchi, 27-1, 980-8576, Japan
Email: kuroyu0725@gmail.com

Abstract
Although seasonal allergies caused by airborne pollen are detrimental to physical and mental
health and impair daily activity, discussion on their social cost is scarce in the economics
literature. Large amounts of airborne pollen can not only increase health care costs and reduce
worker productivity, but also cause people to stay at home, thereby stagnating economic
activity. This study uses daily purchase records from scanner data to investigate the effect ofpollen exposure on consumption behavior. Exploiting the daily variation in the pollen counts
at 120 observation stations in Japan, I find that consumption expenditure decreases by about
2% on days when airborne pollen is unusually high. A reduction in consumption due to pollen
exposure is also observed in estimates using weekly and monthly panel data. This finding
suggests that exposure to pollen may reduce total expenditure as opposed to delay spending.
The results highlight the overlooked economic burden of pollen and seasonal allergies. Hence,
they underline the importance of urban planning to reduce airborne pollen and health policy todeal with seasonal allergies.
Keywords: Seasonal allergies, Pollen, Consumption, Scanner data, Hay fever
JEL classifications: Q51, D12, Q53, I10, E21, R111

1.

Introduction
Seasonal allergy, also known as hay fever or pollinosis, is a common chronic condition anda global health problem (Bousquet et al., 2008). Seasonal allergy causes sneezing, nasal
congestion, and itchy eyes, and its medications also bring about side effects such as sleepinessand lethargy (Jáuregui et al., 2009, Meltzer et al., 2012). The prevalence rates vary between
country and region, but are generally 10% to 40% in developed countries, with an estimated
400 million sufferers worldwide (Greiner et al., 2011; Meltzer et al., 2012). Furthermore,
numerous studies demonstrate that the prevalence is increasing (Caillaud et al., 2015;
Linneberg et al., 2000; Nakae and Baba, 2010; Selnes et al., 2005) for a variety of reasons.
Increases in temperature and precipitation due to global climate change increase pollen, whichis the cause of seasonal allergy (Bajin et al., 2013; Shea et al., 2008; Todea et al., 2013; Zhang
et al., 2015; Ziello et al., 2012). The urbanization and westernization of lifestyles also raise theprevalence of seasonal allergy (Kaneko et al., 2005; Krämer et al., 2010; Sakashita et al., 2010).
Many developed countries are greening their cities to improve the air quality and landscape,which in turn leads to airborne pollen (Asero, 2002; Cariñanos and Casares-Porcel, 2011; Xu
et al., 2016). Thus, since the burden of seasonal allergy can increase with economic activity,
there is a need to study its social cost.
Despite the potential economic burden of seasonal allergy, economists pay little attention topollen compared with pollutants. The medical and epidemiological literature indicates thatseasonal allergy results in direct medical costs and indirect costs due to reduced laborproductivity (Cardell et al., 2016; Hellgren et al., 2010; Lamb et al., 2006). However, these are
not the only potential burdens of seasonal allergy. As with severe air pollution, pollen can also
prevent people from going out (Graff Zivin and Neidell, 2009; Neidell, 2009) and affect theirconsumption behavior (Kang et al., 2019; Sun et al., 2019). High levels of airborne pollen makepeople stay at home, and the resulting loss of expenditure can be considered to be the economic2

cost of pollen. However, the relationship between airborne pollen and consumption behavior
remains unclear.
To bridge this gap in the body of knowledge, this study investigates the impacts of pollen
exposure on consumption behavior. By combining detailed purchase records from 2013 to 2019
based on home scanner data with daily pollen counts collected at 120 monitoring stations
throughout Japan, I analyze changes in consumer spending due to daily pollen variations. The
main results provide evidence that consumer spending declines by about 2% on days whenairborne pollen is unusually high (above the 95th percentile). Considering that consumption
expenditure accounts for about 55% of GDP in Japan, this decline has serious implications.
The estimates using weekly and monthly panel data also show a decrease in consumption
expenditure for each additional day of high pollen, suggesting that pollen exposure causes
shopping to be canceled rather than postponed. To the best of my knowledge, this is the firstresearch to investigate the relationship between daily pollen exposure and consumption
expenditure.
An important contribution of this study is that it presents novel evidence that pollen exposure
impedes economic activity, thereby revealing the economic benefits of pollen reduction. First,
understanding the social costs of pollen is important when formulating urban planning or
greening policies, as considering the negative externalities caused by pollen in urban planning
can improve social welfare. In addition, it can take a long time from planting to pollen
production. In Japan, cedar was artificially planted nationally after World War II; cedar pollen
is the biggest cause of seasonal allergies today. Some types of trees such as Japanese cedar takedecades to start producing pollen, so ignoring it could be a huge burden in the future. Second,quantifying pollen costs contributes to the climate change literature. As climate change affects
the production, dispersion, and allergen content of pollen (Ziska et al., 2019), my results reveal
a potential novel channel through which climate change can affect social costs. Finally, the3

effects of pollen should also be considered when using scanner data. High-frequency economic
indicators using daily scanner data will become more common, and immediate policymaking
using them could also increase in the future (De Haan and Van Der Grient, 2011; Melser, 2018).
Although daily data are useful, they are susceptible to temporary exogenous variation.
Considering the effects of pollen as well as daily weather and air pollution could thus allow for
more accurate decision making.
The remainder of this paper is structured as follows. Section 2 explains pollen and seasonalallergies as well as related studies. Section 3 discusses the data and setting used in the study.
Section 4 describes the empirical strategies. Section 5 presents the results, Section 6 explores
their robustness, and Section 7 discusses the lasting effects of pollen. Finally, Section 8
concludes.

2.

Background and related studies

2.1. Pollen and seasonal allergies
Seasonal allergy is a chronic disease caused by allergic pollen in the air. The causative plantand pollen season differ by country and region, but the symptoms are generally similar globally.
Sufferers of seasonal allergies have an allergic reaction when they inhale airborne pollen. The
most common reactions are allergic rhinitis such as sneezing, runny nose, and stuffy nose as
well as allergic conjunctivitis such as itchy eyes and tears. In rare cases, asthma and atopy may
also occur (Gonzalez-Barcala et al., 2013; Hanigan and Johnston, 2007; Sun et al., 2016). In
the worst cases, it can cause bronchitis, bronchial asthma, pulmonary heart disease, and caneven be life-threatening (Brunekreef et al., 2000; Weichenthal et al., 2016). In addition, allergy
medications lead to side effects such as drowsiness, dryness, and lethargy (Jáuregui et al., 2009,
Meltzer et al., 2012). These symptoms worsen sleep quality (Craig et al., 2004; Kremer et al.,
2002; Santos et al., 2006), reduce cognitive performance (Bensnes, 2016; Marcotte, 2017),
4

increase absenteeism (Hellgren et al., 2010; Lamb et al., 2006), and sometimes lead to suicide(Stickley et al., 2017). Thus, seasonal allergies reduce people’s quality of life, affect their daily
activities, and cause economic losses.
Seasonal allergy is a common disease, but the exact prevalence is difficult to ascertain
because many sufferers use over-the-counter medications without visiting hospital or simply
endure until the pollen season has passed because of the relatively mild and chronic symptoms.
Several studies have estimated the annual per capita cost of seasonal allergies to be $600 to$1000, taking into account direct medical costs and indirect costs due to lost productivity(Cardell et al., 2016; Hellgren et al., 2010; Lamb et al., 2006). However, the burden of seasonalallergies may be underestimated.
A distinctive part of seasonal allergies is that sufferers’ performance and behavior are
strongly influenced by airborne pollen count on that day. More patients visit the asthma
emergency department (Erbas et al., 2012) and more over-the-counter medications for allergies
are sold (Ito et al., 2015; Johnston et al., 2009) on days with high pollen counts. Bensnes (2016),
Marcotte (2015), and Walker et al. (2007) show that high airborne pollen on exam days reduces
students’ cognitive performance. Chalfin et al. (2019), who investigated pollen counts andcrime, suggested that people may spend less time outdoors on high pollen days. They also
found that not only outdoor crime but also indoor crime decreased on high pollen days,
suggesting that high pollen may not only reduce outdoor activity, but also affect individualmood and behavior.
The results of Chalfin et al. (2019) suggest a new potential effect of airborne pollen. Similar
to air pollution (Kang et al., 2019; Sun et al., 2019) and bad weather (Bloesch and Gourio,
2015; Parsons, 2001), the day’s airborne pollen count may be associated with daily expenditure.
In other words, airborne pollen not only causes people to avoid going out, but may also decrease
the desire to consume due to apathy and lethargy. Additionally, consumer spending could5decline to offset the higher medical costs caused by seasonal allergy. However, to the best of
my knowledge, empirical evidence of the impact of daily pollen exposure on consumer
spending is lacking.

2.2. Seasonal allergies in JapanThe main sources of seasonal allergies in Japan are cedar and cypress (Yamada et al., 2014;
Yoshida et al., 2013). Cedar and cypress, which are not only suitable for the Japanese climate,
but also have excellent characteristics for processing wood such as fast growth and easy
shaping, were planted in large numbers during and after World War II when demand formaterials was high but it was impossible to import timber. About 20% of Japan’s land is coveredby artificial cedar and cypress forests (MAFF, n.d.), thereby playing important roles in publichealth by improving soil, air, and water quality. However, cedar and cypress begin to produce
allergic pollen when they are about 30 years old.
Most pollen in Japan is distributed from February to April. Since pollen can be dispersed
more than 100 kilometers away and can stay in the air for more than 12 hours, almost the entire
area, even in sparsely forested cities, can be contaminated (Yamada et al., 2014). The regional
prevalence rate is mostly 10–40% except for Okinawa1 (Okuda, 2003; Sakashita et al., 2010;
Yamada et al., 2014). Similar to the global trend, the prevalence rate in Japan is increasing
steadily (Kaneko et al., 2005; Okano et al., 2012). Prevalence is particularly high in urban
areas; in Tokyo, the prevalence rate reached 48.8% in 2016 (Tokyo Metropolitan Institute of
Public Health, 2017), rising from 19.4% in 1996. As wearing protective masks as well as
cutting down artificial forests and converting them to trees with less pollen have only marginal
beneficial effects, a better understanding of the burden of seasonal allergies is necessary to help
The climate of Okinawa prefecture, the southernmost remote island in Japan, is different than that in otherprefectures. The prevalence of seasonal allergies is low because there is almost no pollen. Therefore, Japan’s
46 prefectures other than Okinawa are investigated in this study.
16

introduce effective policies and improve public health.
3.

Data
To investigate the effect of daily airborne pollen count on consumption behavior, this study

uses a combination of three types of data: pollen, weather, and individual purchases. The data
period runs from February to June (pollen season) of each year, from 2013 to 2019. Data on air
pollution are only used to check the robustness of the results because the available period isshort (from 2013 to 2017).

3.1. Airborne Pollen
Daily pollen data were obtained from the pollen observation system (Hanakosan) operated
by the Ministry of the Environment. There are 120 pollen monitoring stations in Japan,
operating from February to May each year (until June in Hokkaido). Figure 1 shows thelocations of pollen observation stations that except for sparsely populated areas cover mostJapanese cities. Automatic pollen monitors in each station report the pollen count per cubic
meter every hour.2 The measured pollen count is automatically published on the website of the
Ministry of the Environment to allow allergy sufferers to make decisions about medication use
and outdoor activities based on real-time pollen information.
Table 1 shows the descriptive statistics. The mean pollen count is 43.327 and the standard
deviation is 252.918, indicating that the variance is large.3 This study uses the effect of pollen
thresholds following the literature (Chalfin et al., 2019; Ito et al., 2015; Marcotte, 2015;

There is no distinction between cedar and cypress because of their close properties. There are somedeficiencies in the data due to equipment failure, data communication errors, or unusual yellow sand.
3
The maximum pollen count is 66403.792, which may seem like an outlier. This value is the maximum for2013, but the maximum for the other years is around 2000 to 7000. However, the Ministry of theEnvironment reports that outliers are excluded before the release of the data. Hence, this study treats thisvalue as a non-outlier. In any case, the main analysis uses dummy variables, so there is no major impact.
2

7

Sheffield et al., 2011). A day when the airborne pollen count is in the 95th percentile (158.167)
or higher is defined as a high pollen day. Therefore, the main analysis captures the effect of
extreme pollen counts. This is reasonable because the medical and epidemiological literatureshows that symptoms of seasonal allergies can rise sharply with increasing pollen counts andflatten above the threshold (Caillaud et al., 2014; Erbas et al., 2007; Johnston et al., 2009). In
addition, using the same threshold for all the monitoring stations is not problematic because,
as mentioned in the previous section, pollen trees are distributed nationally and the variance of

Latitude

the average pollen count at each monitoring station is small.

◆ Monitoring station

Longitude
Figure 1. Pollen monitoring stations in Japan
Note: The black rhombus symbols represent pollen monitoring stations. Source: Ministry of the Environment.

8Pollen and weather
Pollen countHigh pollen day dummyRain dummy
Average temperature (°C)
Average wind speed (m/s)
Daily home scanner data
Total in-store purchases
Number of monitors purchased
Total in-store purchases per monitor
Total online and mail order purchases
Total purchases of pollen-related goods
Air pollution
3

Average PM25 (μg/m )Critical PM25 dummy
Average SO2 (ppm)
Critical SO2 dummy
Average NOX (ppm)
Number of pollen monitoring stations
Number of observation days
Observations

Mean

Std. Dev

Minimum

Maximum

43.3270.050
0.365
13.2862.555

252.918
0.218
0.481
7.250
1.345

0
0
0
-16
0

66403.792
1
1
30.15015.650000001830594844
30407
902713
49742

0
0
0
0
0

86.0281
63.2971340.500

107519.750 170719.621
68.894110.525
1599.797
727.67311401.165 23078.241
1179.982
2552.407
14.389
0.101
1.835
0.001
13.303

8.198
0.301
1.742
0.025
9.847
120
1051
98361

Table 1. Descriptive statistics
Note: Home scanner and air pollution data are aggregated for each pollen station within 15 km. The detailsof pollen-related goods are given in Appendix A. The number of observations is the number of pollen stations
multiplied by the number of observation days minus the number of days when pollen data are missing.

3.2. Weather
As daily weather has a huge impact on consumer behavior (Bloesch and Gourio, 2015;
Parsons, 2001), it must be controlled for. The weather data are obtained from the AutomatedMeteorological Data Acquisition System (AMeDAS) operated by the Japan Meteorological
Agency. Pollen data and weather data can be matched one-to-one because pollen monitoring
stations are intentionally located near AMeDAS stations. The variables used are a rain dummy(taking one when precipitation per hour is 15 mm or more), temperature (°C), and wind speed(m/s) (see Table 1). Intuitively, pollen counts are likely to be strongly influenced by rain;
9however, the weather has a small impact on pollen counts in reality (D’Amato et al., 2007).
Consistent with the results of Chalfin et al., (2019), the R-squared value is only 0.06 whenregressing pollen counts on the weather variables and all the other fixed effects using the datain the main analysis.

3.3. Home scanner data
To measure daily consumption expenditure, I use home scanner data called Quick Purchase
Report (QPR) from Macromill. This dataset aims to provide detailed marketing information by
constructing nationally representative panel data on consumers’ buying behavior. Since 2011,about 30,000 monitors 4 provided with barcode readers have reported daily purchasing data.
Monitors scan all barcoded items purchased each day using handheld scanners and report thecode, price, and quantity of each product. This dataset contains rich personal information about
the monitors, including ZIP codes, age, income, and family structure, which is updated annually.
Based on the ZIP code, I can combine the QPR data with the pollen station. 5 In addition, the
QPR data include detailed information such as the date and time of the scan and location of thepurchase (e.g., supermarket, mail order, vending machine) as well as the product name. An
important feature of the QPR data is their high representativeness. Monitors are selected toensure they represent the regional gender, age, marital status, and family structure shares basedon the census. In addition, monitors are incentivized to scan correctly using rewards, andmonitors who do not regularly scan are forced to leave; hence, the data are highly reliable.
However, as Leicester (2013), who compared home scanner data with other household
survey data, pointed out, there are several problems with home scanner data. The most serious

The QPR has maintained a sample size of about 30,000, replenished with individuals with similar
characteristics when monitors drop out. Thus, it mitigates the problem of sample attrition, which is a serious
shortcoming of daily purchasing data.
5
A small number of monitors did not report the correct ZIP code and these were excluded from the sample.
4

10

is that the QPR data do not capture all household expenditure. Items without a barcode such as
perishables, cars, and properties are not included in the data. Further, even if a barcode is
attached, some products such as imported products cannot be used for the analysis because theunit price is unknown. Therefore, the consumption expenditure used in this analysis is less than
the actual value. In addition, the monitor must scan every day, so the sample may be biased
toward industrious people. However, such deficiencies and biases are likely to be uncorrelatedwith pollen exposure and seasonal allergies, and should not seriously bias the estimates.
In the main analysis, the data are used at the aggregate level of each pollen monitoring
station. 6 The distance from the center of the ZIP code of the monitor to the nearest pollen
monitoring station is calculated, and the QPR data are connected to the pollen and weather data.
In the main estimation, data from monitors living within 15 km of the pollen monitoring station
are aggregated and used as the consumption activity data in the area around the station. For
each pollen monitoring station, I use the following daily information: total in-store purchases,
number of monitors making purchases of at least one yen, per capita in-store purchases, total
online and mail order purchases, and total purchases of pollen-related products. 7 Table 1
presents the descriptive statistics.

3.4. Air pollution
As with weather, air pollution levels can affect outdoor activities (Kang et al., 2019; Sun etal., 2019) and airborne pollen counts (D’Amato et al., 2007; Yamada et al., 2014). Therefore,
air pollutants should be used as a control. The data on air pollutants are obtained from the
National Institute for Environmental Studies. There are more than 2,000 automatic air quality

Another technical problem is that because of the huge sample size, it is difficult to control for the monitorlevel fixed effects in the monitor’s daily data and make estimates that account for the non-linearities causedby the many “zeros” (i.e., days when the monitor does not go shopping).
7
Table A in the Appendix provides the definitions of pollen-related goods.
611monitors in Japan, each reporting hourly airborne pollutants. In this study, air pollution isdefined as the air quality at each pollen monitoring station (i.e., the average daily value reportedby the air quality monitors within a certain distance of each pollen station). Unlike the pollen,
weather, and purchasing data, the air pollution data are only available from 2013 to 2017.
Therefore, they are not used in the main analysis, only in the robustness check.
Particulate matter (PM25 , μg/m3), sulfur dioxide (SO2, ppb), and nitrogen oxides (NOX, ppb)
are used as the air pollution variables. Each pollutant is used not only as a value, but also as adummy variable that takes one if it is above the critical threshold defined by the World HealthOrganization (WHO) (Bensnes, 2016). The threshold for PM25 and SO2 is 25 or more per hourand 20 or more per hour, respectively (WHO, 2005). NOX is only used linearly because there
is no defined threshold. Table 1 shows the descriptive statistics of the pollutants used in theestimates.

4.

Empirical strategies
To investigate the effects of pollen exposure on consumption expenditure, I estimate thefollowing model:
𝑃𝑃𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = 𝛼𝛼 + 𝛽𝛽𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 + 𝛾𝛾𝑋𝑋𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 + 𝑆𝑆𝑖𝑖 + 𝑌𝑌𝑦𝑦 + 𝑀𝑀𝑚𝑚 + 𝐷𝐷𝑑𝑑 + 𝑊𝑊𝑖𝑖𝑖𝑖 + 𝑍𝑍𝑖𝑖𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖(1)

where, 𝑃𝑃𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 represents consumption behavior in the catchment of pollen monitoring station𝑖𝑖 on day 𝑑𝑑, month 𝑚𝑚, and year 𝑦𝑦. 8 In the main analysis, consumption behavior is proxiedby total in-store purchases. 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 is a dummy variable that indicates whether pollen is

above the threshold and 𝛽𝛽 is the coefficient of interest. 9 𝑋𝑋𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 is a control vector thatOne concern with home scanner data is the timing of the scan. If the timing of the scan is after midnight,
the purchase amount is recorded on the next day. Therefore, I also analyzed purchases made before 8 a.m.
on the next day as being purchases for that day (except for purchases made at 24-hour convenience storesand vending machines). The results are similar to the main results (see Appendix B).
9
Estimates using alternative measures of pollen were also carried out (see Appendix C). Although there are812includes typical weather conditions such as daily average temperature, wind speed, andprecipitation. For the robustness checks, air pollution levels are also included in 𝑋𝑋𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 . 𝑆𝑆𝑖𝑖 ,𝑌𝑌𝑦𝑦 , 𝑀𝑀𝑚𝑚 , and 𝐷𝐷𝑑𝑑 are the fixed effects of the pollen monitoring station, year, month, and day,respectively. 𝑊𝑊𝑖𝑖𝑖𝑖 and 𝑍𝑍𝑖𝑖𝑖𝑖 are the interaction of the station-year and station-month fixed

effects, respectively, controlling the station-specific time trends. This allows us to address local
economic trends and seasonality. To account for the serial correlation of the errors, standard
errors are clustered at the station-year-month level. Controlling for the fixed effects in suchdetail allows me to identify the impact of exogenous variation in pollen on consumption
behavior.
To better understand the impact of pollen on purchasing behavior, I also make estimatesusing dependent variables other than total in-store purchases. First, the number of people whowent out shopping is used as the dependent variable to examine whether the increase in pollen
decreases outdoor activities. Second, to ensure that the results are not driven by the number ofmonitors assigned to each pollen monitoring station, I use the purchase amount per monitor.
Third, using online and mail order shopping expenditure, I confirm that allergy symptoms
caused by airborne pollen do not affect the accuracy and frequency of home scanner data. Since
online shopping can be ordered without going out and delivery usually takes a few days, itcannot be affected by the pollen on that day. If airborne pollen affects scanning behavior rather
than purchasing behavior, then spending on online and mail order shopping will also decrease.
Finally, I investigate pollen-related goods to confirm whether people’s behavior is affected bypollen. Since clinical studies such as Johnston et al. (2009) and Yamada et al. (2014) show that
medication, masks, and glasses sell well on high pollen days, I define such products as pollenrelated goods. The specific definitions are given in Appendix A.
some differences in significance levels, the results consistently show the negative effect of pollen on
consumption. This is also consistent with the relationship shown in the medical literature that it flattens when
the threshold is exceeded.
13

When considering the economic burden of pollen, the key question is whether the decline in
consumption will bounce back. Although bad weather and air pollution have a negative impact
on outdoor activities and consumption, this may simply be a postponement (Bloesch andGourio, 2015; Graff Zivin and Neidell, 2014; Sun et al., 2019). In other words, outdoor activity
may be postponed while the bad conditions continue and then rapidly increase when they areover. This suggests that the negative effects of high pollen days are transitory and have noserious impact on the economy. However, it is difficult to verify the degree to which theeconomy bounces back from continuously bad pollen conditions. Since clear thresholds arelacking, it is hard to state concretely that the high pollen period is over. Moreover, pollen canaffect sufferers the next day because allergy symptoms may not recover after one day andpollen can stay on clothes and indoor surfaces. Therefore, weekly and monthly data are
estimated using the average pollen level and number of high pollen days. If people avoid goingout on high pollen days and instead shop on low pollen days, then no negative effects of highpollen days should be observed at the weekly or monthly level. Conversely, if there is nobounce back effect, the number of high pollen days at the weekly or monthly level can have anegative effect.10

5.

Main results
Table 2 presents the estimates of the impact of pollen exposure on in-store consumption

expenditure. The treatment variable in each row is a dummy variable that takes one if the dailypollen count of a given monitoring station on a given day exceeds the 95th percentile (158.167).
Each column reports the coefficients, their standard errors, and, for the variable of interest, theeffect size as the percentage change in the dependent variable relative to its mean. Column (1)All the major analyses were performed at the level of pollen monitoring station, but weekly and monthly
data were also used at the monitor level to confirm the robustness of the main results (see Appendix D andAppendix E).
10

14

shows the results estimated by controlling for only the basic fixed effects. When weather is notcontrolled for, there is no significant relationship between high pollen days and consumption
expenditure. This is expected considering that the weather affects both consumption behavior
and pollen dispersal, and this result emphasizes the need to control for the weather. Column (2)uses a specification with additional controls for weather conditions. The results show that
consumer spending falls by 2.93% on high pollen days. Moreover, rainy days significantly
reduce consumption, which is consistent with previous research (Parsons, 2001) and common
sense. Columns (3) and (4) show the results with additional controls for the station-year andstation-month fixed effects, respectively. Controlling for the time-fixed effects at themonitoring station level can reduce the bias caused by differences in the trends in the local
climate, economic activity, and other unobserved factors. The results show that high pollen
days significantly reduce consumer spending by about 1.9%. Column (5) additionally controls
for the average pollen count in the weeks before and after the observation day to account for
the effect of airborne pollen on adjacent days, which can affect purchasing behavior on the day
in question. Column (5) indicates that even when controlling for pollen counts on adjacent days,
there is no noteworthy change in the effect of high pollen days. It is also plausible that higher
pollen levels in the previous week would slightly decrease consumer spending.
Hence, the main results show that regardless of the specification, store spending falls
significantly on high pollen days. The effect size is 2–3%, in line with the findings of related
studies that show that restaurant visitors decrease by about 4% on days with severe air pollution(Sun et al., 2019) and Citibike trips decrease by about 4% on days with high pollen counts(Chalfin et al., 2019).

15

High pollen day(1)
Coeff.
(Std. Err.)
Eff. Size-1261.277(879.019)-1.173%(2)
Coeff.
(Std. Err.)
Eff. Size***-3150.088(885.615)-2.930%(3)
Coeff.
(Std. Err.)
Eff. Size(4)
Coeff.
(Std. Err.)
Eff. Size(5)
Coeff.
(Std. Err.)
Eff. Size-2085.105***(616.992)-1.939%-1995.053(640.979)-1.856%**-2182.306(893.465)-2.030%

Average pollen countfor the previous week

-4.483*(1.866)0.690(2.821)

Average pollen countfor the following week
WeatherRain dummy***-9842.177***(818.949)-10012.620***(820.223)-11457.877(1142.275)***

571.098***(50.717)121.550(136.870)

603.265***(51.954)224.524(138.942)

726.048***(71.750)

YES
YES
YES
YES
YES
YES
95272
0.9364

YES
YES
YES
YES
YES
YES
73160
0.9358892.839(60.650)

Wind speed

1059.979(149.792)

2

Adjusted R***-9858.385(909.512)

Temperature

Fixed Effects
StationYearMonthDay of week
Station × Year
Station × Month
N

*

***

YES
YES
YES
YES

YES
YES
YES
YES

YES
YES
YES
YES
YES

96437
0.902296434
0.902695738
0.9363*418.968(188.590)

Table 2. Effects of daily high pollen on consumption expenditure in storesNote: *, **, and *** indicate statistical significance at 5%, 1%, and 0.1%, respectively. The effect size
indicates the percentage change in the dependent variable relative to its mean on a high pollen day. Standard
errors are adjusted for clustering at the station-year-month level.

16

6.

Robustness checks
The main results are robust even when controlling for the various fixed effects discussed

above. However, this section provides a series of robustness checks to address concerns about
the main analysis, such as the composition of the sample and use of arbitrary thresholds.

6.1. Changing the explained variable
In this section, estimates with altered explained variables are conducted to investigate how
people’s behavior changes on high pollen days. Table 3 shows the results for different explained
variables using the specification in column (4) of Table 2. Unless otherwise specified, thisspecification is used for all the subsequent estimations. Column (1) uses the number ofmonitors who purchased something in a store that day as the dependent variable. The results
show that high pollen has no significant effect on the number of people who make purchases,
which is somewhat unexpected considering that outdoor activities may be hampered byairborne pollen. One explanation is that people spend less time outside on high pollen days,
rather than not going outside at all. For example, on a high pollen day, people might buy the
essentials at a nearby convenience store instead of going to a suburban shopping mall or touringmultiple stores. 11
Column (2) presents the results using in-store purchase amount per monitor, showing thathigh pollen days significantly reduce per capita consumption expenditure, with effect sizes
similar to the aggregated consumption expenditure in Table 2. This indicates that the main
results are not sensitive to the number of monitors assigned to each monitoring station. Column(3) uses expenditure on goods purchased without going out, such as online and mail order
Regrettably, the distance between the monitor’s residence and place of purchase is not available from thedata. To investigate outdoor activities, an analysis was conducted using the differences between weekdays
and holidays. Although many people must go out on weekdays for work, holiday outings are arbitrary,suggesting that pollen may have a greater impact on outdoor activity in holidays. However, the results of theanalysis showed no significant difference between weekdays and holidays.

11

17

shopping, as the dependent variable. As expected, online and mail order shopping is not
significantly affected by high airborne pollen, emphasizing that the main results are not driven
by changes in scanning behavior. Column (4) shows that spending on pollen-related goods
increases significantly by about 32% on days when pollen is high. This exceptionally large
effect presents evidence that people respond sensitively to pollen exposure.

High pollen day

WeatherRain dummy

Number ofmonitors

Purchases permonitorOnline shop &Mail order

Pollen-related
goods

(1)
Coeff.
(Std. Err.)
Eff. Size-0.072(0.126)-0.105%(2)
Coeff.
(Std. Err.)
Eff. Size(4)
Coeff.
(Std. Err.)
Eff. Size-29.966(10.448)-1.87%(3)
Coeff.
(Std. Err.)
Eff. Size-62.681(156.374)-0.550%375.907(34.115)31.857%-4.449(0.213)***-37.644**(12.048)-74.835(160.741)-147.823(23.022)9.519***(1.749)

YES
YES
YES
YES
YES
YES
95272
0.6979**Temperature

0.162(0.010)***5.046***(0.726)Wind speed-0.127(0.029)***5.902(1.841)25.436*(12.336)23.685(32.640)

YES
YES
YES
YES
YES
YES
95272
0.9935

YES
YES
YES
YES
YES
YES
95272
0.1958

YES
YES
YES
YES
YES
YES
95272
0.7855

Fixed Effects
StationYearMonthDay of week
Station × Year
Station × MonthN
2

Adjusted R

**

***

***

*

10.958(4.793)

Table 3. Effects of daily high pollen on other explained variables
Note: *, **, and *** indicate statistical significance at 5%, 1%, and 0.1%, respectively. The effect size
indicates the percentage change in the dependent variable relative to its mean on a high pollen day. Standard
errors are adjusted for clustering at the station-year-month level.

186.2. Data composition
One potential concern with the main results is how the dataset is created. In the main analysis,monitors are aggregated by pollen monitoring station, but the distance from the station isdecided arbitrarily. While using monitors close to a station can reduce measurement errors forpollen exposure, it can also lead to bias by reducing the number of monitors aggregated.
Columns (1)–(5) of Table 4 show the results using monitors living within 5, 10, 20, 25, and 30
km of a pollen monitoring station, respectively. 12 Column (6) also shows the estimation results
using the data generated by assigning all monitors to the nearest pollen monitoring station.
Despite the wide variation in the number of monitors aggregated, from about 13,700 to about
66,700, the effects of high pollen days are negative, significant, and their sizes are remarkably
consistent in all the columns, confirming that the main results are not sensitive to the distanceat which the monitors are aggregated.

The data used in the main analysis are from monitors living within 15 km of a pollen monitoring station,
which numbered 46,080 people.
12

19

High pollen day

WeatherRain dummy
TemperatureWind speed
Fixed Effects
StationYearMonthDay of week
Station × Year
Station × Month# of aggregatedmonitorsN
2

Adjusted R

Monitors
within
5 km

Monitors
within
10 km

Monitors
within
20 km

Monitors
within25 km

Monitors
within30 km

Fullsample(1)
Coeff.
(Std. Err.)
Eff. Size(2)
Coeff.
(Std. Err.)
Eff. Size(3)
Coeff.
(Std. Err.)
Eff. Size(4)
Coeff.
(Std. Err.)
Eff. Size(5)
Coeff.
(Std. Err.)
Eff. Size(6)
Coeff.
(Std. Err.)
Eff. Size*-1258.720(437.871)-1.721%**-2225.964(748.260)-1.715%**-2516.903(789.188)-1.786%**-2622.737(809.036)-1.757%**-2788.157(838.034)-1.676%***-6619.283*** -12085.127***-13057.422***-13644.979 -14553.022***(554.598)(964.223) (1033.277) (1060.811) (1098.126)

146.355(16.426)
47.094(44.586)***

391.308***(35.158)
97.900(95.804)

744.026***(61.790)
194.195(166.556)

823.355***(65.674)
179.625(178.771)

883.805(67.505)133.807(186.045)

948.884***(70.089)
159.180(196.487)

YES
YES
YES
YES
YES
YES

YES
YES
YES
YES
YES
YES

YES
YES
YES
YES
YES
YES

YES
YES
YES
YES
YES
YES

YES
YES
YES
YES
YES
YES

YES
YES
YES
YES
YES
YES

13705

32151

54581

58485

61302

66671

90339
0.878694007
0.928995867
0.938595867
0.937495867
0.936595985
0.9340-409.748(206.295)-1.310%-2549.196(243.810)

***

***

***

Table 4. Estimates when changing the monitor catchment area
Note: *, **, and *** indicate statistical significance at 5%, 1%, and 0.1%, respectively. The effect size
indicates the percentage change in the dependent variable relative to its mean on a high pollen day. Standard
errors are adjusted for clustering at the station-year-month level.

206.3. Pollen threshold
The main analysis defines a high pollen day as a day with a pollen level in the 95th percentile
or higher, but this is also arbitrarily determined. Here, I re-estimate the specification in column(4) of Table 2 by varying the threshold for high pollen days to each percentile from the 90th to
99th percentiles. Figure 2 illustrates that the impact increases as the threshold rises. Naturally,as the threshold increases, the number of days corresponding to it decreases, and thus theprecision of the estimates falls. This indicates that the main results are not sensitive to threshold

Coefficients with 95% confidence bands

changes.

Pollen count percentile
Figure 2. Sensitivity of the threshold for high pollen days
Note: Each point represents a coefficient for a high pollen day defined as a pollen level higher than thepercentile indicated by the value on the horizontal axis. The confidence interval around the point estimate
reflects the 95% confidence interval. The other control variables are the same as in column (4) of Table 2,with the rain dummy, mean temperature, and mean wind speed as well as the fixed effects of the pollen
monitoring station, year, month, day, station-year, and station-month. Standard errors are adjusted forclustering at the station-year-month level.
216.4. Effects of air pollution
A critical concern with this study’s identification strategy is that other variables that correlatewith both daily pollen counts and purchasing behavior affect the results. Since pollen dispersal
is a natural phenomenon, such variables are unlikely to exist other than weather, but air
pollution could be a problem. If the main results are driven by a third factor such as air pollution,
the explanatory power of pollen could be markedly reduced or disappear when controlling forthe air pollution variables.
To address this concern, I re-estimate controlling for the air pollution variables. Columns (1)and (2) of Table 5 consider the air quality of each pollen monitoring station to be the averagevalue of the pollutant level reported by the air pollution monitors within 15 km of each station.
Column (1) includes the pollution variables as linear and column (2) includes them as dummies
that take one if they are above the critical threshold defined by the WHO. However, only NOX
is inputted as linear in both columns because the threshold is undefined. The results show thateven after controlling for the air pollution variable, the effect of high pollen days on store
consumption expenditure remains negative and significant. There is a slight decrease in theeffect size compared with the main results, but this may be due to a decrease in the sample
period. Column (3) shows the results using the same sample from 2013 to 2017, but excluding
the variable on air pollution, to check the impact of the shorter sample rather than air pollution.
The effect size in column (3) is slightly less than that in the main results in Table 2, suggestingthat the impact of controlling for air pollution is not considerable. Finally, columns (4) and (5)use the average pollution levels reported by air pollution monitors within 5 km and 25 km of apollen monitoring station, respectively to ensure that the results are not sensitive to how thepollution variables are generated. Although the effect size changes because of the change in thenumber of available pollen monitoring stations, both coefficients of interest are negative andsignificant, indicating that the results are not sensitive to the way the pollution variables are22generated. The results indicate that pollen has an effect independent of air pollution, enhancing
the credibility of the identification strategy used in this study.

23

High pollen day

WeatherRain dummy
TemperatureWind speed
Air pollution
PM25

Air pollution
monitors
within5 km(4)Coeff.
(Std. Err.)
Eff. Size

Air pollution
monitors
within25 km(5)Coeff.
(Std. Err.)
Eff. Size-1975.793*(925.496)-1.551%-2141.482*(928.093)-1.681%-3223.326**(1236.777)-2.193%-2004.084*(857.472)-1.675%-11243.053*** -12593.997*** -11692.467***(1227.657)(1220.147)(1567.545)-9367.346***(1155.648)

Controlling
air pollution(dummy)(1)
Coeff.
(Std. Err.)
Eff. Size-1929.672*(924.830)-1.515%***-10272.937(1238.302)***

826.207(80.479)-17.582(209.977)

942.390***(80.468)-69.859(209.434)

900.235***(80.037)*519.239(202.124)

202.462***(30.335)

Critical PM25 dummy
SO2(2)
Coeff.
(Std. Err.)
Eff. Size

Restricted
samplewithout air
pollution(3)
Coeff.
(Std. Err.)
Eff. Size

Controlling
air pollution(linear)N
2

Adjusted R

265.099***(36.168)204.790***(28.921)585.017***(162.680)

1032.471***(142.832)-814.116***(55.767)-724.774***(53.166)***

Critical SO2 dummy

Fixed Effects
StationYearMonthDay of week
Station × Year
Station × Month# of aggregated air
pollution monitors

770.860***(74.761)-130.087(198.679)

2802.074(733.510)
837.724***(129.987)

NOX

914.711***(103.323)-51.833(247.846)

333.679(3359.727)-669.092***(50.144)-585.109***(45.954)

YES
YES
YES
YES
YES
YES

YES
YES
YES
YES
YES
YES

YES
YES
YES
YES
YES
YES

YES
YES
YES
YES
YES
YES

YES
YES
YES
YES
YES
YES

1310

1310

NA

444

1731

55664
0.934255664
0.934155667
0.933742168
0.934859776
0.9350

Table 5. Estimates when controlling for air pollution
Note: *, **, and *** indicate statistical significance at 5%, 1%, and 0.1%, respectively. The effect size
indicates the percentage change in the dependent variable relative to its mean on a high pollen day. Standard
errors are adjusted for clustering at the station-year-month level.
24

7.

Transitory vs. lasting effects
To understand the economic impact, it is important to show whether the above-described

negative effects of high pollen are lasting or transitory. One possible concern is that the decline
in consumption due to high pollen may simply be a postponement, as studies of pollution andbad weather have shown (Bloesch and Gourio, 2015; Graff Zivin and Neidell, 2014; Sun et al.,2019). If consumption simply bounces back, the bounce back effect would cancel out the
decline in consumption expenditure and pollen would have no lasting negative impact on theeconomy. High pollen usually does not continue for more than a few days; hence, if there is abounce back effect, no negative effects of pollen should be observed at the weekly or monthly
level. Alternatively, people might use online or mail order shopping instead of going out on ahigh pollen day. Because of the characteristics of online and mail order shopping, daily level
data cannot analyze the impact of pollen. However, by using data at the weekly or monthly
level, an alternative relationship between online and offline shopping due to high pollen levels
may be observed. If the number of high pollen days in a week or month increases online
consumption, the impact on total consumption expenditure may be limited, because pollen onlychanges the way of consumption. To determine whether the effects of pollen are transitory, thissection thus examines in-store and online expenditure, using data aggregated at the weekly andmonthly levels.
Table 6 shows the results using weekly data. In columns (1) and (2), the dependent variable
is weekly in-store consumption expenditure and the control variables are the number of rainy
days, average temperature, and average wind speed as well as the fixed effects of the pollen
monitoring station, year, week, and station-year. Column (1) shows that a one standard
deviation increase in average weekly pollen from the mean reduces in-store consumption
expenditure by about 0.28%. Column (2) shows that one additional high pollen day reduces
weekly in-store consumption expenditure by about 0.4%. The reduction in in-store
25

consumption expenditure due to one additional high pollen day is about 3,000 yen, which isplausible given that the daily-level analysis shows a reduction in consumption on high pollen
days of about 2,000 yen and that the effect of high pollen days stays for several days to someextent. In addition, the rainy day, which showed a large impact in the daily-level analysis, nolonger has a significant effect on consumption expenditure. This indicates that rainfall only
makes people postpone their purchasing behavior rather than reduce their weekly spending.
Hence, in contrast to rainy days, the reduction in consumption caused by high pollen days does
not bounce back, suggesting that the effects of pollen may be lasting.
Columns (3) and (4) present the impact of weekly pollen on weekly online and mail order
shopping expenditure. The increase in pollen levels during the week reduces online spending.
As shown in Column (4), one additional high pollen day significantly reduces weekly online
spending by about 0.74%. One probable reason is the decrease in disposable income due to theincreased medical costs of allergies (Erbas et al., 2012). Alternatively, a feeling of lethargy or
depression caused by the allergy symptoms may reduce the desire to consume (Dahal and Fertig,
2013).

26

Store purchase(1)
Coeff.
(Std. Err.)
Eff. Size
Average pollen countTemperatureWind speed
Fixed Effects
StationYear
Week
Station × YearN
2

Adjusted R(2)
Coeff.
(Std. Err.)
Eff. Size*-14.589(6.004)-0.279%# of high pollen days

Weather# of rainy days

Online shop & Mail order(3)
Coeff.
(Std. Err.)
Eff. Size-2.991(1.571)-0.538%-591.722*(240.541)-0.737%***-3055.505(788.247)-0.405%-153.404(1732.342)-173.214(1735.823)

2286.123(614.140)
2830.789(1394.460)***

2442.636(636.079)
2643.319(1392.457)

YES
YES
YES
YES
13354
0.9952

YES
YES
YES
YES
13354
0.9952***(4)
Coeff.
(Std. Err.)
Eff. Size

883.390(479.209)194.169(149.494)

880.157(479.379)
222.425(151.551)

1119.269(429.666)**

1083.499(430.478)

YES
YES
YES
YES
13354
0.9666

YES
YES
YES
YES
13354
0.9666*

Table 6. Estimates when using weekly data
Note: *, **, and *** indicate statistical significance at 5%, 1%, and 0.1%, respectively. The effect size of theaverage pollen count indicates the percentage change in the dependent variable relative to its mean when
average weekly pollen increases by one standard deviation from the mean. The effect size of the number ofhigh pollen days indicates the percentage change in the dependent variable relative to its mean when one
additional high pollen day is added. Standard errors are adjusted for clustering at the station-year level.

27

Table 7 shows the results using monthly data. Columns (1) and (2) both use monthly in-store
consumption expenditure as the dependent variable (average pollen level and number of highpollen days per month, respectively). The results are consistent with those using the weekly
data, showing that high pollen levels reduce monthly consumption expenditure significantly.
The magnitude of the coefficient is also reasonable, indicating that an additional high pollen
day would reduce monthly consumption expenditure by about 6,700 yen (0.2%). Comparedwith the results of Kang et al. (2019), who found that one additional day of PM10 above thecritical threshold reduces monthly retail sales by about 0.1%, the impact of an additional highpollen day is considerable. Columns (3) and (4) use monthly online and mail order shopping
expenditure as the dependent variable. These results are also consistent with those obtainedusing weekly data, indicating that an increase in average pollen level and additional high pollen
days significantly reduce online spending.
In summary, the reduction in consumer spending due to pollen does not bounce back.
Furthermore, no shift from in-store consumption to online and mail order shopping occurs,
indicating that high pollen levels can reduce consumption expenditure. This suggests that highpollen levels could have a lasting negative impact on the economy.

28

Store purchase(1)
Coeff.
(Std. Err.)
Eff. Size
Average pollen count -244.942**(84.864)-0.597%# of high pollen days

Weather# of rainy days

Online shop & Mail order(2)
Coeff.
(Std. Err.)
Eff. Size(3)
Coeff.
(Std. Err.)
Eff. Size(4)
Coeff.
(Std. Err.)
Eff. Size*-49.745(23.142)-1.144%-1757.858***(475.079)-0.509%**-6713.307(2511.847)-0.206%

Temperature

9797.982(4730.080)*

10736.728(4774.747)

16.756(10.577)
51.969(1013.423)

Wind speed

79113.635(23417.727)***

77581.535***(23287.643)

13889.132(4482.359)

13453.891(4465.736)

YES
YES
YES
YES
2647
0.9949

YES
YES
YES
YES
2647
0.9949

YES
YES
YES
YES
2647
0.9879

YES
YES
YES
YES
2647
0.9880

Fixed Effects
StationYear
MonthStation × YearN
2

Adjusted R

146.982(70.317)*148.005*(70.247)*

17.025(10.549)482.501(1008.089)****

Table 7. Estimates when using monthly data
Note: *, **, and *** indicate statistical significance at 5%, 1%, and 0.1%, respectively. The effect size of theaverage pollen count indicates the percentage change in the dependent variable relative to its mean when the
average monthly pollen increases by one standard deviation from the mean. The effect size of the number ofhigh pollen days indicates the percentage change in the dependent variable relative to its mean when one
additional high pollen day is added. Standard errors are adjusted for clustering at the station-year level.

29

8.

Conclusion
To determine the impacts of pollen exposure on consumption behavior, this study used a

combination of purchase records from 2013 to 2019 based on home scanner data and pollencounts recorded at monitoring stations throughout Japan to analyze changes in consumption
expenditure due to daily pollen variations. The results provided robust evidence that in-store
consumption expenditure decreases by about 2% on high pollen days. Furthermore, this effectis also observed using weekly and monthly data, showing the lasting effect of pollen on the
consumption expenditure reduction.
This study presents novel evidence that pollen exposure inhibits economic activity, thereby
revealing that pollen reduction has economic benefits. The magnitude of the impact of airbornepollen found is close to that of air pollution shown by previous studies (Kang et al., 2019; Sunet al., 2019). The study’s findings emphasize that economists should pay as much attention topollen as they do to air pollution. In addition, although pollen is a natural phenomenon, it canincrease with human activity, suggesting that its potential negative effects could become evengreater in the future. The results of this study may also be an underestimate, as the data used
did not include expenditure on eating out and leisure activities. If pollen exposure has a greater
impact on eating out and leisure activities, the negative effects of pollen would become evenmore critical.
The key findings of this study provide a new perspective that pollen should be considered,
thereby adding to the literature in the fields of urban planning, climate change, public health,and environmental policy. Social welfare can improve by considering the negative externalities
caused by pollen when conducting urban planning. Since many industrialized countries havegreened their cities to improve living conditions and landscapes, ignoring the effects of pollen
could result in an unintended public nuisance. The impact of pollen should also be considered
in forestry given that the cause of seasonal allergies in Japan is the artificial planting policy
30

pursued during the economic growth period. In addition, since it is difficult to remove the causeof the pollen once it has been produced, accurate forecasts about pollen dispersal may help
seasonal allergy sufferers better prepare for it. There is also a need to develop and roll out
medicines to relieve the symptoms of seasonal allergies. The effective prevention of pollen can
not only improve people’s well-being, but also promote economic activity.
Despite the series of fixed effects and robustness checks used in this study, there are somecaveats and limitations to the results. First, the home scanner data used in this study do not
represent total household consumption expenditure, as goods whose prices are unknown are
excluded from the analysis. Therefore, if there is any association between the prevalence of
seasonal allergies and characteristics of the products purchased, the results may be biased.
Second, this study uses weekly and monthly data to confirm that the effects of pollen are
lasting, but does not discuss longer-term effects. The negative effects of bad seasonal weather
such as cold summers and warm winters can bounce back the following season. Similarly,
people may avoid purchasing in high pollen seasons and consume more in non-pollen seasons.
This is a challenge for future research, as longer-term data are needed to examine this.
Finally, the identification strategy of this study does not reveal in detail the mechanisms thatlead to the reduction in consumption. Is the decline in consumption due to less disposable
income or less outdoor activity? If the latter, is it because people are avoiding pollen? Or are
the allergy symptoms so painful that people cannot leaves their homes? To answer thesequestions, it would be fruitful to conduct an analysis with data that could identify seasonalallergy sufferers. The impact of pollen on outdoor activities may also be understood by using
more detailed data on the location of product purchases. In addition, investigating the path ofthe decline in consumption is an interesting challenge. If less outdoor activity and fewer
opportunities for consumption are being turned into savings, then enhanced online and mailorder shopping might solve the problem. If seasonal allergies reduce labor productivity, which31

in turn lowers income and reduces consumption, then the problem is exacerbated. Using
detailed data, including precise household income, savings, and health care costs, to identify
the mechanisms by which airborne pollen affects individual behavior is thus another important
future task.

Acknowledgements
I am grateful to Macromill Inc. for making available the scanner data used in this work. Allremaining errors are my own. This research did not receive any specific grant from funding
agencies in the public, commercial, or not-for-profit sectors.

32

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37

Goods category
Medical supplies
Eye, nose and ear care products
Oral allergy medication
Rhinitis medicine
Nasal congestion remedy
Anti-drowsiness drug
Tissue paper
Mask
Eye drops
Other hygiene and medical products
Other medicines for sensory organs
Other respiratory medicines
Glasses and contact lenses
GlassesSpectacle lensGlasses related productsContact lensChemicals for contact lenses

Appendix A. List of pollen-related goods
Source: Macromill Inc.

38

High pollen day

WeatherRain dummy
TemperatureWind speed
Fixed Effects
StationYearMonthDay of week
Station × Year
Station × MonthN
Adjusted R2

Storepurchases

Number ofmonitors(1)
Coeff.
(Std. Err.)
Eff. Size(2)
Coeff.
(Std. Err.)
Eff. Size-0.091(0.126)-0.134%**-1997.171(640.928)-1.876%Store
purchases
per monitor(3)
Coeff.
(Std. Err.)
Eff. Size**-29.123(10.481)-1.819%Pollenrelated
goods
(5)
Coeff.
(Std. Err.)
Eff. Size

Online shop
& Mailorder(4)
Coeff.
(Std. Err.)
Eff. Size-49.831(155.065)-0.444%362.333(33.889)
31.116%***-9971.891(818.505)***-4.429(0.213)***-37.375(12.077)**-63.327(159.431)-148.876(22.781)***0.157***(0.010)4.871***(0.729)*9.505***(1.747)

YES
YES
YES
YES
YES
YES
95272
0.6947587.790(51.822)230.173(138.655)-0.125(0.029)***6.387**(1.836)27.166(12.199)30.855(32.311)

YES
YES
YES
YES
YES
YES
95272
0.9354

YES
YES
YES
YES
YES
YES
95272
0.9934

YES
YES
YES
YES
YES
YES
95272
0.1941

YES
YES
YES
YES
YES
YES
95272
0.7826****10.866(4.754)

Appendix B. Estimation results when considering the scan lag
Note: *, **, and *** indicate statistical significance at 5%, 1%, and 0.1%, respectively. The effect size
indicates the percentage change in the dependent variable relative to its mean on a high pollen day. Standard
errors are adjusted for clustering at the station-year-month level.

39

Dependent Variable
Pollen count

level
(1)
-0.958(0.495)

level
(2)

log
(3)

**

-2.635(0.853)ln(Pollen count)-8.695(136.001)

Pollen count suqared
WeatherRain dummy
TemperatureWind speed
Fixed Effects
StationYearMonthDay of week
Station × Year
Station × MonthN
2

Adjusted R

level
(4)

-4

-5.952×10-4(29.084×10 )-5*

3.317×10(1.289×10-5 )-9971.008(931.941)***-9955.701(822.566)***-0.114***(0.017)-10011.046(819.997)***

591.264(58.646)225.869(157.925)***

585.567***(52.466)224.943(138.862)

0.005***(0.001)0.001(0.002)600.076(51.959)228.640(139.038)

YES
YES
YES
YES
YES
YES
95272
0.9364

YES
YES
YES
YES
YES
YES
95272
0.9364

YES
YES
YES
YES
YES
YES
95272
0.8478

YES
YES
YES
YES
YES
YES
95271
0.9364***

Appendix C. Estimates of the alternative specification
Note: *, **, and *** indicate statistical significance at 5%, 1%, and 0.1%, respectively. The dependent
variable in columns (1), (2), and (4) is in-store consumption expenditure. The dependent variable in column(3) is in-store consumption expenditure taken as the natural logarithm. Standard errors are adjusted forclustering at the station-year-month level.

40

Store purchase(1)
Coeff.
(Std. Err.)
Eff. Size
Average pollen count(2)
Coeff.
(Std. Err.)
Eff. Size-0.032*(0.012)-0.159%# of high pollen days

Weather# of rainy days

Online shop & Mail order(3)
Coeff.
(Std. Err.)
Eff. Size-0.006(0.005)-0.281%-18.688***(4.039)-0.410%-0.142(5.762)-1.019(5.768)(4)
Coeff.
(Std. Err.)
Eff. Size

Pollen-related goods(5)
Coeff.
(Std. Err.)
Eff. Size
***

0.004
(0.000)1.804%*

5.001***(0.305)9.947%-3.976(1.843)-0.821%
2.957(2.729)0.784(0.990)2.768(2.730)1.173(1.011)-0.399(0.335)-0.146(0.334)***1.709***(0.131)
0.675(0.305)
YES
YES
YES
YES
YES
2351629
0.0125Temperature

9.754(2.166)***1.544***(2.208)Wind speed***

28.992(6.599)YES***

11.530(3.139)YES***

11.334(3.140)
YES

***

2.270
(0.130)0.438(0.374)
YES

YES
YES
YES
YES
23516290.1916

YES
YES
YES
YES
23516290.0256

YES
YES
YES
YES
23516290.0256

YES
YES
YES
YES
2351629
0.012429.914(6.599)Individual characteristics
YES
Fixed Effects
Station
YES
Year
YES
Week
YES
Station × Year
YES
N
23516292
0.1916
Adjusted R(6)
Coeff.
(Std. Err.)
Eff. Size

Appendix D. Estimates when using scan monitor-level data (weekly)
Note: *, **, and *** indicate statistical significance at 5%, 1%, and 0.1%, respectively. The effect size of theaverage pollen count indicates the percentage change in the dependent variable relative to its mean when the
average weekly pollen increases by one standard deviation from the mean. The effect size of the number ofhigh pollen days indicates the percentage change in the dependent variable relative to its mean when one
additional high pollen day is added. Individual characteristics are controlled for: age, gender, marital status,presence of children, whether the individual is the head of the household, whether he or she is the main
shopper in the household, occupation reported in 12 categories, family structure reported in five categories,
housing type reported in six categories, household annual income reported in 14 categories, and family size
reported in five categories. Standard errors are adjusted for clustering at the station-year level.

41

Store purchase(1)
Coeff.
(Std. Err.)
Eff. Size
Average pollen count(2)
Coeff.
(Std. Err.)
Eff. Size(3)
Coeff.
(Std. Err.)
Eff. Size-1.461***(0.229)-0.754%# of high pollen days

Weather# of rainy days

Online shop & Mail order(4)
Coeff.
(Std. Err.)
Eff. Size*(5)
Coeff.
(Std. Err.)
Eff. Size(6)
Coeff.
(Std. Err.)
Eff. Size

0.077***(0.009)3.621%-0.186(0.091)-0.905%-125.262***(9.869)-0.643%Pollen-related goods***

5.022***(0.526)2.349%-18.362(4.028)-0.889%325.967(21.827)***330.032***(21.824)

36.269***(8.969)36.847***(8.974)2.950***(0.878)2.775**(0.877)***17.947(0.869)-3.130(2.876)
YES

YES
YES
YES
YES
5788580.0399Temperature

590.198(22.901)***

657.763***(23.684)

54.003***(8.202)64.262***(8.407)Wind speed***

406.439(79.156)
YES***

103.135(34.141)
YES**

100.209(34.148)
YES

20.418(0.857)-3.923(2.877)
YES

YES
YES
YES
YES
5788580.2890

YES
YES
YES
YES
5788580.0549

YES
YES
YES
YES
5788580.0550

YES
YES
YES
YES
5788580.0398426.339(79.255)
Individual characteristics
YES
Fixed Effects
Station
YES
Year
YES
Month
YES
Station × Year
YES
N
57885820.2888Adjusted R

**

***

Appendix E. Estimates when using scan monitor-level data (monthly)
Note: *, **, and *** indicate statistical significance at 5%, 1%, and 0.1%, respectively. The effect size of theaverage pollen count indicates the percentage change in the dependent variable relative to its mean when the
average monthly pollen increases by one standard deviation from the mean. The effect size of the number ofhigh pollen days indicates the percentage change in the dependent variable relative to its mean when one
additional high pollen day is added. Individual characteristics are controlled for: age, gender, marital status,presence of children, whether the individual is the head of the household, whether he or she is the main
shopper in the household, occupation reported in 12 categories, family structure reported in five categories,
housing type reported in six categories, household annual income reported in 14 categories, and family size
reported in five categories. Standard errors are adjusted for clustering at the station-year level.

42