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The COVID effect: an empirical analysis of the pandemic and the 2020 U.S. presidential election
Abstract
The impact of the COVID pandemic on the 2020 election outcome is analyzed using Iowa Electronic Market data, measures of socially and economically driven market volatility, a measure of COVID severity, and selected election-related events. Building on research regarding two previous U.S. presidential elections, we find that the pandemic helped the incumbent in two ways. The largest impact supporting the incumbent came from the apparent medical severity. A secondary impact came from social and economic volatility with the surprising finding that both risks helped the incumbent relative to the challenger. However, these impacts were not adequate to overcome the relatively large advantage of the challenger.
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Scarcity of ventilators during COVID-19 pandemic has urged public health authorities to develop prioritization recommendations and guidelines with the real-time decision-making process based on the resources and contexts. Nevertheless, patients with COVID-19 who will benefit the most from ventilation therapy have not been well-defined yet. Thus, the objective of this study was to investigate the benefit of ventilation therapy among various patient groups with COVID-19 admitted to hospitals, based on the real-world data of hospitalized adult patients. Data used in the longitudinal study included 599,340 records of hospitalized patients who were admitted from February 2020 to June 2021. All participants were categorized based on sex, age, city of residence, the hospitals' affiliated university, and their date of hospitalization. Age groups were defined as 18–39, 40–64, and more than 65-year-old participants. Two models were used in this study: in the first model, participants were assessed by their probability of receiving ventilation therapy during hospitalization based on demographic and clinical factors using mixed-effects logistic regression. In the second model, the clinical benefit of receiving ventilation therapy among various patient groups was quantified while considering the probability of receiving ventilation therapy during hospital admission, as estimated in the first model. The interaction coefficient in the second model indicated the difference in the slope of the logit probability of recovery for a one-unit increase in the probability of receiving ventilation therapy between the patients who received ventilation compared to those who did not while considering other factors constant. The interaction coefficient was used as an indicator to quantify the benefit of ventilation reception and possibly be used as a criterion for comparison among various patient groups. Among participants, 60,113 (10.0%) cases received ventilation therapy, 85,158 (14.2%) passed away due to COVID-19, and 514,182 (85.8%) recovered. The mean (SD) age was 58.5 (18.3) [range = 18–114, being 58.3 (18.2) among women, and 58.6 (18.4) among men]. Among all groups with sufficient data for analysis, patients aged 40–64 years who had chronic respiratory diseases (CRD) and malignancy benefitted the most from ventilation therapy; followed by patients aged 65 + years who had malignancy, cardiovascular diseases (CVD), and diabetes (DM); and patients aged 18–39 years who had malignancy. Patients aged 65 + who had CRD and CVD gained the least benefit from ventilation therapy. Among patients with DM, patients aged 65 + years benefited from ventilation therapy, followed by 40–64 years. Among patients with CVD, patients aged 18–39 years benefited the most from ventilation therapy, followed by patients aged 40–64 years and 65 + years. Among patients with DM and CVD, patients aged 40–64 years benefited from ventilation therapy, followed by 65 + years. Among patients with no history of CRD, malignancy, CVD, or DM, patients aged 18–39 years benefited the most from ventilation therapy, followed by patients aged 40–64 years and 65 + years. This study promotes a new aspect of treating patients for ventilators as a scarce medical resource, considering whether ventilation therapy would improve the patient's clinical outcome. Should the prioritization guidelines for ventilators allocation take no notice of the real-world data, patients might end up being deprived of ventilation therapy, who could benefit the most from it. It could be suggested that rather than focusing on the scarcity of ventilators, guidelines focus on evidence-based decision-making algorithms to also take the usefulness of the intervention into account, whose beneficial effect is dependent on the selection of the right time in the right patient.
Though mechanical ventilation (MV) is used to treat patients with severe coronavirus disease 2019 (COVID-19), little is known about the long-term health implications of this treatment. Our objective was to determine the association between MV for treatment of COVID-19 and likelihood of hospital readmission, all-cause mortality, and reason for readmission. This study was a longitudinal observational design with electronic health record (EHR) data collected between 3/1/2020 and 1/31/2021. Participants included 17,652 patients hospitalized for COVID-19 during this period who were followed through 6/30/2021. The primary outcome was readmission to inpatient care following discharge. Secondary outcomes included all-cause mortality and reason for readmission. Rates of readmission and mortality were compared between ventilated and non-ventilated patients using Cox proportional hazards regression models. Differences in reasons for readmission by MV status were compared using multinomial logistic regression. Patient characteristics and measures of illness severity were balanced between those who were mechanically ventilated and those who were not utilizing 1-to-1 propensity score matching. The sample had a median age of 63 and was 47.1% female. There were 1,131 (6.4%) patients who required MV during their initial hospitalization. Rates (32.1% versus 9.9%) and hazard of readmission were greater for patients requiring MV in the propensity score–matched samples [hazard ratio (95% confidence interval) = 3.34 (2.72–4.10)]. Rates (15.3% versus 3.4%) and hazard [hazard ratio (95% confidence interval) = 3.12 (2.32–4.20)] of all-cause mortality were also associated with MV status. Ventilated patients were more likely to be readmitted for reasons which were classified as COVID-19, infectious diseases, and respiratory diagnoses compared to non-ventilated patients. Mechanical ventilation is a necessary treatment for severely ill patients. However, it may be associated with adverse outcomes including hospital readmission and death. More intense post-discharge monitoring may be warranted to decrease this associational finding.
[This corrects the article DOI: 10.1007/s11109-022-09826-x.].
From the onset of the first confirmed case of COVID-19 in January 2020 to Election Day in November, the United States experienced over 9,400,000 cases and 232,000 deaths. This crisis largely defined the campaign between former Vice President Joe Biden and President Donald Trump, centering on the Trump administration's efforts in mitigating the number of cases and deaths. While conventional wisdom suggested that Trump and his party would lose support due to the severity of COVID-19 across the country, such an effect is hotly debated empirically and theoretically. In this research, we evaluate the extent to which the severity of the COVID-19 pandemic influenced support for President Trump in the 2020 election. Across differing modeling strategies and a variety of data sources, we find evidence that President Trump gained support in counties with higher COVID-19 deaths. We provide an explanation for this finding by showing that voters concerned about the economic impacts of pandemic-related restrictions on activity were more likely to support Trump and that local COVID-19 severity was predictive of these economic concerns. While COVID-19 likely contributed to Trump's loss in 2020, our analysis demonstrates that he gained support among voters in localities worst affected by the pandemic.
Supplementary information:
The online version contains supplementary material available at 10.1007/s11109-022-09826-x.
An important body of literature shows that citizens evaluate elected officials based on their past performance. In the aftermath of the 2020 presidential election, the conventional wisdom in both media and academic discourse was that Donald Trump would have been a two-term president absent an unprecedented, global force majeure. In this research note, we address a simple question: did exposure to COVID-19 impact vote choice in the 2020 presidential election? Using data from the Cooperative Election Study, we find that Trump’s vote share decreased because of COVID-19. However, there is no evidence suggesting that Joe Biden loses the election when no voter reports exposure to coronavirus cases and deaths. These negligible effects are found at both the national and state levels, and are robust to an exhaustive set of confounders across model specifications.
Objective
This article investigates the impact of public reactions to the Covid‐19 panemic on voting for former President Donald Trump in the 2020 American presidential election.
Methods
The impact of the pandemic on voting is assessed by multivariate statistical analyses of representative national survey data gathered before and after the 2020 presidential election.
Results
Analyses show that voters reacted very negatively to Trump's handling of the pandemic. Controlling for several other relevant factors, these reactions affected voting for Trump and exerted a significant impact on the election outcome.
Conclusion
Before the onset of Covid‐19 Trump had a very narrow path to victory in 2020, and the pandemic did much to ensure his defeat.
What is the effect of the COVID-19 pandemic on the 2020 US presidential election? Guided by a pre-analysis plan, we estimate the effect of COVID-19 cases and deaths on the change in county-level voting for Donald Trump between 2016 and 2020. To account for potential confounders, we include a large number of COVID-19-related controls as well as demographic and socioeconomic variables. Moreover, we instrument the numbers of cases and deaths with the share of workers employed in meat-processing factories to sharpen our identification strategy. We find that COVID-19 cases negatively affected Trump’s vote share. The estimated effect appears strongest in urban counties, in states without stay-at-home orders, in swing states, and in states that Trump won in 2016. A simple counterfactual analysis suggests that Trump would likely have won re-election if COVID-19 cases had been 5 percent lower. We also find some evidence that COVID-19 incidence had a positive effect on voters’ mobilization, helping Biden win the presidency.
Objectives
The contentious 2016 U.S. presidential election was marked by acrimonious televised debates between the two major candidates, Hillary Clinton and Donald Trump, federal investigations of Clinton's emails that were sent from a personal server when she held office as Secretary of State, and the release of a videotape of lewd remarks by Trump about his behavior toward women. Financial market uncertainty also played in role in the election campaign. The objective of the article is to examine the impact of these different factors on the election.
Method
The present article uses a popular vote prediction market to test the impact of these factors on the probability of Trump winning the election.
Results
Results indicate that the debates and videotape release were not statistically significant, but that a letter to Congress released by FBI Director James B. Comey on October 28, 2016, substantially decreased Clinton's probability of winning the popular vote and simultaneously increased Trump's probability. Financial market uncertainty is found to have some marginal positive effect on Trump's probability of winning.
Conclusion
Trump's probability of winning the election received a substantial boost from FBI Director James B. Comey's “last‐minute” announcement on October 28, 2016.
Given the deep polarization of the American political system in recent decades, was the 2020 presidential election an extension of the pre-existing partisan coalitions or did the Covid-19 pandemic and its economic consequences have a significant impact on the outcome? Using a national probability sample provided by AmeriSpeak and voter verification provided by Catalist, we construct a structural equation model to examine the relative influence of age, race, gender, education, religious fundamentalism, ideological partisanship, affective partisanship, and measures of Covid-19 experiences and understanding to predict the 2020 vote. We re-construct the partisan polarization landscape to examine the role of politically interested non-partisans in the center of the ideological spectrum and examine their ability to select candidates in response of specific issues. The Covid-19 pandemic had a significant marginal impact on the outcome of the 2020 presidential election.
This review paper articulates the relationship between prediction market data and event studies, with a special focus on applications in political economy. Event studies have been used to address a variety of political economy questions from the economic effects of party control of government to the importance of complex rules in congressional committees. However, the results of event studies are notoriously sensitive to both choices made by researchers and external events. Specifically, event studies will generally produce different results depending on three interrelated things: which event window is chosen, the prior probability assigned to an event at the beginning of the event window, and the presence or absence of other events during the event window. In this paper we show how each of these may bias the results of event studies, and how prediction markets can mitigate these biases.
Recently, market volatility has been used as an explanatory variable for presidential job approval. Our research builds on such an approach by first extracting the economic and non-economic components of market volatility using the Eta® (c4cast.com, Inc., 750 E. Walnut St., Pasadena, CA 91101) model, which considers 18 economic factors, far more than the unemployment and inflation factors used in most similar studies. Further, our results suggest that it is not aggregated market volatility but rather disaggregated market volatility that has a causal effect on presidential job approval ratings. In addition, the market's expectation of economic and non-economic market volatility exhibits different patterns, in terms of direction and timing, in their relationship to presidential job approval ratings. Copyright © 2011 John Wiley & Sons, Ltd.
Prediction markets - markets used to forecast future events - have been used to accurately forecast the outcome of political contests, sporting events, and, occasionally, economic outcomes. This chapter summarizes the latest research on prediction markets in order to further their utilization by economic forecasters. We show that prediction markets have a number of attractive features: they quickly incorporate new information, are largely efficient, and impervious to manipulation. Moreover, markets generally exhibit lower statistical errors than professional forecasters and polls. Finally, we show how markets can be used to both uncover the economic model behind forecasts, as well as test existing economic models.
Prediction markets have drawn considerable attention in recent years as a tool for predicting elections. But how accurate are they? Do they really outperform the polls, as some would have us believe? Do prices in election markets carry information beyond the horserace that is portrayed in the latest polls? And how well do election markets perform when there are no public opinion polls to provide the cues? This paper compares the accuracy of presidential betting markets in years before and after public opinion polls were introduced. For data, we rely on prices from modern internet-based election markets since 1992 but also from the vigorous election markets that thrived on Wall Street going back from at least as far as 1880 up to 1960. Our results comparing the performance of markets and polls are provocative. First, we find that market prices are far better predictors without polls (1880-1932) than with polls available (1936-2008). Second, we also find that market prices of the pre-poll era predicted presidential elections at least as well as polls have done following the introduction of scientific survey research. Finally, we find that last-minute market prices add nothing to election prediction once we control for trial heat polls during the final week of the campaign. The ordering of these results is not transitive.
Event studies have been used in political science to study the cost of regulation
(Schwert, 1981), the value of political connections (Roberts, 1990a; Fisman,
2001), the effect of political parties on defense spending (Roberts, 1990b), the
importance of rules in congressional committees (Gilligan and Krehbiel, 1988),
the reaction of different interests to trade legislation (Schnietz, 2000), how party
control in parliamentary systems affects broad-based stock indices (Herron,
2000), the value of defense contracts (Rogerson, 1989), the effect of the political
party of the US President and congressional majorities on particular industry
segments (Mattozzi, 2008; Knight, 2006; Herron et al., 1999; Den Hartog and
Monroe, 2008; Monroe, 2008; Jayachandran, 2006), and other questions.
Many observers argued that Barack Obama’s candidacy in the U.S. presidential election of 2008 benefited from the financial
crisis and recessionary economic conditions which voters blamed on the Republican administration. However, an empirical examination
of stock price and public opinion data indicates that improvements in Obama’s electoral prospects led stock price declines
and that gains by Obama were more likely to be followed by falling stock prices than by rising prices. This evidence suggests
that the poor performance of the stock market in the days leading up to Obama’s victory was partly caused by, rather than
causing, Obama’s success.
This paper investigates the relationship of the daily spread between taxable and municipal securities to a daily indicator of the likelihood of a tax reform that would eliminate the preference now accorded to municipal securities, the ‘flat tax.’ The probability of tax reform is proxied by the market price of the shares of Steve Forbes, a champion of the flat tax, in the Iowa Electronic Market. We find evidence that the indicator of tax reform’s prospects was correlated with a decline in the spread on five and ten-year maturity bonds, although not for 30-year maturity bonds. The correlations do not, however, survive in first-difference form, suggesting either that Forbes’ prospects did not immediately affect expectations of future tax policy, that the correlations are coincidental rather than causal, or that the decreased signal-to-noise ratio obscures a real, but small, causal relationship.
Analyses of the effects of election outcomes on the economy have been hampered by the problem that economic outcomes also
influence elections. We sidestep these problems by analyzing movements in economic indicators caused by clearly exogenous
changes in expectations about the likely winner during election day. Analyzing high frequency financial fluctuations following
the release of flawed exit poll data on election day 2004, and then during the vote count we find that markets anticipated
higher equity prices, interest rates and oil prices, and a stronger dollar under a George W. Bush presidency than under John
Kerry. A similar Republican-Democrat differential was also observed for the 2000 BushGore contest. Prediction market based
analyses of all presidential elections since 1880 also reveal a similar pattern of partisan impacts, suggesting that electing
a Republican president raises equity valuations by 2–3 percent, and that since Ronald Reagan, Republican presidents have tended
to raise bond yields.
We analyze financial market data in order to produce an ex-ante assessment of the economic consequences of war with Iraq. The novel feature of our analysis derives from the existence of a market for Saddam Securities,' a new future traded on an online betting exchange that pays only if Saddam Hussein is ousted. A variety of tests suggest that this future's price provides a plausible estimate of the probability of war. The spot oil price has moved closely with the Saddam Security, suggesting that war raises oil prices by around $10 per barrel. Futures prices imply that markets expect these large immediate disruptions to dissipate quickly, with prices returning to pre-war levels within about a year and a half. Evidence on the long-run effects is fragile, and while prices are probably expected to fall a little as a result of war, any oil dividend' will be minimal. We find large effects in equity markets: and war lowers the value of U.S. equities by around 15 percent. This effect is concentrated in the consumer discretionary sector, airlines and IT; the prospect of war bolsters the gold and energy sectors. Analyzing option prices, we find that the large estimated average effects of war reflect the market pricing in a range of different scenarios - a 70 percent probability that it will lead to market declines of 0 to 15 percent, a 20 percent chance of 15 to 30 percent declines, and a 10 percent risk of a fall in excess of 30 percent. Across countries, the most extreme effects are on the stock markets of Turkey, Israel, and several European nations. Countries that are highly enmeshed in the world economy, or net oil importers, are most likely to experience adverse effects from war.
To understand the extent to which partisan majorities in Congress influence economic policy, we compare financial market responses in recent midterm elections to Presidential elections. We use prediction markets tracking election outcomes as a means of precisely timing and calibrating the arrival of news, allowing substantially more precise estimates than a traditional event study methodology. We find that equity values, oil prices, and Treasury yields are slightly higher with Republican majorities in Congress, and that a switch in the majority party in a chamber of Congress has an impact that is only 10-30 percent of that of the Presidency. We also find evidence inconsistent with the popular view that divided government is better for equities, finding instead that equity valuations increase monotonically, albeit slightly, with the degree of Republican control.
This paper analyzes the large and often well-organized markets for betting on U.S. presidential elections that operated between 1868 and 1940. Four main points are addressed. First, we show that the market did a remarkable job forecasting elections in an era before scientific polling. Second, the market was fairly efficient, despite the limited information of participants and active attempts to manipulate the odds. Third, we argue political betting markets disappeared largely because of the rise of scientific polls and the increasing availability of other forms of gambling. Finally, we discuss lessons this experience provides for the present.
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