Partisan Return Gap: The Polarized Stock Market in the Time of a Pandemic Jinfei Sheng Zheng Sun Wanyi Wang * February 2021 Abstract We document sharp differences in stock price responses to COVID-19 related news between public firms headquartered in blue counties (dominated by Democratic supporters) and those in red counties (dominated by Republican supporters). Red-county stocks on average experience 18 basis points higher abnormal returns than blue-county stocks on days with important COVID-19 news. We call this “Partisan Return Gap”. The partisan return gap can be explained by a behavioral channel: investors in red counties are less concerned about COVID-19 and give more favorable interpretation to COVID-19 related news. Using smartphone app data tracking visits to non-essential services (e.g. restaurants), we confirm that individuals in red counties conduct less social distancing behavior in response to the surge of COVID-19 cases and government lockdown orders. Moreover, we find that stocks in counties where investors conduct less social distancing behavior have higher returns on COVID-19 news days. Exploiting Facebook connections as an identification, we further show that the partisan return gap is unlikely fully driven by local economic conditions and policies. Overall, the results are consistent with investors’ partisanship affecting their attitude toward COVID-19, which leads to polarized stock prices in the time of the pandemic. Keywords: Stock market, COVID-19, Partisanship, Return gap, Polarization, Social finance, Political finance * All authors are with Merage School of Business at University of California, Irvine. Emails: [email protected], [email protected], [email protected]. We thank Philip Bromiley, Diego Garcia, Jack Favilukis, David Hirshleifer, Yukun Liu, Kelly Shue, Johannes Stroebel, and seminar participants at University of California Irvine for helpful comments. We thank Zhiqi Rong, Diana He, Margaret Qu, Zoey Zhou, and Benlin Gan for excellent research assistance. All errors are our own.
58
Embed
Partisan Return Gap: The Polarized Stock Market in the ...
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Partisan Return Gap:
The Polarized Stock Market in the Time of a Pandemic
Jinfei Sheng Zheng Sun Wanyi Wang*
February 2021
Abstract
We document sharp differences in stock price responses to COVID-19 related news between
public firms headquartered in blue counties (dominated by Democratic supporters) and those in
red counties (dominated by Republican supporters). Red-county stocks on average experience 18
basis points higher abnormal returns than blue-county stocks on days with important COVID-19
news. We call this “Partisan Return Gap”. The partisan return gap can be explained by a
behavioral channel: investors in red counties are less concerned about COVID-19 and give more
favorable interpretation to COVID-19 related news. Using smartphone app data tracking visits to
non-essential services (e.g. restaurants), we confirm that individuals in red counties conduct less
social distancing behavior in response to the surge of COVID-19 cases and government
lockdown orders. Moreover, we find that stocks in counties where investors conduct less social
distancing behavior have higher returns on COVID-19 news days. Exploiting Facebook
connections as an identification, we further show that the partisan return gap is unlikely fully
driven by local economic conditions and policies. Overall, the results are consistent with
investors’ partisanship affecting their attitude toward COVID-19, which leads to polarized stock
prices in the time of the pandemic.
Keywords: Stock market, COVID-19, Partisanship, Return gap, Polarization, Social finance,
Political finance
*All authors are with Merage School of Business at University of California, Irvine. Emails: [email protected],
“The coronavirus crisis once seemed to be the kind of gut-wrenching shock that would pull
together a politically divided nation. Increasingly, though, it is pulling the nation apart along
familiar lines.”
– Wall Street Journal
1 Introduction
The global pandemic due to COVID-19 has hallmarked 2020 as one of the deadliest years
in human history. As of January 31, 2021, there are over 100 million cases and 2.2 million deaths
due to COVID-19 worldwide. In United States, there are over 26 million confirmed cases and
about 450, 000 deaths. The onset of the black swan event posed many questions: How deadly is
the virus? What should be the best response to curtail the disease? What will be the economic
impact of the pandemic? … Large disagreement persists regarding these issues, and the gap
seems to mainly arise along the line of partisanship. From elite leaders to news media, the
Republicans have consistently downplayed the severity of the disease and the necessity of the
social distancing measures. The Democratic Party, on the other hand, has emphasized the threat
of the disease and argued for strict lockdown policies. Studies show that a similar partisan
division exists among general households, where the Republicans are less likely to practice
social distancing measures (e.g., Allcott et al. 2020, Barrios and Hochberg 2020, Fan, Orhun and
Turjeman 2020, Gadarian, Goodman and Pepinsky 2020).
In this paper we study whether the partisan disagreement about COVID-19 could
generate a similar partisan gap in stock prices. In particular, we examine whether stocks of
companies headquartered in red and blue counties are priced differently during the first few
months of COVID-19, after controlling for the effect of fundamentals. Our study is premised on
the vast empirical evidence that investors tend to concentrate holdings in stocks to which they
2
are geographically close (e.g., Coval and Moskowitz 1999, Grinblatt and Keloharju 2001,
Huberman 2001, Hong, Kubik and Stein 2008). The implication is that the equilibrium prices of
stocks headquartered in red (blue) counties are more likely to be determined by the risk attitude
of Republican (Democratic) investors. Therefore, a divergence of their risk attitude during the
COVID-19 period could lead to polarized stock prices.
Despite the evidence that Americans’ health behaviors divide between partisanship, ex-
ante it is not obvious that such a gap carries over to the financial markets. First, scholars in
political sciences argue that apparent partisan gaps in beliefs can shrink substantially when there
are moderate incentives for accuracy (e.g., Bullock et al. 2015, Prior et al. 2015). Due to direct
consequences on their wealth, investors should have strong incentives to minimize biases from
their political beliefs. Second, even if differences of opinions exist among investors, stock prices
should reflect the average opinions of investors which could still be unbiased. Moreover,
arbitragers may serve as a correcting force that guard against any mispricing caused by non-
fundamental factors, including partisanship. In this paper, we ask if political gaps could persist in
asset prices, in the face of large incentives and arbitrage forces.
To study the effect of political polarization on stock returns during the pandemic, we first
identify the shocks of COVID-19 to the financial market by looking at aggregate stock market
movements. We find days on which the S&P 500 index moves up or down by more than 2.5%
and use news articles to identify whether the main reason for the swing is COVID-19. Our
research question is to examine whether responsiveness of the red-county stocks to COVID
shocks are different from that of the blue-county stocks.
Anecdotal evidence suggests that stocks in red and blue stocks reacted differently to
COVID shocks. For example, Range Resources Corporation and Montage Resources
3
Corporation are two companies in the Crude Petroleum and Natural Gas industry. Range
Resources Corporation is headquartered in Tarrant county of Texas and Montage Resources
Corporation is in the Dallas county of Texas. Tarrant and Dallas are neighboring counties with
similar size of population (2 million). The majority (54%) in Tarrant county voted for the
Republican party during the 2016 Election, while the majority (64%) of Dallas county voted for
the Democratic party. These two companies are in the same industry, located in adjacent counties
and have similar risk exposures.2 However, their stock price reactions to COVID-19 related news
are very different. The average risk-adjusted return of Range is 1.3% during the COVID-19
shocks days, while Montage’s average risk-adjusted return is -1.0%. 3 Figure 1 plots the
cumulative return of these two companies from January 2020 to June 2020 and it shows a big
gap between two stocks. The cumulative return of Range was higher than that of Montage since
early 2020 and the gap increased over time. This comparison suggests that there may be big
return difference in companies in red counties and companies in blue counties.
We examine all stocks in the United States and find that stocks of companies located in
red counties earn higher risk-adjusted returns than companies in blue counties on days when
COVID-19 related news triggered large market movements. In contrast, there is no statistical
difference in returns between the two groups of stocks outside of the Covid-19 news days. We
use risk-adjusted returns to alleviate the concern that our results are driven by the different risk
exposures of the two groups of companies. The economic magnitude is large. Firms in red
counties are associated with 18-21 basis points higher risk-adjusted returns on COVID-19 news
days. The result is robust to the inclusion of an extensive set of control variables such as severity
2 Betas for the Market, SMB, and HML factors are 1.01(1.05), 1.48(1.49), 0.93(0.81), respectively for Range
Resources Corporation (Montage Resources Corporation). 3 On positive COVID-19 news days, the average returns for Range Resources Corporation and Montage Resources
Corporation are 8.3% and 6.0%, respectively. On negative COVID-19 news days, the average returns for Range
Resources and Montage Resources are -4.7% and -7.1%, respectively.
4
of local COVID conditions, government lockdown orders, demographic information, and firm
characteristics. We call this difference “Partisan Return Gap.”
What could be an explanation of the Partisan Return Gap on the Covid-19 news days?
First, psychologists have long found that people display a tendency to search for and interpret
information in a way that confirms or supports their prior beliefs – “the confirmation bias”. (e.g.
Lord, Ross, and Lepper 1979, Taber and Lodge 2006, Westen et al. 2006). When encountering
Covid-19 related news, the confirmation bias could result in each partisan interpreting it as in
support of their existing attitudes, widening rather than narrowing the disagreement between
them. This combined with the well-established finding that investors are more likely to invest in
firms headquartered in the local areas (e.g., Coval and Moskowitz 1999, 2001), could lead to
more polarized stock prices between red and blue county stocks on Covid-19 news days.
Confirmation bias suggests that investors tend to believe in information that supports
their prior view and discount information that goes against it. Since Republicans are generally
less concerned about Covid-19 than Democrats, they will react more strongly to good Covid-19
news and less strongly to bad news. As a result, red county stocks are likely to experience higher
increases in prices on good news days and less price drops on bad news days. To test this
hypothesis, we look at days with positive and negative COVID-19 shocks separately. We find
that firms in red counties have higher risk-adjusted returns than firms in blue counties on good
news days (e.g., vaccine news), suggesting that they overreact to good news. Also, firms in red
counties have higher risk-adjusted returns than firms in blue counties on bad news days,
suggesting that they underreact to bad news. These results are consistent with the prediction of
confirmation bias. They also suggest that the big gap in stock price reactions to COVID-19
related news is unlikely due to missing risk factors. If the higher alpha on good news days by
5
red-county stocks relative to blue-county stocks is due to red-county stocks’ higher loadings on a
missing risk factor, then the same effect should generate a lower alpha for red-county stocks on
bad news days.
To provide more direct evidence that the partisan return gap is due to investors’ different
risk attitude toward COVID-19, we turn to individuals’ social distancing behavior during our
sample period. Using smartphone app data that tracks individuals’ visits to public places, we
confirm that, relative to blue county residents, people living in red counties pay more visits to
non-essential business (e.g. restaurants) in response to COVID-19 cases and lockdown policies,
manifesting their lower risk perceptions about the disease. More importantly, when replacing the
red county variable in our main specification with the change in visits to non-essential business,
we find that firms in counties with less social distancing behaviors earn higher risk-adjusted
returns on COVID-19 news days. Taken together, the results are consistent with that red county
residents perceive less risk in the face of COVID, as revealed by their less social distancing
behavior. Their less concern about COVID leads to a more favorable interpretation about
COVID news, contributing to a higher return earned by red county stocks during COVID-news
days.
Alternatively, the return gap we find can also be explained by the fundamental channel.
Despite extensive controls in the baseline specification, it is possible that our result is due to
omitted local factors that correlate with both local partisanship and local stock returns. To
address this issue, we employ an identification using the Social Connectedness Index (SCI)
introduced by Bailey et al. (2018). It is a county-level measure based on Facebook friendship
links, which captures the relative probability of residents in any two U.S. counties being
Facebook friends. Motivated by the finding that investors are more likely to invest in firms
6
located in counties where they have stronger social ties (Kuchler et al. 2020), we expect that
stock returns are more affected by the political beliefs of investors from more socially connected
counties. Thus, for each county, we construct a social-connection-based partisanship measure.
Because the measure only consists of the partisanship of geographically distant counties, it is
arguably exogenous to local factors of the focal county. We find that companies located in
counties with stronger social ties to Republican areas earn higher stock returns on COVID news
days than firms in counties with more social connections to Democratic areas. This finding
suggests that the difference in local economic fundamentals between red and blue counties
cannot fully explain the partisan return gap.
We also examine the fundamental channel at the firm level. It is possible that firms in
blue counties are fundamentally more negatively affected by COVID-19, and the return gap may
come from investors’ rational expectations of lower future earnings. However, we find no
evidence that companies in blue counties are hit harder by COVID-19. There is no statistical
difference in the changes in ROA between the two groups of companies. Regarding changes in
profitability, red-county firms are more negatively affected than blue-county firms, but the gap
becomes insignificant after we control for industry compositions. Thus, the difference in firm
fundamentals between red and blue counties cannot explain our observed pattern.
To gain further understanding of the sources of our partisan return gap, we conduct
several subsample tests. First, if the partisan return gap is due to home bias, it is likely to be more
pronounced in stocks that are more likely to be affected by local investors. Small firms and less
well-known firms (like non-S&P 500 stocks) are more likely to be held by local investors.
Indeed, we find the gap is concentrated among small firms and non-S&P 500 firms. Second, we
investigate the behavioral channel by comparing stocks that are more likely to be affected by
7
retail investors, because retail investors are more likely to be biased. We split companies by
institutional ownership and transaction cost. We find that the return gap only exists in firms with
low intuitional ownership and firms with high transaction cost. Third, presumably people with
higher income and higher education have more resources to learn about the disease so should be
less biased. Indeed, we find that the effects we document concentrate on companies
headquartered in low income and low education counties. Taken together, these findings support
a behavioral explanation that the difference in risk perceptions about Covid-19 between
Democratic and Republican investors leads to a gap in stock returns.
There may be concerns that our results are driven by unobservable differences between
companies in red and blue counties, and these differences have nothing to do with COVID-19
risk attitudes. To address the concern, we run a placebo test and repeat our procedures in 2018-
2019. During this earlier period, we do not observe the performance gap as we documented in
the main regression. Firms located in red counties do not have higher risk-adjusted returns than
firms in blue counties on stock market jump days.
Our results are robust to a number of alternative specifications, including alternative
benchmarks of risk-adjusted returns (CAPM; Fama-French Carhart four-factor model; Fama-
French five-factor model), alternative thresholds of market movement (1%, 3%, 5%), and
alternative measures of partisanship (county-level vs. state-level; continuous vs. discrete).
Our paper adds to a growing literature on how partisanship affects financial decisions and
outcomes. Kaustia and Torstila (2011) find that left-wing voters and politicians are less likely to
invest in stocks. Hong and Kostovetsky (2012) find that Democratic mutual fund managers hold
less of their portfolios in companies that are deemed socially irresponsible. Relatedly, Giuli and
Kostovetsky (2014) find that Democratic-leaning firms perform better in corporate social
8
responsibility. Hutton, Jiang, and Kumar (2014) demonstrate that personal political preferences
of corporate managers influence corporate policies. Hutton, Jiang, Kumar (2015) study whether
the political culture of a firm determines its propensity for corporate misconduct. Jiang, Kumar,
and Law (2016) show that analysts who contribute primarily to the Republican Party adopt a
more conservative forecasting style. Meeuwis, Parker, Schoar, and Simester (2019) show that
individuals in Republican areas invest their retirement assets more aggressively after the 2016
election. Like our paper, several papers link partisanship and economic decisions in the context
of COVID-19. For example, partisanship has been shown to affect risk preferences (Barrios and
Hochberg 2020) and stock market beliefs (Cookson, Engelberg and Mullins 2020). Unlike our
paper, most of the existing studies focus on micro-level data of individual preferences and
behaviors. While the individual data provide a more direct link between individual’s political
beliefs and their economic behavior, macro-level evidence is still needed to show that the
divergent political beliefs among different partisans do not end up netting each other in affecting
equilibrium outcomes. Our paper is one of the first to show that political beliefs could have an
important effect on asset prices, arguably one of the most important equilibrium variables in
finance.
The financial market experienced unprecedented volatility in the early weeks of the
COVID-19 pandemic. Several papers examine the causes of the price movement during this
period. Boudoukh, Liu, Moskowitz, and Richardson (2020) study the factor structure of returns
of different asset classes during COVID-19 times. Gormsen and Koijen (2020) use data from the
aggregate equity market and dividend futures to quantify investors’ expectations about economic
growth. Ding, Levine, Lin, and Xie (2020) study cross-firm stock price reactions to COVID-19
as functions of pre-shock corporate characteristics. These papers focus on the stock price
9
movement driven by fundamentals. On the other hand, Cox, Greenwald, and Ludvigson (2020)
use a dynamic asset pricing model to estimate the prices of the stock market risk and find that the
price fluctuation is mainly driven by shift in risk aversion or sentiment. Our paper shows that at
least part of the sentiment-driven price movement is due to investors’ political beliefs.
Out paper also contributes to the growing literature on social finance. Social finance
studies how social interaction affects financial decisions (Hirshleifer 2020, Han, Hirshleifer and
Walden 2020). Bailey et al (2018) show that social interaction measured by Facebook connection
can affect housing purchase decisions. Kuchler et al (2020) show that institutional investors are
more likely to invest in firms from regions to which they have stronger social connections. Our
paper shows that political belief can generate big impact on stocks that are geographically far
away through social networks. Our finding highlights the significant role of social interactions in
understanding asset pricing.
The rest of the paper is organized as follows. Section 2 offers a description for datasets,
measures and summary statistics. Section 3 shows the baseline results that there are significant
differences in asset prices between firms headquartered in blue counties and firms in red counties
during the COVID-19 period. Section 4 explores both behavioral and fundamental channels as
potential explanations of our baseline findings. Section 5 provides further discussions and
conducts robustness tests. Section 6 concludes.
2 Data and Measurement
2.1 Data
We start from a list of public companies from CRSP/Compustat Merged Database. We
restrict our sample to common share stocks listed on NYSE, Nasdaq and AMEX and exclude
10
companies that have no book value in the fiscal year ending in 2019 or no market value by the
end of 2019. We also exclude firms whose headquarter is outside the United States. Using tickers
of these companies, we download daily stock prices and trading volume from January 1, 2020 to
June 30, 2020 from CRSP. We then drop penny stocks whose price falls below $1 on any trading
day during the sample period. In total, there are 3,030 firms in our sample.
We merge firm headquarter information from Compustat with the HUD-USPS Crosswalk
File to convert zip codes to county FIPS codes. This allows us to link financial information with
geographic information such as partisanship. We obtain 2016 Presidential Election voting results
from MIT Election Data & Science Lab. We measure local partisanship with the proportion of
votes to the Republican and Democratic candidates in the area. A county is labeled as red (blue)
if the Republican candidate received more (less) votes than the Democratic candidate in the
county.
To measure individuals’ social distancing behavior, we use anonymized foot traffic data
provided by SafeGraph. Partnering with smartphone applications, SafeGraph obtains GPS
location data from 45 million smartphones and aggregates it into customer visits to public places.
There are over 6 million uniquely identified public places in the dataset, including shops,
restaurants, hotels, airports, etc. For each place, we observe its address, industry classification,
and the number of visits every day.
To measure social connections between counties in the United States, we use the Social
Connectedness Index developed by Bailey et al. (2018). Based on anonymized friendship links of
Facebook users, the county-level pairwise index captures the likelihood of residents in any two
U.S. counties being Facebook friends. Because Facebook has a large user base and requires
11
mutual consent to establish friendship links, Facebook friends provide a good proxy for real-
world social connections.
We obtain other state and county-level variables from various sources. Daily cumulative
COVID-19 cases are from the New York Times. Government lockdown orders are extracted
from the dataset collected by Keystone Strategy. Weekly unemployment claims are from U.S.
Department of Labor website. Demographics are from the 2012-2016 American Community
Survey (ACS) and the U.S. Census Bureau. Religiosity information is from “U.S. Church
Membership Data” collected by the Association of Religion Data Archives (ARDA).
2.2 Measurement
In order to examine the impact of COVID-19 on the stock market, the first and most
intuitive method that people usually think of is to use variables that directly measure the severity
of COVID-19 (e.g., cases, deaths, growth rate). However, in the United States, there is a huge
disconnect between the development of COVID-19 and the response of the stock market: the
market collapsed when there were only a few COVID-19 cases and rebounded and remained
high when COVID-19 cases grew rapidly. It turns out that the government and the Federal
Reserve’s policy responses to COVID-19 and investors’ prospects for the future development of
COVID-19 most affect the stock market. Therefore, we focus on days with COVID-19-related
news. For example, on March 12, 2020, the USA government declared a travel ban to Europe
and the S&P 500 index dropped 10%. In contrast, on March 24, 2020 and March 26, 2020,
medias reported that Congress and the government were about to reach a $2 trillion stimulus plan,
and the stock market rebounded strongly by 9% and 6%.
To identify important dates with COVID-19 related news, we focus on days when the
stock market moves up or down by more than 2.5%, which is about the threshold of top and
12
bottom 10% of S&P 500 returns during the sample period. The results are robust to using
alternative thresholds (see section 5.3). From January 1, 2020 to June 30, 2020, there are 33 days
when the stock market swings by more than 2.5%. We use news articles to identify whether the
main reason for the swing is COVID-19. Figure 2 plots some major events during the period.
The complete list of the 33 days with related news articles is in Appendix B. Among these 33
days, only 5 days are not related to COVID-19. The remaining 28 days are driven by COVID-19
news and we label them as COVID Shock days. We further define Positive (negative) COVID
Shock to distinguish market rises from market falls. There might be concern that COVID-19
related news occurs almost every day in 2020 and these 5 days might be also related to COVID-
19. In a robustness test in section 5.3, we also include these 5 days and the results are similar.
To measure firm-level stock responses to COVID-19 news, we use risk-adjusted daily
returns as the dependent variable in our main analysis. Specifically, we examine individual
stocks’ risk exposures to Fama-French three factors (Fama and French, 1993). We regress daily
excess returns on excess market return, SMB and HML from January 1, 2018 to June 30, 2020.
We then subtract the expected return based on Fama-French 3 factor model from the raw return.
We also calculate turnover as the daily trading volume divided by total shares outstanding. To
measure corporate earnings in the first two quarters of 2020, we define ROA as income before
extraordinary items divided by total assets. Following Novy-Marx (2013), we calculate gross
profitability as returns on gross profits (revenues minus cost of goods sold) scaled by total assets.
We also denote the year-over-year change of ROA and gross profitability as ROA and
Profitability.
To measure individuals’ social distancing behavior as a proxy for COVID-19 risk
perception, we calculate the change in visits to non-essential services compared to pre-pandemic
13
levels. Specifically, we define non-essential services as places whose 2-digit NAICS code is 71
(Arts, Entertainment, and Recreation) or 72 (Accommodation and Food Services).4 We then
count the total number of non-essential visits for each county on each day and calculate its 5-day
moving average to adjust for weekly seasonality.5 To measure the degree of social distancing, we
define visits as the 5-day moving average on day t divided by the visits at the beginning of 2020:
∆𝑣𝑖𝑠𝑖𝑡𝑠𝑖,𝑡 =(∑ 𝑣𝑖𝑠𝑖𝑡𝑠𝑖,𝜏)/5𝑡+2
𝜏=𝑡−2
(∑ 𝑣𝑖𝑠𝑖𝑡𝑠𝑖,𝜈)/55 𝑣=1
− 1
To measure local economic conditions and policy responses to COVID-19, we define
new cases as the state-level new COVID-19 cases per 1000 residents every day. % Unemp is the
unemployment claim rate during a week in a state. NPI indicates whether there is a state-level
“shelter-in-place”, “non-essential services closure” or “closing of public venues” order in effect
on a given day. We focus on the three types of lockdown orders as they have direct impact on
business operations. Besides, we define % Female as the percentage of female in a county’s total
population and HH Income as the median household income in the past 12 months. We measure
local religiosity as the proportion of a county’s total population that attends church, using the
survey conducted by the Association of Religion Data Archives (ARDA) in 2010. The total
religiosity ratio (TRR) is calculated as the number of adherents of all 217 religious
denominations divided by the total population of the county.
2.3 Summary statistics
Table 1 presents summary statistics of key variables in our main analysis. The average
risk exposure to Rm − Rf, SMB and HML is 0.93, 0.78 and 0.31, respectively. The average FF3
risk-adjusted return is 0.045%, and the average daily turnover is 1.28%. During the sample
4 Specifically, they are: theaters; sport centers; museums; historical sites; zoos; amusement parks; casinos; golf
courses; hotels and inns; RV parks and campgrounds; bars; restaurants; cafeterias. 5 There are only trading days in the data (Monday-Friday), so 5-day moving average eliminates weekly seasonality.
14
period, 22% of trading days are labeled as COVID Shock days, among which 10% are positive
shocks and 12% are negative shocks. Out of 3,030 firms in the sample, 20% reside in red
counties (i.e., counties dominated by Republican supporters), and the average county-level
voting shares to the Republican candidate in the 2016 Presidential Election is 37%. Regarding
COVID-19 severity, the average daily new COVID-19 cases are 0.074 per 1000 residents.
During Jan 1, 2020 – Jun 30, 2020, people reduces their visits to non-essential services by 38%.
The average unemployment claim rate is 13.9% at the state-level, and 43% of firm-date pairs are
associated with at least one of “shelter-in-place”, “non-essential services closure” or “closing of
public venues” orders. For counties in our sample, the average median household income is $68k
per year. The average proportion of women and proportion of residents attending church are both
51%. For firm characteristics, the average firm in the sample has a market value of $10.4 billion,
a book-to-market ratio of 0.57, and an institutional ownership of 54%. In the first two quarters of
2020, the average return on assets (ROA) and gross profitability is -2.4% and 4.9%, a decrease of
0.7 and 1.1 percentage points compared with the same period in 2019.
3 Partisan return gap
In this section, we document that there are striking differences between firms
headquartered in blue counties and firms headquartered in red counties. We examine both the
characteristics of firms and their stock prices during COVID-19 period.
3.1 Firms with color: red vs. blue
We first examine whether there are systematic differences in characteristics between
firms in blue counties and firms in red counties. Table 2 Panel A presents the comparison of
several firm characteristics. We find that firms headquartered in blue counties are bigger in terms
of market capitalization and have lower book-to-market ratio. Given the focus of this paper is on
15
stock returns, we also examine their risk exposures to Fama-French three factors. We find that
while there is no big difference in firms’ exposures to market factor and size factor, firms in red
counties have significantly higher exposure to the value risk factor. Given this finding, it is
important to control risk exposures when comparing stock returns of firms. Therefore, we use
Fama-French 3-factor alphas as a measure of stock performance.
We also look at the industry distributions of these firms based on Fama-French 12
industry classification. Table 2 Panel B shows that firms in red counties are concentrated in
industries like manufacturing, wholesale, retail, and some services, while firms in blue counties
are mainly in industries like healthcare, medical equipment, and drug, business equipment. Given
the important difference in industry distribution among these firms, we include industry fixed
effects in our regression to control for that.
3.2 Partisan return gap: result
We now examine how stock returns behave for firms in blue counties, compared to firms
in red counties. Motivated by the fact firms have different risk exposure to common risk factors,
we use the abnormal return adjusted by Fama-French 3 factors, 𝐴𝑏𝑛𝑅𝑒𝑡𝑖,𝑡 , as the dependent
Hutton, I., Jiang, D. and Kumar, A., 2014. Corporate policies of Republican managers. Journal of
Financial and Quantitative Analysis, 49(5-6), pp.1279-1310.
Hutton, I., Jiang, D. and Kumar, A., 2015. Political values, culture, and corporate litigation. Management
Science, 61(12), pp.2905-2925.
Jiang, D., Kumar, A. and Law, K.K., 2016. Political contributions and analyst behavior. Review of
Accounting Studies, 21(1), pp.37-88.
Jin, Z. and Li, F.W., 2020. Geographic Links and Predictable Returns. Working paper. Available at SSRN
3617417.
Kaustia, M. and Torstila, S., 2011. Stock market aversion? Political preferences and stock market
participation. Journal of Financial Economics, 100(1), pp.98-112.
Korniotis, G. and Kumar, A., 2013. State-Level Business Cycles and Local Return Predictability. Journal
of Finance, 68(3), pp. 1037-1096.
Kuchler, T., Li, Y., Peng, L., Stroebel, J. and Zhou, D., 2020. Social proximity to capital: Implications for
investors and firms. Working paper. National Bureau of Economic Research.
Kushner Gadarian, S., Goodman, S.W. and Pepinsky, T.B., 2020. Partisanship, health behavior, and
policy attitudes in the early stages of the COVID-19 pandemic. Working paper. Available at
SSRN 3562796.
Lord, C., Ross, L. and Lepper M., 1979. Biased assimilation and attitude polarization: The effects of prior
theories on subsequently considered evidence. Journal of Personality and Social Psychology, 37
(11), pp. 2098–109
Meeuwis, M., Parker, J.A., Schoar, A. and Simester, D.I., 2018. Belief disagreement and portfolio choice.
Working paper. National Bureau of Economic Research.
Memon, S.A., Razak, S. and Weber, I., 2020. Lifestyle disease surveillance using population search
behavior: Feasibility study. Journal of Medical Internet Research, 22(1), p.e13347.
Miller, E., 1977. Risk, uncertainty, and divergence of opinion. Journal of Finance, 32(4), pp.1151-1168.
MIT Election Data Lab, Science, 2018. County Presidential Election Returns 2000-2016. V42. Harvard
Dataverse. https://doi.org/10.7910/DVN/NH5S2I.
31
Novy-Marx, R., 2013. The other side of value: The gross profitability premium. Journal of Financial
Economics, 108(1), pp.1-28.
Petty, R. E., Gleicher, F. & Baker, S. M. 1991. Multiple roles for affect in persuasion. In: FORGAS, J. P.
(ed.) International series in experimental social psychology. Emotion and social
judgments. Elmsford, NY, US: Pergamon Press.
Prior, M., Sood, G., Khanna, K., 2015. You cannot be serious: the impact of accuracy incentives on
partisan bias in reports of economic perceptions. Quarterly Journal of Political Science, 10 (4),
p.p. 489–518.
Schwarz, N., 1990. Feelings as information: Informational and motivational functions of affective states.
The Guilford Press.
Sinclair, R.C. and Mark, M.M., 1995. The effects of mood state on judgemental accuracy: Processing
strategy as a mechanism. Cognition & Emotion, 9(5), pp.417-438.
Taber, C. and Lodge M., 2006. Motivated Skepticism in the Evaluation of Political Beliefs. American
Journal of Political Science 50 (3), pp. 755-769.
Tuzel, S. and Zhang, M.B., 2017. Local risk, local factors, and asset prices. Journal of Finance, 72(1),
pp.325-370.
Westen, D., Blagov, P., Harenski, K., Kilts, C. and Hamann S., 2006. Neural Bases of Motivated
Reasoning: An fMRI Study of Emotional Constraints on Partisan Political Judgment in the 2004
U.S. Presidential Election. Journal of Cognitive Neuroscience 18 (11), pp. 1947–1958
Wright, W.F. and Bower, G.H., 1992. Mood effects on subjective probability assessment. Organizational
Behavior and Human Decision Processes, 52(2), pp.276-291.
32
Figure 1. Cumulative returns of Range and Montage
This figure plots the cumulative stock returns of Range Resources Corporation and Montage Resources
Corporation between January 1, 2020 and June 30, 2020. The blue line indicates Montage Resources
Corporation (MR), and the red line proxies for Range Resources Corporation (RRC).
33
Figure 2. Major events triggering large market movements
This figure plots the historical price of S&P 500 index and major events that triggered large market movements between January 1, 2020 and June 30, 2020.
Mar 2, +4.6%.
Expectation on
Fed to cut rate.
Mar 12, -9.5%
Trump declares
travel ban on Europe
Mar 13, +9.4%.
Trump declares national
emergency; Pelosi says
House will pass a relief bill.
Mar 11, -4.9%
WHO declares
global pandemic
Mar 9, -7.6%
Oil price crashes
Mar 16, -12.0%
FED cuts rate by
100bp and plans to
buy $700B bonds.
Feb 24/25/27,
-3.4/-3.0/-4.4%
Surge of cases in
Europe and signs of
spread in the U.S.
Mar 3, -2.8%
Fed cut rate by 50bp.
Mar 17, +6.0%.
Fed to launch funding
facility; Trump seeks $1
trillion to fight covid.
Mar 18, -5.2%
U.S. - Canada
border close
Mar 24/26, 9.4%/6.2%
Agreement on $2T
coronavirus bill.
Apr 6/8, 7.0%/3.4%
Slower case growth in
NY and lower death
rate in Europe.
Apr 21, -3.1%
Crude oil contract
dropped to negative.
Apr 17/29, +2.7%/2.7%
Gilead’s breakthrough
on Remdesivir.
May 18, +3.2%
Moderna’s
progress on
vaccine.
Jun 11, -5.9%
COVID-19
infections resurge.
34
Figure 3. 2016 Presidential Election Result
This figure plots the share of votes received by Donald Trump in the 2016 Presidential Election. Red indicates more votes to the Republican party. Blue indicates
more votes to the Democratic party. The darker the color, the greater the difference in votes between the two parties.
35
Figure 4. COVID-19 cases, government orders, and unemployment across states
This figure plots the geographic distribution of COVID-19 cases, government lockdown orders and unemployment
rate across U.S. states. Panel A shows the cumulative covid-19 cases as of 6/30/2020; panel B presents the earliest
start date of three types of government orders: non-essential business closure, closing of public venues, and shelter-
in-place; panel C plots the unemployment claim rates as of 7/4/2020.
Panel A: Cumulative COVID-19 cases (as of June 30, 2020)
Panel B: Government NPI start date
Panel C: Unemployment claim rate (as of July 4th, 2020)
36
Figure 5. Time trends of non-essential visits and COVID-19 cases
This figure plots the time trend of visits to non-essential services and cumulative COVID-19 cases for Republican
and Democratic counties. Panel A shows the non-essential visits pattern. We define non-essential services as places
whose 2-digit NAICS code is 71 (Arts, Entertainment, and Recreation) or 72 (Accommodation and Food Services).
For each county on each day, we count the total number of visits to non-essential services, calculate its 5-day
moving average to adjust for weekly seasonality, and then normalize it with the visits at the beginning the year.
Panel B shows the cumulative COVID-19 cases. The red line indicates Republican counties, and the blue line
indicates Democratic counties.
Panel A: Visits to non-essential services
Panel B: cumulative COVID-19 cases
37
Figure 6. Examples of Facebook Social Connectedness Index
This figure shows two examples of Facebook Social Connectedness Index. Social Connectedness Index measures
the relative probability of people in two counties being Facebook friends. It is calculated as the number of Facebook
friend pairs between two counties divided by the product of the two counties’ population and then scaled up by a
factor of 1012. Panel A presents the social connectedness to New York County, NY. It is the county with the greatest
number of firm headquarters (N=184) among Democratic-dominated counties. Panel B shows the social
connectedness to Maricopa County, AZ, the county with the greatest number of firm headquarters in our dataset
(N=42) among Republican-dominated counties.
Panel A: New York County, New York
Panel B: Maricopa County, Arizona
38
Figure 7. Social-Connection-based Partisanship
This figure plots the Social-Connection-based Partisanship (SCP) measure across U.S. counties. SCP is the
logarithm of a weighted average of the share of votes to the Republican party in the 2016 Presidential Election,
where the weight is the Social Connectedness Index between the focal county and other counties (excluding own
county). Social Connectedness Index measures the relative probability of people living in two counties being
Facebook friends. It is calculated as the number of Facebook friend pairs between two counties divided by the
product of the two counties’ population. It is then scaled up by a factor of 1012 to become an integer. A county with
a high SCP means that it is more socially connected to the Republican party. Thus, high SCP counties are colored
red, and low SCP areas are colored blue.
39
Table 1. Summary statistics
This table presents the summary statistics of key variables at the firm-date level. The sample period is from January
1, 2020 to June 30, 2020. Panel A shows variables related to stock performances. Raw return measures daily stock
returns winsorized at the top/bottom 1% on COVID shock days. Excess return equals raw return minus risk-free rate.
FF3 α, CAPM α, FFC4 α, and FF5 α are daily returns adjusted by Fama-French three-factor model, CAPM model,
Fama-French-Carhart four-factor model, and Fama-French five-factor model, respectively. 𝛽𝑅𝑚−𝑅𝑓, 𝛽𝑆𝑀𝐵 and 𝛽𝐻𝑀𝐿
are risk exposures on excess market return, SMB and HML factors. All factor loadings are estimated using daily
returns from Jan 1, 2018 to Jun 30, 2020. Turnover is calculated as the daily trading volume divided by total shares
outstanding. Panel B presents variables related to COVID-19 shocks. COVID Shock indicates days on which
COVID-19 related news triggered S&P 500 index to move up or down by more than 2.5%. Positive (negative)
COVID Shock indicates days on which COVID-19 related news triggered S&P 500 index to move up (down) by
more than 2.5%. Panel C summarizes measures of partisanship. Red is a dummy variable that equals to 1 if the firm
is headquartered in a republican county (i.e., counties where Trump received more votes). Red (state) indicates
whether the firm is headquartered in a republican state. % Rep Vote (county /state) is the percentage of votes the
Republican party at the county/state level. Social-Connection-based Partisanship (SCP) is the logarithm of a
weighted average of the Republican voting shares, where the weight is the Social Connected Index between the
focal county and other counties based on Facebook friendship links (excluding own county or own state). All voting
shares are measured using the 2016 Presidential Election data. Panel D displays summary statistics of local variables.
Visits is the change in non-essential visits compared to the beginning of 2020. For each county, we replace non-
essential visit with its 5-day moving average to eliminate weekly seasonality. New Cases is the number of new
COVID-19 cases per 1000 residents in a state on a day. % Unemp is the state-level new unemployment claim rate
during a week. NPI indicates whether there is a state-level “shelter-in-place”, “non-essential services closure” or
“closing of public venues” order in effect on a day. % Female is the percentage of female in the county’s total
population. HH Income is the median household income in the past 12 months. Total Religiosity Ratio (TRR) is the
proportion of a county’s total population that attends church. Panel E shows firm characteristics. ME is the market
value in millions of dollars on December 31, 2019. BE is the book value in millions of dollars for the fiscal year
ending in 2019. B/M is the book-to-market ratio. S&P 500 is a dummy variable that equals to 1 if the stock is a S&P
500 index constituent as of December 31, 2019. Institutional Ownership equals the shares of stocks held by
institutional investors divided by total shares outstanding. Corporate earnings measures are at the firm-quarter level.
ROA is income before extraordinary items divided by total assets. Following Novy-Marx (2013), gross profitability
is calculated as returns on gross profits (revenues minus cost of goods sold) scaled by total assets. ROA is the year-
over-year change of ROA, and Profitability is the year-over-year change of gross profitability.
N Mean STD Min P25 P50 P75 Max
Panel A: stock performance (%)
Raw return 384,425 -0.0038 5.42 -23.8 -2.35 0 2.19 22.1