The Mystery of the COVID-19 Infection Fatality Rate (IFR) Unraveled (#14) with Dr. Linus Wilson

https://www.podbean.com/media/share/pb-uhib6-db1b5f

Dr. Linus Wilson discusses and reads his new study:

SARS-CoV-2, COVID-19, Infection Fatality Rate (IFR) Implied by the Serology, Antibody, Testing in New York City

Wilson, Linus, SARS-CoV-2, COVID-19, Infection Fatality Rate (IFR) Implied by the Serology, Antibody, Testing in New York City (May 1, 2020). Available at SSRN: 

https://ssrn.com/abstract=3590771

“Abstract

 The SARS-CoV-2, COVID-19, infection fatality rate (IFR) has been hard to accurately estimate. It is a key parameter for disease modeling and policy decisions. Asymptomatic spread and limited testing have understated infections in hard to predict ways across jurisdictions. We survey serology, antibody, studies of the COVID-19 infection to find official cases are understated by an average of 25-to-1. Further, we analyze the deaths and infections in New York City to estimate an overall IFR for the United States of 0.863 percent.

…5. Conclusion

 

The COVID-19 pandemic has a lot of uncertainty about the ratio of deaths to total infections. That confounds the calculation of how deadly the novel coronavirus is. The serology sampling in New York City and elsewhere makes estimates of infections more reliable. We estimate that the infection fatality rate (IFR) from serology studies in nine different sampling locations in the United State and Europe is on average 0.38 percent. We analyze the data from New York City in-depth to estimate that the IFR for all ages and genders in New York City was 0.85 percent. New York City is a preferable location to estimate IFR because it has one of the highest infection rates in the world. Thus, random sampling is less prone to an upward bias in false positives. In addition, New York City’s official counts are less likely to understate deaths than in other locations in the United States. We find that the infection fatality rates from New York vary a great deal by age and gender. Females ages 0 to 17 can expect infection fatality rates of 0.001 percent while males of age 75 and over can expect infection fatality rates of 9.127 percent.”

Dr. Linus Wilson[1]

Associate Professor of Finance

Department of Economics & Finance

B.I. Moody III College of Business

University of Louisiana at Lafayette

Moody Hall, Room 253

P.O. Box 43709

Lafayette, LA 70504

(337) 482-6209

linus [dot] wilson {at} louisiana [dot] edu

https://www.linuswilson.com

www.financeprofessor.org

 

COVID-19: Save Lives or the Economy? The Benefits of Social Distancing in the Pandemic and the Value of Statistical Life (#13)

https://www.podbean.com/media/share/pb-mth9s-d9b37b

Dr. Wilson reads and discusses his new paper:

Wilson, Linus, Estimating the Life Expectancy and Value of Statistical Life (VSL) Losses from COVID-19 Infections in the United States (April 19, 2020). Available at SSRN: https://ssrn.com/abstract=3580414

His the “Download this paper” button to get a free copy.

Estimating the Life Expectancy and Value of Statistical Life (VSL) Losses from COVID-19 Infections in the United States

 By 

Dr. Linus Wilson

Associate Professor of Finance

Department of Economics & Finance

B.I. Moody III College of Business

University of Louisiana at Lafayette

Theses are the views of the author alone.

 “

Abstract

 

Americans aged sixty or older stand to lose 153 to 222 days of life expectancy from contracting COVID-19. Over 90 percent of the U.S. population was under stay at home orders by April 2020. These social distancing measures to slow the spread of the SARS-CoV-2 or novel coronavirus have led to over 20 million new applications for unemployment benefits. Are these economic losses justified? We find the value of statistical lives lost (VSL) from an unconstrained spread of the virus which hypothetically infected 81 percent of the population would amount to $8 to $60 trillion.

 

 

Journal of Economic Literature Codes: G22, I1, I18, J31, J65, K32

 

Keywords: actuarial tables, mortality, death rates, CFR, COVID-19, IFR, life expectancy, SARS-CoV-2, school closures, social distancing, stay at home orders, VSL

1. Introduction

This paper attempts to open the discussion of how to model the benefits of social distancing measures in terms of the value of statistical lives (VSL) saved in the SARS-CoV-2 or COVID-19 pandemic. To do this we compare the infection fatality rates IFR’s of Ferguson et al. (2020) to the VSL from several studies. We find in figure 3 panels A and B that the costs of 50 percent of the U.S. population being infected with COVID-19 in lives lost and VSL are, respectively, between 0.659 million and 2.305 million lives lost and between $5 trillion and $37 trillion in VSL losses. The Gross Domestic Product (GDP) was only $21.7 trillion at the end of 2019 according to Mataloni and Aversa (2020).

We use U.S. Census data to control for the age and gender of the population and show how a COVID-19 infection affects an individual’s life expectancy and compares to a typical year’s mortality. Life expectancy losses of between 153 and 222 days can be expected for Americans over 60 with a novel coronavirus infection, according to figure 2, panel C. Americans younger than forty can expect to lose less than two weeks of life expectancy from contracting the virus. In figure 1, panel C, persons over fifty can expect COVID-19 to be about as deadly or up to 70 percent more deadly than a year’s mortality risks. Persons younger than forty-years-old can expect less than half a year’s mortality risk in a COVID-19 infection.

To argue against the social distancing measures, either the IFR of COVID-19 must be nearer to the low end of Ferguson et al. (2020)’s 95 percent confidence interval or the social distancing measure must be very ineffective in reducing the reproductive number, R0, of the SARS-CoV-2 virus. As to the former, a few studies suggest a much lower mean IFR than the 0.9 percent from Ferguson et al. (2020). Ioannidis (2020) argues that, after adjusting for the age of the infected on the Diamond Princess cruise ship, the IFR for the U.S. population should be 0.3 percent, which is below the lower bound of the 95 percent confidence interval calculated in Ferguson et al. (2020) and used here. Likewise, a population-weighted study, Bendavid et al. (2020), recruited people to be tested in Santa Clara County, California regardless of symptoms for COVID-19. It found infection rates were severely under-reported. They calculated an IFR between 0.12 and 0.2 percent.

On the other hand, social distancing may be effective in reducing the spread of COVID-19. R0 is the number of additional persons that an infected person goes on to infect on average. Anecdotal evidence indicates that social distancing in late March and early April 2020 has been effective. Governor Andrew Cuomo of New York State, which has had the highest number of deaths and confirmed cases of COVID-19 of the U.S. states on April 16, 2020, argued in his daily briefing in CNBC (2020) that his modeling teams believed that R0 fell from a median of 2.5 in Wuhan before social distancing and 2.2 on the Diamond Princess Cruise ship to 0.9 in New York State after the mitigation efforts. Governor Cuomo argued that his advisors’ projected hospitals in the state would be overwhelmed with COVID-19 patients if R0 was persistently above 1.2.[1]  Rocklöv et al. (2020) estimate that uncontrolled R0 for COVID-19 on the Diamond Princess cruise ship was 14.8 before social isolation and 1.8 afterward. Chowell et al. (2011) argued that school closures in Mexico reduced the R0 of the H1N1 outbreak by more than 30 percent.

This paper will not attempt to measure the costs of social distancing which, no doubt, number in the many trillions of dollars in the United States alone. By April 7, 2020, Secon and Woodward (2020) reported that 95 percent of the U.S. population was under a stay at home order that meant all but “essential” businesses were shuddered. Morath and Chaney (2020) report that by April 16, 2020, 13 percent of the U.S. workforce or 22 million workers had filed unemployment insurance claims. The COVID-19 multi-state stay at home orders, and associated non-essential business shutdowns, began with California, on March 19, 2020, according to Mervosh et al. (2020). Before the SARS-CoV-2 disruptions, the U.S. unemployment rate stood at a record low 3.5 percent in February 2020 according to the Bureau of Labor Statistics.

Eichenbaum et al. (2020) estimate containing COVID-19 “optimally” with social distancing will lead to consumption dropping by 22 percent versus 7 percent without containment of the virus. Since consumption is about 68.1 percent of GDP, according to the St. Louis Fed, and 2019 GDP was $21.7 trillion, they are arguing macroeconomic consumption losses are about (0.22 – .07)*$21.7 trillion = $3.26 trillion. The loss of freedom cannot just be measured in just macroeconomic statistics. Aggregate consumption does not measure the loss of consumer and producer surplus. There may also be long-term effects to school-age children or households facing bankruptcy that may not be captured fully in Eichenbaum et al. (2020). People losing their jobs also lose their health insurance and may be more likely to die as a result. Certainly, more empirical work can be done to estimate the actual costs of social distancing measures relative to their R0 benefits.

One press release, Yale News (2020), from the Yale Tobin Center for Economic Policy estimated the daily losses of shutdowns at $19 billion per day or about $7 trillion per year. Nevertheless, $3 to $7 trillion annually is less than this paper’s low-end estimates of the VSL losses for anything resembling the unconstrained spread of the virus infecting 81 percent of the population that Ferguson et. al (2020) project. Our low-end VSL losses are $8 trillion for an 81 percent infection rate. For the high-end estimate, the value of statistical lives lost is about $60 trillion.

Overall, the results of this paper point to sizable personal risks for individuals over sixty becoming infected with COVID-19 in terms of the increased chance of death and reduced life expectancy. We also find that the number of deaths and value of statistical life (VSL) losses are extremely high from high rates of COVID-19 infection. Thus, major economic disruptions from social distancing, stay at home orders, and school closures will be justified if the infection fatality rate estimates are reasonable. Measurement error of IFR, pharmaceutical treatments reducing the fatality rates, or a vaccine could make social distancing economic disruptions not worth the cost.

 

[1] See the discussion around the 24:45 minute mark of CNBC (2020).”

The 2020 Presidential Election: A Race Against Mortality (#12) by Linus Wilson

https://www.podbean.com/media/share/pb-4uzda-d07db6

A year from the inauguration, four of the top five Democratic 2020 U.S. Presidential election candidates in the polls are in their seventies. Using actuarial data and the history of Presidential assassinations, the top two contenders, Former Vice President Joe Biden and Vermont Senator Bernie Sanders, have a 24 to 29 percent chance of not surviving to the end of a hypothetical first term. The 77 and 78-year-old men’s chances of dying before the end of a second term as POTUS are between 46 and 56 percent.

The 2020 Presidential Election: A Race Against Mortality

by Dr. Linus Wilson, University of Louisiana at Lafayette

 Dr. Wilson reads his paper on episode 12 of The Finance Professor Podcast

 

 

Download the full paper at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3523251 or go to linuswilson.com or financeprofessor.org 

 

Below is the introduction of the paper:

  1. Introduction

 

Generations of Americans have never lived to see an American President die in office. Indeed, the last President to die was John F. Kennedy who in 1963 died from an assassin’s bullet at the relatively young age of 46 years. That was over 56 years from the start of the first in the nation nominating contest in 2020, the February 3, Iowa Democratic Caucuses. You would have to go back to Franklin D. Roosevelt’s death in office in 1945 in his record 4th term to find a U.S. President who died of “natural causes”. To be alive for that event in 2020, someone would have to be 74 years old. To have voted for FDR, someone would have to be at least 96 years old. (The voting age was not lowered from 21 to 18 years old until March 21, 1971, with the ratification of the 26th Amendment to the U.S. Constitution.)

According to Panetta (2020), on January 20, 2017, Donald Trump became the oldest President to be sworn in to his first term at 70 years 222 days old. Joseph Biden, Bernie Sanders, and Michael Bloomberg, aged 77, 78, and 77 were all at least three years older than Donald Trump, who was 73, on January 15, 2020. Biden, Sanders, and Bloomberg were 1st, 2nd, and 5th in the Real Clear Politics Democratic Primary polling average on that day. The 3rd place democratic candidate, Elizabeth Warren on that date was 70 and would be older than Donald Trump was at his inauguration in 2017 if she won the election and took office on January 20, 2021.

The likely winner of the 2020 election based on betting markets will be pushing the bounds of life expectancy at birth at some time during his tenure in office. He or she will likely be the oldest person to take the oath of office of President of the United States. This paper seeks to quantify the chances that the leading candidates will die before their first or second term in office using actuarial life expectancy data.

If we project out a year out from the January 20, 2021, inauguration, Former Vice President Joseph Biden, Vermont Senator Bernie Sanders, and Billionaire Michael Bloomberg have 24 to 29 percent chance of dying before the end of their first term as President of the United States. The range depends on the age and gender of the candidate and how we account for the chances of assassination. Those same leading Democratic challengers have a 46 to 56 percent chance of not surviving two terms as President of the United States (POTUS).

Kenski and Jamieson (2010) found that perceptions of the septuagenarian Republican nominee John McCain changed through the course of the 2008 election. Voters increasing perceived him a “too old” as election day neared. Thus, the current preference for older candidates by voters may change as the 2020 election progresses. Senator McCain died in 2018 at the age of 81, according to Pitzl (2018).

If the field of viable candidates survives to inauguration day, January 20, 2021, this paper finds that there is a 16 to 21 percent chance that the man or woman sworn in will die before the end of his or her four-year Presidential term on January 20, 2025.

In section 2, we discuss our data collection and how we determined if a 2020 Presidential candidate was “viable.” In section 3, we discuss the death probability calculations in table 2. In section 4, the paper concludes.

www.financeprofessor.org

http://www.linuswilson.com

Blow Up YT Algorithm 2019: Watch Time v. % Retention on YouTube #YouTubeAlgorithm #GetMoreViews

There is a big debate among video creators if they should focus on percent audience retention versus minutes watch time per view. Dr. Linus Wilson uses his groundbreaking study to settle this debate as to which metric will trigger the YouTube algorithm and allow you to have a viral video that blows up the internet.

This is based on Dr. Wilson’s study “Clickbait Works! The secret to getting views with the YouTube algorithm”

Wilson, Linus, Clickbait Works! The secret to getting views with the YouTube algorithm (April 9, 2019). Available at SSRN:
https://ssrn.com/abstract=3369353

Click the link. Hit the download button. Click the link to download without registering below the dude’s picture. Prove you are not a computer, and you have the algorithm study that can blow up your channel for FREE.

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Abstract
In 2018, YouTube began releasing click-through rates (CTR) data to its video creators. Since 2012, YouTube has emphasized how it favors watch time over clicks in its recommendations to viewers. This is the first academic study employing that data to test what matters more for views on YouTube. Is watch time or CTR more important to getting views on YouTube? This paper finds no to limited evidence that higher percent audience retention or and total average watch time per view are associated with more views on YouTube. Instead, videos with higher CTR got significantly more views as did videos on trending or newsworthy topics. The marginal benefit in terms of views scaled by subscribers of increased CTR is between 71 and 318 times larger than the marginal benefits of increased watch time per view.

http://www.linuswilson.com
http://www.financeprofessor.org

P1-Intro Mueller Report Audiobook

I read the Mueller Report released on April 18, 2019. This is an introduction to the Special Counsel’s Russia Investigation Report that stretches 448 pages. This is a reading of the first two pages. If a lot of you watch, I’ll keep recording readings. You can download the full Mueller Report.

report

Special Counsel Robert S. Mueller III was a former FBI director appointed by Rod Rosenstein Deputy Attorney General in May 2017 to investigate Russian Interference into the 2016 election between Hilary Clinton and Donald J. Trump. Mueller finds that the Russians did try to influence the 2016 election in favor of Donald Trump and did have contacts with the Trump campaign in the 2016 election, but they did not find enough evidence to bring the charge of conspiracy.

Clip from public domain WH.gov video “President Trump Delivers Remarks and Signs an Executive Order on Energy & Infrastructure”

Public domain Department of Justice photo of Robert S. Mueller as FBI director.

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Don’t LIKE me, YT Algorithm! View me. YouTube likes & dislikes % ratio 2019

If you want more views on YouTube, get more DISLIKES. Getting a lot of dislikes, contrary to popular opinion, is not a bad thing. It may even be an indicator that you have set off the YouTube search and discovery algorithm. Dr. Linus Wilson discusses his new research on the YouTube algorithm that shows that getting more dislikes or a lower like percentage is associated with getting MORE views and a higher click-through rate (CTR). CTR is the most important factor in triggering the YouTube algorithm’s search and discovery recommendations. Video creators who try to manipulate the like to dislike ratios or percentages are ultimately misguided. There are much better calls to action that they could make to their views like subscribing or recommending another video. Controversial or newsy topics may be more clickable and inspire higher CTR and higher watch time.

This is based on Dr. Wilson’s study “Clickbait Works! The secret to getting views with the YouTube algorithm”

Wilson, Linus, Clickbait Works! The secret to getting views with the YouTube algorithm (April 9, 2019). Available at SSRN:
https://ssrn.com/abstract=3369353

Click the link. Hit the download button. Click the link to download without registering below the dude’s picture. Prove you are not a computer, and you have the algorithm study that can blow up your channel for FREE.

Abstract
In 2018, YouTube began releasing click-through rates (CTR) data to its video creators. Since 2012, YouTube has emphasized how it favors watch time over clicks in its recommendations to viewers. This is the first academic study employing that data to test what matters more for views on YouTube. Is watch time or CTR more important to getting views on YouTube? This paper finds no to limited evidence that higher percent audience retention or and total average watch time per view are associated with more views on YouTube. Instead, videos with higher CTR got significantly more views as did videos on trending or newsworthy topics. The marginal benefit in terms of views scaled by subscribers of increased CTR is between 71 and 318 times larger than the marginal benefits of increased watch time per view.

Keywords: YouTube, algorithm, search, discovery, video, CTR, click-through rates, clickbait, watch time, audience retention, neural networks, recommendation systems

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http://www.linuswilson.com
http://www.financeprofessor.org

Ep. 11: “Clickbait Works! The secret to getting views with the YouTube algorithm” by Linus Wilson on the Finance Professor Podcast

Professor Linus Wilson discusses his new paper “Clickbait Works! The secret to getting views with the YouTube algorithm on episode 11 of the Finance Professor Podcast.” There is a lot of contradictory advice about what metrics the largest video sharing site in the world and the second largest social network promotes. Using a new data set available to YouTube creators starting in 2018, Dr. Wilson finds that click-through rates are by far the most important predictor of a new video getting views from YouTube’s black-box recommendation system.

The link is https://ssrn.com/abstract=3369353

Click the download button for a free download. Next, if you don’t want to register or login with SSRN, click “Download without registration” under the gray text under the picture of Greg Gordon.

“Clickbait Works! The secret to getting views with the YouTube algorithm”

By Dr. Linus Wilson

Abstract

In 2018, YouTube began releasing click-through rates (CTR) data to its video creators. Since 2012, YouTube has emphasized how it favors watch time over clicks in its recommendations to viewers. This is the first academic study employing that data to test what matters more for views on YouTube. Is watch time or CTR more important to getting views on YouTube? This paper finds no to limited evidence that higher percent audience retention or and total average watch time per view are associated with more views on YouTube. Instead, videos with higher CTR got significantly more views as did videos on trending or newsworthy topics. The marginal benefit in terms of views scaled by subscribers of increased CTR is between 71 and 318 times larger than the marginal benefits of increased watch time per view.

Journal of Economic Literature Codes:  D12, D22, D26, D83, D85, L15, L21, L82, L86, M15

Keywords:  YouTube, algorithm, search, discovery, video, CTR, click-through rates, clickbait, watch time, audience retention, neural networks, recommendation systems

Wilson, Linus, Clickbait Works! The secret to getting views with the YouTube algorithm (April 9, 2019). Available at SSRN: https://ssrn.com/abstract=3369353

For all of Linus Wilson’s research go to

www.financeprofessor.org

clickbait

Secret Wall Street Bailout Uncovered – Broken Bucks: Money Funds & Taxpayer Guarantees

Find out about the secret $2.7 trillion bailout of Money Market Mutual Funds MMMFs in 2008. The collapse of Lehman Brothers its commercial paper default caused the Primary Reserve Fund to “break the buck” or sell for less than $1.00 per share. Linus Wilson presents his paper at the Southwest Finance Association (SWFA) & Federation of Business Disciplines (FBD) conference at the Hyatt Regency in Houston, Texas on March 15, 2019.

Wilson, Linus, Broken Bucks: Money Funds that Took Taxpayer Guarantees in 2008 (August 28, 2015). Available at SSRN: https://ssrn.com/abstract=2195358 or http://dx.doi.org/10.2139/ssrn.2195358

The U.S. Treasury rolled out a bailout guarantee on September 19, 2008 without Congressional approval using the exchange rate stabilization fund led by Hank Paulson, David Nason, and Steve Shafran. Paulson and Shafran were Goldman Sach alums (p. 263) ON THE BRINK by Henry Paulson.

Broken Bucks: Money Funds that Took Taxpayer Guarantees in 2008
42 Pages Posted: 2 Jan 2013 Last revised: 29 Aug 2015
Linus Wilson
University of Louisiana at Lafayette – College of Business Administration

Date Written: August 28, 2015

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Abstract
This is the first study to look at the characteristics of funds accepting the $2.7 trillion taxpayer guarantee of money market mutual funds during the 2008 financial crisis. Funds with lower asset maturities were significantly less likely to need federal or sponsor bailouts. Fund shares that benefited from Federal Reserve’s asset-backed commercial paper program were significantly more likely to get bailed out by taxpayers and sponsors. Finally, the paper tests if funds adhering to the SEC’s 2010 liquidity reforms prior to their enactment were less likely to be bailed out in 2008.

Keywords: breaking the buck, bailout, Dodd-Frank, DLA, exchange rate stabilization fund, Financial Stability Oversight Council (FSOC), guarantees, liquidity, money market mutual funds, Primary Reserve Fund, regulation, SEC, Securities and Exchange Commission, U.S. Treasury, WAL, WAM, WLA

JEL Classification: G01, G18, G22, G23, G28, H12, H81, L5

Music by http://www.BenSound.com
(c) Linus Wilson, 2019
http://www.linuswilson.com
http://www.financeprofessor.org

U.S. Treasury portrait of Secretary Henry Paulson was completed in 2010 image at https://www.treasury.gov/about/history/pages/Steven Shafranhmpaulson.aspx
by Aaron Shikler

Ranking the Fed Doves & Hawks | Yellen to Martin FOMCs by Linus Wilson

This video ranks the Federal Reserves based on the tenure of their chairs from William McChesney Martin, Jr. to Janet L. Yellen, using data from 1958 through 2018.

This reading of “A Dove to Hawk Ranking of the Martin to Yellen Federal Reserves” by Linus Wilson.

Inflation “doves” are willing to tolerate more inflation than inflation “hawks.” Comparing the Taylor (1993) rule and core inflation to the effective fed funds rates, it is found that the Yellen Fed is the most dovish Fed since 1958.

The tenures of the following Federal Reserve (FOMC) Fed Open Market Committees are analyzed based on core inflation (CPI-U) without food or energy prices, unemployment, fed funds rates and the Taylor rule:
Janet L. Yellen
Ben S. Bernanke
Arthur F. Burns
G. William Miller
William McChesney Martin, Jr.
Alan Greenspan
Paul A. Volcker

The paper quotes or cites some of the speeches of Janet Yellen.

It was written by Linus Wilson, Associate Professor of Finance at the University of Louisiana at Lafayette. The views expressed are his alone.

The Yellen Fed is found to be the most dovish in history based on its setting of short-term interest rates relative to inflation. This paper looks at the interest rate setting policy of the Federal Reserve going back to the chairmanship of William McChesney Martin, Jr. and ending with the Janet Yellen’s tenure as chair. The Yellen Fed lacked a recession or banking crisis that may have justified the negative real interest rate policy of the Bernanke Fed. For its four years, the Yellen Fed succeeded in having falling unemployment and low inflation with negative real interest rates.

The link to get the paper is below:
Wilson, Linus, A Dove to Hawk Ranking of the Martin to Yellen Federal Reserves Available at SSRN: https://ssrn.com/abstract=2529195 or http://dx.doi.org/10.2139/ssrn.2529195

The views expressed are of Linus Wilson alone.
Music by http://www.BenSound.com
Public domain photos of the Fed chairs from the U.S. Federal Reserve.
(c) 2018, Linus WilsonDoveThumb1280by720