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

Income Receipt, Economic Activities, and Health:
Evidence from Ambulance Transport Patterns
井深陽子、濱秋純哉
2024 年 3 月 25 日
DP2024-006
https://ies.keio.ac.jp/publications/23635/

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

Income Receipt, Economic Activities, and Health: Evidence from Ambulance Transport
Patterns
井深陽子、濱秋純哉

IES Keio DP2024-006
2024 年 3 月 25 日

JEL Classification: H55, H75, I12
キーワード: Emergency Medical Services, Social Security Payment, Time Stamp, Excess
sensitivity【要旨】

Studies suggest that mortality increases after income receipt. To examine whether theadverse effect of income on health is induced by economic activities and how certain
economic activities are related to specific health conditions, we investigate withinmonth cycles in ambulance transport, utilizing detailed information on the locations of
the origin and timing of the transports. Our analysis exploits the difference in thenumber of patients on the same day between payment and non-payment months,
using the Japanese National Pension for the elderly that is distributed bi-monthly. We
observe a 4.5% increase in ambulance transports on the day of pension payment,
primarily attributed to heightened economic activities such as gambling or
amusement, shopping, and dining out. We have suggestive evidence indicating that
this increase in transport is linked to a relaxation in liquidity. These findings have
implications for healthcare system preparedness and the optimal design of publicbenefit payment.
井深陽子
慶應義塾大学経済学部
東京都港区三田二丁目15-45
ibuka@econ.keio.ac.jp
濱秋純哉
法政大学経済学部
東京都町田市相原4342
hamaaki@hosei.ac.jp

Income Receipt, Economic Activities, andHealth: Evidence from Ambulance Transport
Patterns ∗Yoko Ibuka †Junya Hamaaki ‡March 25, 2024
Abstract

Studies suggest that mortality increases after income receipt. To examine whether the adverse
effect of income on health is induced by economic activities and how certain economic activities are
related to specific health conditions, we investigate within-month cycles in ambulance transport,
utilizing detailed information on the locations of the origin and timing of the transports. Our
analysis exploits the difference in the number of patients on the same day between payment andnon-payment months, using the Japanese National Pension for the elderly that is distributed bimonthly. We observe a 4.5% increase in ambulance transports on the day of pension payment,
primarily attributed to heightened economic activities such as gambling or amusement, shopping,and dining out. We have suggestive evidence indicating that this increase in transport is linked toa relaxation in liquidity. These findings have implications for healthcare system preparedness andthe optimal design of public benefit payment.
Keywords: Emergency Medical Services; Social Security Payment; Time Stamp; Excess
sensitivity.
JEL classification : H55; H75; I12∗

We are grateful for comments from seminar participants at the University of Hawai‘i at Mānoa and session
participants at the 15th IHEA World Congress. This research is supported by JSPS Kakenhi 19K01703, 20K01731
and 21H04396. The authors declare that they have no competing financial interests that could have appeared toinfluence the work reported in this study.
†
Corresponding author. Faculty of Economics, Keio University & Department of Economics, University of Hawai‘i
at Mānoa. 2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan. Email: ibuka@econ.keio.ac.jp‡
Faculty of Economics, Hosei University. 4342 Aihara, Machida City, Tokyo, 194-0298, Japan. Email:hamaaki@hosei.ac.jp1

1

Introduction

Recent research has increasingly focused on the causal effects of income on health to better understand the socioeconomic determinants of health and formulate effective designs for public policy.
The effects could depend on the duration over which the impact of income receipt is observed because different mechanisms exist within the time frame of health- and non-health-related decision
making and the emergence of their consequences. In the medium and long run, it is theoretically
expected that income generally encourages health investment, through improved nutrition intake
and enhanced preventive care.1
For short-term effects, the theoretical expectation is not very clear. Moreover, empirical studies
consistently demonstrate a negative effect of health, showing that mortality, as an ultimate measureof health status, tends to increase upon income arrival. The negative impact of income receipt
on health is crucial for understanding the intricate relationship between economic conditions andhealth, highlighting the possibility that income not only positively affects people’s health throughan increase in health investment, as often expected, but could also harm one’s health throughcertain economic mechanisms.
One such economic mechanism is an increase in the consumption of harmful goods or engagement in economic activities that potentially elevate health risks. Consistent with this, a study
revealed health deterioration following the arrival of income, as evidenced by an increase in healthcare visits and mortality related to illicit drug use (Dobkin and Puller, 2007). Subsequently, Evans
and Moore extended the analysis to encompass a broader spectrum of causes of death, demonstrating that this phenomenon is more universal (Evans and Moore, 2011, 2012). Their studies revealthat the increase in mortality includes deaths due to external causes such as traffic accidents or
non-accident-related injuries as well as deaths due to illnesses such as heart disease, heart attack,and stroke. Moreover, Evans and Moore show a parallel monthly cycle in economic activities withmortality patterns, measured by an increase in lottery sales and foot traffic in shopping malls(Evans and Moore, 2012). These studies suggest that economic activities induced by income are
detrimental to health, impacting diverse dimensions of well-being.
There are still important unanswered questions about the relationship among income receipt,
1

Interestingly, empirical evidence does not necessarily support the theoretical expectation, demonstrating that
income per se has only limited effects on an individual’s physical health and health-related activities, using lottery
winnings as a random assignment of positive income shocks (Cesarini et al., 2016; Östling et al., 2020) or tax
reform as an exogenous change in wealth (Erixson, 2017).

2

economic activity, and health. The first and most important question is whether economic activities
indeed contribute to health deterioration after income arrival. Thus far, no analyzed data records
both daily activities and health conditions at the individual level around the day of income receipt,
and as a result, there is no observed direct link between the two. Establishing a connection between
activities and the corresponding health changes within a short time frame is a challenging yet
crucial task to enhance our understanding of the relationship. The second question is what typesof economic activities are more likely to be boosted by income arrival than others. A previous
study indicated that shopping, watching movies, and buying lottery tickets increase around income
arrival but watching Major League Baseball games and using the subway do not (Evans and Moore,2012). Examining a comprehensive set of activities based on detailed information about where theyoccur enhances the understanding of the various pathways between income arrival and health anddetermines the relative risk of each type of economic activity. The third question is how particular
health conditions are related to certain economic activities. Medical literature shows that various
activities could elevate the risk of cardiovascular events (Schwartz et al., 2018). Whether and how
the relationship extends to other health risks beyond cardiovascular events provides knowledge ona physiological path from economic activities to health.
We address these questions utilizing ambulance transport records. Using ambulance transport
data has several advantages. First, it captures nonfatal health changes while being better suited
to measuring immediate health changes. Other types of medical utilization are often scheduled
in advance and therefore have a slight discrepancy with health changes. Second, the ambulance
records encompass detailed information on the location of incidence as well as its severity and diagnosis. The location information is key information in identifying the type of economic activities.
In addition, ambulance transport provides a disease code for each case, aiding in linking economic
activities with specific health problems. Third, the ambulance transport data records the exact
time when emergency medical services are called, providing insights into how people’s behavior
changes not only over the course of a month but also throughout the day.
We hypothesize that income arrival increases with ambulance transports reflecting health deterioration, and that the increase is generated by increased economic activities upon income arrival.
Previous studies use various types of income shocks, including regular payment and one-time
payment, to investigate short-term responses to income arrival.2 We use pension payment as
2

These include social security payments, government transfers, salary payments, tax rebates, and dividend payments.

3

an income shock following previous studies (Evans and Moore, 2011; Gross et al., 2022).3 The
Japanese setting confers benefits for analyzing the impact of income receipt on health for at least
two reasons. First, pension benefits in Japan are, in principle, distributed on the 15th every twomonths. This allows for a comparison of within-month cycles in ambulance transport between themonths of payment and non-payment, controlling for some day-specific effects within a month.
While within-month cycles in mortality studies often focus on the first day of the month as a dayof payment, changes in economic activities could be driven by the “beginning of month effect”
or nonincome factors that occur around the first day. In this study, we aim to provide evidence
that health deterioration upon income arrival occurs, controlling for a specific day effect within agiven month. Some previous studies have addressed this issue utilizing differences in the timingof income receipt to identify the effect. For example, one study uses the different timing of salary
payment in a month (Andersson et al., 2015). Another study uses the difference in the timing of
economic stimulus payments in 2008, which are determined randomly by people’s social security
numbers (Gross et al., 2014).
Second, pension payments represent a large share of elderly people’s income, and there are no
significant income shocks on other days for the majority of these individuals. Furthermore, thepension system is compulsory and covers all elderly individuals in the country with no opt-out
options.
In addition to the pension system, the Japanese setting offers two additional merits to theanalysis in the context of the response to income shocks. First, in Japan’s emergency medical
services, calling for and using an ambulance is free of charge, helping disentangle the health effectfrom the income effect in the observed change in ambulance transport. There are potentially two
pathways to explain an increase in healthcare utilization upon income arrival: immediate healthdeterioration (the health effect) and a service utilization increase due to a relaxation of liquidity
constraints (the income effect) (Gross et al., 2014, 2022). The use of healthcare with costs prevents
us from distinguishing between these two effects, whereas a setting with no costs for emergency
medical services allows us to concentrate solely on the direct impact of health changes, independentof income.
Another noteworthy aspect of the Japanese setting is the deep dependence on cash in transac3

Evans and Moore (2011) examine the effect of income receipt on mortality using social security payment as anincome shock. Gross et al. (2022) examine the effect of gaining liquidity on prescription drug purchases using
within-month variation in social security payment.

4

tions. The literature shows that one of the major reasons for health changes is stimulated economic
activity through improved liquidity (Evans and Moore, 2012). This suggests that the health effects
associated with receiving income may be more noticeable among individuals who lack the means
to effectively smooth their consumption patterns. Japan has a deeply ingrained reliance on cash asa mode of payment, especially among the elderly population, who may not be familiar with electronic payment methods.4 Consequently, the use of credit cards, a typical device for consumption
smoothing, is less prevalent than in other countries.
Related to this point, to illustrate the motivation of our study, Figure 1 shows the numberof persons who withdraw cash from an ATM provided by Mizuho Bank Inc., one of the threelargest commercial banks in Japan, for those aged 75 years old in 2016. Red dashed vertical lines
show the pension payday, which is, in principle, the 15th of even-numbered months, and dark graydashed lines show a corresponding date in odd-numbered months. ATM cash withdrawal spikes
only on the day of pension payment that comes every other month, and no such hike is observed
for the corresponding day in odd-numbered months, or on any other days. The figure suggeststhat pension payments are made promptly on the scheduled day without any expeditions or delays
and that a significant number of elderly individuals eligible for pensions tend to withdraw cash on
the specific day of their pension payment. The figure shows only one age group for one year, but
the pattern is consistent across age groups and years (Appendix Figure B1).
This study contributes to four key areas of literature. First, as noted above, it contributes
to the short-term effect of income on health by connecting income receipts with health changes
following economic activities. Second, our result has an implication regarding excess sensitivity inconsumption. Previous studies show that people do not always smooth their consumption and areoften responsive to the arrival of anticipated income changes. We show that such a change occurs
in the broader range of consumption and it causes a negative impact on health, an important
aspect of welfare. Third, recent studies show the unintended effect of government transfers on
various welfare outcomes, such as education and crime (Carr and Packham, 2019; Bond et al.,2022). This study contributes to the literature, indicating that income transfer elevates healthrisks among elderly individuals. Fourth, there is a series of studies on the optimal payment cycle(LaPoint and Sakabe, 2022), and this study has implications for the literature by demonstrating
4

The ratio of currency circulation to GDP in Japan was slightly above 20% in 2017, nearly double that of theEurozone and India, more than double that of the US and Korea, and four times higher than Canada, the UK,
and Sweden (Sakamaki, 2019). Yoshizawa et al. (2021) argue that a greater preference for cash among elderly
individuals is the reason for the high circulation of banknotes in Japan.

5

Figure 1: Daily number of individuals aged 75 years who withdraw cash, 2016

Notes: The figure shows the daily number of individuals who withdrew cash aged 75 years in 2016, obtained froma large commercial bank in Japan. The red dashed lines show the pension payday, typically on the 15th of evennumbered months, and the gray dashed line shows the corresponding day in odd-numbered months.

that the payment cycle affects one’s welfare not only through consumption but also health status.

2

Institutional Background

2.1

The Japanese National Pension System

This section briefly introduces Japan’s public pension system (Japanese National Pension System).
The public pension system is compulsory, and there is almost no opt-out option. The public pension
system was established in 1961, and since then nearly all elderly individuals in Japan have received
pension payments under the public system.5 The system consists of two tiers. The first tier isthe basic pension scheme (Rorei kiso nenkin). The basic pension requires a minimum of ten yearsof contributions, and a full pension requires 40 years of contributions. The benefits are adjusted
proportionally for shorter contribution periods. The contribution is flat-rate, and the payment isalso fixed across individuals. In 2020, the full annual basic pension benefit was 781,700 Japaneseyen (5,390 US dollars). The second tier is built on top of the first tier and is salary-based, andthose who were employed receive additional payments (Rorei kosei nenkin). For the second tier,
5

There is also a voluntary pension scheme provided by employers in addition to the public pension system. In 2019,31% of those aged 25 to 64 years were eligible for the employer-provided pension.

6

the contribution is earning-related and the amount of pension payment is also earning-dependent.
The earning-related pension benefit is based on the remuneration and the length of the period thatan individual enrolls in (that is, how long they were employed). Most importantly, in both thefirst and second tiers, the payments are combined and paid on the same day, and the distinction
between the first and second tiers does not matter for pensioners who receive both.
For the basic pension, pension payment, in principle, starts in the next month in which one
reaches 65 years of age.6 For the second tier, pensionable age depends on cohorts and genderbecause the pensionable age is gradually shifting from 60 years old to 65 years old.7 However,
all the cohorts become eligible to receive a pension no later than 65 years of age. The pension
system allows some flexibility for individuals to receive it earlier or later than at 65 years of age,and the payment amount is adjusted accordingly. Those who chose to receive their pension before
their pensionable age represent 26.1% for the first-tier basic scheme and 0.5% for the second-tiersalary-based pension scheme in 2020. Together with cohorts who become eligible after 65 years ofage to receive a pension from the second tier, a non-negligible number of individuals start receiving
the pension between 60 and 65 years of age. In contrast, those who choose to receive it later than
65 years of age represent only 1.8% and 1.0% for the first and second tiers in 2020, indicating that
almost all of those aged 65 years or above receive pension income. Thus, our analysis focuses on
the elderly aged 65 years or above.8 Our main analysis is an intention-to-treat analysis, although
the probability of being treated is nearly 100%. The average monthly pension income including
both first and second tiers if applicable for those 65 years old or above is 123,000 Japanese yen(848 US dollars) in 2019.
Since 1995, the pension payday has been set to be the 15th of even-numbered months, andindividuals receive the pension for the preceding two months. For example, pensions for December
and January are paid in February. If the 15th is a Saturday or Sunday, the payment is deliveredon the Friday immediately preceding the 15th. There are no holidays on or near pension paydays
during the study period and thus no change in pension paydays associated with holidays.
6

Specifically, for those who were born on the first day of a month, the pension starts in the birth month. For allothers, the pension starts in the next month7
Specifically, the pensionable age is 60 for males born before April 1, 1953, and for females born before April 1,
1958. For males born after April 1, 1961, and for females after April 1, 1966, the pensionable age is 65. Cohorts
born between these ranges gradually experience a delay in pensionable age. As those who were born in 1953 were
66 years old in 2019, which is the most recent year of our study, the pensionable age is 60 for the majority of
individuals in our analysis.
8
As we explain later, we also set the upper limit because, based on our hypothesis, we want to focus on relatively
active age groups.

7

2.2

Emergency medical services

As in many developed countries, emergency medical services provide out-of-hospital acute careand transport to definitive care in hospitals. These services are public and operated by the fire
department, which is run by either a municipality or a group of municipalities. As of 2020, there
were 723 fire departments with 1,714 fire stations in the country (Fire and Disaster Management
Agency, 2020). Emergency medical services cover 99.9% of the population, and ambulances, located in local fire departments, are typically dispatched from the closest fire department to thepatient. In case of a medical emergency, people dial 119 to request assistance. Note that callingand using an ambulance does not incur any cost for patients. Given higher health risk among theelderly population, individuals aged 65 or above accounted for 62.3% of all transports in 2020.
There are 6,579 ambulance cars in Japan, which corresponds to 5.3 per 100,000 population.

3

Methods

3.1

Empirical model

We analyze the health response to income arrival using the Japanese National Pension System
for the elderly population. To analyze the relationship between pension payment and ambulancetransport, we construct “synthetic” months following previous literature (Stephens, 2003; Evans
and Moore, 2012). The synthetic month begins 12 days prior to the 15th (or the 14th or 13th
if the 15th is Saturday or Sunday) and ends 12 days after the payment, resulting in 25 days persynthetic month. The payment is made every other month, but the variable of interest, Payday(d),is defined every month following the rule above. This means that the variable Payday(0) indicates
not only the day of pension payment but also the corresponding day of nonpayment months.
Our identification relies on the difference between payment (treatment) and nonpayment (control)
months on Payday(d) of a month. This allows us to control for specific date effects such as busier
business days on certain days of a month.9 The idea of comparing payment and nonpayment
months is used by Stephens and Unayama (2011) in assessing the impact of anticipated pension
payments on the consumption of households using the Japanese data. Their study uses variations
across months, and we further rely on daily fluctuations within a month as a source of variation.
9

For example, dates that are multiples of five are common for business transactions in Japan.

8In the main analysis, we use the following equation:ln(Ydmy ) = α +12Xβd {P ayday(d) × P aymonth(m)}d=−12,d̸=−3+6Xγj W eekday(j)dmy + ϕHolidaydmy + µm + νy + εdmy , (1)j=1where Ydmy shows the number of ambulance transports of d-th day from the payday of month min year y.10 P ayday(d) is an indicator variable that takes value one if the day is the d-th day fromthe payday and 0 otherwise, with −12 ≤ d ≤ 12. P ayday(0) thus indicates the 15th of each monthor one or two days before the 15th if the 15th is Saturday or Sunday. P aymonth(m) takes value
one if the month is February, April, June, August, October, or December, zero otherwise. We
set three days before the payday as the reference day in the main analysis and use an alternative
day of reference, one day before the payday, in the robustness check. Our coefficient of interestis βd for each d, and it expresses the difference between payment and nonpayment months in
the relative increase in the number of ambulance transports on the d-th day compared to thereference day. W eekday(j) is a dummy variable for different weekdays and set to 1 for Monday
and 6 for Saturday where Sunday is the reference day. Holiday is an indicator variables thatindicate national holidays as well as New Year days that consist of New Year’s Eve and January
1st to 3rd.11 µm is a month fixed effect, and νy is a year fixed effect. As a robustness check, weallow for greater flexibility, including year-month fixed effects (ωym ). We use clustered standard
errors within a synthetic month. The source of variation comes from the daily fluctuation of
ambulance cases, and our identification further relies on between-month variation. Specifically,
we compare outcomes on the payday and three days before the payday as the reference day, andthen compare the difference between payment and nonpayment months, conditional on day-ofweek, holiday effects, month effects, and year effects. In some of the analysis, we obtain five-day
average of the impact. In these analyses, we prepare four groups each of which consists of five days(−10 ≤ d ≤ −6; −5 ≤ d ≤ −1; 0 ≤ d ≤ 4; and 5 ≤ d ≤ 9). Then, we apply the same analysis as
Equation (1) with the preceding group (i.e., −5 ≤ d ≤ −1) as the reference group in the analysis.
The control variables include month and year fixed effects but exclude day-level variables.
10Since payday is around the 15th, the synthetic month aligns with the calendar month. Thus, we use calendar
months for fixed effects.
11Japan’s largest holiday is the New Year period, during which many people take days off from New Year’s Eve
until January 3rd, with only January 1st being the official national holiday.

9In addition to analyzing data at the daily level, we explore the evolution of changes in transport
within the day of pension payment compared with the reference day to determine what time of the
day transport increases. In the analysis, the sample is limited to Payday(0) (i.e., d = 0) and thereference day (i.e., d = −3), and we employ the difference-in-difference approach with the same
spirit as in Equation (1) with payment and nonpayment months as the treatment and control
groups:ln(Ydmy ) = α + β{P ayday(0) × P aymonth(m)}+6Xγj W eekday(j)dmy + ϕHolidaydmy + µm + νy + εdmy , (2)j=1where Ydmy shows the number of ambulance counts in one of the four-hour intervals of day d ofthe synthetic month m in year y. P ayday(0) is an indicator variable that takes value one if the
day is the 15th (or 13 or 14 if the 15th is Saturday or Sunday) and 0 if it is the reference day.
As in Equation (1), P aymonth(m) takes value one if the month is an even-numbered month, zerootherwise. We set six time intervals (0 am to 4 am, 4 am to 8 am, 8 am to 12 pm, 12 pm to 4
pm, 4 pm to 8 pm, and 8 pm to 12 pm), and we conducted six regressions and report β for eachinterval. Our coefficient of interest is β, which shows a difference in the change in the number of
ambulance transports on the pension payday from the reference day.

3.2

Ambulance transport data

Data structure. Our ambulance transport data are based on individual ambulance transport
cases in Japan between 2007 and 2019 and is provided by the Ministry of Internal Affairs and Communications for research purposes.12 Until 2015, the provided data covered all cases nationwide
except for those completed by the Tokyo Fire Department, which corresponds to the entire Tokyo
Prefecture except for Inagi-city and island municipalities that operate their own fire departments.
Starting in 2016, the dataset covers the entire county (see Appendix Table A1). The data record
information on basic patient attributes such as age and sex, severity of condition, and cause ofincidence, as well as transport details including the time of emergency calls and location of theincidence by category. Since 2015, additional information has been provided regarding location
12

The data is called the “Number of Transported Persons Data (Kyukyu-hansou jininsu deita)” and is available
only upon application for research purposes. Our application was approved on September 9, 2021.

10and physical condition.

Location. One of the key variables for our analysis is the location of the incidence. Location isprovided in five major categories: home, public space, road, workplace, and other. Furthermore,
starting in 2015, detailed codes became available for specifying the location of the incidence. Thisdetailed information breaks down public space, one of the major categories, into 27 subcategories.
In the analysis, we aggregate the 27 categories based on the similarity of activities to produce
the following nine categories: movie theater or museum; restaurant or bar; place of amusement13 ;department store or supermarket; hospital or nursing home; school; station or airport; hotel; andother. The correspondence between the four major categories and nine subcategories of the public
space used in our analysis is depicted in Figure 2a. The examples of locations for each category isfound in Appendix Table A2.

Incidence type. The cause of incidence, or incidence type, is categorized into the following 12
categories: fire, natural disaster, water-related accident, traffic accident, labor-related accident,
sports and exercise, injury, violence, self-harm, illness, hospital transport, and other. Our primary
focus is traffic accidents, injuries, and illnesses, which are considered the main causes related toeconomic activities, and we therefore aggregate all other categories as “other causes”. The dataset
also includes a diagnosis for illness given by a physician after 2015. The diagnosis for illness
category is categorized into the following ten groups defined by the emergency medical service department: cerebrovascular diseases; heart diseases; digestive diseases; respiratory diseases; mental
illness or alcohol- or substance-related conditions; diseases related to the nervous system; cancer;
unknown or cannot be classified; and other. The ten groups are defined based on the ICD-10 codes,
and the correspondence between the ICD-10 classification and the 10 disease categories by emergency medical service department is found in Appendix Table A2. The correspondence between
the major categories and 10 subcategories of diagnosis used in our analysis is depicted in Figure 2b.

Other information. Another important piece of information is health condition. Throughout
the period, the severity of health conditions determined by the first physician who examines thepatient at the hospital is recorded. Severity is classified into five levels: dead, fatal, serious, severe,
13

This includes the establishment of pachinko, a prevalent form of gambling, wherein prizes earned from games areexchanged for cash

11

Figure 2: Major categories and subcategories for incidence location and type(a) Incidence location(b) Incidence type

Notes: The figure illustrates the correspondence of major categories for incidence location and type and subcategories. The major categories, enclosed in a square box, include subcategories for public space as seen in Panel (a)and illness as seen in Panel (b). The examples of each location category is provided in the Appendix Table A2.

12and mild. Mild refers to a condition that does not require hospitalization. Severe refers to acondition that requires hospitalization for less than three weeks. Serious refers to a condition that
requires hospitalization for three or more weeks. Finally, a fatal condition is a life-threatening
condition.

In this analysis, we concentrate on individuals aged 65 to 79, as they are typically more active
and, consequently, more likely to respond to income arrival through economic activities. Appendix
Figure B2 shows the ambulance transport cases of those aged 65 to 79 in 2016. We observe an
increasing trend over time in the number of transport cases partly due to an increasing populationof elderly persons. There is seasonality according to which transports increase in winter and the
middle of summer, while an association with pension payment is not clearly observed. In particular,
unlike the record of cash withdrawals in Figure 1, we do not observe a clear spike on the pension
payday in Panel (b). We will thus perform statistical analyses, controlling for holiday effects,
day-of-the-week effects, and month and year fixed effects. Appendix Table A3 shows summary
statistics of the key variables for the original dataset including all age groups. Among them, onequarter of all of the transports falls in the age group of interest for our analysis, and they are
shown in Appendix Table A4. We aggregate the case-level data to generate ambulance transport
counts at the daily level. The summary statistics for the daily-level data used for our analysis ispresented in Appendix Table A5. We have a total of 3,900 observations of 25 days in a synthetic
month × 12 months × 13 years for the entire dataset. Among these observations, 1,500 after 2015
contain detailed information regarding public space and illness.
We additionally examine the surge in ambulance transports on the specific day of pension payment by analyzing recorded timestamps of what time the emergency call is made. To compare thenumber of ambulance calls for each four-hour slot on the payday between payment and nonpayment months, we aggregate the number of calls for 4 hours starting at 12 am on the payday until
12 am on the next day. Then, we statistically analyze the data as stated in Section 3.1.

13

Figure 3: Impact of pension payday on ambulance transport

Notes: This figure shows the coefficient estimates of βd , where d = {−12, −11, ..., −1, 0, 1, ..., 11, 12} from Equation(1) using all the ambulance transport data between 2007 and 2019. The number of observations is 3,900. The
dotted bar shows the 95 percent confidence interval. d = 0 shows the payday. The coefficient estimates show thedifference in the relative increase in ambulance transport cases compared to the reference day, three days before
the payday, between payment months and nonpayment months.

4

Results

4.1

The impact of pension payment on ambulance transport

To understand how ambulance transport pattern changes following a payday, we plot βd from
Equation (1) in Figure 3. The number of transport cases does not show any pretrend during the two
weeks before the payday, suggesting that payment and nonpayment months are not systematicallydifferent, conditional on daily characteristics such as day of week or seasonality. The difference inambulance transport sharply increases on the day of pension payment compared to the three days
before the payday, and the magnitude is 4.5%. The mean number of transports is approximately
3,200, and the increase corresponds to approximately 150 cases per day. The increase in thetransports persisted for four days after the payment with a lesser magnitude on the days following
the payday and quickly vanished after that. The increase in ambulance transport lasts only for ashort period of time. The total impact over the four days is 12.1% (p < 0.001).
The spontaneous and very short-term effect of an increase is consistent with Andersson et al.
(2015), who find an impact on mortality only on the day of payment but contrasts with that of14

Evans and Moore (2011), who show the impact persisted for several days. Andersson et al. argue
that the difference is likely due to variations in payment methods. Specifically, Swedish public
sector employees, studied by Andersson et al. (2015), received payments through direct deposit,
while social security payments in the study by Evans and Moore (2011) were made via physicalchecks at the time of the study, which takes several days to deposit and cash it. Our findings,
which are consistent with the Swedish data, further support this argument, as pension payments
in Japan are made through direct deposit.
Appendix Figure B3 shows a breakdown by payment (treatment) and nonpayment (control)
months. The figure indicates that the increase on the payday is driven by a change in payment
months and is not observed in nonpayment months. If the increase is driven by factors other thanpension payment and instead reflects a specific date effect within a month, we should observe an increase in nonpayment months. However, the coefficient remains at approximately zero throughout
the synthetic month, indicating that no such effects exist.
The increase in transports may merely reflect a shift from later in payment cycles, often called
the “harvest effect”. To test whether the increase is offset by a decrease later in the same payment
cycle, we analyze a payment cycle that consists of two synthetic months. If an increase in patients
is a shift from the rest of the days until the next payday, we would not observe a total increase in
the number of transports in two synthetic months in this analysis. As we do not take a difference
between payment and nonpayment months, the regression coefficient of Day d shows a differencein ambulance transports from the reference day in this analysis. Then, we test the null hypothesisthat the sum of all the coefficients of a binary variable to indicate Day d over two synthetic months
is equal to zero. If the “harvest effect” exists, the increase on the payday would be offset by adecrease on other days of the rest of the payment cycle, and the sum of all the coefficients of thebinaries, which shows the total change in a payment cycle, becomes zero. The total increase in
the two synthetic months in ambulance transport is 85% (p = 0.07). Although the increase is
not statistically significantly different from zero at the conventional level, the total effect shows apositive sign with a greater magnitude than the sum of the first four days 12.1%, which is discussed
in relation to Figure 3. The result suggests that the increase is not merely a shift in the timingof emergency service utilization but rather an increase in ambulance transport after the pension
payday.

15

4.2

Incidence location and economic activities

The subsequent inquiry pertains to what type of economic activities individuals typically engage
in upon receiving income. To address this question, we analyze the impact by the location ofincidence, and the results are found in Figure 4. Figure 4a illustrates the impact of the payday
by the four major location categories. In the left panel displaying the payday coefficient (β0 ), thelargest impact is found in transports from roads with a 14.4% increase, followed by public spaces
with 7.2%. The increase is consistent with the hypothesis that people increase their activities.
There are not a large number of transports through the synthetic month from the workplace (seeAppendix Tables A4 and A5), resulting in a large confidence interval for the workplace coefficient.
The right panel shows the five-day average effect starting from a payday. The rise in transportationis less pronounced than the notable increase observed on paydays for roads and public spaces. This
indicates that the impact diminishes in the first five days following payment, consistent with theobservations from the main results depicted in Figure 3. The full results for βd (−12 ≤ d ≤ 12) arefound in Appendix Figure B4.
These results indicate that people are more likely to have health problems in places outside
their homes on and following the pension payday. One may think that this increase in ambulancetransports in public spaces and roads might reflect a change in where people are located and thus ashift in incidence location, rather than health deterioration triggered by income receipt. However,
we can exclude this possibility for the following two reasons. First, we observe an overall increase intransport on the day of pension payment in the main analysis in Figure 3. Second, we do not finda decrease in transport from residences, which is most likely the place where incidence would occur
in the absence of pension payment, to offset the observed increase for roads and public spaces.
Figure 4b shows the further breakdown of the public space category into the nine subcategories,
which provides useful information on the activities people engaged in upon income arrival. The
most substantial surge is observed in transports from amusement arcades, with one popular venue
among adults in Japan being referred to as pachinko parlors. In Japan, gambling is not legallypermitted except for the following four public gambling: horse racing, bicycle racing, motorboat
racing, and auto racing. However, pachinko parlors, a form of gambling establishment, are very
common, where individuals participate in machine-based games for nonmonetary rewards, which
can later be exchanged for cash.14 Moreover, note that pachinko parlors exclusively accept cash as
14

There were 7,665 pachinko parlors in Japan in 2022.

16

Figure 4: Impact of income receipt on ambulance transport on the payday and five-day average ofthe impact by incidence location(a) Large category(b) Public space

Notes: This figure shows the coefficient plot of β0 in the left panel and the five-day average in the right panel using
all the ambulance transport data between 2007 and 2019 for Panel (a) and between 2015 and 2019 for Panel (b).
For Panel (a), the number of observations is 3,900 for the left panel and 624 for the right panel. In Panel (b),the number of observations is 1,500 for the left panel and 240 for the right panel. The thin bar shows the 99%
confidence interval and the thick bar shows the 95% confidence interval. The numbers show the difference in therelative increase from the reference day in ambulance transport cases between payment months and nonpayment
months. The reference day is three days before the payday for the left panel and the preceding five-day block before
the payday for the right panel. The examples of each location category are presented in Appendix Table A2.

17the mode of payment, which could contribute to a sharp response on the payday due to liquidity
relaxation. The magnitude of the increase on the payday is substantial and 49.3%. The secondlargest increase is found in shopping places such as department stores and supermarkets with a
34.8% increase, followed by stations and airports with a 33.9% increase, and restaurants and bars
with a 24.1% increase. In contrast, there was no notable increase observed in transport from
hospitals or nursing homes, where we would not expect an increase on paydays.
Persistence in the impact exhibits variation across locations. Appendix Figure B5 shows thecomplete result for amusement arcades including 12 days before and after the payday. There is adistinct pattern in ambulance transports on pension paydays, characterized by a notable surge on
the payday followed by a gradual decline over the subsequent days. In the right panel of Figure 4b,
the average impact over the first five days is most pronounced in amusement arcades. Examining
the cumulative effect, there is a 110% increase over the first three days and a 147% increase overthe five days. In comparison, the corresponding increases are 44.5% and 57.0% for shopping centersand 43.0% and 53.3% for restaurants and bars. Thus, the impact in amusement arcades including
pachinko parlors is not only distinct in magnitude but also in its duration of persistence.
These results indicate that economic activities are stimulated upon the receipt of income, withamusement and gambling, shopping, and dining out being the primary activities that respond to
the arrival of income and have adverse health effects.

4.3

Health changes

The next question concerns the nature of health changes that occurred. Figure 5 shows thebreakdown by incidence type and by diagnosis. Figure 5a shows the impact by incidence type.
The increase in transport was a product of external causes such as traffic accidents and injuries
as well as illnesses. This is consistent with previous findings for mortality that find an increasein mortality due to illnesses including cardiovascular events (Evans and Moore, 2011; Andersson
et al., 2015). Specifically, traffic accidents increased by 7.9%, followed by 6.3%, and 5.1% forinjuries and illnesses, respectively. By contrast, no such increase is found in transport due to otherreasons, which include fire, natural disasters, and work-related health hazards. Appendix Figure
B6 shows the full results.
We further examine the impact by diagnosis that is given for illnesses in Figure 5b. Transports
due to mental and behavioral disorders, including alcohol-related behavioral disorders, show an
18

Figure 5: Impact of income receipt on ambulance transport on the payday and five-day average ofthe impact by incidence type and diagnosis(a) Incidence type(b) Diagnosis for illness

Notes: This figure shows the coefficient plot of β0 in the left panel and the five-day average in the right panel using
all the ambulance transport data between 2007 and 2019 for Panel (a) and between 2015 and 2019 for Panel (b).
For Panel (a), the number of observations is 3,900 for the left panel and 624 for the right panel. For Panel (b),the number of observations is 1,500 for the left panel and 240 for the right panel. The thin bar shows the 99%
confidence interval, and the thick bar shows the 95% confidence interval. The numbers show the difference in therelative increase from the reference day in ambulance transport cases between payment months and nonpayment
months. The reference day is three days before the payday and the preceding five-day block before the payday forthe right panel. The definition of each incidence type and diagnosis category is presented in Appendix Table A2.

19increase. For some conditions such as cerebrovascular diseases or heart disease, the coefficientof payday is positive but not significant at conventional levels. The full result for mental andbehavioral disorders is found in Appendix Figure B7.15
Next, we consider a breakdown by location and diagnosis. We focus on four locations underpublic space where the rise in ambulance transports on pension payday is large. The results
are shown in Appendix Figure B8. There is a noteworthy divergence in patient diagnoses acrossthese locations. This suggests that distinct economic activities are associated with different healthproblems, rather than a simple increase in transport due to higher traffic in specific areas. For
example, there are increases in heart disease cases linked to amusement places that are significant at
the 5% level, whereas mental and behavioral disorders, including alcohol-related issues, are moreprevalent in transport from restaurants and bars. Transport due to injuries from airports andstations also increases. To summarize, activities in amusement arcades tend to increase diseases
such as heart disease, drinking and dining increase mental and behavioral disorders, and travelingincreases injuries.

4.4

Within-day variation

Finally, we examine in detail when the increase occurred at a particular time of day to explore
when people change their behavior on payday and if these changes are related to business hours.
Figure 6 shows a plot of the number of transport cases of the payday in each 30-minute interval(Figure 6a) and the corresponding figure for the reference day (Figure 6b). In each panel, there are
two lines: the blue line is for payment months, and the black line is for nonpayment months. On
the payday, as illustrated in Figure 6a, there is no distinction between payment and nonpayment
months during midnight and early morning. However, the two lines begin to diverge around 9am approximately when business hours commence.16 This gap persists throughout the day, withthe margin gradually decreasing in the evening. Notably, such deviation is not observed on thereference day in Figure 6b or for the pension ineligible age group in Appendix Figure B9. The
results suggest that elderly individuals start engaging in economic activities after business hours
15

Figure 5a shows a statistically significant increase in illness on payday, whereas Figure 5b indicates significant
impact for a single category, with wider confidence intervals. The difference partly arises from variations in the
study period:the former incorporates all data from our study period spanning 2007 to 2019, while the latter only
includes data from 2015 to 2019, during which diagnosis information is available.
16
As the pension payment is made using direct deposit, it is delivered before 8:45 am on the payday. In Japan, banks
and many supermarkets open at 9 am. Pachinko parlors usually open between 8 am and 10 am. Department
stores typically open at 10 am.

20

begin.
The results are shown by incidence location and type: for four major location categories inAppendix Figure B10; for amusement arcades in Appendix Figure B11; for incidence type inAppendix Figure B12; and for mental and behavioral disorders in Appendix Figure B13.
The analysis by incidence location in Appendix Figure B10 clearly shows that the increase after
9 am is driven by transports from public spaces and roads, not from the workplace. The figure
shows a slight increase in the number of transports from home in the afternoon. Additionally, inAppendix Figure B11, the difference between payment and nonpayment months is pronounced intransports from amusement arcades, as we expected from the main analysis. For the analysis byincidence type in Appendix Figure B12, the difference between payment and nonpayment months
is observed in all causes except for others, consistent with the analysis with daily data in Figure
5a. The analysis for mental and behavioral disorders in Appendix Figure B13 is noisy and does
not show a clear pattern. However, the increase in transport during the evening is only observed in
payment months and not in nonpayment months. This trend is likely caused by payment-related
behavior.
Figure 7 shows the coefficient of the interaction term of Payday and the pay-month dummy
in Equation (2) to conduct the formal statistical test of whether a change on the payday inthe number of transports from a reference day differs between payment months and nonpayment
months for all and for age and gender groups. Overall, we confirm a significant increase after 8
am. The regression results by incidence location and type are found in Appendix Figure B14. The
difference starting in the morning is significant in transports from public spaces and roads but notfrom the workplace or residence. We also find a statistically significant increase in injuries andtraffic accidents at night.

4.5

Heterogeneous impact

Finally, we investigate heterogeneity in the impact of pension payment by gender and age group,
severity of condition, and month. As stated in the introduction, we hypothesize that the increaseis driven by a rise in economic activities. Therefore, we expect a greater response among those
who are relatively younger, and among those in relatively good health conditions, particularly
during favorable seasons for going out, such as spring between March and May and fall between
September and November. The coefficient of β0 is plotted in Figure 8, and the full results are21

Figure 6: Ambulance transport by half-hour on the payday and reference day(a) Payday(b) Reference day

Notes: This figure shows the plot of the average number of ambulance transport cases in each 30 minute bin overthe sample period, 2007 to 2019. Panel (a) shows the number of cases on the day of pension payment, and Panel(b) shows the trend on the reference day, three days before the payday. In each panel, there are two lines. The blueline shows payment months, and the black line shows nonpayment months.

Figure 7: Increase in ambulance transport on the payday, regression analysis

Notes: This figure shows the coefficient plot of β from Equation (2) for each four-hour interval. The dotted barshows the 95 confidence interval. The regressions are performed separately for each of the six intervals. For eachanalysis, the number of observations is 312. The results are presented for all individuals between 65 and 79 as well
as by age and gender.

22

Figure 8: Heterogeneous impact by age and gender, severity, and month

Notes: This figure shows the coefficient plot of β0 from Equation (1) using the ambulance transport data between
2007 and 2019. The number is 3,900 for the analysis by age and severity and 2,275 for the analysis by month.
The bar shows the 95% confidence interval. The numbers show the difference in the relative increase from thereference day in ambulance transport cases between payment months and nonpayment months. The reference dayis three days before the payday. The blue color shows males, and the red color shows females. Three age groups
are analyzed: 65-69, 70-74, and 75-79. The definition of severity is presented in Appendix Table A2.

plotted in Appendix Figure B15 for age group and Appendix Figure B16 for severity. In Figure 8,
the rise in ambulance transports on the pension payday is most notable among individuals in theyoungest age group, specifically those aged 65 to 69 years. The impact diminishes with increasing
age for both genders. This pattern across age groups suggests that health shocks are more prevalent
among the younger demographic. The effect is greater among males in all groups than females.
Additionally, the increase is the greatest for mild conditions and monotonically decreases withseverity, and we do not obtain a significant impact for those with fatal conditions, who died, or
those with serious conditions. While we lack information on the patients’ health conditions before
they require emergency medical services, an increase in mild cases could imply that those who
fell ill were not in significantly poor health at the time of ambulance transport, indicating anacute change in health status triggered by activities. We observe seasonality in the effect. Theinterpretation of the results concerning seasonality is not straightforward. Nevertheless, it appears
that there are fewer ambulance transports observed during periods of extreme weather such as inFebruary and August.

23How ambulance transports change on the payday in all gender-age groups between 65 and 79 isfound in Appendix Figure B17. There is a difference in the timing of increases by age and gender.
Overall, the impact is more pronounced for men. In younger males, the increase persists until
midnight, while for females, the effect begins to diminish after 4 pm.

4.6

Mechanism

4.6.1

Liquidity

The remaining concerns relate to what contributed to a change in activities on the payday. Regarding the mechanism of the increase, two main reasons could be considered for the rise in ambulancetransport from the classical economic model.17 The first channel involves a relaxation of liquidity
constraints. Previous studies indicate that individuals increase consumption on the day of pension
payment, and this effect is more pronounced among those with more constrained liquidity. Our
results, showing larger jumps in transports from places of amusement—some of which only accept
cash—support this rationale.
Although the administrative ambulance record does not include the patient’s socioeconomic
status and we cannot test this directly, we attempt to examine whether the relaxation of liquidity
is likely to drive the result through two analyses. First, we utilize an original survey we conducted
to ask about people’s behavior and their economic situation in the three days around the pension
payday. While our primary analysis relies on comprehensive ambulance transport data encompassing all of Japan, we incorporate insights from the survey to enhance the understanding of the roleof the economic factors in the mechanism. The survey spanned three consecutive days surrounding
the pension payday (i.e., one day before the payday, the payday, and one day after the payday)
in June 2023. We asked 1,000 individuals aged 65 years or older, inquiring about their awarenessof the pension payment schedule, their activities on each day, and their overall financial situation.
The survey was distributed to the registered individuals via a commercial survey company. We
obtained approval from the Internal Review Board at Hosei University (Approved on June 16,2023, with the approval number 2023S02).
The primary objective of utilizing the survey data is to investigate the difference in the relationship between a pension payment and activities by liquidity status. Specifically, we ask whether
individuals used ATMs or banks on each of the three days to examine a change before and after17

We will discuss other factors related to behavioral economics in Section 5.

24

a payment. The reason why we employ ATM use, rather than specific activities such as shopping, is that all economic activities should start with obtaining cash if income receipts increase
economic activities. Additionally, we incorporate self-reported step counts for each day into ouranalysis. Participants were instructed to provide their step counts using an application on theirsmartphones, smartwatches, or similar devices.
To assess liquidity, we posed two questions. First, we inquired about the economic situation
just before the pension payday, offering two response options: whether the pension payment issufficient to cover living expenses and some money is left, or if the entire pension payment isconsumed before the next payday. For those indicating the latter, they were directed to respondto a more detailed question about their economic situation, choosing from five responses: having
financial assets but avoiding their use for daily living expenses, attempting to reduce expenses;
using some of the assets alongside the pension payment for daily living expenses; lacking financial
assets and relying on family support; lacking financial assets and resorting to borrowing money;and lacking financial assets, family support, and borrowing. We classify individuals who lackassets and family support as liquidity-constrained. Specifically, we define those who are liquidityconstrained if they respond with either “lacking financial assets and resorting to borrowing money”
or “lacking financial assets, family support, and borrowing”; otherwise, they are considered notliquidity constrained.
To analyze the data, we construct a panel dataset at the participant level with three survey
days (i.e., a respondent-survey-day panel). We set one day before the payday as the reference day
and compared a change in their activities on the following two days in footstep counts as well
as ATM or bank use. In the regression, we control for individual fixed effects. We restrict theanalysis to respondents who reported recognizing the dates of pension paydays. Additionally, we
identify and treat records with more than 50,000 steps per day as potential misrecordings, andreplace them with missing values.
In Table 1, we compare ATM/bank use and footsteps before and after the pension payment
based on perceived liquidity constraints. As in previous literature, the number of those who are
liquidity constrained is not large, and the vast majority are categorized as those who are notliquidity constrained. The coefficients show an increase in the probability of using an ATM orbank and an increase in footstep counts on the payday and the preceding day compared to one
day before the payday.

25Table 1: Bank or ATM use and footstep count by perceived liquidity constraintAll

Not constrained Constrained

Probability of using ATM or bankCoefficient of P ost

0.105***(0.0120)0.100***(0.0124)0.161**(0.0492)

2640

2463

177

Coefficient of P ost

315.4*(144.1)274.3(147.3)1231.8(683.1)

Observations

1354

1292

62

Observations
Footstep counts

Notes: ∗ p < 0.1;∗∗ p < 0.5;∗∗∗ p < 0.01. The coefficient of Post shows the difference in the probability of using an
ATM or bank and footstep counts on the day of payment or the following day compared to one day before the
payday, based on the survey conducted in June 2023. We control for individual fixed effects. Standard errors arein parentheses. We classify individuals as liquidity constrained if they respond with either “lacking financial assets
and resorting to borrowing money” or “lacking financial assets, family support, and borrowing”; otherwise, they
are considered not liquidity constrained.

In the results using the full sample, we find that individuals are more inclined to use ATMsor banks on the payday and the following day, compared to one day before the payday. Thisaligns with the outcomes of our primary analysis. The increase in using ATMs or banks is foundamong both those who are liquidity constrained and those who are not. However, the difference
between the days before and after the payday is more pronounced among those who reported
being constrained in their liquidity. Specifically, the probability of using an ATM or bank is higher
among the liquidity constrained than those who are not constrained by 6.1 percentage points. The
increase in footstep counts is three times higher among those who are constrained compared tothose who are not although the coefficients are not statistically significant in either case. Thesefindings provide suggestive evidence that at least some of the observed behavioral response isinfluenced by individuals who increase consumption upon the arrival of income due to an upsurge
in liquidity.
Second, we examine the correlation between the magnitude of the increase in ambulance transports on the payday, obtained from the main analysis, and the individual economic situation,
obtained from government statistics, both aggregated at the prefecture level. The magnitude of
the increase is measured by β0 in Equation (1), where the analysis is conducted separately for eachof the 47 prefectures. The coefficients for each prefecture are found in Appendix Figure B18. The

26

economic situation at the prefecture level is measured by the following three variables: financial
assets per household with people aged 65 or above; per capita bank savings; and per capita postal
savings. While none of the variables directly or perfectly measures the liquidity of the elderly
population, we aim to observe the overall tendency using the three supplementary measures of
wealth. The health response is considered to be larger in prefectures where a higher proportionof people are liquidity constrained. In Figure 9, there are negative correlations between the effectsize of the response on the payday and all the economic variables with degrees varying with themeasures. The correlation coefficients are −0.25 (p = 0.09) for financial assets per household withpeople aged 65 or above; −0.46 (p = 0.001) for per capita bank savings; and −0.46 (p = 0.001)
for per capita postal savings. Since the results are not evaluated at the individual level and thisis not a causal analysis, we cannot dismiss the possibility of unobserved factors influencing therelationship between the two variables. Nevertheless, at the prefecture level, a greater degree of liquidity constraint is associated with a greater increase in ambulance transport on payday, providing
suggestive evidence of liquidity constraint as a reason for an increase.
4.6.2

Supply-side factors

The second channel involves supply-side factors. Given that pension paydays are common among
the elderly population, suppliers may adjust prices through sales or other means to attract morecustomers or boost their revenue. While we are unable to directly test this hypothesis, we believethat it is unlikely to play a significant role in our results. Appendix Figure B9 shows the resultsfrom Equation (1) for those 55-59 years old, who are not eligible for a pension. The increase on
the payday is not observed among those aged 55 to 59 years, suggesting that the increase is not
driven by common factors across age groups. Furthermore, our triple difference analysis, where
the additional difference comes from age groups (those aged 55 to 59 years and those aged 65 to
79 years) in Appendix Figure B19 reveals that the magnitude of the increase is approximately thesame when compared with the main results in Figure 3. These findings suggest that the response isevident only among those eligible for a pension. Since price discounts are not restricted by the ageof consumers, the increased ambulance transport is most likely driven by demand-side factors. Thisis consistent with a report of non-adjustment of prices by retailers based on predictable demand
patterns for government benefits in the US (Goldin et al., 2022).

27

Figure 9: Correlation between the effect size and wealth at the prefecture level(a) Assets per household with those 65+(b) Per capita bank deposits(c) Per capita postal savings

Notes: This figure shows the correlation between the β0 coefficients estimated for each of the 47 prefectures using
Equation (1) and three different measures of wealth, which approximate liquidity. The three measures are financial
assets per household with people aged 65 or above; per capita bank savings; and per capita postal savings. The
correlation coefficients are −0.25 (p = 0.09) for financial assets per household with people aged 65 or above; −0.46(p = 0.001) for per capita bank savings; and −0.46 (p = 0.001) for per capita postal savings. All the economic data
are obtained from ”Statistical observations of prefectures” reported by the Statistics Bureau of Japan. The size ofthe circles shows the population aged 65 years or above. The red line shows a linear fit.

28

4.7

Robustness check

We conduct two robustness checks to examine the sensitivity of our results. First, we use a different
reference day. In the main analysis, we set three days before the payday as a reference day, andwe use one day before the payday as a reference day in the robustness check. Second, we use fixed
effects for a specific year and month that allow for greater flexibility in the control.
Appendix C shows the set of main results from the analysis with one day before payday as thereference day. Appendix D shows the main results with a different specification with year-month
fixed effects. In both analyses, the overall results are consistent with the main analysis.

5

Discussion

Our analysis bridged the gap between income arrival, economic activities, and health, identifying
certain economic activities that negatively affect people’s well-being upon income arrival. These
activities include amusement, gambling, shopping, and dining out. We also found that the type of
health problems incurred differs by economic activity. Our data with detailed time stamps revealed
that ambulance transport starts to increase during business hours and continues until midnight.
All of these findings clearly demonstrate a connection between income, economic activity, andhealth deterioration.

5.1

Relation to the consumption literature

Our results indicate an increase in economic activities on payday. Rational consumers typicallysmooth their consumption over time, and we do not anticipate a sudden change in their behavior
solely due to the arrival of expected income. However, considerable empirical research demonstrates
that people increase consumption upon anticipated income arrival, often called excess sensitivity(Stephens, 2003; Gelman et al., 2014; Kueng, 2018). One major reason for excess sensitivity could
be a relaxation of liquidity, as discussed in Section 4.6. However, excess sensitivity is reported not
only among the economically disadvantaged but also in the more general population (Olafsson andPagel, 2018). Our analysis using the survey reveals a result that is in line with this perspective.
Possible explanations for excess sensitivity among people who are not liquidity constrained could
include short-run impatience, mental accounting, and social interactions, as discussed in priorstudies (Hastings and Washington, 2010; Kueng, 2018). Regarding short-run impatience, studies
29

highlight the role of time and risk preferences to explain consumption changes associated withpayment cycles. For example, the literature on within-month cycles in food consumption finds adecrease in calorie intake before payday (Shapiro, 2005) due to hyperbolic discounting (Mastrobuoniand Weinberg, 2009). In addition, a recent study shows that economically disadvantaged people
tend to change their risk preferences around payday likely due to mental health and relative
deprivation (Akesaka et al., 2023).
Mental accounting is the individual and household practice of recording, summarizing, analyzing, and reporting financial transactions and events, akin to organizational managerial accounting,aimed at tracking expenditures and maintaining fiscal discipline (Thaler, 1999). It is used to explain consumption reactions to an annual payment from the Alaska Permanent Fund, where people
could regard unearned income as a windfall (Olafsson and Pagel, 2018). As pensions are the major
source of income for the elderly population, this is less likely to be true in our case. By contrast,social interactions are likely to explain people’s reaction in the context of pension payment. Whenthe prominent and anticipated pension payment occurs simultaneously for everyone, individuals
might perceive these moments as opportunities to engage in something noteworthy with otherpeople such as dining together.
Our study setting provides a good environment to observe excess sensitivity in two ways.
First, a previous study has shown that people tend to smooth consumption by delaying mortgage
payments or shifting credit card payments if such a financial tool is available (Gelman et al., 2020).
However, as discussed in Section 2, Japan is a cash-oriented country, and many individuals rely on
cash for their economic activities. Notably, not having a credit card is common, particularly amongolder generations, signifying a lack of means for consumption smoothing. Second, expenditure andconsumption are often recognized as distinct events because people do not necessarily consume
goods when they purchase. Service utilization considered by our study such as dining out andplaying in amusement arcades are considered typical examples of excess sensitivity because people
utilize the service upon purchase.

5.2

Macro dynamics on health

The literature shows mortality decreases in economic downturns in the US, and the result is replicated with data from different countries (Ruhm, 2000; Gerdtham and Ruhm, 2006; Ariizumi and
Schirle, 2012), although this tendency changed recently due to an increase in suicide and substance30

use (Ruhm, 2015). There are several channels to explain the procyclical nature of mortality. These
include a decrease in labor time, a decrease in harmful consumption such as alcohol intake due toincome decrease, fewer physical accidents from reduced economic activities, and reduced exposure
to environmental toxicity during economic downturns. Among these channels, harmful consumption and reduced economic activities are related to our results. The consumption channel suggeststhat an increase in income leads to greater consumption of potentially harmful products, such as
alcohol. The activity channel proposes that accidents decrease during economic downturns. In ourstudy, we identify positive income shocks at the individual level that increase economic activities
and subsequently negatively impact one’s health. Our results also indicate that the health change
is associated with alcohol consumption, as revealed in the analysis using diagnosis data, although
the findings are not conclusive because the diagnosis category is broad and includes other types ofhealth conditions.

5.3

Policy implications

Our findings suggest that the utilization of the healthcare system could be concentrated on a
specific day, particularly on the day of pension payment. In many countries, including Japan,
emergency medical services play a crucial role in the healthcare system by saving lives. Nevertheless, issues such as overcrowding in these services persist, highlighting the importance of efficiently
allocating resources as a key concern in healthcare provision. With the payment cycles alreadydetermined, healthcare resources could be better prepared for these specific days. Detailed information, such as the timing of the increase within a day, would further enhance preparedness.
Specifically, business hours on paydays in certain months experience a more pronounced increase
and therefore require better preparation. Additionally, our results provide suggestive evidence thatthe surge in economic activity on paydays is triggered by the realization of liquidity, emphasizing
the need for increased attention to economically disadvantaged areas.
While our primary focus is on the implications for the design of the healthcare system, ourresults are also relevant to the timing of public payments (LaPoint and Sakabe, 2022). The timingof payment for some public assistance programs in developed countries is arranged on an individual
basis. For instance, economic stimulus packages were distributed based on the last digit of thesocial security number, and the payment timing for SNAP in the United States is based on theinitial letter of the surname in certain places. As varying the timing of payment incurs some costs,
31

decisions regarding differentiation in timing should carefully weigh the costs and benefits. Our
results provide insight into one potential benefit—varying the timing of healthcare utilization—asa means of optimizing the allocation of limited resources in healthcare.

5.4

Conclusion

The literature has demonstrated the health response to income arrival. Our analysis, based on
ambulance transport data, serves to bridge the gap between health changes and economic activity.
We identify an increase in health changes following income receipt, which we attribute to specificeconomic activities, leveraging detailed data on the location and time of diseases or accidents.

32

References
Akesaka, M., Eibich, P., Hanaoka, C. and Shigeoka, H. (2023). ‘Temporal instability of risk preference among the poor: Evidence from payday cycles’, American Economic Journal: Applied
Economics, vol. 15(4), pp. 68–99.
Andersson, E., Lundborg, P. and Vikström, J. (2015). ‘Income receipt and mortality — evidence
from swedish public sector employees’, Journal of Public Economics, vol. 131, pp. 21–32.
Ariizumi, H. and Schirle, T. (2012). ‘Are recessions really good for your health? Evidence fromcanada’, Social Science & Medicine, vol. 74(8), pp. 1224–1231.
Bond, T.N., Carr, J.B., Packham, A. and Smith, J. (2022). ‘Hungry for success? SNAP timing,
high-stakes exam performance, and college attendance’, American Economic Journal: Economic
Policy, vol. 14(4), pp. 51–79.
Carr, J.B. and Packham, A. (2019). ‘SNAP benefits and crime: Evidence from changing disbursement schedules’, The Review of Economics and Statistics, vol. 101(2), pp. 310–325.
Cesarini, D., Lindqvist, E., Östling, R. and Wallace, B. (2016). ‘Wealth, health, and child development: Evidence from administrative data on swedish lottery players’, The Quarterly Journalof Economics, vol. 131(2), pp. 687–738.
Dobkin, C. and Puller, S.L. (2007). ‘The effects of government transfers on monthly cycles in drug
abuse, hospitalization and mortality’, Journal of Public Economics, vol. 91(11), pp. 2137–2157.
Erixson, O. (2017). ‘Health responses to a wealth shock: Evidence from a swedish tax reform’,Journal of Population Economics, vol. 30(4), pp. 1281–1336.
Evans, W.N. and Moore, T.J. (2011). ‘The short-term mortality consequences of income receipt’,
Journal of Public Economics, vol. 95(11), pp. 1410–1424.
Evans, W.N. and Moore, T.J. (2012). ‘Liquidity, economic activity, and mortality’, The Review of
Economics and Statistics, vol. 94(2), pp. 400–418.
Fire and Disaster Management Agency (2020). The 2020 White Paper on Fire Service.

33

Gelman, M., Kariv, S., Shapiro, M.D., Silverman, D. and Tadelis, S. (2014). ‘Harnessing naturally
occurring data to measure the response of spending to income’, Science, vol. 345(6193), pp.
207–212.
Gelman, M., Kariv, S., Shapiro, M.D., Silverman, D. and Tadelis, S. (2020). ‘How individuals
respond to a liquidity shock: Evidence from the 2013 government shutdown’, Journal of Public
Economics, vol. 189, p. 103917.
Gerdtham, U.G. and Ruhm, C.J. (2006). ‘Deaths rise in good economic times: Evidence from theOECD’, Economics and Human Biology, vol. 4(3), pp. 298–316.
Goldin, J., Homonoff, T. and Meckel, K. (2022). ‘Issuance and incidence: SNAP benefit cycles andgrocery prices’, American Economic Journal: Economic Policy, vol. 14(1), pp. 152–178.
Gross, T., Layton, T. and Prinz, D. (2022). ‘The liquidity sensitivity of healthcare consumption:
Evidence from social security payments’, American Economics Review: Insights, vol. 4(2), pp.
175–190.
Gross, T., Notowidigdo, M.J. and Wang, J. (2014). ‘Liquidity constraints and consumer
bankruptcy: Evidence from tax rebates’, The Review of Economics and Statistics, vol. 96(3),pp. 431–443.
Hastings, J. and Washington, E. (2010). ‘The first of the month effect: Consumer behavior andstore responses’, American Economic Journal: Economic Policy, vol. 2(2), pp. 142–162.
Kueng, L. (2018). ‘Excess sensitivity of High-Income consumers*’, The Quarterly Journal of
Economics, vol. 133(4), pp. 1693–1751.
LaPoint, C. and Sakabe, S. (2022). ‘Coming in at a trickle: The optimal frequency of public benefit
payments’, Mimeo.
Mastrobuoni, G. and Weinberg, M. (2009). ‘Heterogeneity in intra-monthly consumption patterns, Self-Control, and savings at retirement’, American Economic Journal: Economic Policy,
vol. 1(2), pp. 163–189.
Olafsson, A. and Pagel, M. (2018). ‘The liquid Hand-to-Mouth: Evidence from personal financemanagement software’, The Review of Financial Studies, vol. 31(11), pp. 4398–4446.
34

Östling, R., Cesarini, D. and Lindqvist, E. (2020). ‘Association between lottery prize size and selfreported health habits in swedish lottery players’, JAMA Netw Open, vol. 3(3), pp. e1919713–
e1919713.
Ruhm, C.J. (2000). ‘Are recessions good for your health?*’, The Quarterly Journal of Economics,
vol. 115(2), pp. 617–650.
Ruhm, C.J. (2015). ‘Recessions, healthy no more?’, Journal of Health Economics, vol. 42, pp.
17–28.
Sakamaki,T. (2019). ‘The advancement of the digital economy and diversificationof payment methods [Dezitaru keizai no shinten to shiharai syudan no tayoka]’,
Report

on

the

Study

Group

on

Innovation

in

the

DigitalAge,

Chapter1,

available at: https://www.mof.go.jp/pri/research/conference/fy2018/digital2018r eport01.pdf.
Schwartz, B.G., Kloner, R.A. and Naghavi, M. (2018). ‘Acute and subacute triggers of cardiovascular events’, The American Journal of Cardiology, vol. 122(12), pp. 2157–2165.
Shapiro, J.M. (2005). ‘Is there a daily discount rate? evidence from the food stamp nutrition
cycle’, Journal of Public Economics, vol. 89(2), pp. 303–325.
Stephens, M. (2003). “‘3rd of the month”: Do social security recipients smooth consumption
between checks?’, The American Economic Review, vol. 93(1), pp. 406–422.
Stephens, M. and Unayama, T. (2011). ‘The consumption response to seasonal income: Evidence
from Japanese public pension benefits’, The American Economic Review, vol. 3(4), pp. 86–118.
Thaler, R.H. (1999). ‘Mental accounting matters’, Journal of Behavioral Decision Making,vol. 12(3), pp. 183–206.
Yoshizawa, K., Maebashi, K., Yanagihara, H., and Kadokawa, Y.(2021). ‘Trends in banknotes during the COVID-19 pandemic [Koronaka niokeru ginkouken no doukou]’, Bank of Japan Review,2021-J-15, available at: https://www.boj.or.jp/research/wpsr ev/rev2 021/data/rev21j15.pdf.

35

Appendix

1

A

Data
Table A1: Data availability
Data availability

Availability of detailed categories
Public space

2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019

All others Tokyo All others

Tokyo

n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
✓
✓
✓
✓

n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
✓
✓
✓
✓

✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓

n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
✓
✓
✓
✓
✓

Disease code
All others Tokyo
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
✓
✓
✓
✓
✓

n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
✓
✓
✓
✓

Notes: Ambulance transport data are obtained from the Fire and Disaster Management Agency. The data areprovided separately by the Tokyo Fire Department and by all other fire departments. A ✓indicates that the data
are available, while n.a. indicates that the data are not available.

2

Table A2: Definition of categories
Main category
Category
Location

Subcategory

Definition

Category

Example/Definition

Theater/Museum
Restaurant/Bar
Amusement
arcadeTheaters, movie theaters, halls, libraries,
museums, galleries
restaurants, bars, night clubs
Amusement place, pachinko parlor, bowlingcenter, public bath
Department store, supermarket, small
shops, shopping malls

Residence
Public space

Shopping center
Hospital/Nursing
homeSchoolStation/Airport
Hotel
Other

Hospitals, clinics, nursing homes
Preschool, K12, universities, vocational
colleges, special needs schools
Stations, trains, airports
Hotels, accommodations
Temples, churches, parks, city halls, pools

WorkplaceRoad
Incidence type

Traffic accident

Traffic-involved accidents

InjuryInjuries caused by events other than traffic
accidents, criminal incidences, self-harms,
work-related accidents, sports competition,
swimming, or natural disasters
Cerebrovasculardiseases

Illness

Heart diseases
Digestive
diseases
Respiratory
diseases
Mental andbehavioral
disorders
Diseases of
nervous system
Diseases ofgenitourinary
systemCancerOther
Undiagnosed or
not classified

Other

Severity

IX Diseases of the circulatory system,
Stroke(a-0904) and Other cerebrovasculardiseases(a-0905)
IX Diseases of the circulatory system,
Hypertension(a-0901) to other heartdiseases(a-0903), and other cardiovasculardiseases(a-0906)
XI Diseases of the digestive system
X Diseases of the respiratory system
V Mental and behavioral disorders
VI Disease of the nervous system, VII
Diseases of the eye and adnexa, VIII
Diseases of the ear and mastoid process
XIV Diseases of the genitourinary system
II Neoplasms
I, III, IV, XII, XIII, XV, XVI, XVII, XIX,
XX, and XXIIVIII Symptoms, signs and abnormal
clinical and laboratory findings, notelsewhere classified

Criminal incidences, self-harms,
work-related accidents, sports competition,
swimming, or natural disasters, notelsewhere classified

Death
Fatal

Fatal is a life-threatening conditions

SevereRequires hospitalization for 3 or more weeks

Serious

Does not fall in other categories

Mild

Does not require hospitalization

Notes: The main categories and subcategories of illness are defined in the dataset. We further specify subcategories for public spaces by consolidating the 27 categories provided in the dataset into nine overarchingcategories. The definition of diagnosis categories is based on the Overview of Emergency Aid available athttps://www.fdma.go.jp/publication/rescue/items/kkkgr 020 1k yukyu.pdf.

3

Table A3: Summary statistics of the original data, all
N

2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Total

35551273931716405398743272564543799
46031735224172527941748052245594941
582667358677165978155
6.36e+07% Female

Age

Location (%)

Percent

Mean

47.62
47.48
47.54
47.84
47.95
48.17
48.08
48.08
48.51
48.56
48.68
48.97
49.03
48.30

56.345 26.184
57.469 26.036
57.800 26.189
58.956 25.964
59.650 25.900
60.160 25.914
60.287 25.768
60.759 25.769
61.456 25.742
61.310 25.959
61.963 25.954
62.485 25.816
62.668 26.064
60.541 25.988

SD

Type (%)

Severity (%)

Home

Public Space

WorkplaceRoad

Accident

InjuryIllness

Other

Death/Fatal

Serious

Severe

Mild

55.52
55.81
56.62
57.03
57.76
55.96
57.21
57.26
57.28
57.59
57.36
57.87
58.10
57.20

24.54
24.86
24.74
24.93
24.92
28.18
25.67
26.10
26.81
26.72
27.22
27.23
27.54
26.14

2.75
2.68
2.39
2.51
2.49
2.30
2.48
2.48
2.47
2.51
2.50
2.67
2.49
2.53

17.18
16.65
16.24
15.53
14.83
13.56
14.64
14.16
13.44
13.18
12.92
12.23
11.87
14.13

13.24
12.39
12.01
11.44
10.83
10.48
10.15
9.70
9.18
8.35
8.17
7.34
6.94
9.73

13.03
13.42
13.51
13.57
13.97
14.09
14.12
14.52
14.58
15.25
15.52
15.43
15.62
14.48

59.90
60.35
60.91
61.68
62.19
62.64
63.09
63.21
63.56
65.00
64.78
65.87
66.17
63.32

13.83
13.83
13.56
13.30
13.02
12.79
12.65
12.57
12.68
11.41
11.53
11.36
11.27
12.47

2.35
2.11
2.01
2.04
2.01
2.06
1.94
1.91
1.84
2.30
1.85
1.72
1.70
1.97

10.17
9.87
9.67
9.41
9.19
8.30
8.73
8.60
8.44
11.56
7.93
7.76
7.72
8.96

37.09
37.86
38.35
38.78
38.85
38.74
39.49
40.34
40.88
42.90
41.64
41.67
42.57
40.22

50.39
50.16
49.97
49.77
49.95
50.91
49.84
49.15
48.85
43.24
48.58
48.85
48.01
48.86

Notes: Ambulance transport data are obtained from the Fire and Disaster Management Agency. The table describes
the original case-level data using all the available data in Appendix Table A1 before aggregating at the daily level
data that we use for our analysis.

Table A4: Summary statistics of the original data, 65-79 years old
N

2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Total

7183048023078692749597931013810105341112822421315231130744614778511526934
1566268
1588206
1.55e+07% Female

Age

Location (%)

Type (%)

Severity (%)

Percent

Mean

SD

Home

Public Space

WorkplaceRoad

Accident

InjuryIllness

Other

Death/Fatal

Serious

Severe

Mild

44.61
44.31
44.43
44.72
44.76
44.77
44.51
44.31
44.53
44.22
43.91
44.00
43.84
44.32

72.62972.67772.67972.77872.87272.83872.68372.60472.58772.56572.62372.68872.85172.694

4.281
4.292
4.306
4.277
4.227
4.273
4.341
4.394
4.397
4.361
4.297
4.236
4.208
4.302

64.11
63.97
64.16
64.68
65.18
62.65
64.33
64.08
64.21
64.00
63.82
64.48
64.17
64.22

23.37
23.61
23.40
23.21
22.93
25.83
23.02
23.25
23.47
23.28
23.54
23.02
23.47
23.34

1.06
1.09
1.00
1.03
1.07
0.98
1.18
1.25
1.31
1.39
1.45
1.56
1.53
1.28

11.47
11.33
11.44
11.08
10.83
10.54
11.48
11.41
11.01
11.33
11.18
10.94
10.83
11.16

7.25
7.06
7.10
6.81
6.50
6.42
6.32
6.23
6.15
5.85
5.75
5.34
5.18
6.16

13.31
13.76
14.00
14.05
14.48
14.57
14.68
14.99
14.73
15.37
15.54
15.43
15.68
14.83

66.09
65.80
65.88
66.53
66.81
66.98
67.42
67.27
67.41
68.05
68.00
68.62
68.41
67.39

13.35
13.37
13.02
12.61
12.21
12.04
11.57
11.51
11.70
10.74
10.71
10.61
10.73
11.63

2.96
2.75
2.58
2.52
2.39
2.46
2.14
2.12
2.03
2.60
2.08
1.91
1.91
2.27

13.29
13.23
12.70
12.01
11.51
10.51
10.24
10.00
9.77
12.94
9.24
9.09
9.08
10.75

43.36
43.81
43.79
43.73
43.50
43.28
43.34
43.96
44.07
45.03
44.83
44.66
45.57
44.22

40.39
40.21
40.92
41.73
42.60
43.75
44.28
43.92
44.14
39.44
43.85
44.33
43.44
42.75

Notes: The data show ambulance transports for people aged between 65 and 79 years. Ambulance transport data
are obtained from the Fire and Disaster Management Agency. The table describes the original case-level data forpatients aged 65 to 79 using all the available data in Appendix Table A1 before aggregating at the daily level datathat we use for our analysis.

4Table A5: Summary statistics

Total
LocationType

Severity

Public space

Diagnosis

Residence
Public space
Work place
Road
Accident
InjuryIllness
Other
Death/Fatal
Serious
SevereMild
Theater/Museum
Restaurant/Bar
Amusement arcade
Shopping center
Hospital/Nursing home
SchoolStation/Airport
Hotel
Other
Cerebrovascular diseases
Heart diseases
Digestive diseases
Respiratory diseases
Mental and behavioral disorders
Neurological diseases
Kidney diseases
CancerOther
Undiagnosed or not classified

N
3900
3900
3900
3900
3900
3900
3900
3900
3900
3900
3900
3900
3900
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500
1500

Mean
3259.63
1943.02707.1838.72
337.55199.74481.532191.25376.51
72.87
344.341414.29
1369.14
24.40
57.77
34.66
75.42
571.63
6.55
32.91
29.76
73.09
280.28294.25259.27238.7243.67
121.9783.69
72.01
509.98954.37

S.D.
Min Max
852.62 1569 5849
696.45 313 3844
263.25 100 1413
23.02
0
173
124.88 37
723
45.23
72
353
151.75 179 1086
602.05 1030 4250
125.85 113 726
22.80
24
167
85.71 161 759
413.08 674 2787
409.89 589 2814
10.48
4
73
23.36
14
194
14.07
7
89
17.13
30
148
144.12 246 943
5.74
0
69
9.22
2
75
8.97
10
68
17.18
35
161
32.96 194 404
40.79 198 441
25.78 179 388
64.48 147 623
7.90
19
79
15.33
78
189
15.84
47
150
12.41
40
132
112.32 340 1304
150.96 600 1555

Notes: The table describes the summary statistics of daily data of ambulance transport cases for the entire sample
as well as by incidence location, type, severity, subcategories of public space, and diagnosis for illness. Ambulancetransport data are obtained from the Fire and Disaster Management Agency. The definition or examples of eachcategory is shown in Appendix Table A2.

5

B

Additional figures and tables
Figure B1: Daily number of individuals who withdraw cash by age group, 2014 and 2017
(a)

(b)

Notes: This figure shows the daily number of individuals who withdrew cash in each age category in 2014 (Panel(a)) and in 2017 (Panel (b)). The red dashed lines show the pension payday, typically on the 15th of even-numberedmonths, and the gray dashed line shows the corresponding day in odd-numbered months.

6

Figure B2: Daily number of ambulance transports(a) 2007-2019(b) 2016

Notes: This figure shows the daily number of ambulance transport cases in our study sample. Panels (a) and (b)show the plot for the entire study period (2007-2019) and 2016, respectively. In Panel (b), the red dashed line
shows the pension payday, and the gray dashed line shows the corresponding day in nonpayment months.

7

Figure B3: Relative number of ambulance transports for pension payment months and nonpayment
months

Notes: This figure shows the change in ambulance transport cases on each day of the synthetic month from thethree days before the payday in payment and nonpayment months, using all the ambulance transport data between
2007 and 2019. The number of observations is 1,950. The dotted bar shows the 95 confidence interval. d = 0 shows
the payday for payment months and the corresponding day for nonpayment months. The blue line shows payment
months, and the red line shows nonpayment months. The numbers show an increase in ambulance transport cases
compared to three days before the payday.

8Figure B4: Impact of pension payday on ambulance transport by incidence location(a) Residence(b) Public space(c) Workplace(d) Road

Notes: This figure shows the coefficient estimates of βd , where d = {−12, −11, ..., −1, 0, 1, .., 11, 12} from Equation(1), using all ambulance transport data between 2007 and 2019. The number of observations is 3,900. The dottedbar shows the 95 percent confidence interval. d = 0 shows the payday. The coefficient estimates show the differencein the relative increase in ambulance transport cases compared to the reference day, three days before the payday,
between payment and nonpayment months.

9

Figure B5: Impact of pension payment on ambulance transport from amusement arcades

Notes: This figure shows the coefficient plot of βd , where d = {−12, −11, ..., −1, 0, 1, .., 11, 12} from Equation (1),using all the ambulance transport data between 2007 and 2019. The number of observations is 1,500. The dotted barshows the 95 confidence interval. d = 0 shows the payday. The numbers show the difference in the relative increasein ambulance transport cases compared to the reference day, three days before the payday, between payment andnonpayment months. The examples of amusement arcades is presented in Appendix A2.

10

Figure B6: Impact of pension payday on ambulance transport by incidence type(a) Traffic accident(b) Injury(c) Illness(d) OtherNotes:This figure shows the coefficient estimates of βd , where d = {−12, −11, ..., −1, 0, 1, .., 11, 12} from Equation(1), using all ambulance transport data between 2007 and 2019. The number of observations is 3,900. The dottedbar shows the 95 percent confidence interval. d = 0 shows the payday. The coefficient estimates show the differencein the relative increase in ambulance transport cases compared to the reference day, three days before the payday,
between payment and nonpayment months. The definition of incidence type is presented in Appendix Table A2.

11

Figure B7: Impact of pension payday on ambulance transport due to mental and behavioral
disorderNotes: This figure shows the coefficient estimates of βd , where d = {−12, −11, ..., −1, 0, 1, .., 11, 12} from Equation(1), using all ambulance transport data between 2015 and 2019. The number of observations is 1,500. The dottedbar shows the 95 percent confidence interval. d = 0 shows the payday. The coefficient estimates show the differencein the relative increase in ambulance transport cases compared to the reference day, three days before the payday,
between payment and nonpayment months. The definition of mental and behavioral disorder is presented inAppendix Table A2.

12

Figure B8: Payday impact by location of incidence and diagnosis(a) Cerebrovascular diseases(b) Heart diseases(c) Mental and behavioral disorders(d) Injury

Notes: This figure shows the coefficient plot of β0 of Equation (1), using the ambulance transport data between
2015 and 2019. The number of observations is 1,500. The thin bar shows the 99% confidence interval, and thethick bar shows the 95% confidence interval. The numbers show the difference in the relative increase from thereference day in ambulance transport cases between payment and nonpayment months. The reference day is threedays before the payday. The definition of disease category and examples of each location category are presented inAppendix Table A2.

13

Figure B9: Falsification test: Analysis for those 55 to 59 years old(a) Main results(b) By hour

Notes: Panel (a) shows the coefficient estimates of βd , where d = {−12, −11, ..., −1, 0, 1, .., 11, 12} from Equation(1), using all the ambulance transport data between 2007 and 2019. The number of observations is 3,900. The
dotted bar shows the 95 percent confidence interval. d = 0 shows the payday. The coefficient estimates show thedifference in the relative increase in ambulance transport cases compared to the reference day, three days before the
payday, between payment and nonpayment months. Panel (b) shows the plot of the average number of ambulancetransport cases in each 30 minute bin over the sample period, 2007 to 2019, for those aged 55 to 59 years. The blueline shows payment months, and the black line shows nonpayment months.

14

Figure B10: Ambulance transport by half-hour on the payday by incidence location(a) Home(b) Public space(c) Workplace(d) Road

Notes: This figure shows the plot of the average number of ambulance transport cases in each 30 minute bin overthe sample period, 2007 to 2019, for each incidence location. In each panel there are two lines. The blue line shows
payment months, and the black line shows nonpayment months. The vertical line represents 9:00 am.

15

Figure B11: Ambulance transport from amusement arcades by half-hour on the payday

Notes: This figure shows the plot of the average number of ambulance transport cases in each 30 minute bin overthe sample period, 2015 to 2019, for each incidence location. The blue line shows payment months, and the blackline shows nonpayment months. The vertical line represents 9:00 am. The examples of amusement arcades arepresented in Appendix Table A2.

16

Figure B12: Ambulance transport by half-hour on the payday by incidence type(a) Traffic accident(b) Injury(c) Illness(d) OtherNotes: This figure shows the plot of the average number of ambulance transport cases in each 30 minute bin overthe sample period, 2007 to 2019, for each incidence location. In each panel, there are two lines. The blue line
shows payment months, and the black line shows nonpayment months. The vertical line represents 9:00 am. The
definition of each incidence type is presented in Appendix Table A2.

17

Figure B13: Ambulance transport due to mental and behavioral disorder by half-hour on the
paydayNotes: This figure shows the plot of the average number of ambulance transport cases in each 30 minute bin overthe sample period, 2015 to 2019, for each incidence location. The blue line shows payment months, and the blackline shows nonpayment months. The vertical line represents 9:00 am. The definition of mental and behavioral
disorder is presented in Appendix Table A2.

18

Figure B14: Increase in ambulance transport on the payday by incidence location and incidence
type, regression analysis(a) Incidence location(b) Incidence type

Notes: This figure shows the coefficient plot of β from Equation (2) for each four-hour interval. The dotted barshows the 95 percent confidence interval. The regressions were performed separately for each six intervals. For
each analysis, the number of observations is 312. The results are presented for all individuals between 65 and 79
by incidence location and incidence type. The definition of incidence type and examples of incidence location arepresented in Appendix Table A2.

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Figure B15: Impact of pension payday on ambulance transport by age group and gender(a) Female, 65-69(b) Female, 70-74(c) Female, 75-79(d) Male, 65-69(e) Male, 70-74(f) Male, 75-79

Notes: This figure shows the coefficient estimates of βd , where d = {−12, −13, ..., −1, 0, 1, ..., 12} from Equation (1),using all the ambulance transport data between 2007 and 2019. The number of observations is 3,900. The dottedbar shows the 95 percent confidence interval. d = 0 shows the payday. The coefficient estimates show the differencein the relative increase in ambulance transport cases compared to the reference day, three days before the payday,
between payment and nonpayment months.

20

Figure B16: Impact of pension payday on the ambulance transport by severity(a) Fatal/Death(b) Serious(c) Severe(d) Modest

Notes: This figure shows the coefficient plot of βd , where d = {−12, −13, ..., −1, 0, 1, ..., 12} from Equation (1), using
all the ambulance transport data between 2007 and 2019. The number of observations is 3,900. The dotted barshows the 95 percent confidence interval. d = 0 shows the payday. The numbers show the difference in the relative
increase in ambulance transport cases between payment and nonpayment months. The reference day is three days
before the payday. The definition of severity is presented in Appendix Table A2.

21

Figure B17: Ambulance transport by half-hour on the payday by gender and age group(a) Female, 65-69(b) Female, 70-74(c) Female, 75-79(d) Male, 65-69(e) Male, 70-74(f) Male, 75-79

Notes: This figure shows the plot of the average number of ambulance transport cases in each 30 minute bin overthe sample period, 2007 to 2019, for each age gender group. In each panel, there are two lines. The blue line shows
payment months, and the black line shows nonpayment months. The vertical line represents 9:00 am.

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Figure B18: Impact of pension payday on ambulance transport by prefecture(a) Coefficient estimates(b) Geographical representation

Notes: This figure shows the coefficient plot of β0 of Equation (1), using the ambulance transport data between
2015 and 2019. In Panel (a), the bar shows the 95% confidence interval. The number of observations is 3,900. The
numbers show the difference in the relative increase from the reference day in ambulance transport cases between
payment and nonpayment months. The reference day is three days before the payday.

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Figure B19: Triple-difference analysis

Notes: This figure shows the coefficient plot of interest in the triple-difference regression analysis, where the additional difference by age group (65-79 and 55-59) is added to the analysis. The number of observations is 3,900.
The dotted bar shows the 95 percent confidence interval. d = 0 shows the payday. The reference day is three days
before the payday.

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C

Robustness check 1: An alternative reference day
Figure C1: Impact of pension payday on ambulance transport(a) Difference in difference analysis(b) Payment months and non-payment months

Notes: Panel (a) shows the coefficient plot of βd , where d = {−12, −13, ..., −1, 0, 1, ..., 12} from Equation 1, using all
the ambulance transport data between 2007 and 2019. The number of observations is 3,900. The dotted line shows
the 95 percent confidence interval. d = 0 shows the payday. The numbers show the difference in the relative increasein ambulance transport cases between payment and nonpayment months. Panel (b) shows the change in ambulancetransport cases on each day of the synthetic month from the one day before the payday in payment and nonpayment
months, using all the ambulance transport data between 2007 and 2019. The number of observations is 1,950. The
dotted bar shows the 95 confidence interval. d = 0 shows the payday for payment months and the corresponding
day for nonpayment months. The blue line shows payment months, and the red line shows nonpayment months.
The numbers show an increase in ambulance transport cases compared to one day before the payday.

25Figure C2: Impact of income receipt on ambulance transport on the payday and five-day averageof the impact by incidence location(a) Large category(b) Public space

Notes: This figure shows the coefficient plot of β0 of Equation (1) using all the ambulance transport data between
2007 and 2019 for Panel (a) and between 2015 and 2019 for Panel (b). For Panel (a), the number of observations
is 3,900. For Panel (b), the number of observations is 1,500. The thin bar shows the 99% confidence interval, andthe thick bar shows the 95% confidence interval. The numbers show the difference in the relative increase from thereference day in ambulance transport cases between payment and nonpayment months. The reference day is threedays before the payday. The examples of each location category are presented in Appendix Table A2.

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Figure C3: Impact of income receipt on ambulance transport on the payday and five-day averageof the impact by incidence type and diagnosis(a) Incidence type(b) Diagnosis for illness

Notes: This figure shows the coefficient plot of β0 of Equation (1), using all the ambulance transport data between
2007 and 2019 for Panel (a) and between 2015 and 2019 for Panel (b). For Panel (a), the number of observations is
3,900. For Panel (b), the number of observations. The thin bar shows the 99% confidence interval and the thick barshows the 95% confidence interval. The numbers show the difference in the relative increase from the reference day
in ambulance transport cases between payment and nonpayment months. The reference day is three days before
the payday. The definition of each incidence type and diagnosis category is presented in Appendix Table A2.

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Figure C4: Heterogeneous impact by age and gender, severity, and month

Notes: This figure shows the coefficient plot of β0 of Equation (1) using the ambulance transport data between 2007
and 2019. The number of observations is 3,900 for the analysis by age and severity and 2,275 for the analysis bymonth. The bar shows the 95% confidence interval. The numbers show the difference in the relative increase fromthe reference day in ambulance transport cases between payment and nonpayment months. The reference day isthree days before the payday. Blue color shows males, and red color shows females. Three age groups are analyzed:
65-69, 70-74, and 75-79. The definition of severity is presented in Appendix Table A2.

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D

Robustness check 2: Year-month fixed effectFigure D1: Impact of pension payday on ambulance transport(b) Payment months and non-payment months(a) Difference in difference analysis

Notes: Panel (a) shows the coefficient plot of βd , where d = {−12, −13, ..., −1, 0, 1, ..., 12} from Equation (1) withthe year and month fixed effects replaced by the year-month fixed effect, using all the ambulance transport data
between 2007 and 2019. The number of observations is 3,900. d = 0 shows the payday. The numbers show thedifference in the relative increase in ambulance transport cases between payment and nonpayment months. Panel(b) shows the change in ambulance transport cases on each day of the synthetic month from the three days before
the payday in payment and nonpayment months, using all the ambulance transport data between 2007 and 2019.
The dotted bar shows the 95 confidence interval. d = 0 shows the payday for payment months and the corresponding
day for nonpayment months. The blue line shows payment months, and the red line shows nonpayment months.
The numbers show an increase in ambulance transport cases compared to three days before the payday.

29

Figure D2: Impact of income receipt on ambulance transport on the payday and five-day averageof the impact by incidence location(a) Large category(b) Public space

Notes: This figure shows the coefficient plot of β0 of Equation (1) with the year and month fixed effects replacedby the year-month fixed effect, using all the ambulance transport data between 2007 and 2019 for Panel (a) andbetween 2015 and 2019 for Panel (b). For Panel (a), the number of observations is 3,900. For Panel (b), thenumber of observations is 1,950. The thin bar shows the 99% confidence interval, and the thick bar shows the 95%
confidence interval. The numbers show the difference in the relative increase from the reference day in ambulancetransport cases between payment and nonpayment months. The reference day is three days before the payday. The
examples of each location category are presented in Appendix Table A2.

30

Figure D3: Impact of income receipt on ambulance transport on the payday and five-day averageof the impact by incidence type and diagnosis(a) Incidence type(b) Diagnosis for illness

Notes: This figure shows the coefficient plot of β0 of Equation (1) with the year and month fixed effects replacedby the year-month fixed effect in the right panel, using all the ambulance transport data between 2007 and 2019
for Panel (a) and between 2015 and 2019 for Panel (b). For Panel (a), the number of observations is 3,900. For
Panel (b), the number of observations is 1,500. The thin bar shows the 99% confidence interval, and the thick barshows the 95% confidence interval. The numbers show the difference in the relative increase from the reference day
in ambulance transport cases between payment and nonpayment months. The reference day is three days before
the payday. The definition of each incidence type and diagnosis category is presented in Appendix Table A2.

31

Figure D4: Heterogeneous impact by age and gender, severity, and month

Notes: This figure shows the coefficient plot of β0 of Equation (1) with the year and month fixed effects replacedby the year-month fixed effect in the right panel using the ambulance transport data between 2007 and 2019. The
number of observations is 3,900 for the analysis by age and severity and 2,275 for the analysis by month. The 95%
confidence interval. The numbers show the difference in the relative increase from the reference day in ambulancetransport cases between payment and nonpayment months. The reference day is three days before the payday.
Blue color shows males, and red color shows females. Three age groups are analyzed: 65-69, 70-74, and 75-79. The
definition of severity is presented in Appendix Table A2.

32