Does a Pandemic Affect Household Portfolio Choices?
Evidence from the 2009 H1N1 Pandemic∗
Naijia Guo

Charles Ka Yui Leung

Shumeng Zhang

September 7, 2023
Abstract
This paper examines the short- and long-term effect of the 2009 H1N1 pandemic onhousehold portfolio choices using the Panel Study of Income Dynamics. Using thecross-state variations in the H1N1 death rate during the pandemic, the difference-indifferences estimates suggest that the H1N1 pandemic does not affect stock market
participation but reduces the share of liquid assets invested in risky assets (the conditional risky share). A 1 percent increase in the H1N1 death rate reduces the conditional risky share by 0.27 percent. The event study further shows that the impact of
H1N1 has a two-year lag and remains persistent eight years after the pandemic. The
decomposition analysis suggests that the decline in risky share associated with the
pandemic is mainly due to households’ rebalancing decisions rather than their realized
asset returns. Furthermore, the effect of H1N1 on risky share is more substantial forhouseholds whose household heads are females, singles, have larger income fluctuation,
do not work in a government job, and are not represented by a union. The findings
suggest that the portfolio choices for households with larger income risks are moresensitive to the pandemic shock.
Keywords: Pandemic, Portfolio Choice, Heterogeneity
JEL Classification: D10, G50, I10∗

Guo: Faculty of Business and Economics, the University of Hong Kong. Email: njguo@hku.hk. Leung: Department of Economics and Finance, City University of Hong Kong. Email: kycleung@cityu.edu.hk.
Zhang: School of Economics, Nankai University. Email: s.m.zhang@link.cuhk.edu.hk. We are grateful to(alphabetical order) Erqi Ge, Jia He, Xiaoyang Li, Xiangjun Ma, Shiko Maruyama, Petra Todd, Ming-ang
Zhang, and participants at various conferences and workshops including Asian Meeting of the EconometricSociety in China (2022), 6th CCER Summer Institute, the Seminar at HCEO-IESR Summer School on Socioeconomic Inequality, Econometric Society Australasia Meeting (2022), and 19th Chinese Finance Annual
Meeting for helpful comments and discussions. All errors are our own.

1

Introduction

Over the last century, viruses have taken more lives than all armed conflicts (Adda,
2016). For instance, the 1918 Spanish flu, the most severe influenza pandemic in recent
history, resulted in about 50 million deaths (Taubenberger and Morens, 2006). The 2019
COVID-19 outbreak, as of May 2023, has infected 760 million people and caused over 6.9
million deaths.1 Given its uncontrollable emergence, unpredictable consequences, and uncertain duration, such public health crisis exposes people to increased risks and thus elicits
behavior responses in different aspects (Rasul, 2020). Previous research has established that
influenza pandemics can change health behaviors (Lau et al., 2005; Agüero and Beleche,2017), alter household consumption (Baker et al., 2020; Chetty et al., 2020) and savings(Hurwitz et al., 2021), affect time allocation decisions (Restrepo and Zeballos, 2020), andshape household expectations (Hanspal et al., 2021). Still, we know little about how a pandemic affects households’ risk preferences and portfolio choices. As such, this study focuseson the impact of the 2009 H1N1 pandemic on US household portfolio choices.
The H1N1 pandemic was detected first in the US in April 2009, which was caused bya novel human-to-human transmissible A (H1N1) virus. Following the epidemiological literature, we alternatively refer to the 2009 H1N1 influenza virus as “(H1N1)pdm09 virus”
or “novel H1N1 virus” throughout this paper.2 The H1N1 pandemic was the first influenza
pandemic in 40 years3 , infecting 11 to 21 percent of the world population at the time (Kelly
et al., 2011). In particular, the US had the world’s largest number of infections and deaths
during the pandemic (43.3–89.3 million infections and 8,868–18,306 deaths, see Section 2.1).
Although WHO declared an end to the pandemic on August 11, 2010, the (H1N1)pmd09
virus continues to circulate as a regular seasonal flu virus and affects human life.
As the H1N1 pandemic was arguably unexpected, it can be used as a natural experiment
to analyze whether and how households adjust their portfolio choices in response to a pandemic. Several possible channels can be explored in this regard. First, the pandemic may
increase the household’s background risk, which is referred to as the exogenous risks that are
not under the agent’s control (Eeckhoudt et al., 1996). Specifically, the background risks
1

See statistics on the World Health Organization website, https://covid19.who.int/ (accessed May
13, 2023). Pandemics can also incur severe economic costs at the macro level: the annual global cost ofmoderately severe to severe pandemics accounts for 0.7% of global income. See the World Report in 2017,https://www.worldbank.org (accessed May 13, 2023).
2
This new virus was initially referred to as “swine flu” because its gene components were analogous toviruses that were known to infect pigs. However, this virus cannot circulate by eating pork products.
3
Before the 2009 H1N1 pandemic, the most recent pandemic occurred in 1968, caused by the H3N2 virus.

1driven by the pandemic can affect household finance through multiple aspects. For instance,
Almond and Mazumder (2005) show that a pandemic can have a persistent effect on health,and an increase in health risk can affect stock holdings (Fan and Zhao, 2009). Similarly, a
pandemic can affect labor market outcomes, such as employment, occupational choice, andearnings (Albanesi and Kim, 2021; Larrimore et al., 2022), which will also change portfolio
choices (Guiso et al., 1996; Heaton and Lucas, 2000). Second, a pandemic can lead to atemporary or permanent change in risk preference and, therefore, investment in risky assets.
Following the literature, we study both the extensive margin (i.e., whether to participate in
the stock market or not) and the intensive margin (i.e., the share of the risky asset, conditional on the stock market participation) (Fagereng et al., 2017).
Our analysis mainly uses data from three sources. First, to measure the intensity of
the H1N1 pandemic, we manually collect state-level H1N1 deaths during the pandemic (i.e.,
April 2009–August 2010) on an online forum FluTracker and calculate the death rate ofeach state, which is defined as the number of laboratory-confirmed deaths per 100,000 people. We also gather laboratory-confirmed case counts at the Health and Human Services(HHS) region level from the Centers for Disease Control and Prevention (CDC) website asa robustness check.4 Second, we pool data from nine waves of the Panel Study of Income
Dynamics (PSID) (2001–2017, in odd-numbered years) to obtain a comprehensive data set
on wealth, investments, and demographics at the household level. The longitudinal natureof the data set allows us to separate the household fixed effects and identify the effects ofcovariates on the portfolio choices. We construct two measures for portfolio choices, stock
market participation (an indicator variable of whether owning any risky assets) and risky
share (share of risky assets among liquid assets conditional on positive risky asset holdings).
Third, we collect state-by-year macroeconomic indicators and medical resources from various
sources.
The major challenge for identifying the causal impact of the H1N1 pandemic on household portfolio choices is that the source of variations in H1N1 intensity is cross-sectional.
Even if a negative correlation exists between H1N1 intensity and stock holdings, households
in higher H1N1 intensity states may intrinsically hold less risky assets. We use a differencein-differences identification strategy to overcome this problem by exploiting the state-level
differences in the H1N1 intensity and the differences between the pre- and post-pandemic
period within households. The panel data allows us to control for time-invariant household4

Due to data limitations, we can only obtain case numbers at the HHS level because the CDC did notdisclose state-level data. See the description of the H1N1 data in Section 2.2.1.

2

characteristics and analyze the dynamic effects of the pandemic. In our main specification,
we also include a large set of time-varying household- and state-level controls that may becorrelated with the outcome variables and the measure of H1N1 intensity.
Our empirical analysis clearly shows that the exposure to the 2009 pandemic affects theintensive margin (risky share) but not the extensive margin of stock holdings (stock market
participation). The effect on the intensive margin emerges after the pandemic. In particular,
in 2009, when the crisis emerged, the risky share did not change. In contrast, from the second to the eighth year after the pandemic, a 1 percent increase in the 2009 H1N1 death rate
led to a decline in the risky share by 0.27 percent. We address the potential confounding
effect of the 2007–2008 financial crisis by adding interactions between year dummies andcross-state variations in economic indicators during the financial crisis (i.e., GDP per capita,
unemployment rate, and housing price). Our results are also robust to different criteria ofsample selections and alternative measures of the H1N1 intensity. In addition, we test theeffect of exposure to seasonal influenza on risky shares as a placebo test. Unlike the seasonalflu, which is regular, anticipated, and under control, the novel H1N1 virus had never been
detected before and could invalidate existing vaccines. Hence, we conjecture that households
will only respond to an unexpected and more dangerous influenza virus but not the seasonalflu, and the data confirm our speculation.
To inspect how the impact changes over time, we estimate the year-specific effects of
exposure to the H1N1 pandemic in an event study framework by taking advantage of themulti-waves of the PSID data set we use. The changes in the risky share are small andstatistically insignificant before 2007, which supports the parallel trends assumption. The
difference is also little in 2009, thus suggesting that investment decisions have some lagged
effects. In 2011 and onward, the coefficient estimates for the risky share started becoming
statistically different from zero. They stabilized at about -0.24 by 2017 (the end of thesample period), thus implying that the impact of the H1N1 pandemic is long-lasting.
Understanding structural changes in household portfolio choices can provide insight into
households’ evolving risk preferences and investment strategies. The decline in the risky
share with the H1N1 death rate can result from a reduction in the number of risky assets,
an increase in liquid assets, or a combination of both. Our findings reveal that a 1% risein the H1N1 mortality rate leads to a roughly 40-dollar decrease in the net investment instocks at the household level. However, no significant change in the amount of liquid assets
is observed. Therefore, the decrease in the intensive margin of stock holdings in response to3the H1N1 pandemic is mainly driven by the net sales of risky assets rather than the change
in liquid assets.
However, households may exhibit inertia in rebalancing their portfolios, as noted by
Brunnermeier and Nagel (2008) and many other studies. Furthermore, measurement errors
may occur in the net amount put in risky assets. To address these issues, we follow Calvet et al. (2009) and decompose the risky share into passive changes resulting solely fromrealized asset returns and active changes that stem from households’ rebalancing decisions.
Our analysis shows that the passive change remains unaffected by H1N1 intensity, while
the active change falls by around 0.07 percentage points in response to a 1% increase in
the H1N1 death rate. Therefore, households exposed to the pandemic appear to be actively
reducing risky assets and shifting their assets from risky investments to safer, risk-free assets.
We also find rich heterogeneity in the effect of the H1N1 pandemic on risky shares. The
pandemic impact is more substantial for households whose heads are females or single thanthose whose heads are males or married. The effect is also stronger for households whose
heads earn more unstable income, do not have a government job, or are not represented bya union. These results suggest that households with more volatile incomes are more likely
to be influenced by the pandemic.
Moreover, we examine the life-cycle effect of the pandemic. The exposure effect of thepandemic on the share of risky assets displays a hump shape over the life cycle. Specifically,
the effect (in magnitude) of the H1N1 pandemic is largest for households whose heads are
aged 20–29. The effect decreases at age 20–59 and slightly increases at age 60–69. Considering that risk aversion decreases with age (e.g., Schooley and Worden, 1996) and people
who approach retirement tend to rebalance their portfolio composition away from stocks(Fagereng et al., 2017), our findings suggest that the pandemic has larger effects on morerisk-averse individuals.
In addition to focusing on the exposure impact of the H1N1 pandemic on liquid assets,
we also investigate its potential effect on illiquid assets. We show that a 1 percent increasein the H1N1 death rate reduces the probability of being an incorporated business ownerby 0.028 percent significantly. We also observe an increase in the probability of owning anunincorporated business by 0.021 percent associated with a 1 percent increase in the H1N1
intensity, despite this coefficient being statistically insignificant. However, little evidence
indicates that the probability of owning a house or car will change with the H1N1 intensity.
4

Finally, we conduct an investigation to shed light on why the H1N1 pandemic affects
risky assets. We examine a range of factors related to health, demographics, and labor market outcomes, such as self-reported health status, marital status, number of children, income,and employment status. While these characteristics may be affected by the pandemic andcould result in background risks that lead to changes in stock holdings, our findings revealthat the pandemic does not contribute to these potential factors. This rules out the health,demographics, and labor market assumptions. We also find that family wealth remains unaffected by H1N1 intensity and does not influence the proportion of risky shares.
This study contributes to the literature that examines the socioeconomic consequencesof pandemics. Despite a growing body of literature on pandemics, the effect of exposure toa pandemic on household portfolio choice has been insufficiently explored.5 To complement
the works on the ongoing COVID-19 pandemic, the current study investigates the effect ofthe 2009 H1N1 pandemic on portfolio choices. Using panel data from 2001 to 2017, we cananalyze the contemporaneous and long-term effects of the H1N1 pandemic on stock holdings’
extensive and intensive margins. In addition, we use the difference-in-differences framework
to control for time-invariant unobserved characteristics at the household level, which provides more rigorous causal evidence of the pandemic impact.
Our study also adds to the literature on the determinants of household portfolio choices.
A broad list of factors that determine household asset choices includes housing (Cocco, 2005;
Chetty et al., 2017), income risk (Angerer and Lam, 2009; Betermier et al., 2012), physicalhealth (Fan and Zhao, 2009), mental health (Bogan and Fertig, 2013), financial literacy (Liet al., 2020), cognitive ability (Christelis et al., 2010), and social interaction (Hong et al.,
2004; Liang and Guo, 2015). We contribute to this literature by shedding light on the roleof pandemics in household asset allocations. We provide one of the first pieces of evidence
that exposure to a pandemic can affect the intensive margin but not the extensive marginof stock holdings, which deepens our understanding of how portfolio choices can be affectedby a public health crisis.
5

To the best of our knowledge, there are three extant studies related to this topic (Yue et al., 2020;
Coibion et al., 2020; Agarwal et al., 2020), all of which examine the impact of the COVID-19 pandemic on
the intensive margin of assets holding using a survey conducted within a few months. Yue et al. (2020), andCoibion et al. (2020) show that COVID-19 exposure reduces household investments. By contrast, Agarwal
et al. (2020) find that more confirmed COVID-19 cases in a city result in more upward and downward
adjustments of risky assets.

5This paper is organized as follows. Section 2 introduces the background of the 2009
H1N1 pandemic and describes the data we use. Then, Section 3 discusses our differencein-differences empirical strategy. Section 4 reports findings on how exposure to the H1N1
pandemic affects household stock holdings. Afterward, Section 5 focuses on the heterogeneous effect of the pandemic on the risky share. Section 6 examines the exposure effectof the pandemic on illiquid assets. Next, Section 7 provides a discussion on the possible
explanation of why the H1N1 pandemic affects household portfolio choices. The last section
provides the conclusion of the study.

2

Background and Data

2.1

The US and the 2009 H1N1 Influenza Pandemic

The outbreak of the 2009 H1N1 influenza (informally called “swine flu”) was declaredby WHO as the first global flu pandemic since 1968. During the pandemic, more than 214
countries and overseas territories or communities reported cases of 2009 H1N1 infection,
including more than 18,449 laboratory-confirmed deaths.6
The novel influenza A (H1N1) virus surfaced in the spring of 2009. It was first detectedin California on April 15 and spread quickly across the US. By June 19, confirmed cases of2009 H1N1 infection were reported by all 50 states, the District of Colombia, Puerto Rico,and the US Virgin Islands.
The 2009 H1N1 influenza virus is typically transmitted from person to person throughdroplets. It shares some similar symptoms to other flus: fever, cough, headache, etc. However, the disease patterns in severe cases are remarkably different from those of seasonal flus.
The health of infected people can deteriorate within three to five days after they have symptoms and progress to respiratory failure. This virus differs significantly from other known
H1N1 viruses at the time of the pandemic because it contains a unique segment of flu genes
not identified by humans previously. As a result, children and young and middle-aged adults
have almost no existing antibodies against it, while almost a third of people over 60 years
old had immunity probably due to their exposure to an older H1N1 at some time in theirearlier lives. It is estimated that 80% of 2009 H1N1 deaths were people less than 65 years
old, while about 80–90% of typical seasonal influenza were those aged 65 or older (Dawood
et al., 2012). Given the uniqueness of the novel H1N1 virus, seasonal flu vaccines offeredlittle protection against it.
6

See
WHO
pandemic(H1N1)
2009-update112,
https://www.who.int/emergencies/disease-outbreak-news/item/2010_08_06-en (accessed March 2, 2022.) Note that the number oflab-confirmed deaths, 18,499, is likely to be only a fraction of the true number and is widely considered anunderestimate. For instance, Dawood et al. (2012) estimate 151,700–575,400 deaths globally.

68k

Jun.14−20

Oct.18−24

100

Apr.4−10

7284

80

60
4k

3874

40

% H1N1pdm09

Weekly cases

6k

2k
20

224

0

39
10

20

20

w
10

10
20

w

15

1
w

43
20

09

w

36
20

09

w

25
w
09
20

20

09

w

17

0

Week of Year
Percent of H1N1pdm09

Weekly Cases of H1N1pdm09

Notes: The solid line represents the number of weekly lab-confirmed H1N1 cases, while thedashed line denotes the proportion of weekly lab-confirmed H1N1 cases in total lab-confirmed
influenza cases. (Data source: WHO Collaborating Laboratories )Figure 1. Weekly Cases of H1N1pdm09 in the US
From April 12, 2009 to April 10, 2010, the CDC estimated 43.3–89.3 million cases of thenovel H1N1 virus, including 195,086–402,719 hospitalizations and 8,868–18,306 deaths in the
US (Shrestha et al., 2011). Figure 1 plots the trend of the 2009 H1N1 influenza lab-confirmed
cases. Specifically, the novel H1N1 flu cases in the US peaked in June and October of 2009.
From June 2009 to April 2010, the (H1N1)pdm09 virus accounted for more than 90% of thetotal influenza cases. From then on, the H1N1 cases decreased gradually and dropped tozero by the end of August 2010.
In response to this pandemic, the US government took a series of measures. In the initialstage, schools were closed if cases were confirmed7 . In addition, H1N1-related news were
published on cdc.gov. Public health advice was also provided on these websites, including
educating people to practice good hygiene, such as washing hands properly and using handsanitizers, encouraging the public to keep social distancing, and encouraging sick people withmild symptoms to stay home from work or school until their symptoms subsided. On theother hand, CDC started working on the 2009 H1N1 flu vaccine as early as April 28, 2009.
7

By May 5, 2009, 980 schools across the countries were closed, involving 607,778 students.

7

However, given the slow production process, the vaccine was not available until October
2009, when the government launched the 2009 H1N1 vaccination campaign. Only a few
million were available at the beginning. Therefore, the priority of the first available vaccines
was given to health care workers, children, and pregnant women. In late December 2009,
free vaccines were opened up to anyone who wanted it.
In early August 2010, scientists reviewed the global epidemiological situation and madean important discovery: the H1N1 virus was circulating as a regular seasonal flu virus, andthe level of pandemic flu activity had returned to normal levels that were considered typicalfor seasonal flu. These findings provided strong evidence that the (H1N1)pdm09 virus wastransitioning to a seasonal influenza virus. As a result, the World Health Organization(WHO) declared an end to the H1N1 pandemic on August 11, 2010. From then on, thenovel H1N1 virus spreads as a seasonal flu virus.
2.2
2.2.1

Data
H1N1 Data

We first describe data on the severity of the H1N1 pandemic. Three potential measures
are confirmed cases, hospitalizations, and deaths. The CDC publishes influenza surveillance
information in a weekly report called FluView.8 When the novel H1N1 virus first emerged,
FluView reported state-level individual case counts. However, with the rapid spread of thevirus, individual cases became not representative of the outbreak and were thought to be asignificant underestimate of the actual number.9 As a result, the CDC discontinued reporting confirmed cases on July 24, 2009. Nevertheless, it continued to track hospitalizations
and deaths and reported the country-level hospitalizations and deaths due to the 2009 H1N1
virus. After August 30, 2009, the CDC further revised reporting criteria and asked states
to report all influenza and pneumonia-related hospitalizations and deaths every week, notjust those due to the 2009 H1N1 virus. From September 11, 2009 on, FluView publishedthe country-level total hospitalizations and deaths (including the 2009 H1N1 and seasonalflu). Unfortunately, we can not obtain useful information from the CDC reports since thecountry-level figures masked the regional variations in the H1N1 outbreak.
We use data collected from FluTrackers (FluTrackers.com), an online forum and early
warning system that has gathered information on infectious diseases since 2006. During the8

See www.cdc.gov/h1n1flu/updates/ for a collection of FluView reports on the novel H1N1 virus (accessed March 10, 2022).
9
Many people with respiratory symptoms did not seek medical care. Only a small proportion of themsaw a doctor and did an (H1N1)pdm09 test. Reed et al. (2009) find that every confirmed case reported from
April 2009 to July 2009 in the US represented an estimated 79 cases.

8

2009 H1N1 pandemic, whenever a state health department announced the H1N1 surveillance
information (usually at the state level), FluTrackers collected it and posted the content of
the state surveillance report and the source link on the forum. As individual case counts andhospitalizations were not reported by the state in most cases, we extract the lab-confirmed
H1N1 death information for each state from FluTrackers and compute the state-level total
number of death during the pandemic (i.e., April 2009–August 2010). Then we define ourmain measurement for the state-level H1N1 intensity as the number of lab-confirmed death
toll per 100,000 residents. We use death rates rather than death numbers because death
numbers can be positively correlated with the local population. Figure 2 shows the geographic distribution of H1N1 death rates for 50 states (excluding the District of Columbia),
in which a darker color implies a higher death rate. All 50 states reported deaths from the(H1N1)pdm09 virus during the pandemic. Significant variations are observed in the distribution of death rates across states, ranging from 0.29 (Missouri) to 2.85 (South Dakota) (seeAppendix Table B1 for specific death rates).

Per 100,000
residents
2.85

0.29
Missing (D.C.)

Notes: The state H1N1 death rates are defined as the state-level lab-confirmed death toll
per 100,000 residents during the 2009 Pandemic (April 2009–August 2010). The state-level
death toll is aggregated from FluTrackers.com. The number of state residents denotes theintercensal estimate for 2009 (source: the US Census Bureau, Population Division).

Figure 2. Geographic Distribution of H1N1 Death Rates in the 2009 Pandemic
Besides the H1N1 death rate, we construct a second measure as a robustness check.
Approximately 80 WHO Collaborating Laboratories are located throughout the US. They
reported the total number of respiratory specimens tested and the number of positive forthe influenza types A and B, including the (H1N1)pdm09, in a weekly publication called

9FluView. These data were reported at the HHS region level, a broader level than a state.10
We collect the weekly data on H1N1 case numbers via an R package called “cdcfluview.”
Then, we aggregate the number of confirmed cases of the 2009 H1N1 virus for each HHS
region during the pandemic and calculate the H1N1 case rate (at HHS region), which isdefined as the H1N1 case counts per 100,000 residents.
Examining the correlation between the H1N1 intensity and indexes for stock market
performance during the pandemic is significant because stock market performance is closely
related to household portfolio choices. We construct three weekly indicators of stock market
performance: the weekly real S&P 500 price index, the weekly return of the real S&P 500
index, and the weekly stock market volatility.11 We plot the time trends of these stock market
indexes versus the weekly H1N1 cases during the H1N1 pandemic in Appendix Figures A1–
A3. During the peaks of the first and second waves of the H1N1 pandemic, the weekly S&P
price index and corresponding returns decreased to some extent. However, the correlation
coefficients between these indicators and confirmed cases were all no more than 0.1 in absolutevalue.12 Generally, these three indicators did not exhibit a clear uniform or contrasting
trend with the confirmed cases of H1N1. In Appendix Table B3, we report the results of
the Granger causality test, which suggests that the lagged values of weekly H1N1 cases
do not Granger-cause the aforementioned three weekly stock market indicators at the 5%
significance level. In turn, these indicators do not Granger-cause the weekly H1N1 cases
either. Overall, the H1N1 pandemic had a very mild impact on the stock market, which isin distinct contrast with the case of the COVID-19 pandemic.13
2.2.2

PSID

Our primary data source is the PSID. It is a longitudinal and nationally representative
project that tracks over 82,000 individuals from more than 9,500 families for more than 50
years. The PSID data were released annually until 1997 and became a biannual survey fromthen on.14 The wealth module on asset holdings was first included in 1984, which initiallyrepeated every five years (i.e., in 1989 and 1994). Since 1999, the wealth data have beencollected in every wave. At the time of our research, due to the lack of some state-by-year10

The country is divided into 10 HHS regions. For more details about HHS regions, see the website of
the US Department of Health and Human Services.
11
The data source is the Center for Research in Security Prices (CRSP) of Wharton Research Data
Services. Please refer to the notes in Appendix Figures A1–A3 for details on the variable construction.
12
The correlation between the weekly H1N1 cases and the weekly real S&P 500 price index, the weekly
return of the real S&P 500 index, and weekly stock market volatility are -0.1, -0.05, -0.09, respectively.
13
The COVID-19 pandemic and the measures taken to control it caused the S&P 500 to experience ahistoric decline of one-third of its value in the steep drop of stock prices in February and March 2020 (Hanspal
et al., 2021) and have a substantially adverse impact on stock returns (Al-Awadhi et al., 2020).
14
The most recent available PSID survey is the 2020 wave by March 2023.

10

level control variables in 1999 and 2019, we use nine waves of data in the PSID (i.e., 2001,
2003, 2005, 2007, 2009, 2011, 2013, 2015, and 2017).15
Variable Definitions.—Wealth data are collected at the household level and refer to thevalue at the time of the survey. Following Brunnermeier and Nagel (2008), we define risky
assets as the sum of stock in publicly held corporations, stock mutual funds, and investment
trusts, including stocks in the Individual Retirement Account (IRA). Risky-free assets aredefined as the sum of checkings, savings, bonds, trusts, and IRA invested in interest earnings. We further denote the sum of risky and riskless assets as liquid assets. Stock market
participation is defined as equal to 1 if the household has positive risky assets and zerootherwise. Following the common practice, the risky share is calculated as the proportion of
the liquid assets held in risky assets, which is conditional on stock market participation.
Figure 3 plots the average changes in the household portfolio over time. In our study
period (2001–2017), the fraction of households that owned risky assets displayed an overall
declining trend (Panel A). In particular, it dropped substantially from 2007 to 2009 due tothe financial crisis. On the other hand, the risky share revealed high volatility (Panel B).

(A) Fraction of Stock Market Participation(B) log Risky Share

.55

−.75
−.8

.5

−.85
.45
−.9
.4

−.95

.35

−1
2001 2003 2005 2007 2009 2011 2013 2015 2017
Year

2001 2003 2005 2007 2009 2011 2013 2015 2017Year

Notes: Stock market participation is defined as owing positive risky assets. Risky share is the value ofrisky assets divided by liquid assets, conditional on stock market participation. (Data source: PSID,
waves 2001, 2003, 2005, 2007, 2009, 2011, 2013, 2015, and 2017.)

Figure 3. Aggregate Changes in Household Portfolio: 2001–2017
We proxy the household head (i.e., the “reference person” defined by PSID since 2017) as
the main decision maker in a family. Therefore, the head’s individual characteristics (e.g.,
15

Most of the state-by-year control variables are missing in or before 1999, and some of them are missing
in 2019. As we will introduce in Section 4.1, our rigorous causal analysis must control these variables, as
they are closely related to the outcome variables and the H1N1 intensity. Therefore, we use PSID data from2001 to 2017 such that they can be matched to the balanced panel of state-by-year characteristics.

11

age, gender, education, race, marital status) are used in priority.16 All monetary values,
such as wealth and income, are defined in 2017 dollar adjusted by the CPI-U index.
Sample restrictions.—We impose several sample restrictions to our analysis. Our original
sample is restricted to those who have non-missing basic individual and household characteristics (introduced in detail in Section 4.1), which consists of 59,512 observations in nine
waves. We then exclude observations with asset changes due to a family member moving
into or out of the family, which drops 1,759 observations. We also drop observations if thehousehold head is a student (634 observations) or retired (11,592 observations). We establish
a minimum number of liquid assets of $1,000 because the asset allocation decision is not relevant for households with liquid assets less than $1,000 (Gomes and Smirnova, 2021), whichleaves us with 27,739 observations. We further exclude households with a single observation
to conduct a panel-data analysis. Finally, we are left with a sample with 25,947 observations
from 5,049 family units. For the risky share analysis, the sample is conditional on owning
risky assets and consists of 9,759 observations from 2,239 family units.
Table 1. Summary Statistic for Households
Variable

Mean Median Std.

Age
43.35
Male
0.83
White
0.74
Years of schooling
14.52
Stock market participation 0.41
Risky share
0.53
Number of family
Observations

43.00
1.00
1.00
15.00
0.00
0.50

11.46
0.37
0.44
2.13
0.49
0.29

5049
25,947

Notes: The table reports summary statistics for all
households that satisfy our sample selection rule.
Head information is used in priority as individual
characteristics, and if head information is missing,
spouse information is used instead. Stock market
participation is defined as equal to 1 if the household has positive risky assets and zero otherwise.
The risky share is defined as the proportion of theliquid assets held in risky assets, conditional on
stock market participation. (Data source: PSID,
waves 2001, 2003, 2005, 2007, 2009, 2011, 2013,2015, and 2017.)
16

If head information is missing, we use the wife’s characteristics instead, which accounts for only a trivial
proportion (less than 0.1% of the sample size).

12

Summary Statistics—Table 1 presents pooled cross-sectional statistics for all households
in our analysis sample. During our sample period, the average age of household head is 43.
The head’s gender is skewed to males (83%). More than 70% of the heads are white. The
average number of educational years is 14.5 years. The fraction of households participatingin stock market is 0.41, and the (conditional) risky share is 0.53.
2.2.3

Macroeconomic Indicators and Medical Resources

We collect a wide set of state-by-year-level variables on macroeconomic indicators andmedical resources from different sources (see Appendix Table B2 for the original data anddata sources). To make a fair comparison among states, we use the per capita (or per
100,000 population) basis for GDP, personal income, bankruptcy cases, assets, deposits,
hospital beds, active physicians, physicians in patient care, and registered nurses.
Table 2. Balanced Checks for States
Variable

Low death-rate High death-rate

Diffstatesstates(1)(2)(2)-(1)

0.050

0.048

-0.0016*(0.001)(0.001)(0.0009)45088.406

41851.403-3237.0030***(509.401)(374.250)(632.1015)

5.537

5.666

0.1289(0.127)(0.132)(0.1832)

0.689

0.691

0.0021(0.004)(0.003)(0.0049)

189.679214.473

24.7942***(2.181)(3.464)(4.0936)416.831414.141-2.6898(12.965)(12.664)(18.1237)

0.032

0.139

0.1068***(0.003)(0.030)(0.0301)

2.563

3.746

1.1828(0.504)(1.163)(1.2680)258.404

124.507-133.8972***(18.549)(14.023)(23.2528)

A. Macroeconomic indicators
GDP per capita (in chained 2012 million dollars)Personal income per capita
Unemployment rate
Homeownership rate
Housing price index
Bankruptcy cases per 100,000 people, filed by state
Assets per capita (in million dollars)
Deposits per capita (in million dollars)
Population density

Continued on next page13

Table 2 – Continued from previous page
Variable

Low death-rate High death-rate

Diffstatesstates(1)(2)(2)-(1)291.813275.800-16.0132**(5.221)(6.089)(8.0209)

2.833

2.435

-0.3982***(0.043)(0.029)(0.0515)

2.614

2.260

-0.3541***(0.038)(0.028)(0.0478)869.831805.805-64.0266***(8.903)(11.443)(14.4984)

States25

25

Observations

225

225

B. Medical resources
Hospital beds per 100,000 population
Active physicians per 100,000 population
Physicians in patient care per 100,000 population
Registered nurse per 100,000 population

Notes: This table reports the summary statistics by H1N1 death rates for state characteristics
in 2000, 2002, 2004, 2006, 2008, 2010, 2012, 2014, and 2016 (i.e., one year before the PSID
wave). States in which H1N1 death rates are below the median level of all states are defined as
low death-rate states. States in which H1N1 death rates are above the median level of all states
are defined as high death-rate states. Standard deviations are reported in parentheses. * p <0.1, ** p < 0.05, *** p < 0.01. Standard errors are shown in parentheses. GDP per capita is inchained 2012 dollars. Homeownership rate is defined as the proportion of owner households tothe total number of occupied households. The housing index price data, with the first quarterin 1991 normalized as 100, are for the fourth quarter of the year (seasonally adjusted) andreflects average price changes in repeat sales or refinancings on the same properties. Assets
per capita is referred to as assets per capita in Federal Deposit Insurance Corporation (FDIC)
insured financial institutions. Deposits per capita is defined as deposits per capita in FDIC
insured financial institutions. Population density is defined as population per square mile ofland area. Hospital beds are referred to beds in community hospitals, which are defined as all
non-federal, short-term general, and other special hospitals, excluding hospitals not accessibleby the general public. Except indicated, all monetary variables are in 2017 dollar, adjusted by
CPI-U index. (Data sources: see Appendix Table B2.)

Table 2 reports balanced test results for these state characteristics from 2000 to 2016
(i.e., the years prior to the PSID waves). We use the data from these years to mitigate
concerns regarding reverse causality. We define a state as a high death-rate state if its H1N1
death rate during the pandemic was higher than the median level of all 50 states. As shownin Table 2, high death-rate states has lower GDP per capita, personal income per capita,
14and population density but had higher housing price and more assets per capita in FDICinsured financial institutions than low death-rate states. In terms of medical resources, highdeath-rate states tended to have fewer hospital beds, physicians, and nurses for every 100,000
population. In summary, H1N1 intensity is correlated with state macroeconomic indicatorsand medical resources, for which we will provide thorough analysis in Section 4.1.

3

Empirical Strategy

The primary goal of our study is to estimate the causal impact of the 2009 H1N1 pandemic
on the extensive and intensive margins of stock holdings. As introduced in Section 2.2.1,
we use state-level H1N1 death rates during the pandemic (i.e., April 2009–August 2010) tomeasure H1N1 intensity. We adopt a difference-in-differences identification strategy, in which
we exploit the state-level differences in the H1N1 intensity and the differences between the
pre-pandemic and post-pandemic periods. Specifically, the following equation is estimated:
Yijt = β1 Duringt × H1N 1p + β2 Af tert × H1N 1p + αt + αi + αj + δtαj + γXijt + ϵijt ,(1)

where i indexes family, t indexes year, and j indexes the state where the family resides. The
outcome of interest, Yijt , denotes either an indicator variable for stock market participationor the natural log of risky share. H1N 1p is the natural log of the lab-confirmed H1N1 death
rate in the state p during the 2009 H1N1 pandemic. The state p is where the household
resided in 2009. Our baseline analysis does not require the sample to stay in the same state.
We will focus on families that did not move in the robustness analysis later. Duringt is anindicator equal to 1 if t = 2009. Af tert is an indicator equal to 1 if t > 2009. αt , αi , and αjare year, family, and state fixed effects, respectively.17 The model also includes state-specifictime trends, tαj , to mitigate the concern that the effect of the 2009 H1N1 pandemic can be
driven by preexisting state-specific trends. Xijt includes a large set of household- and statespecific time-varying characteristics, which are described when introduced in the results.
The inclusion Xijt mainly has two advantages. On the one hand, controlling these variables
helps reduce the variance of the error term and hence improves the precision of the estimateof β1 and β2 . On the other hand, despite the unpredictability of the H1N1 pandemic, thestate H1N1 death rates may be correlated with other factors that can cause the change inasset allocations, thus being correlated with household stock holdings. Including Xijt may
help to control for these observable differences across states. Standard errors are clustered
17

Given that we include αi , which controls time-invariant family characteristics, αj captures the effect oftime-invariant state-level characteristics for migrants (i.e., families that changed their residency state during
our study period.)15

at the family level.
In our model, H1N 1p works as a continuous treatment variable. Our identification
relies on the assumption that in the absence of the 2009 H1N1 pandemic, household stock
holdings would have changed in a parallel way in both the treatment and pre-pandemic
control group, controlling for year fixed effects, family fixed effects, state fixed effects, statespecific time trends, and other time-varying control variables. This assumption should beplausible because the outbreak of the H1N1 pandemic is unexpected and uncontrollable.
Coefficients β1 and β2 are our key coefficients of interests. Assuming parallel trends
without treatment, we can then interpret β1 as the average percentage change in Yijt in 2009
when the H1N1 intensity increases by 1%, relative to the pre-pandemic year. Analogously,
β2 represents the average percentage change in Yijt after 2009 when the H1N1 intensity
increases by 1%, relative to the pre-pandemic year. In other words, β1 and β2 can beconsidered measurements for elasticities, that is, how sensitive stock market participation
or risky share is to H1N1 intensity. We interact H1N 1p with Duringt and Af tert dummy
separately rather than construct the classic interaction between H1N 1p and a P ostt dummy(P ostt is equal to 1 if t >= 2009). In our analysis sample, the majority of families (morethan 60%) in 2009 took the PSID survey before June (i.e., the peak of the first wave in theUS) (see Appendix Figure A4). As a result, these households could have had less exposure
to the pandemic at the time of the 2009 survey than others who lived in the same state
but took the survey later in 2009. To be conservative, we define the Duringt and Af tert
dummies following Kolstad and Kowalski (2012).
We next use an event study framework to estimate year-specific treatment effects relative
to the base year 2007. Formally, the model is written as:
Yijt = βt H1N 1p + αt + αi + αj + δtαj + γXijt + ϵijt .

(2)

Notably, β is now allowed to vary by year. The estimates βt measure the dynamics oftreatment effect over time and help to examine whether pre-trends exist.
In our heterogeneity analysis, we modify Eq. (1) by allowing the treatment effect to bedifferent in different groups. Formally, we estimate the following model:
Yijt = β1g During × H1N 1p + β2g Af ter × H1N 1p + αt + αi + αj + γXijt + ϵijt

(3)where β1g and β2g denote the treatment effect of the group g during and after the pandemic,
respectively. By testing the equality of β1g and β1g′ (β2g and β2g′ ), we can examine whether
the difference in treatment effects between different groups is significant.

16

4

Exposure Effect of the H1N1 Pandemic on Stock Holdings

4.1

Main Results

Table 3 shows the results of the effect of the 2009 H1N1 pandemic on stock holdings
based on Eq. (1). Columns (1)–(4) and columns (5)–(8) examine the extensive margin(stock market participation) and the intensive margin (risky share), respectively.
Table 3. Impact on the 2009 H1N1 Pandemic on Household Portfolio
Stock market participation(1)
During × log(H1N1 death rate)
After × log(H1N1 death rate)

(2)

(3)

(4)

log risky share
(5)

(6)

(7)

(8)

0.008 0.008 0.013 0.012-0.030-0.028-0.078-0.088(0.017) (0.017) (0.018) (0.018) (0.060) (0.060) (0.063) (0.063)-0.013 -0.012 0.004 -0.002 -0.209∗∗∗ -0.206∗∗∗ -0.228∗∗∗ -0.273∗∗∗(0.022) (0.021) (0.023) (0.023) (0.073) (0.073) (0.078) (0.079)

Average of the outcome variable 0.410 0.410 0.410 0.410
Family, state and year FEs✓✓✓✓
State-specific time trend✓✓✓✓
Household features✓✓✓
Macroeconomic indicator (lag)✓✓Medical controls (lag)✓Number of family
5,049 5,049 5,049 5,049
Observations
25,947 25,947 25,947 25,947
Adj.R20.475 0.476 0.476 0.476-0.886✓✓-0.886✓✓✓-0.886
✓
✓
✓
✓

2,239
9,790
0.285

2,239
9,790
0.285

2,239
9,790
0.285

-0.886
✓
✓
✓
✓
✓
2,239
9,790
0.286

Notes: This table shows baseline results on the change of stock market participation and risky share in response to
the 2009 H1N1 pandemic. All regressions control for family fixed effects, year fixed effects, state fixed effects, andstate-specific time trend. Columns (2) and (6) add controls for household features. Columns (3) and (7) further
include time-varying state-level macroeconomic indicators. Columns (4) and (8) additionally control for timevarying state-level medical resources. See the description in Section 4.1 for the list of control variables. Standard
errors are clustered at the family level and shown in parentheses. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01. (Data source:
PSID, waves 2001, 2003, 2005, 2007, 2009, 2011, 2013, 2015, 2017.)

Columns (1) and (5) control family fixed effects, year fixed effects, state fixed effects, andstate-specific time trend without controlling Xijt . For the extensive margin, no significant
change occurs in stock market participation related to H1N1 death rates during or after
the pandemic. For the intensive margin, although no clear pattern is observed during the
pandemic (in 2009), we find that when the H1N1 death rate increases by 1%, the risky sharedecreases by 0.2% after the pandemic (in 2011 and beyond).
In columns (2) and (6), we add a wide set of household characteristics, motivated bypast studies on household portfolio decisions, most notably Brunnermeier and Nagel (2008).
Specifically, we include the age and age square of the household head. Given that the head’s
race, educational attainment, and gender are time-invariant and absorbed by family fixed
effects, we add their interactions with the head’s age and age square. Fagereng et al. (2017)17

show that there are cohort-specific patterns in stock market participation and risky share.
Inspired by this finding, we include interactions between the age and cohort indicators ofheads.18 We do not include variables that can be affected by a pandemic, such as marital
status, health status, income, and financial situation (e.g., whether they own mortgages,
debts, and business) because these variables are bad controls. As can be shown in Table 3,
for stock market participation, the coefficients of two interaction terms are still close to zeroand insignificant (column (2)). While for the risky share, the effect of H1N1 intensity barelychanges (column (6)).
We further control for a series of time-variant state-specific macroeconomic indicators(one-year lagged) in columns (3) and (7). These indicators include GDP per capita, personal income per capita, unemployment rate, homeownership rate, the Federal Housing
Finance Agency (FHFA) housing price index (HPI), bankruptcy cases per 100,000 people,
assets per capita and deposits per capita in FDIC-insured financial institutions, and population density. We control for these macroeconomic indicators for two main reasons. First,
macroeconomic experiences can affect individuals’ financial risk taking and hence influence
household portfolio choices (Malmendier and Nagel, 2011). Second, the spread of epidemics
is correlated with the macroeconomic situation and business cycles (Adda, 2016).19 In otherwords, the macroeconomic variables we include may potentially be correlated with both thehousehold portfolio choice and the H1N1 death rate. As shown in column (7), the estimatedexposure effect remains stable after including these macroeconomic indicators.
Although the novel H1N1 virus outbreak is unexpected, the H1N1 death rate during the
pandemic is possibly correlated with healthcare resources. As shown in Appendix Figure A5,
states with fewer physicians, nurses, or hospital beds tend to have higher death rates during
the pandemic (Appendix Figure A5). If unobserved time-varying confounders are correlatedwith the medical resources and lead to changes in household portfolio choices, our estimation
strategy may suffer from biases. However, the results hold up when we additionally control
for a set of medical resources (hospital beds, active physicians, physicians in patient care,and registered nurses per 100,000 people) in columns (4) and (8).
Two main conclusions can be drawn from the above analysis. First, stock market participation does not change with H1N1 intensity. Second, for risky share, we find little changes
related to the H1N1 death rate in the year of the pandemic outbreak but a significant decrease after the pandemic. In our subsequent analysis, we will mainly focus on the risky
18

Heads who were born before 1928, between 1928 and 1944, between 1945 and 1964, between 1965 and1984, and after 1984, were defined as the “greatest generation,” “silent generation,” “baby boom,” “babybust,” and “echo boom,” respectively.
19
Adda (2016) show that epidemics spread faster during economic booms because people are more likely
to travel, which increases interpersonal connection and hence accelerate the spread of infectious diseases.

18

share and include year fixed effects, family fixed effects, state fixed effects, state-specificlinear trend, and a full set of time-varying controls, as we have done in column (8).
4.2Does the 2007–2008 Financial Crisis Confound Our Result?

One may have concern that the estimated exposure effect of the 2009 H1N1 pandemic
on household stock holdings was confounded by the 2007–2008 financial crisis, which had aprofound impact on the United States economy. The crisis was triggered by the collapse of
the US housing market. As the crisis unfolded, many major financial institutions in the US
faced significant losses, thus leading to a wave of bank failures and a sharp contraction incredit markets. Notably, evidence shows that the crisis could largely increase households’
risk aversion and reduce the share of risky assets (e.g., Guiso et al. (2018)). Although theoutbreak of the H1N1 pandemic was unexpected and uncontrollable, the H1N1 intensity ina region is likely correlated with the local severity or serious economic consequences of thefinancial crisis and hence creates bias in our estimation.
We develop three measures to assess the local impact of the 2007–2008 financial crisis.
For each state j, we compute the change rate of GDP per capita, unemployment rate, andhousing price index in 2008 compared with 2006 following this equation:%∆xj = (xj,2008 − xj,2006 )/xj,2006 × 100,
where xj,t denotes GDP per capita, unemployment rate, or housing price index of state
j in year t. The geographic distribution of the percentage changes in these indicators isshown in Appendix Figure A6. Intuitively, most states in the west (excluding Alaska) andsoutheast experienced larger decreases in GDP per capita and housing prices, as well aslarger increases in the unemployment rate in 2008 compared with 2006. In contrast, as
demonstrated in Figure 2, the western regions of the US were exposed to higher H1N1
intensity during the pandemic. We further perform a simple regression analysis to examine
the relationship between H1N1 death rates during the pandemic and changes in state-level
economic indicators during the financial crisis. The results, as presented in Appendix Table
B5, indicate that the correlation between H1N1 intensity and changes in these economic
indicators is negligible and statistically insignificant.
We now investigate whether the exposure effect of the H1N1 pandemic on the risky sharepersists when we control for the local impact of the 2007–2008 financial crisis. The impact of
the financial crisis is captured by the interactions between year dummies and the percentagechanges in GDP per capita, unemployment rate, and housing price during the financial
crisis. This scenario allows us to capture the potential time-variant effect of the financial
19crisis on risky shares. The results are presented in Table 4. Compared with the baseline
result (column (1)), the elasticity of the risky share with respect to the H1N1 death rate
slightly decreases in magnitude but remains statistically significant after we control for theinteractions between year dummies and GDP per capita, unemployment rate, and housing
price in columns (2)–(4), respectively. In column (5), we further control for all three series
of interactions. The effect of H1N1 intensity on risky share is -0.26, which is statisticallysignificant at the 1% level. In summary, the significant exposure effect of the H1N1 pandemic
on household stock holdings is quite robust even when we control for the potential impactof the financial crisis.
Table 4. Examine the Potential Bias Driven by the Financial Crisis
Log(Risky share)(1)
During × log(H1N1 death rate)

(2)

(3)

(4)

(5)

-0.088-0.091-0.080-0.076-0.098(0.063) (0.064) (0.064) (0.065) (0.068)-0.273∗∗∗ -0.269∗∗∗ -0.259∗∗∗ -0.263∗∗∗ -0.260∗∗∗(0.079) (0.079) (0.080) (0.081) (0.085)

After × log(H1N1 death rate)
Average of the outcome variable
Family, state, year FEs
State-specific time trend
Household- and state-level controls%∆ in GDP per capita × year indicators%∆ in unemployment rate × year indicators%∆ in housing price index × year indicators
Number of family
Observations
Adj.R2-0.886
✓
✓
✓

2,239
9,790
0.286

-0.886✓✓✓✓-0.886
✓
✓
✓

2,239
9,790
0.286

2,239
9,790
0.285

✓

-0.886
✓
✓
✓
✓
2,239
9,790
0.285

-0.886
✓
✓
✓
✓
✓
✓
2,239
9,790
0.285

Notes: The percentage change of a state-level economic indicator during the financial crisis
is defined as the change rate of this economic indicator in 2008 relative to 2006. The formulais ∆xj = (xj,2008 − xj,2006 )/xj,2006 × 100%, where xj,t is the economic indicator of state jin year t. See Appendix Table B2 for the data source of GDP per capita, unemployment
rate, and housing price index. See the description in Section 5.1 in the paper for the listof other control variables. Standard errors are clustered at the family level and shown inparentheses. * p < 0.1, ** p < 0.05, *** p < 0.01. (Data source: PSID, waves 2001, 2003,
2005, 2007, 2009, 2011, 2013, 2015, 2017.)

4.3

Other Robustness Checks

As shown in the previous section, the H1N1 pandemic does not affect stock market participation but results in a decrease in the share of liquid assets invested in risky assets. We
now examine the robustness of the findings for the risky share in Table 5.
Column (1) of Table 5 replicates the baseline result from column (8) of Table 3. In column
20

(2), we restrict the sample to households who always lived in the same state in our study
period, dropping about 6.8% of the sample size. The state fixed effects are thus absorbed
because the residence state for the same household had no variation across years. It showsthat the coefficients on our two key interactions barely change.
We then check the sensitivity of our findings to the outliers. Among 50 states of the US,
South Dakota and New Mexico can be treated as outliers because their H1N1 death rates are
the highest, nearly three deaths per 100,000 people during the pandemic and much higher
than in other states (see Appendix Table B1). As can be seen from column (3), the result
remains substantively the same if we exclude these two states.
Next, we use the H1N1 case rate (i.e., the laboratory-confirmed case numbers per 100,000
people) as an alternative measure for H1N1 intensity. As has already been noted in Section
2.2.1, this measure is at the HHS region level. In column (4), the finding that risky share isnegatively affected by H1N1 intensity still holds in the years after the pandemic. However,given that the H1N1 case rate is measured at the HHS region level, it may fail to capture
accurate information within regions, which reduces the precision of the estimated exposure
effects.
Theory models in economic epidemiology predict that agents engage in protective behavior only when the contagious disease passes a threshold prevalence (Philipson, 2000).20
Inspired by the theory, we would like to ask whether the change in risky shares is only triggered by an unexpected and widespread public health crisis, such as the H1N1 pandemic.
According to the CDC, regular seasonal influenza viruses co-circulated with the 2009 H1N1
influenza.21 Although both the seasonal flu and the 2009 H1N1 virus posed a public healthrisk to people, the H1N1 outbreak was unexpected, unknown to humans, caused a large scaleof infections and deaths, and invalidated the existing vaccines at the time of its onset. Therefore, we examine whether exposure to the regular seasonal flu (including (A)H1, A(H3), B,
and other subtypes of influenza A viruses) has a similar impact on the risky share. We use
the laboratory-confirmed number of any other flu-related cases (excluding the (H1N1)pdm09
virus) per 100,000 people during the 2009 H1N1 pandemic (i.e., April 2009–August 2010) tomeasure the intensity of the seasonal flu. This seasonal flu case rate is reported by WHO
Collaborating Laboratories in FluView at the HHS region level. We estimate a model similar
to Eq. (1) except that the seasonal flu case rates now interact with Duringt and Af tert
dummies (column (5)).
Alternatively, we collect annual case rates of seasonal flu (excluding the novel H1N1
20
In a study on the impact of the H1N1 pandemic on health behaviors in Mexico, Agüero and Beleche(2017) find no effect for the seasonal flu on diarrhea outcomes but a negative impact for the H1N1, which
provides supporting empirical evidence for Philipson (2000)’s model.
21
http:/www.cdc.gov/h1n1flu/reportingqa.htm, accessed February 2022.

21

virus) at the HHS region level from FluView. Due to data availability, this measurement
only covers from 2007 to 2017 in our sample period. With this time-varying variable, weestimate the following model and present the result in column (6):Yijt = σF lurt + αt + αi + αj + δtαj + γXijt + ϵijt .

(4)

where F lurt denotes the natural log of laboratory-confirmed seasonal flu case rates (casenumbers per 100,000 people) in the HHS region r in year t. σ captures the difference-indifferences impact of exposure to the seasonal flu. As shown in columns (5) and (6), we
find little correlation between the risky share and the case rate of seasonal flu. It indicatesthat an expected health shock caused by the seasonal flu does not affect households’ stock
holdings. By contrast, an unexpected and severe health crisis, such as the H1N1 pandemic,
does.
If, for some reason (e.g., policy change), families exposed to higher H1N1 death rates
tended to reduce their risky share prior to the 2009 H1N1 pandemic, then the coefficients ofour key interaction terms would be misunderstood as the result of the pandemic. Therefore,
we conduct a falsification test by choosing a “pseudo” time of the pandemic. We assume 2007
is in the post-pandemic period and retain data from 2001 to 2007. In this way, 2001, 2003,
and 2005 are hypothetical pre-treatment periods, and 2007 is the post-treatment period.
We can see from column (7) that the coefficient of the post-treatment dummy (Y ear2007)
interacted with the H1N1 death rate is close to zero and loses statistical significance. It
suggests that in the pre-period, families in states with higher H1N1 death rates do notchange with the risky share significantly. This falsification test is also used in the literature(e.g., Yurukoglu et al., 2017) as a diagnosis for the parallel trends.
4.4

Event Study

We now estimate a more flexible functional form specified in Eq. (2). By interacting thenatural log of the H1N1 death rate with year indicators, we can better understand how thetreatment effect of the pandemic varies with time.

22

Table 5. Robustness Checks
Dependent variable

log risky share
(1)

(2)

(3)

Baseline

Non-

Drop 2 states

(4)

(5)

(6)

HHS-level case rate

(7)
“Pseudo”

migrants (SD and NM) time-invariant time-varying pandemic year
During × log(H1N1 death rate)
After × log(H1N1 death rate)-0.088-0.092(0.063) (0.067)-0.273∗∗∗ -0.271∗∗∗(0.079) (0.095)-0.078(0.067)-0.279∗∗∗(0.084)

During × log(H1N1 case rate)-0.123(0.079)-0.157∗(0.093)After × log(H1N1 case rate)
During × log(Flu case rate, excluding H1N1)-0.037(0.071)-0.031(0.082)After × log(Flu case rate, excluding H1N1)
log(Annual flu case rate, excluding H1N1)-0.027(0.032)

Year2007 × log(H1N1 death rate)
Average of the outcome variable
Family, state year FEs
State-specific time trends
Household features
Macroeconomic index (lag)Medical controls (lag)Number of family
Observations
Adj.R2-0.048(0.110)-0.886
✓
✓
✓
✓
2,239
9,790
0.286

-0.886
✓
✓
✓
✓
✓
2,141
9,123
0.281

-0.884
✓
✓
✓
✓
✓
2,220
9,713
0.285

-0.886
✓
✓
✓
✓
✓
2,239
9,790
0.285

-0.886
✓
✓
✓
✓
✓
2,239
9,790
0.285

-0.859
✓
✓
✓
✓
✓
1,687
5,897
0.328

-0.900
✓
✓
✓
✓
✓
1,459
4,349
0.298

Notes: See Section 4.1 for the list of variables controlled as household features, macroeconomic index, and medical resources.
Column (1) replicates the result of column (8) in Table 3. Column (2) restricts the sample to family units who did not
emigrant to other states during our study period. Column (3) drops families who lived in South Dakota or New Mexico
in 2009. These two states are outliers that have the highest H1N1 death rate during the pandemic. Column (4) adopt the
H1N1 case rate during the pandemic as the measure for H1N1 intensity. The H1N1 case rate is defined as the number oflab-confirmed case counts per 100,000 people in an HHS region from April 2009 to August 2010. Column (5) conducts aplacebo test by using the constant case rate of other flu during the pandemic. The case rate of other flu is defined as thenumber of lab-confirmed cases of other influenza virus per 100,000 people in an HHS region from April 2009 to August 2010.
Column (6) does an alternative placebo test by using the time-varying case rate of other flu. Column (7) drops data from2009 and onward, assuming 2007 is the post-pandemic year. Standard errors are clustered at the family level and shown inparentheses. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01. (Data source: PSID, waves 2001, 2003, 2005, 2007, 2009, 2011, 2013, 2015,
and 2017.)

23

(A) Stock market participation(B) log Risky Share

.1

.2

.05

0

0
−.2
−.05
−.4

−.1
−.15

−.6
2001 2003 2005 2007 2009 2011 2013 2015 2017
Year

2001 2003 2005 2007 2009 2011 2013 2015 2017Year

Notes: The figure plots the estimates of βt in Eq. (2). The regression controls family fixed
effects, state fixed effects, year fixed effects, state-specific year trend, household features,state-by-year-level macroeconomic indicators, and medical resources (the same controls
as columns (4) and (8) of Table 3). The capped spikes indicate 95 percent confidence
intervals, with robust standard errors clustered at the family level. (Data source: PSID,
waves 2001, 2003, 2005, 2007, 2009, 2011, 2013, 2015, 2017.)

Figure 4. Dynamic Effects of the 2009 H1N1 Pandemic on Household Portfolio
In Figure 4, we plot the estimated coefficients of βt with 2007 as the base year. For bothstock market participation and risky share, βt is close to zero and statistically insignificant
prior to 2007, thus indicating that stock holdings changed similarly in higher death-rate states
and lower death-rate states prior the treatment. Relative to 2007, no immediate change isobserved in either the extensive margin or the intensive margin of stock holdings during
the 2009 pandemic. However, after the pandemic, we find that the risky share experienced
more decreases in states with higher mortality rates than those with lower mortality rates.
The estimated coefficient of βt is about -0.21 in 2011. It then decreases to -0.31 in 2013
and stabilizes at around -0.24 by 2017. These results suggest that the exposure effect of thepandemic has a two-year lag and is long-lasting at least until 2017. Columns (1) and (3) inTable 6 report the estimates of βt that are plotted in Figure 4. Joint F-tests show that theplacebo effects in all years prior to 2007 are jointly insignificant for both the extensive andintensive margin of stock holdings, with p-values of 0.66 and 0.17, respectively. On the other
hand, the treatment effects in the post-pandemic years (i.e., 2009 and onward) are jointly
significant for intensive margin only, with a p-value of 0.043.

24

Table 6. Parallel Trends Test
Stock market participation
(1)
β2001
β2003
β2005
β2009
β2011
β2013
β2015
β2017

0.019
(0.031)
0.022(0.025)0.002(0.020)0.005(0.020)-0.011(0.025)-0.005(0.030)-0.032(0.035)-0.056(0.042)log(H1N1 death rate) × Survey year
Average of the outcome variable
0.410
p-value: Joint Sign. of pre-treated Coef.
0.758
p-value: Joint Sign. of post-treated Coef. 0.427
Family, state, year FEs✓
State-specific time trend✓
Household features✓
Macroeconomic indicator (lag)✓Medical controls (lag)✓Number of family
5,049
Observations
25,947
Adj.R2
0.476

(2)

0.014
(0.021)
0.006(0.028)0.020(0.035)0.000(0.041)-0.015(0.047)-0.004(0.005)
0.410
0.587
✓
✓
✓
✓
✓
5,049
25,9470.476

log risky share(3)-0.019(0.104)-0.118(0.092)0.058(0.075)-0.080(0.066)-0.213∗∗(0.084)-0.313∗∗∗(0.103)-0.254∗∗(0.122)-0.241∗(0.140)-0.886
0.181
0.048
✓
✓
✓
✓
✓
2,239
9,790
0.286

(4)

-0.121∗(0.072)-0.279∗∗∗(0.097)-0.399∗∗∗(0.122)-0.363∗∗(0.147)-0.375∗∗(0.168)0.012(0.017)-0.886
0.028
✓
✓
✓
✓
✓
2,239
9,790
0.285

Notes: This table presents results for parallel trend tests. Columns (1) and (3) report
estimates of βt s in Eq. (2). Columns (2) and (4) report estimates of βt s and θ in Eq.
(5). All regressions control for family fixed effects, year fixed effects, state fixed effects,
and state-specific time trend, household features, and state-by-year-level macroeconomic
indicators. See Section 4.1 for the list of variables controlled as household features,
macroeconomic index, and medical resources. Standard errors are clustered at the familylevel and shown in parentheses. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01. (Data source: PSID,
waves 2001, 2003, 2005, 2007, 2009, 2011, 2013, 2015, and 2017.)

An alternative way to test parallel trends is to examine whether the differential slope
between higher death rate states and lower death rate states is significantly different prior
to the 2009 pandemic (e.g., Akosa Antwi et al., 2013; Muralidharan and Prakash, 2017).
25

Formally, we estimate the following model:
Yijt = βt H1N 1p + θH1N 1p t + αt + αi + αj + δtαj + γXijt + ϵijt

(5)where βt s are year-specific treatment effects of the pandemic, relative to the pre-pandemic
periods (i.e., 2001, 2003, 2005, and 2007). θ is our key of interest, the differential slope. If theestimate of θ is statistically insignificant (p-value greater than 0.05), we may conclude thatthe parallel trends existed prior to the pandemic. Different from Eq. (2), we only estimate
treatment effects for the post-pandemic years (i.e., 2009 and onward). In this way, theparameter θ fits the trend difference only for the pre-pandemic period. As shown in columns(2) and (4) of Table 6, the estimates of θ are close to zero and statistically insignificant for
both stock market participation and log risky share. Therefore, we can not reject the null
hypothesis that higher death rate states and lower death rate states have different trends
before the pandemic.
4.5

Decomposition of the Risky Share

Recall that the risky share is defined as the share of liquid assets invested in risky assets.
Given this definition, a decline in the risky share can be attributed to either a decrease inrisky assets or an increase in liquid assets, or both. With this in mind, we now explore which
determinant plays a key role in driving the decline in risky shares in Table 7.
We first examine whether the value of risky assets changes with H1N1 intensity. As shownin column (1), we observe a decrease in the value of risky assets by approximately $70 to $120
per 1% increase in the H1N1 death rate. This reduction, while not statistically significant,accounts for 10% to 17% of the average value of risky assets ($680). Contrastingly, column (2)reveals that risk-free assets exhibit a growth trend with rising H1N1 intensity. Specifically,
we note a significant increase of $207 in the value of risk-free assets for every 1% rise in the
H1N1 death rate, which represents nearly a third of the mean value of these assets ($671).
We further test the exposure effect of H1N1 on liquid assets in column (3). We do not findany significant correlation between the H1N1 death rate and changes in liquid assets.22 One
potential issue with utilizing risky, risk-free, and liquid assets as dependent variables lies inthat the observed changes in response to H1N1 intensity could be attributable to fluctuationsin asset returns, rather than household portfolio rebalancing decisions. To address this, we
22

In 2009, an increase of 1% in H1N1 intensity corresponded to a decline of $53 in the value of liquid
assets. This decline is statistically insignificant and represents only 3.7% of the mean value of liquid assets.
Similarly, from 2011 onward, a 1% rise in H1N1 intensity results in a decrease of $136 in the value of liquid
assets. However, this reduction is not statistically significant, accounting for a mere 10% of the mean liquid
assets.

26

examine the net purchases or sales of risky assets directly. A positive value in the net amountinvested in stocks suggests net buying, while a negative value indicates net sales. Column(4) shows that when the H1N1 death rate increases by 1%, the net amount put in stocks at
the household level decreases by about 37.74 dollars significantly.
Table 7. Structural Changes in Household Portfolio Choices
Dependent variable(in US$ 1000)

During × log(H1N1 death rate)
After × log(H1N1 death rate)
Average of the outcome variable
Family, state, year FEs
State-specific time trend
Household features
Macroeconomic indicator (lag)Medical controls (lag)Number of family
Observations
Adj.R2Risky
assetsRisk-freeassetsLiquidassetsNet amount
put in stocks
in the past 2 years

(1)

(2)

(3)

(4)

-11.829
6.495-5.335(7.904) (9.795) (12.633)-7.128 20.758∗∗ 13.631(10.994) (10.460) (15.002)-1.394(2.801)-3.774∗∗(1.801)68.021
✓
✓
✓
✓
✓
5,048
25,938
0.462

2.781
✓
✓
✓
✓
✓
4,699
22,6900.045

67.151
✓
✓
✓
✓
✓
5,048
25,9400.306

135.167
✓
✓
✓
✓
✓
5,048
25,9400.512

Notes: Liquid assets is the sum of risky and risk-free assets. See the description
in Section 4.1 for the list of control variables. All monetary variables are adjusted
in 2017 US dollar. Standard errors are clustered at the family level and shown inparentheses. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01. (Data source: PSID, waves 2001,
2003, 2005, 2007, 2009, 2011, 2013, 2015, and 2017.)

To sum up, the decrease in risky share that we have found in previous sections mainly
stems from the net sales of stocks instead of the increase in liquid assets. With the valueof liquid assets remaining largely unchanged, households tend to hold more risk-free assets
when exposed to high H1N1 intensity. This scenario suggests that households are more likely
to reduce their stock holdings and transfer their assets to safer, risk-free options in responseto the pandemic.
Extensive studies have documented that a significant proportion of households do notrebalance their portfolios frequently (e.g., Brunnermeier and Nagel (2008), Calvet et al.
(2009), Bilias et al. (2010)). This pattern is also evident in our analysis of the PSID 20012017 sample. We find that only about 20% of households have a non-zero value of net
purchases or sales of stocks. This result may be due to inertia (as Brunnermeier and Nagelsuggests) or measurement errors, as people may not remember their stock trading behavior
27

precisely over the past few years. To address these concerns, we adopt Calvet et al. (2009)’s
approach and decompose the change in the risky share into a passive change caused by the
returns of assets and an active change that arises from household decisions to rebalance theirportfolios.
P
We compute the passive risky return ωi,t
, which is the proportion of risky assets thatwould have been held without any trading occurring between time t − 1 and t. Formally,
P assive=
ωi,t

ωi,t−1 Rt
ωi,t−1 Rt + (1 − ωi,t−1 )Rtf

(6)

where ωi,t−1 is the initial risky share (i.e., the risky share at t-1), Rt is the return on risky
assets, and Rtf is the return on risk-free assets.
The passive change is the change in the risky share, assuming that the household did not
make any trades involving risky assets between t − 1 and t:
P assive
P assive∆ωi,t= ωi,t− ωi,t−1 .

(7)

As suggested by Calvet et al. (2009), the passive change should be zero if the investor’s
initial investment is solely in cash or risky assets.
The active change refers to the change in risky share, which is not driven by realized
returns but rather arises from intentional portfolio rebalancing. In other words, the active
change is a result of strategic decisions aimed at achieving a desired level of risk exposure or
taking advantage of market opportunities. Formally, the active change is defined as follows:
ActiveP assive∆ωi,t= ωi,t − ωi,t.

(8)

Combined with Eqs. (7) and (8), we can write the total change in the risky share as thesum of the passive and the active change:
ActiveP assiveωi,t − ωi,t−1 = ∆ωi,t+ ∆ωi,t.

(9)

In our empirical analysis, the return on the risk-free assets (Rtf ) is the real return on
the 90-Day T-Bill. In terms of the return on the risky assets (Rt ), we try five different
returns from CRSP to ensure robust results: Vwretd (value-weighted return, including all
distributions), Vwretx (value-weighted return, excluding dividends), Ewretd (equal-weighted
return, including all distributions ), Ewretx (equal-weighted return, excluding dividends),
and Sprtrn (return on the S&P composite index). Accordingly, we compute five versions ofpassive changes and five versions of active changes. As we do not know the household-specific28Active
P assive
returns on risky assets (Rt ), the active change (∆ωi,t) and the passive change (∆ωi,t)may have measurement errors.
Appendix Figures A8 and A9 draw the scatterplots of the passive and active changes(calculated with Vwretd) in 2001–2017, respectively.23 As shown in Appendix Figure A8,
the passive changes in 2001, 2003, and 2009 are a U-shaped function of the initial share,
as is typical in a bear market (i.e., Rt < Rtf ). In other years, the passive changes are a
hump-shaped function of the initial share, thus indicating that Rt > Rtf . The active changes
plotted in Appendix Figure A9 show that the households have considerable diversity, withmany exhibiting substantially positive or negative active changes. In addition, we observethat a large portion of households have trading values of risky assets close to zero, thus
indicating that they either trade very infrequently or not at all throughout the year.
We investigate whether active and passive changes in stock holdings change with the
H1N1 pandemic, as presented in Table 8. To facilitate the coefficient interpretation, we
present the passive and active changes in the percentage form. In the first five columns,
the dependent variable is the passive change. Since passive changes solely depend on therealized return on risky assets, all five types of passive changes do not change significantlywith the H1N1 death rate during or after the pandemic. The next five columns (6) to (10),
use the active change as the dependent variable. Consistent with our previous finding on therisky share, households do not actively trade their risky assets during the pandemic (i.e., in2009). However, since 2011, the active change declines by about 0.07 percentage points if
the H1N1 intensity increases by 1%. In other words, people tend to sell their stocks actively
in response to an increase in the H1N1 death rate.
23

The scatterplots of the passive and active changes computed with Vwretd, Vwretx, Ewretd, Ewretx, andSprtrn are similar to the ones computed with Vwretd. The summary statistics of active and passive changes
using five different returns on risky assets are presented in Appendix Table B6.

29

Table 8. Active and Passive Changes in Household Portfolio Choices
P assive∆ωi,t
× 100%

Dependent variable
Return on risky assets
During × log(H1N1 death rate)
After × log(H1N1 death rate)
Average of the outcome variable
Family, state, year FEs
State-specific time trend
Household features
Macroeconomic indicator (lag)Medical controls (lag)Number of family
Observations

Active∆ωi,t
× 100%

(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
Vwretd Vwretx Ewretd Ewretx Sprtrn Vwretd Vwretx Ewretd Ewretx Sprtrn-0.209 -0.261 -0.284 -0.321 -0.269(0.332) (0.344) (0.380) (0.389) (0.351)
0.077 0.078 -0.100 -0.080 0.078(0.339) (0.317) (0.441) (0.419) (0.321)-0.620(3.293)-6.945∗(3.827)-0.569 -0.556 -0.519 -0.561(3.296) (3.291) (3.293) (3.296)-6.946∗ -6.768∗ -6.789∗ -6.947∗(3.832) (3.816) (3.821) (3.832)

1.413
✓
✓
✓
✓
✓
1,497
6,559

-1.258
✓
✓
✓
✓
✓
1,497
6,559

-0.577
✓
✓
✓
✓
✓
1,497
6,559

0.732
✓
✓
✓
✓
✓
1,497
6,559

2.758
✓
✓
✓
✓
✓
1,497
6,559

2.127
✓
✓
✓
✓
✓
1,497
6,559

0.699
✓
✓
✓
✓
✓
1,497
6,559

-2.603
✓
✓
✓
✓
✓
1,497
6,559

-1.973
✓
✓
✓
✓
✓
1,497
6,559

-0.544
✓
✓
✓
✓
✓
1,497
6,559

Notes: As introduced in the main text, we compute five types of passive and active changes. The return on risk-free
assets is the real return on the 90-Day T-bill. The return on risky assets is computed with Vwretd (the valueweighted return, including all distributions), Vwretx (the value-weighted return, excluding dividends), Ewretd (theequal-weighted return, including all distributions), Ewretx (the equal-weighted return, including all distributions),
Sprtrn (Return on the S&P Composite Index), respectively. Both the passive and active changes are expressed inpercentages. Standard errors are clustered at the family level and shown in parentheses. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗p < 0.01. (Data source: PSID, waves 2001, 2003, 2005, 2007, 2009, 2011, 2013, 2015, and 2017.)

5

Heterogeneity Analysis

We now turn to the heterogeneous effect of the H1N1 pandemic on the risky share by
estimating Eq. (3). We visualize the estimates of β1g and β2g in Figures 5 and 6.24 Capped
spikes represent the 95 percent confidence interval for each coefficient. In each panel of the
figures, the p-value in the parentheses below the x-axis comes from a Wald test, where thenull hypothesis is that the estimates of the H1N1 impact for groups g and g ′ are equal toeach other.
In general, the estimates of β1g s, which capture the change in the risky share for group g in2009, are statistically insignificant (at the 5% level) and close to zero, which is consistent with
our main results that no immediate change occurs in the risky share during the pandemic. In
contrast, we find some interesting heterogeneous changes in risky shares after the pandemic.
Therefore, we now focus on discussing the estimates of β2g s.
24

We report the estimates of β1g and β2g in Appendix Tables B7 and B8.

30

5.1

Heterogeneity by Demographics

We first display in Figure 5 the heterogeneous patterns in terms of demographic features,
including the heads’ gender, race, marital status, and education.

(A) Gender (Obs: 9,790)(B) Race (Obs: 9,790)

.2

.2

0

0

−.2

−.2

−.4
−.6
−.8

−.4
Male (88.8%)−.6

Female (11.2%)

During*log(H1N1 death rate)(p−value: 0.486)−.8
After*log(H1N1 death rate)(p−value: 0.046)

Minority (12.7%)
White (87.3%)

During*log(H1N1 death rate)(p−value: 0.972)(C) Marital status (Obs: 9,406)

After*log(H1N1 death rate)(p−value: 0.647)(D) Education (Obs: 9,790)

.2

.2

0

0

−.2
−.2
−.4
−.6
−.8

−.4

Not married (20.3%)
Married (79.7%)

During*log(H1N1 death rate)(p−value: 0.384)−.6
After*log(H1N1 death rate)(p−value: 0.023)

High school or below (25.3%)
College or above (74.7%)

During*log(H1N1 death rate)(p−value: 0.387)

After*log(H1N1 death rate)(p−value: 0.968)

Notes: This figure plots the estimates of β1g and β2g in Eq. (3). We mainly use household heads’
gender, race, marital status, and education. In rare cases when heads’ information is missing, we use
spouses’ instead. Particularly, marital status is measured in 2007 to avoid potential endogeneity.
Capped spikes represent the 95 percent confidence interval for each coefficient. In each panel ofthe figures, the p-value in the parentheses below the x-axis comes from a Wald test, where the null
hypothesis is that the estimates of the H1N1 impact for groups g and g ′ are equal to each other.
(Data source: PSID, waves 2001, 2003, 2005, 2007, 2009, 2011, 2013, 2015, and 2017.)

Figure 5. Heterogeneous Effects of the 2009 H1N1 Pandemic, by Demographic Features
Gender.—On average, when the H1N1 death rate increases by 1%, families with maleheads reduce by nearly 0.25% of the risky share, while families with female heads reduce
more than 0.5% of the risky share (Panel A). The p-value of the difference between males
and females is less than 0.05. This finding suggests that the portfolio choice of households
whose head is female is more affected by the pandemic. This result may have several possible
explanations. First, women are more risk-averse than men (Borghans et al., 2009) and less31

willing to take financial risks (Charness and Gneezy, 2012). Second, female heads are more
likely to be single than male heads. In our sample, almost all female heads are single (about
99.4%), while only one-fifth of the male heads are single.25 It is difficult for single females toshare risks with others. Hence, they tend to reduce their risky shares more than males. This
scenario is consistent with our subsequent analysis of marital status. Third, females earnmore volatile income than males. Based on our calculation, the standard deviation of female
income is larger than that of male income, and the difference is significant at the 1% level.26
This accords with our later heterogeneous results by income fluctuation, which demonstratethat the exposure effect of a pandemic is stronger for those with more fluctuated income.
Race.—For the minority and the white, when the H1N1 death rate increases by 1%, risky
share decreases by about 0.2% and 0.3%, respectively (Panel B).27 The large p-value (0.65)
suggests that no significant difference exists between the minority and the white.
Marital status.—In case that marital status in the post-pandemic year is correlated with
the H1N1 intensity, we use the marital status of the household head in 2007. According toPanel C, the H1N1 effect for households whose heads are not married is more than twicethat for those whose heads are married. Moreover, the difference is statistically significant,with a p-value of 0.023. Intuitively, a married individual carries less risk than a single one
because a couple can share risk. As a result, households whose heads are married can beless sensitive to the pandemic.
Education.—As shown in Panel D, no significant difference exists in the risky-share elasticity of H1N1 intensity between people with high school degrees or below and those withcollege degrees or above. For both groups, the H1N1-intensity elasticity is about -0.27.
5.2

Heterogeneity by Labor Market Characteristics

We next discuss the heterogeneity related to job features of the head in Figure 6, including
income fluctuation, entrepreneurship, government employment, and union affiliation.
25

To alleviate endogeneity, marital status is measured in the PSID 2007 wave.
The standard deviations of female and male income are 0.31 and 0.27, respectively. For more details
about how to compute the standard deviations, see descriptions of the heterogeneous analysis by income
fluctuation.
27
Note that the estimate for the minority is insignificant and imprecise, which may be due to the small
sample size of the minority.
26

32

(A) Income fluctuation (Obs: 9,055).2(B) Entrepreneurship (Obs: 9,399)
.2

0

0

−.2
−.2
−.4
−.6
−.8

Low inc fluctuation (73.6%)
High inc fluctuation (26.4%)

During*log(H1N1 death rate)(p−value: 0.480)−.4Not entrepreneur (74.8%)
Entrepreneur (25.2%)−.6

After*log(H1N1 death rate)(p−value: 0.014)

During*log(H1N1 death rate)(p−value: 0.562)(C) Government job (Obs: 9,396)(D) Union affiliation (Obs: 8,681)

.6

.6

.3

.3

0

0

−.3

−.3

−.6
−.9

Not work for gov (83.8%)
Work for gov (16.2%)

During*log(H1N1 death rate)(p−value: 0.063)

After*log(H1N1 death rate)(p−value: 0.430)

−.6
−.9

After*log(H1N1 death rate)(p−value: 0.017)

Not union representaion (86.9%)
Union representaion (13.1%)

During*log(H1N1 death rate)(p−value: 0.101)

After*log(H1N1 death rate)(p−value: 0.019)

Notes: This figure plots the estimates of β1g and β2g in Eq. (3). We mainly use household heads’
information on income fluctuation, entrepreneurship, whether have a government job, and whether
have union representation. In rare cases when heads’ information is missing, we use spouses’
instead. To deal with the endogenous concern, a Mincer regression of income on age, age square,
education, race, gender, and state fixed effects is estimated, using the data before 2009. A family
is defined as being high (low) income-fluctuated if the standard deviation of the Mincer residual
is above (below) the average level. Similarly, the information on entrepreneurship, whether have agovernment job, and whether have union representation is related to 2007. Capped spikes represent
the 95 percent confidence interval for each coefficient. In each panel of the figures, the p-value inthe parentheses below the x-axis comes from a Wald test, where the null hypothesis is that theestimates of the H1N1 impact for groups g and g ′ are equal to each other. (Data source: PSID,
waves 2001, 2003, 2005, 2007, 2009, 2011, 2013, 2015, and 2017.)

Figure 6. Heterogeneous Effects of the 2009 H1N1 Pandemic on Risky Share, by Job
Features
Income fluctuation.—To address the endogeneity concern that income stability can beaffected by H1N1 intensity, we use the fluctuation of income before the pandemic as a proxy.
Specifically, we run a Mincer regression using data before the pandemic (i.e., data in 2001,
2003, 2005, and 2007) and obtain residuals.28 Then, we calculate the standard deviation of28

The Mincer equation includes age, age square, educational attainment, race, gender, and state fixed33

the residual for each household before the pandemic. In particular, a family is defined as
being high-(low-)income fluctuating if the standard deviation of the Mincer residual is above(below) the average level. Panel A shows that families with more fluctuating income havea risky share elasticity of -0.46, which is significantly different from that of the low-income
fluctuating ones, which is -0.24.
Entrepreneurship.—Panel B examines whether heterogeneous responses by entrepreneurship exist. Entrepreneurs are defined as owning part or all of a farm or business, which
account for 25% of our sample size. Considering that entrepreneurship can be time-varying
and associated with the H1N1 pandemic, we define entrepreneurship using the informationin 2007, the year prior to the pandemic.29 When the H1N1 death rate increases by 1%,
entrepreneurs reduce their risky shares by about 0.35%, while households without a farm orbusiness reduce risky shares by 0.26%. However, the difference between entrepreneurs andemployees is statistically insignificant.
Government job.—Government jobs are defined as working for the federal, state, or local
government or in a public school system. Relative to a private sector job, a government job
is more stable. It comes with a lot of benefits, including competitive pay, health insurance,
allowances, housing benefits, social security, job security, etc. About 16% of the participants in our sample work for the government. Panel C shows that for people working forthe government, their risky shares barely change after the pandemic. By contrast, for those
not working for the government, their risky shares decrease by almost 0.35% if the H1N1
mortality rate increases by 1%.
Union contract.—According to the US Bureau of Labor Statistics (BLS), in 2021, workers represented by a union had median weekly earnings of $1,158, while nonunion workers
had median weekly earnings of $975.30 Union workers are better protected and harder tobe laid off. Unions not only provide workers with higher earnings but also offer them better
fringe benefits, including paid leave and health care. In our sample, workers represented bya union account for about 13%. As shown in Panel D, when the H1N1 death rate increases
by 1%, workers represented by a union do not change risky shares. By contrast, people noteffects.
29
Likewise, due to the endogeneity concern, we use the information from 2007 to define working for thegovernment and having a job covered by the union contract.
30
The median weekly earnings are reported by BLS based on the data from the Current Population Survey(see https://www.bls.gov/news.release/pdf/union2.pdf). According to BLS, workers represented byunions refer to both union members and workers who have no union affiliation but whose jobs are coveredby a union contract.

34

represented by a union decrease their risky shares by 0.33%.
Overall, we find that the effect of H1N1 on risky shares differs between males and females,
married and unmarried individuals, those with lower income stability and those with higher
income stability, those employed in government versus non-government positions, and those
represented by unions versus those who are not. Our results suggest that households withgreater income risk are more vulnerable to the pandemic shock.
5.3

Life-cycle Effects of the H1N1 Pandemic

We now examine how the H1N1 pandemic affects the risky share differentially for peopleof different age groups. Before proceeding to the result, it is necessary to get a picture of
the life-cycle profile of risky shares. We plot the 25th percentile, the median, and the 75th
percentile of risky shares in Appendix Figure A7.31 For people aged 20 to 69, the risky share
increases with age, and it increases faster at an early age. Generally, older people tend toinvest more in risky assets and hold a higher risky share.32
To evaluate the life-cycle impact of the pandemic on stock holdings, we estimate Eq.
(3), with the log risky share as the outcome of interest, to measure the age-specific elasticity
during and after the pandemic. The index g in Eq. (3) now represents an age group (i.e., 20–
29, 30–39, 40–49, 50–59, or 60–69). The estimated coefficients of β1g s and β2g s are plotted in
Figure 7.33 Consistent with our previous findings in Section 4.1, the age profile of changes inrisky share in 2009 is insignificant and pretty flat. By contrast, the life-cycle profile after the
pandemic delivers a hump-shaped pattern. The risky share elasticity decreases in magnitude
from around 0.45 at ages 20–29, to 0.2 at ages 50–59, and then increases to about 0.3 atages 60–69. While interpreting the coefficients for different age groups, it is important toexercise caution as their differences lack statistical significance. Intuitively, young investors
are faced with higher background risk when they have just entered the labor market andhave very little buffer savings (Guiso and Sodini, 2013). The outbreak of a pandemic may
lead to large uncertainty in their jobs and income. As a result, they tend to hold fewer
risky assets but more conservative portfolios relative to older people. For those aged 60–69,
they are getting close to retirement and are faced with deductions in their stock of humanwealth. As suggested by Fagereng et al. (2017), these people should lower their exposure
31

In case that the age profile is affected by the pandemic, we drop the data starting from 2009 and only
use the data in 2001, 2003, 2005, and 2007.
32
This pattern broadly supports Guiso and Paiella (2008)’s finding. Using a household survey in Italy,
they show that for people less than 61 years, the risky share increases by 2% on average when the age
increases by 10 years.
33
The estimates of β1g s and β2g s are reported in Appendix Table B9.

35

to the stock market as compensation. With an exogenous shock from the H1N1 pandemic,
people approaching retirement are more susceptible and reduce their risky share more when
exposed to a higher H1N1 mortality rate.
.3

0

−.3

−.6

During the pandemic
After the pandemic

−.9
20−29

30−39

40−49
Age group

50−59

60−69

Notes: This figure plots the age profiles of the H1N1-intensity elasticities for the risky shareby estimating Eq. (3). Capped spikes represent the 95 percent confidence interval for eachcoefficient. (Data source: PSID, waves 2001, 2003, 2005, 2007, 2009, 2011, 2013, 2015, and2017.)

Figure 7. Life-cycle Impact of the 2009 H1N1 Pandemic on Risky Share36

6

Exposure Effect of the H1N1 Pandemic on Illiquid Assets

Figure 8. Portfolio Composition Chart
As illustrated in Figure 8, a household can have liquid and illiquid assets. In previous
sections, our analysis of the exposure effect of the H1N1 pandemic concentrates on the liquid
assets holding. In this section, we turn to explore the change in illiquid assets in responseto the H1N1 pandemic.
In columns (1) and (2), we first report the results on two types of business owners. Afterthe pandemic, the probability of being an incorporated business (IB) owner falls statisticallysignificantly. A 1 percent increase in H1N1 intensity decreases the probability of owning an
IB by 0.028%. By contrast, we do not find a significant change in the probability of owning
an unincorporated business (UB). Even so, the exposure effect of H1N1 on UB is positive.
Rubinstein and Levine (2020) show that entrepreneurship is procyclical, while other selfemployment is countercyclical. Our result provides suggestive evidence that a pandemic canact as a recession, during which the incorporated self-employed are “procyclical,” and theunincorporated self-employed are “countercyclical.”In addition to business, we also examine non-business outcomes, such as owning housing
and cars, in columns (3) and (4) in Table 9. However, no significant exposure effect on being
a home or car owner exists. As shown in this table, more than 70% and over 90% of thehouseholds in our sample own housing or cars, respectively. Housing and cars are somewhat
37

analogous to necessities in most American families. Hence, the H1N1 pandemic has little
impact on the household holding of these assets.
Table 9. Exposure Effect of H1N1 Pandemic on Illiquid Assets
Outcome variables

Business(1)
IB

During × log(H1N1 death rate)(2)
UB

Housing

Car

(3)

(4)

-0.0150.019
0.005-0.001(0.010) (0.012) (0.014) (0.011)-0.028∗∗ 0.021
0.006
0.012
(0.013) (0.016) (0.024) (0.015)

After × log(H1N1 death rate)
Average of the outcome variable
Family, state, year FEs
State-specific time trend
Household- and state-level time-variant features%∆ in GDP, unemployment, HPI, bankruptcy rateduring the financial crisis
Medical controls (lag)Number of family
Observations
Adj.R2

0.074
✓
✓
✓
✓

0.096
✓
✓
✓
✓

0.740
✓
✓
✓
✓

0.921
✓
✓
✓
✓

✓
5,095
26,711
0.552

✓
5,095
26,711
0.415

✓
5,100
26,846
0.639

✓
5,100
26,846
0.314

Notes: IB denotes incorporated business owner. UB denotes unincorporated businessowner. See the description in Section 4.1 for the list of control variables. Standard
errors are clustered at the family level and shown in parentheses. ∗ p < 0.1, ∗∗p < 0.05, ∗∗∗ p < 0.01. (Data source: PSID, waves 2001, 2003, 2005, 2007, 2009,2011, 2013, 2015, and 2017.)

7

Discussions of Potential Mechanisms

We now move to a primary investigation into why the H1N1 pandemic affects household portfolio choices. Different channels can explain the exposure effect. First, a pandemic
may have a profound impact on people’s health. It can cause respiratory symptoms that
range from mild to severe and may require hospitalization or even lead to death. Stress andanxiety associated with a pandemic can also take a toll on one’s mental health. Second, apandemic can affect demographic features in a variety of ways. On the one hand, it canlead to changes in the number of children within a family, as economic uncertainties andconcerns about health and safety may lead some couples to postpone having children or even
opt to have fewer children. On the other hand, extended periods of isolation and stress canexacerbate existing marital issues, thus potentially leading to divorce or separation. Furthermore, a pandemic can cause shocks to the labor market. It can lead to a decrease ineconomic activity as businesses shut down or scale back their operations, thereby causing38

reduced earnings and even job losses.
In Appendix Table B11, we examine whether the H1N1 pandemic has an impact onvarious factors, such as people’s health, marital status, number of children, earnings, andemployment status. We did not observe any significant changes in response to the H1N1
intensity for most of the outcomes. However, significant evidence shows that the unemployment rate increases with the H1N1 death rate during and after the pandemic, which coincides
with our earlier finding in Section 6 that the H1N1 pandemic can affect labor market risks
by reducing the probability of being an IB owner.
We then additionally control for these aforementioned possible channels. We expect that
if any channel is effective in explaining this relationship, we will observe a significant change
in the exposure effect of the H1N1 pandemic (i.e., coefficients β1 and β2 in Eq. (1)). However,as presented in Appendix Table B12, the estimate of the exposure effects remains significant
at -0.27 in columns (2)–(6) compared with the baseline case (column (1)). Therefore, we
conclude that these channels (i.e., health, marital status, number of children, earnings, andemployment status) do not provide a satisfactory explanation for why the H1N1 pandemic
affected the intensive margin of stock holdings.
Several studies, such as Calvet et al. (2009) and Calvet and Sodini (2014), suggest thathouseholds tend to invest in a greater proportion of risky assets when their overall family
wealth increases. It is plausible that the H1N1 pandemic might diminish family wealth,resulting in a corresponding decrease in this risky investment share. Therefore, we analyze
the impact of the H1N1 pandemic’s mortality rate on two versions of total family wealthcalculations: one that excludes home equity and another that includes it. Our findings, as
outlined in the first two columns of Appendix Table B13, indicate a minor rise in family
wealth associated with the H1N1 death rate. However, the effect of this exposure is statistically negligible. In the last two columns in Appendix Table 3 B13, we add the control
of total wealth in Eq. (1) but we do not observe a notable shift in the log risky share in
relation to total wealth.34 Consequently, it seems less likely that the decrease in risky sharein response to H1N1 intensity is primarily driven by changes in family wealth.

8

Conclusion

Taking advantage of the exogenous nature of the 2009 H1N1 pandemic and the statelevel variation in the H1N1 death rate, we examine the extent to which the H1N1 intensity
influences the allocation of household portfolios in a difference-in-differences framework.
34

This observation aligns with several studies, including those by Brunnermeier and Nagel (2008) and
Chiappori and Paiella (2011), which suggest a lack of significant correlation between risky investment sharesand family wealth.

39

Using nine waves of the PSID (2001–2017), we find that the exposure of the pandemic affects
the intensive margin (risky share) but not the extensive margin (stock market participation).
During the pandemic in 2009, we do not observe changes due to the H1N1 death rate in therisky share. After the pandemic, however, the risky share is reduced by approximately 0.26
percent if the death number per 100,000 people increases by 1%. The exposure effect isrelatively stable and long-lasting until the end of our sample. Our decomposition analysis
suggests that this change is driven by a change in risky assets rather than a change inliquid assets. Specifically, the active change, as opposed to the passive change, appears tobe the driving force behind this shift. We also conduct a rich analysis on heterogeneity interms of demographics and job features. The effect of the pandemic is more substantial forhouseholds whose heads are females and single, earn more unstable income, do not work forthe government, and are not represented by a union. Besides, our result suggests that theage profiles of the exposure effect display a hump-shaped pattern.
The interesting patterns shown in our heterogeneity analysis suggest that more risk-averse
people and those who have more volatile income and unstable jobs are more susceptible tothe pandemic. Our preliminary investigation suggests that family wealth and a range offactors related to health, demographic features, and labor market outcomes can not help us
understand the relationship between the H1N1 pandemic and risky assets. Given that therisky share does not change immediately during the pandemic, the impact of the pandemic
is unlikely to be driven by the background-risk theory.

40

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46

Appendix
Figures

Mean S&P index per week1000

1st H1N1 case

Pandemic ended

8k

6k

Pandemic occured

800

4k

Weekly cases

A

2k

600

32
20

10

w

25
w
09

20

20

09

w

17

0

Week of the Year
Weekly Cases of H1N1pdm09

Mean S&P index per week

Notes: The weekly lab-confirmed cases of H1N1 flu are obtained from the World HealthOrganization (WHO) Collaborating Laboratories. The weekly S&P index is the average ofreal S&P price index between Monday and Friday in a week. The S&P 500 Index is computedby adjusting the nominal S&P 500 Index using the daily Consumer Price Index (CPI). The
daily CPI is calculated by interpolating the monthly CPI, which, along with the nominal
S&P 500 Index, is sourced from CRSP.

Figure A1. Time Trends of Weekly Real S&P 500 Price Index

1

1st H1N1 case

Pandemic ended

4

8k

6k

Pandemic occured

1
4k
−2

Weekly cases

Weekly return of real S&P index

7

2k
−5

w
32
20

10

w
25
09

20

20

09

w
17

0

Week of the Year
Weekly Cases of H1N1pdm09

Weekly return of real S&P index

Notes: The weekly lab-confirmed cases of H1N1 flu are obtained from the World HealthOrganization (WHO) Collaborating Laboratories. The weekly return for the real S&P 500
Index is computed as: Rω = [(PF ri,ω − PM on,ω )/PM on,ω ] × 100%, where PF ri,ω is the real S&P
500 index on the Friday of week ω, and PM on,ω is the real S&P 500 index on the Monday
of week ω. The real S&P 500 index is adjusted with the daily CPI, which is calculated byinterpolating the monthly CPI. The monthly CPI, along with the nominal S&P 500 Index,
is sourced from CRSP.

Figure A2. Time Trends of Weekly Real Return for S&P 500 Index

2

1st H1N1 case

Pandemic ended

8k

6k

Pandemic occured

0.02
4k

Weekly cases

Within−week Stock Price Volatility

0.03

0.01
2k

w
32
10
20

09
20

09
20

w
25

0

w
17

0.00

Week of the Year
Weekly Cases of H1N1pdm09

Within−week Stock Price Volatility

Notes: The weekly lab-confirmed cases of H1N1 flu are obtained from the World HealthOrganization (WHO) Collaborating Laboratories. The within-week stock price volatility isdefined as the weekly standard deviation of the return for the S&P price index. The returnfor the real S&P 500 Index is computed as: R(t) = [(P (t) − P (t − 1))/P (t − 1)] × 100%,
where P (t) is the real S&P 500 index on day t. The daily CPI is calculated by interpolating
the monthly CPI, which, along with the nominal S&P 500 Index, is sourced from CRSP.

Figure A3. Time Trends of Within-week Stock Price Volatility

3

30

25

Percent

20

15

10

5

0
1

2

3

4

5
6
7
8
Survey month in 20099

10

Data source: PSID wave 2009.

Figure A4. Proportion of Survey Month in 2009

4

11

12

(A) Active physicians(B) Physicians in patient care

1

1

.5

.5

0

0

−.5

−.5

−1
−1.5

−1
2

ln_h1n1 = 0.94 − 0.94 ln_phys_~t
.6

.8

1

R = 14.8%

1.2

−1.5

1.4

.6

(C) Registered nurse(D) Hospital beds

.5

.5

0

0

−.5

−.5

−1

R = 14.6%

1

t statistic of the beta coef: −3.32

1

−1.5

.8

t statistic of the beta coef: −3.3112

ln_h1n1 = 0.91 − 0.98 ln_phys_~e

1.2

1.4

−1

ln_h1n1 = 2.18 − 0.99 ln_nurse
1.8

2

2.2

2

R = 12.8%
2.4

−1.5

2.6

ln_h1n1 = 0.33 − 0.28 ln_beds.6t statistic of the beta coef: −2.18

.8

1

1.2

2

R = 2.3%
1.4

1.6

t statistic of the beta coef: −0.90

Notes: This figure plots the relationship between lab-confirmed H1N1 death rate and medical
resources of 50 states. In each panel, the vertical axis is log H1N1 death rate during the
pandemic and the horizontal axis denotes medical resources in 2008. In Panel (A), thehorizontal axis is active physicians per 100,000 population. In Panel (B), the horizontal axis
is physicians in patient care per 100,000 population. In Panel (C), the horizontal axis isregistered nurse per 10 million population. In Panel (D), the horizontal axis is hospital beds
per 10 million population.

Figure A5. Relationship between H1N1 Death Rate and Medical Resourcesin 2008

5

(A) Change rate (%) in GDP per capita(B) Change rate (%) in Unemployment rate

11.47

82.35

−9.09

−7.32

(C) Change rate (%) in Housing price

6.77−39.66

Notes: The percentage change of a state-level economic indicator during the financial crisis
is defined as the change rate of this economic indicator in 2008 relative to 2006. The formulais ∆xj = (xj,2008 − xj,2006 )/xj,2006 × 100%, where xj,t is the economic indicator of state j inyear t. See Appendix Table B2 for the data source of GDP per capita, unemployment rate,and housing price index.

Figure A6. Percentage Changes of GDP Per Capita, Unemployment Rate,and Housing Price, During the 2007-2008 Crisis

6

2001

2003

2005

2007

2009

2011

2013

2015

2017

.2
.1
0
−.1

Passive change

−.2

.2
.1
0
−.1
−.2

7

.2
.1
0
−.1
−.2
0

.5

1

0

.5

1

0

.5

1

Graphs by Survey year

Notes: This figure illustrates the passive change versus the initial risky share using our analysis sample in PSID
2001–2017. The return on risk-free assets is the real return on the 90-Day T-bill. The return on risky assets iscomputed with Vwretd (the value-weighted return, including all distributions). Data source: CRSP.

Figure A8. Scatterplots of Passive Change versus the Initial Risky Share

2001

2003

2005

2007

2009

2011

2013

2015

2017

1
.5
0
−.5

Active change

−1

1
.5
0
−.5
−1

8

1
.5
0
−.5
−1
0

.5

1

0

.5

1

0

.5

1

Graphs by Survey year

Notes: This figure illustrates the active change versus the initial risky share using our analysis sample in PSID
2001–2017. The return on risk-free assets is the real return on the 90-Day T-bill. The return on risky assets iscomputed with Vwretd (the value-weighted return, including all distributions). Data source: CRSP.

Figure A9. Scatterplots of Active Change versus the Initial Risky Share

.8

.7

Risky share

.6

.5

.4

.3

.2
20−30

30−39

40−49
Age group

25th percentile

Median

50−59

60−69

75th percentile

Notes: This figure plots the age profiles of the 25th percentile, the median, and the 75th
percentile of risky share, respectively. The risky share is defined as the share of risky assets(stock in publicly-held corporations, stock mutual funds, investment trusts, including stocks
in IRA) in liquid financial assets of stock markets participants. (Data sources: PSID waves
2001, 2003, 2005, 2007.)

Figure A7. Cross-sectional Risky Share Across Age Groups

9(A) Incorporation business owner(B) Unincorporated business owner

.04

.1
.08

.02

.06
0
.04
−.02
.02
−.04

0

−.06

−.02
−.04

−.08
2001

2003

2005

2007

2009
Year

2011

2013

2015

2017

2001

2003

2005

2007

2009
Year

2011

2013

2015

2017

Notes: The figure plots the estimates of β̂t in Eq. (2). In Panel A, the dependent variable isa dummy variable that indicates whether the household owns an incorporation business. In
Panel B, the dependent variable is a dummy variable that indicates whether the householdowns an unincorporated business. The capped spikes indicate 95 percent confidence intervals,
with robust standard errors clustered at the family level.

Figure A10. Exposure Effect of the 2009 H1N1 Pandemic on Entrepreneurship

10

B

TablesTable B1. H1N1 Death Rate During the 2009 H1N1 Pandemic
State
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25

Missouri
Ohio
Virginia
New Jersey
Vermont
Massachusetts
New York
Mississippi
North Dakota
IndianaPennsylvania
Nebraska
MarylandNew Hampshire
Georgia
Illinois
Michigan
Kentucky
Hawaii
Texas
Wisconsin
Connecticut
South Carolina
KansasTennessee

Deaths per
100,000 people
0.285
0.451
0.467
0.480
0.480
0.506
0.508
0.541
0.602
0.604
0.750
0.772
0.785
0.836
0.842
0.867
0.889
0.950
0.965
0.968
0.970
1.011
1.089
1.094
1.110

State
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50

Alabama
North Carolina
Rhode Island
OklahomaLouisiana
West Virginia
FloridaIowa
Minnesota
Colorado
NevadaWyoming
Delaware
Idaho
Washington
AlaskaMaine
California
Utah
Montana
Arkansas
OregonArizona
New Mexico
South Dakota

Deaths per
100,000 people
1.114
1.132
1.139
1.157
1.180
1.191
1.233
1.352
1.363
1.408
1.415
1.429
1.458
1.480
1.485
1.574
1.580
1.613
1.689
1.829
1.830
2.074
2.340
2.848
2.850

Notes: This table presents the state-level lab-confirmed H1N1 death rates
from April 2009 to August 2010 in the US. Death numbers for 50 states arecollected from the website of https://FluTrackers.com.

11

Table B2. Data Sources for State-by-year Level Characteristics
Original data

Data source

Macroeconomic indicators
GDP (in chained (2012) billions of Bureau of Economic Analysis (BEA), “Regional Economic Accounts”,
dollars)
http://www.bea.gov
Personal income per capita
BEA, “Regional Economic Accounts, State Annual Personal Income and Employment”, http://www.bea.gov
Unemployed rate
Bureau of Labor Statistics (BLS), “Local Area Unemployment Statistics”,
http://www.bls.gov
Homeownership rate
CensusBureau,“Housing
Vacancies
andHome
Ownership”,
http://www.census.gov
Housing price index
Federal Housing Finance Agency, “House Price Index Datasets, Quarterly
Data, Purchase-Only Indexes”, http://www.fhfa.gov
Bankruptcy cases (in 1,000) filed by Administrative Office of the United States Courts, “Caseload Statistics Data”,
state
http://www.uscourts.gov
Assets (in billion dollars) in FDIC- Federal Deposit Insurance Corporation, http://www.fdic.gov
insured financial institutions
Deposits (in billion dollars) in Federal Deposit Insurance Corporation, http://www.fdic.gov
FDIC-insured financial institutions
Population density
Census Bureau, http://www.census.gov
Medical controls
Beds (1,000) in community hospital
Active physicians per 10,000 resident population
Physicians in patient care per 10,000
resident population
Registered nurse per 100,000 residents

American Hospital Association, http://www.ahadata.com
National Center for Health Statistics. (2003–2019).
National Center for Health Statistics. (2003–2019).
Bureau of Labor
http://www.bls.gov

12

Statistics,“Occupational

Employment

Statistics”,

Table B3. Results of Granger Causality Test
Equation

ExcludedF statistics p-value

Panel A: Weekly S&P index price and Weekly H1N1 cases
FD of S&P index price
FD of H1N1 cases.069
FD of H1N1 cases
FD of S&P index price
.012

.793
.914

Panel B: Weekly return of S&P index and Weekly H1N1 cases
S&P index return
FD of H1N1 cases
3.539
FD of H1N1 cases
S&P index return
3.615

.063
.060

Panel C: Within-week stock market volatility and Weekly H1N1 cases
Within-week stock market volatility FD of H1N1 cases.734
FD of H1N1 cases
Within-week stock market volatility
.320

.483
.727

Notes: FD denotes “first-difference”. The table reports results of pairwise Granger causality
tests for vector autoregressive (VAR) models, using the vargranger command. The unit-root
tests (Appendix Table B4) show that the FD of S&P index price, the FD of H1N1 cases, theweekly S&P index return, and the within-week stock market volatility are stationary. We selectthe lag order for VAR models based on the final prediction error (FPE), Akaike’s information
criterion (AIC), Schwarz’s Bayesian information criterion (SBIC), and the Hannan and Quinn
information criterion (HQIC) lag-order selection statistics. In Panel A and Panel B, the selected
lag order is 1. In Panel C, the selected lag order is 1 and 2 (the result barely changes if wechoose lag order 2). F statistics is the small-sample F statistics. The null hypothesis is thatthe lagged variable (the excluded ones) does not Granger cause the variable in the column of“Equation”.

Table B4. Results of Unit-root Test
MacKinnon approximate p-value(1)(2)
Augmented Dickey-Fuller Phillips-Perron
unit-root test
unit-root test
Weekly S&P index price
FD of Weekly S&P index priceWeekly return for S&P index
Within-week stock market volatility
Weekly cases of H1N1 cases
FD of Weekly cases of H1N1 cases

.887
0.000
0.000
0.000
.503
0.000

.849
0.000
0.000
0.000
.170
0.000

Notes: Columns (1) and (2) report the p-value of Augmented Dickey-Fuller unitroot test and Phillips-Perron unit-root test, respectively. The null hypothesis isthat the data are non-stationary.

13

Table B5. Correlation between H1N1 Intensity During the Pandemic andPercentage Change of State-level Economic Indicators During the Financial Crisis
Outcome variable

State-level H1N1 death rate
(1)%∆ in GDP per capita(2)-0.010(0.021)%∆ in unemployment rate

-0.002(0.004)%∆ in housing priceObservations
Adj.R2(3)-0.004(0.008)50-0.01650-0.01850-0.017

Notes: The percentage change of a state-level economic
indicator during the financial crisis is defined as thechange rate of this economic indicator in 2008 relative to
2006. The formula is ∆xj = (xj,2008 − xj,2006 )/xj,2006 ×
100%, where xj,t is the economic indicator of state j inyear t. See Appendix Table B2 for the data source ofGDP per capita, unemployment rate, and housing priceindex. The state-level H1N1 death rate is assessed during the H1N1 pandemic.

14

Table B6. Summary Statistics of Passive Risky share, Active and Passive Changes
N

mean

sd

min

p25

p50

p75

max

Panel A: Passive risky share
Vwretd 9,792 0.549 0.290
Vwretx 9,792 0.542 0.290
Ewretd 9,792 0.562 0.289
Ewretx 9,792 0.556 0.289
Sprtrn 9,792 0.542 0.291

0.000
0.000
0.000
0.000
0.000

0.326
0.317
0.339
0.330
0.316

0.542
0.532
0.558
0.548
0.531

0.814 1.000
0.808 1.000
0.827 1.000
0.822 1.000
0.807 1.000

Panel B: Passive change
Vwretd 9,792 0.014 0.056
Vwretx 9,792 0.007 0.056
Ewretd 9,792 0.027 0.062
Ewretx 9,792 0.020 0.062
Sprtrn 9,792 0.006 0.057-0.156 -0.021-0.166 -0.025-0.181 0.000-0.190 -0.001-0.170 -0.026

0.021
0.017
0.030
0.024
0.017

0.051 0.162
0.042 0.150
0.060 0.219
0.052 0.208
0.042 0.153

Panel C: Active change
Vwretd 7,110 -0.011 0.305
Vwretx 7,110 -0.005 0.306
Ewretd 7,110 -0.025 0.304
Ewretx 7,110 -0.019 0.305
Sprtrn 7,110 -0.004 0.306-1.000 -0.179 -0.011 0.160 0.971-1.000 -0.172 -0.006 0.167 0.971-1.000 -0.194 -0.020 0.142 0.970-1.000 -0.187 -0.016 0.150 0.970-1.000 -0.172 -0.006 0.168 0.971

Notes: This table reports summary statistics of passive risky share, active changes, and passive changes in our study period, 2001–2017. The
return on risk-free assets is the real return on the 90-Day T-bill. The
return on risky assets is computed with Vwretd (the value-weighted
return, including all distributions), Vwretx (the value-weighted return,excluding dividends), Ewretd (the equal-weighted return, including all
distributions), Ewretx (the equal-weighted return, including all distributions), Sprtrn (Return on the S&P Composite Index), respectively.
Data source: CRSP.

15

Table B7. Heterogeneous Effects of the H1N1 Pandemic by Demographic Features
Dependent variable

log risky share(1)
GenderMale × During × log(H1N1 death rate)

(2)
Race

(3)
(4)
Marital Education
status-0.072(0.067)-0.193(0.163)-0.236***(0.081)-0.531***(0.151)

Female × During × log(H1N1 death rate)
Male × After × log(H1N1 death rate)
Female × After × log(H1N1 death rate)
Minority × During × log(H1N1 death rate)-0.095(0.181)-0.088(0.066)-0.185(0.211)-0.280***(0.080)White × During × log(H1N1 death rate)
Minority × After × log(H1N1 death rate)
White × After × log(H1N1 death rate)
Not married × During × log(H1N1 death rate)-0.166(0.126)-0.047(0.070)-0.481***(0.117)-0.228***(0.085)

Married × During × log(H1N1 death rate)
Not married × After × log(H1N1 death rate)
Married × After × log(H1N1 death rate)
High school or below × During × log(H1N1 death rate)-0.171(0.113)-0.061(0.072)-0.275**(0.118)-0.271***(0.083)

College or above × During × log(H1N1 death rate)
High school or below × After × log(H1N1 death rate)
College or above × After × log(H1N1 death rate)
Average of the outcome variable
Family FE
Year FE
State-specific time trend
Household features
Macroeconomic indicator (lag)Medical controls (lag)Number of family-0.886
✓
✓
✓
✓
✓
✓
2,239

16

-0.886-0.881-0.886
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
2,239
2,113
2,239
Continued on next page

Table B7 – Continued from previous page
Dependent variable

log risky share

Observations
Adj.R2(1)
Gender

(2)
Race

(3)
(4)
Marital Education
status

9,790
0.286

9,790
0.285

9,406
0.283

9,790
0.286

Notes: This table reports the heterogenous effects of the H1N1 pandemic on the risky share by
estimating Eq. (3). All regressions control for family fixed effects, year fixed effects, state fixed
effects, and state-specific time trend, household features, and state-by-year-level characteristics.
See Section 4.1 for the list of variables controlled as household features, macroeconomic index,
and medical resources. We mainly use household heads’ gender, race, marital status, and education. In rare cases when heads’ information is missing, we use spouses’ instead. Particularly,
marital status is measured in 2007 to avoid potential endogeneity. Standard errors are clusteredat the family level and shown in parentheses. (Data source: PSID, waves 2001, 2003, 2005,
2007, 2009, 2011, 2013, 2015, and 2017.)

17

Table B8. Heterogeneous Effects of the H1N1 Pandemic by Job Features
Dependent variable

log risky share

(1)
(2)
(3)
(4)
Income Entrepre- Government Union
fluctuation neurshipjob
affiliation
Low inc fluctuation × During × log(H1N1 death rate)-0.074(0.075)High inc fluctuation × During × log(H1N1 death rate) -0.155(0.115)
Low inc fluctuation× After × log(H1N1 death rate)-0.238***(0.090)High inc fluctuation × After × log(H1N1 death rate) -0.464***(0.116)Not entrepreneur × During × log(H1N1 death rate)-0.053(0.075)
Entrepreneur × During × log(H1N1 death rate)-0.127(0.110)Not entrepreneur × After × log(H1N1 death rate)-0.262***(0.085)
Entrepreneur × After × log(H1N1 death rate)-0.346***(0.114)
Not work for gov × During × log(H1N1 death rate)-0.120*(0.068)Work for gov × During × log(H1N1 death rate)
0.172(0.149)Not work for gov × After × log(H1N1 death rate)-0.331***(0.086)Work for gov × After × log(H1N1 death rate)-0.061(0.114)No union contract × During × log(H1N1 death rate)-0.106(0.067)A union contract × During × log(H1N1 death rate)
0.179(0.178)No union contract × After × log(H1N1 death rate)-0.327***(0.083)A union contract × After × log(H1N1 death rate)-0.006(0.144)
Average of the outcome variable
Family FE
Year FE
State-specific time trend
Household features
Macroeconomic indicator (lag)Medical controls (lag)Number of family-0.879
✓
✓
✓
✓
✓
✓
2,013

18

-0.881
✓
✓
✓
✓
✓
✓
2,111

-0.882-0.881
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
2,111
2,102
Continued on next page

Table B8 – Continued from previous page
Dependent variable

log risky share
(1)
(2)
(3)
(4)
Income Entrepre- Government Union
fluctuation neurshipjob
affiliation

Observations
Adj.R2

9,055
0.282

9,399
0.283

9,396
0.284

9,377
0.284

Notes: This table reports the heterogenous effects of the H1N1 pandemic on the natural log of risky
share by estimating Eq. (3). All regressions control for family fixed effects, year fixed effects, state
fixed effects, and state-specific time trend, household features, and state-by-year-level characteristics. See Section 4.1 for the list of variables controlled as household features, macroeconomic index,
and medical resources. We mainly use household heads’ information on income fluctuation, entrepreneurship, whether have a buffer, and whether have union representation. In rare cases whenheads’ information is missing, we use spouses’ instead. To deal with the endogeneous concern, a
Mincer regression of income on age, age square, education, race, gender, and state fixed effects is
estimated, using the data before 2009. A family is defined as being high (low) income-fluctuated
if the standard deviation of the Mincer residual is above (below) the average level. Similarly, theinformation on entrepreneurship, whether have a buffer, and whether have union representation isrelated to 2007. Standard errors are clustered at the family level and shown in parentheses. (Datasource: PSID, waves 2001, 2003, 2005, 2007, 2009, 2011, 2013, 2015, and 2017.)

19

Table B9. Life-cycle Impact of the H1N1 Pandemic on Risky Share
Dependent variable

log risky share(1)

20-30 × During × log(H1N1 death rate)
30-39 × During × log(H1N1 death rate)
40-49 × During × log(H1N1 death rate)
50-59 × During × log(H1N1 death rate)
60-69 × During × log(H1N1 death rate)
20-30 × After × log(H1N1 death rate)
30-39 × After × log(H1N1 death rate)
40-49 × After × log(H1N1 death rate)
50-59 × After × log(H1N1 death rate)
60-69 × After × log(H1N1 death rate)
Average of the outcome variable
Family FE
Year FE
State-specific time trend
Household features
Macroeconomic indicator (lag)Medical controls (lag)Number of family
Observations
Adj.R2-0.129(0.205)-0.074(0.120)-0.095(0.120)-0.109(0.093)-0.078(0.127)-0.457∗∗(0.230)-0.287∗∗∗(0.109)-0.239∗∗(0.108)-0.202∗∗(0.092)-0.281∗∗(0.111)-0.887
✓
✓
✓
✓
✓
✓
2,218
9,661
0.281

Notes: This table reports the age profiles of the H1N1intensity elasticities for the natural log of risky share by
estimating Eq. (3). All regressions control for family
fixed effects, year fixed effects, state fixed effects, andstate-specific time trend, household features, and stateby-year-level characteristics. See Section 4.1 for the listof variables controlled as household features, macroeconomic index, and medical resources. Standard errors
are clustered at the family level and shown in parentheses. (Data source: PSID, waves 2001, 2003, 2005, 2007,2009, 2011, 2013, 2015, and 2017.)

20

Table B10. Available Information on Consumption in PSID 2001–2017
Domains

Items

Waves available

Food

Food at home, food away from home, food stamps

Transportation

Including gasoline, parking and carpool, bus fares and train fares, taxicabs,
other transportation, carpyments

Health care

Including payment for nursing home and hospital bills, payment for doctor,
outpatient surgery, and dental bills, payment for prescriptions, in-homemedical care, special facilities, and payment for health insurance premiums

Housing

Including rent, monthly loan payments, heating, water and sewer, housing
insurance premium, housing property taxes, electricity, and other utility
expenses

Education

Including tuition or tutoring expenses and other school-related expenses

Child Care

Payment for child care

2001–2017

Home Repairs & Maintenance Including materials plus any costs for hiring a professional
Household Furnishings& Equipment

Including household textiles, furniture, floor coverings, major appliances,small appliances and miscellaneous housewares

Clothing & Apparel

Including footwear, outerwear, and products such as watches or jewelry

Trips & Vacations

Including transportation, accommodations, and recreational expenses on trips

Recreation & Entertainment

Including performing arts and hobbies

Telephone & Internet expenses Payment for telephone, including cellphone, cable or satellite TV, Internet service

21

2005–2017Table B11. Mediation Analysis (First Stage)
Outcome variable

During × log(H1N1 death rate)
After × log(H1N1 death rate)
Average of the outcome variable
Family, year, state FEs
State-specific time trend
Household features
Macroeconomic indicator (lag)Medical controls (lag)Number of family
Observations
Adj.R2

Poor health Married Have children
log of
Unemployed(dummy) (dummy)(dummy)total income (dummy)
(1)
(2)
(3)
(4)
(5)
-0.016(0.010)-0.013(0.014)0.003(0.009)0.005(0.014)-0.004(0.014)0.013(0.020)-0.001(0.022)-0.014(0.032)0.019∗(0.011)0.023∗(0.014)

0.072
✓
✓
✓
✓
✓
5,100
26,846
0.397

0.714
✓
✓
✓
✓
✓
5,099
26,844
0.835

0.484
✓
✓
✓
✓
✓
5,100
26,8460.644

11.372
✓
✓
✓
✓
✓
5,097
26,830
0.588

0.056
✓
✓
✓
✓
✓
5,100
26,8460.142

Notes: “Poor health” is a dummy variable that is equal to 1 if the household head reports
his/her health status is poor or fair. “Have children” is a dummy variable that is equal to 1
if the household has children aged between 0–17. Income is defined in 2017 dollar adjusted
by the CPI-U index. All regressions control for family fixed effects, year fixed effects, state
fixed effects, and state-specific time trend, household features, and state-by-year-level macroeconomic indicators. See Section 4.1 for the list of variables controlled as household features,
macroeconomic index, and medical resources. Standard errors are clustered at the family leveland shown in parentheses. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01. (Data source: PSID, waves
2001, 2003, 2005, 2007, 2009, 2011, 2013, 2015, and 2017.)

22

Table B12. Exposure Effect of H1N1 Pandemic on Risky Share
Outcome variable
Additional controlslog(Risky share)
Baseline(1)

During × log(H1N1 death rate)
After × log(H1N1 death rate)
Poor healthHave children 0–17
MarriedLog total income
Unemployed
Average of the outcome variable
Family, year, state FEs
State-specific time trend
Household features
Macroeconomic indicator (lag)Medical controls (lag)Number of family
Observations
Adj.R2

Healthstatus(2)

Familystructure(3)

Labor market
outcomes
(4)
(5)

All
(6)

-0.088-0.087-0.089-0.087-0.087-0.086(0.063) (0.064)(0.064)(0.063) (0.063) (0.064)-0.273∗∗∗ -0.273∗∗∗ -0.274∗∗∗ -0.274∗∗∗ -0.271∗∗∗ -0.273∗∗∗(0.079) (0.079)(0.079)(0.079) (0.079) (0.079)
0.024
0.022
(0.062)(0.062)0.008
0.008(0.034)(0.035)-0.110∗∗-0.092(0.055)(0.056)-0.043∗∗-0.041∗∗(0.020)(0.020)-0.059-0.069(0.049) (0.049)-0.886
✓
✓
✓
✓
✓
2,239
9,790
0.286

-0.886
✓
✓
✓
✓
✓
2,239
9,790
0.286

-0.886
✓
✓
✓
✓
✓
2,239
9,790
0.286

-0.886
✓
✓
✓
✓
✓
2,239
9,789
0.286

-0.886
✓
✓
✓
✓
✓
2,239
9,790
0.286

-0.886
✓
✓
✓
✓
✓
2,239
9,789
0.286

Notes: “Poor health” is a dummy variable that is equal to 1 if the household head reports
his/her health status is poor or fair. “Have children” is a dummy variable that is equal to 1
if the household has children aged between 0–17. Income is defined in 2017 dollar adjusted
by the CPI-U index. All regressions control for family fixed effects, year fixed effects, state
fixed effects, and state-specific time trend, household features, and state-by-year-level macroeconomic indicators. See Section 4.1 for the list of variables controlled as household features,
macroeconomic index, and medical resources. Standard errors are clustered at the family leveland shown in parentheses. ∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01. (Data source: PSID, waves
2001, 2003, 2005, 2007, 2009, 2011, 2013, 2015, and 2017.)

23

Table B13. Exposure Effect of H1N1 Pandemic on Family Wealth
Outcome variable

During × log(H1N1 death rate)
After × log(H1N1 death rate)

Total wealth
Total wealth(exc. home equity)) (inc. home equity)(in 10k US$))(in 10k US$)
(1)

(2)

0.262
(1.020)0.286(1.156)0.256(1.036)0.371(1.175)-0.090-0.090(0.063) (0.063)-0.274∗∗∗ -0.274∗∗∗(0.079) (0.079)-0.000(0.001)-0.000(0.001)

5.466
✓
✓
✓
✓
✓
2,239
9,790
0.591

7.292
✓
✓
✓
✓
✓
2,239
9,790
0.648

-0.886
✓
✓
✓
✓
✓
2,239
9,790
0.285

Total wealth (exc. home equity)
Total wealth (inc. home equity)
Average of the outcome variable
Family, state, year FEs
State-specific time trend
Household features
Macroeconomic indicator (lag)Medical controls (lag)Number of family
Observations
Adj.R2log risky share

(3)

(4)

-0.886
✓
✓
✓
✓
✓
2,239
9,790
0.285

Notes: Following PSID’s definition, We define Total wealth (exc. home equity) as the aggregate
value of all types of assets — encompassing risky assets, risk-free assets, business or farmownership, other real estate, and vehicles — minus the total debt value, with home equity
expressly excluded from this calculation. On the other hand, Total wealth (inc. home equity)follows the same summation of assets as Total wealth (exc. home equity), with the distinction
being the inclusion of home equity in the total, which is then deducted by the overall debt
value. Wealth variables (in 10k US$) are defined in 2017 dollar adjusted by the CPI-U index.
All regressions control for family fixed effects, year fixed effects, state fixed effects, and statespecific time trend, household features, and state-by-year-level macroeconomic indicators. See
Section 4.1 for the list of variables controlled as household features, macroeconomic index, andmedical resources. Standard errors are clustered at the family level and shown in parentheses.
∗ p < 0.1, ∗∗ p < 0.05, ∗∗∗ p < 0.01. (Data source: PSID, waves 2001, 2003, 2005, 2007, 2009,2011, 2013, 2015, and 2017.)

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