I was fortunate to spend the majority of my hedge fund career working for some of the best short sellers that have ever practiced. Uncovering fraud is a big part of that job, so, through osmosis alone, one develops an ear for the common signatures.
So when the State of New York said it was changing the number of fatalities it had previously reported from Covid-19, my ears perked up. When I read further that the revised historical number of New York fatalities would now include deaths from “probable, but not tested cases,” my first instinct was to check to see if it was possible to short the credibility of the State of New York.
This move will artificially inflate the historical death rate that is being used today to justify a response policy that put 20 million out of work and set the Bill of Rights on fire. We should consider carefully if we are using the right number.
The Berkeley Center for Law and Business hosted a fraud symposium in San Francisco last April, where some of the best experts in the world gave tips and insights for identifying fraud. Legendary short seller Jim Chanos walked through some accounting tricks, while investigative journalists Bethany Mclean (Enron) and John Carreyrou (Theranos) gave lessons learned from writing their respective books about the frauds they exposed. By the end of the day, dozens of experts had given insights.
One theme present in every case was that fraudulent companies all, at some point, change numbers they had previously reported.
Enron did it. Tyco, Worldcom, Qwest, Cendant, Bernie Madoff, they all did too. Lehman Brothers and Bear Stearns changed previously reported delinquency rates on their non-performing loan portfolios. Theranos did it with their blood tests – both in numbers tested and accuracy of the test itself.
The State of New York is not wrong that there are likely thousands of fatalities from Covid-19 that occurred from January to April and were either misrecorded as flu or not reported at all. But the number of infected who were not tested during that period is also likely to be hundreds of thousands.
When it turned out that Covid-19 would result in about 1/2 the fatalities the state projected just two weeks earlier, New York changed the historical numbers to make the virus more deadly by adding to the historical numerator, but not the denominator. And it is this death rate that is being used to justify the “Stay Home!” response policies that are being extended for weeks and months to come.
It has become popular at this point in the discussion to weaponize the debate over response policy into binary identity politics, where you have to choose between supporting social distancing, first line defenders, and people who are dying, or choose the side that is against inducing a Great Depression in order to stop the spread of a virus that has, as of April 15, taken as many lives as a mild flu season. That false choice is being advanced in order to deflect from the most important question:
Is what we are doing now correct? Is it even thoughtful?
Most sensible people agree the travesty was not being able to test our entire US population by early February. It shouldn’t have been that hard:
Incentivize big pharma to develop the test capacity by February, use national election polling centers and mall parking lots to test the entire country in a few days. Then use the corresponding empirical data to make thoughtful, go-forward policy decisions.
That didn’t happen. Who is ultimately blamed for it, and who gets credit for restarting the economy will be revealed in November. But the “what-should-have-happened” ship has sailed, and discussion of it deflects from what is still the most important question today:
What are we doing now?
It appears we are doubling down on policies that were based on guesses, which are now proven to have been wrong. And when New York realized it was basing much of its policy response on the wrong death rate, they… changed it?
On the same day New York announced it was changing the State’s historical Covid-19 fatalities, it also mandated that all New Yorkers wear a mask in public because the virus is more infectious than they previously thought.
Given this realization, one would assume the state is also adjusting the denominator in its death rate calculation (infected cases), because the virus is more infectious than we thought, and hundreds of thousands of Chinese and millions of Europeans traveled through New York in the months after the outbreak of the virus, before the testing began. But New York will not include even more than one case of, “infected, but not tested” in the denominator for its death rate calculation.
New York and California have been the national leaders for setting virus response policy from the outset. What if other states follow New York’s lead and start to make up their own death rate? How many future policy decisions will be based on assumptions that turned out to be wrong, but were then changed and inflated so as to not have appeared wrong in the first place, so that the flawed policies based on them could be reiterated and continued?
The thing about numbers fraud is it never ends well. That is the reason short sellers work so hard to find the companies that do it. Reality always catches up to the company and, eventually, the fraud is exposed. In this case, however, the fallout is far more tragic than a stock collapse.
A +1% increase in unemployment results in 37,000 deaths according to “Corporate Flight: The Causes and Consequences of Economic Dislocation” (Bluestone, Harrison and Baker, 1981). If +1% incremental unemployment caused +37,000 deaths in 1981, that number would be 50,000 when adjusted to the size of the workforce in 2020 vs 1981. The Bluestone book therefore implies that the 10% increase in unemployment caused by the response to Covid-19 will lead to between 370,000 and 500,000 additional US deaths. Meanwhile, total deaths directly attributed to Covid-19 stands at 28,529 as of April 15 (a number that was later changed to 32,588 to include “probable, but not tested.”)
It will have been worth it if this response policy saved even more lives than it cost. However, as empirical data grows in the US, it appears that the studies issued in late March by disease specialists from Stanford are closer in their death rate estimation (perhaps as low as 0.1% or even 0.01%) than the rate of 1.3%-1.8% that has been assumed from the outset.
In middle March, policy makers did not know the numerator or denominator (deaths/infections), but they made a plan for their States, with the best information available to them at that time. That is strong leadership.
Four weeks later we have a lot more data and it is pretty clear the death rate leaders were initially assuming was too high. New York’s response is to revise these numbers so it appears higher.
Short sellers have seen it hundreds of times – how fraudulent companies change historical numbers to validate flawed decisions of the past and justify their plans going forward.
We have just never seen it from a state.
Joe Voboril is the co-founder and managing partner of Farvahar Partners.