Upon Further Review ...

The patient is dying.

The patient in question is the Institute for Health Metrics and Evaluation (IHME) COVID-19 model, which I discussed at length in my last post. And what is the model dying from?

Starvation. And malnutrition.

You may recall the theme of my last post: Data informs models. And I noted that, as more data becomes available, it is fed into the model, and the model gains predictive value. As with we humans, if you don't feed a model sufficiently, it may eventually starve to death. At a minimum, it will become so malnourished that it can't do its job effectively, just as with a human.

And with the IHME model, that's where we are today.

Before I explain why the model is starving, let me explain how I came to the realization that it is. As I said in the previous post, I analyzed the projections from the first run of the model that was made available for public consumption. That was on April 1. I updated the analysis on April 5, when IHME released the next revision. I updated it again on April 8, when a subsequent revision was released. And in looking at how the data changed, how it moved, from one release to another, just a few days apart, I saw behavior that you'd never see from a "healthy patient" in the modeling world. In other words, I saw symptoms of starvation, and of malnutrition on a major scale.

You've probably seen in the news how projected total deaths have dropped significantly with each update of the model. The initial count was over 95,000. After being fed a few days of additional data, the count dropped by 14,000, to about 81,000. And just a few days later, it dropped to about 60,000. (The media has tried to mislead you regarding why, but more on that later.)

But it's what happened state-by-state that really revealed just how malnourished the model is. Some states are seeing their projected death totals drop by half or more with each update. Dates for the curves to peak and flatten have also changed markedly. The projected date for the curve to flatten in Virginia was originally July 15; in the latest update, it's June 1. Nearly all states are now projected to peak and flatten earlier than in the original release, and nearly all within the April-May timeframe. However, a few states inexplicably have seen their projected deaths and time to peak/flatten increase. And there's little rhyme or reason as to why.

Or is there? The states that appear to be displaying the most counter-intuitive movements in the projections tend to be lower in population than the ones whose trends appear logical. Thus the model appears to be suffering from the law of large numbers.

Let's look at my home state of Kansas, which ranks 35th among all 50 states in terms of its population (about 2.9 million people, or about 15% of the population of the New York City Metropolitan Statistical Area, spread out over 82,277 square miles). The original release of the model projected 640 deaths in Kansas by Aug. 4 (the end date for the model's projections, by which time all curves have long since completely flattened). That's about 220 deaths per 1 million (1M) residents. The curve was projected to peak on May 3 and flatten on June 10.

At the time, Kansas had experienced 10 deaths as reported, so the model was projecting another 630 reported deaths by the time the curve was projected to flatten on June 10. The first death in Kansas was recorded on March 12, so by the first model release, the state had experienced an average of 0.5 deaths per day. The model projected that average to increase to about 10 per day - an increase of nearly 20-fold - through June 10. (Through April 9, the highest number of daily deaths in Kansas has been 5, so that average would be pretty hard to attain.)

The next update projected 265 deaths in Kansas. That's a reduction of 375 projected deaths, or almost 60%, from the original projection. The new date for the curve to peak was April 25 - 8 days earlier than originally projected - and the new date for the curve to flatten was May 23 - 18 days earlier than originally projected. (Some states saw those dates come in by more than a month, like Virginia.) So now, the model was projecting an average of about 5 deaths per day - half the original projection - in a state that to that point had still seen an average of less than one per day.

Those dramatic changes were the result of the model being fed four more days of data. During those four days, Kansas experienced just 264 new reported cases, and 12 new reported deaths. That's a big change in the projections produced by a very small number of data points.

Are you beginning to feel the model's hunger pangs?

In the next release, the model forecast an increase in total deaths in Kansas, to 299 from 265, an increase of about 13%. And it projected that the curves would peak and flatten one day later than in the previous update. Yet Kansas was still averaging just 1.26 deaths per day. This time, the change was driven by 12 new deaths and less than 300 new cases. Again, not a lot of data to produce reliable results. Those projections appear to have been skewed by the fact that, on the last day of data that was fed into the model at that point, Kansas saw a peak in daily cases at 123 (.004% of the state's population) and 5 deaths.

Wyoming is also interesting: There have been zero deaths to date in the state, out of 230 cases at this writing (about the same number of cases per 1M as Kansas or Iowa), but the model projected 67 deaths between the April 8 update and the May 22 flattening of the curve. On what basis? As Wyoming's governor said, "We've been social distancing for the entire 130 years we've been a state."

**Note: the model has been updated again as of April 10. The latest projections are as nonsensical as the earlier ones. I'm not even going to bother updating my analysis anymore. Suffice it to say that projected deaths in Kansas are now back up to 426, an increase of 127, or 42%. Why? Because there were 8 deaths on April 9.**

More broadly, here's the first thing that's wrong with the IHME model, and any other model making projections about COVID-19 cases or deaths: the data is simply insufficient in quantity to produce statistically significant results.

Let's put it into perspective. To date, there have been about 490,000 cases of the virus in the U.S., and about 18,000 deaths. That's an incredibly small number relative to the U.S. population. In the last flu season, the CDC reported more than 35 million cases, nearly 500,000 hospitalizations, and over 34,000 deaths. So last flu season as many people were hospitalized from the flu than have been reported to have COVID-19 (and remember, 96% of cases are mild), and about twice as many people died of the flu than have been reported to have died from COVID. The flu data is a lot richer dataset to feed into a model.

The average number of auto accidents in the U.S. each year is about six million. Average injuries are about three million, and average deaths are about 33,000. Again, a far richer dataset that would produce a more robust model.

Remember my discussion of mortgage prepayment models in the last post? In a year when rates are falling and more people are refinancing their mortgages, millions of mortgage loans will prepay, resulting in billions of dollars of prepayments. In a year. And during the Great Recession, billions of dollars in mortgages defaulted, resulting in full prepayment. So the mortgage prepayment models are far more robust than a model predicting auto accidents or influenza would be. And those models are twice as robust as any of the COVID models at this point in time.

But the problem with the IHME and other COVID models runs far, far deeper. It is not an insufficient amount of data alone that is producing such inaccurate results and wild swings in results based on tiny amounts of additional data.

The quality of the data is horrible.

When a mortgage loan prepays, we know for certain that it has prepaid. And we know exactly why, whether it is from refinancing, default, death, or any of the other factors that drive prepayments.

When there's an auto accident, we know it (unless it isn't reported and nobody gets hurt or dies). We know exactly how many people are injured in auto accidents (again, unless the injury is very minor and it's not reported), and we know how many people die from auto accidents. (We also don't state cause of death as "auto accident" if the decedent had a fatal heart attack that then resulted in the car veering off the road.)

We know less about the flu, because some people probably get mild cases and don't go to the doctor, and thus are not tested or diagnosed. Doctors can't report data they don't have. But there are a lot more data points, so the data and the modeling are more reliable.

It's even worse with COVID. What are the data points needed to feed the model? Number of cases, number of deaths, and number of recoveries (daily and total for each).

We have no earthly clue how many cases there have been, or how many active cases there are, for two reasons. One, since 96% of cases are mild, there are probably a huge number of people who have had COVID-19 during this cold and flu season who didn't know it. Their symptoms were mild. They might have thought they had a cold or the flu. I know a number of people who believe they may have had it in December or January, though I am always cautious about self-diagnosis, especially with something like this.

My not at all curmudgeonly wife and I went on a cruise in late January. We flew to Tampa, spent the night in a hotel, went out for dinner, then went to the cruise port to board the ship. We cruised to several Western Caribbean ports: Belize, Cozumel, Costa Maya and Roatan. We got off the ship at each port. We ate lunch in Costa Maya and Belize, and had a day pass to a resort on Cozumel, where there were numerous other vacationers from all over the world. We shopped. We touched things. We washed our hands, as we always do, and used hand sanitizer when entering the ship's dining room. Of course, we saw the usual random foul louts who walked out of a bathroom stall without washing their hands, didn't use tongs in the buffet line, etc.

We disembarked and flew home from Tampa on Feb. 1. I flew to San Francisco for business on Feb. 4. The next day, I developed a dry cough. I may have had a fever; I didn't check it until after I returned home. On Feb. 10 I went to the doctor, and the PA I saw said that it looked like I had "this virus we've been seeing going around." She asked about all the symptoms that are now associated with COVID-19, including shortness of breath. I didn't have all of them, and I also tested positive for Influenza A (despite getting the vaccine last October). On that basis, I do not believe that I had COVID. But I may have.

So there are people who likely had it that we don't know about, but even if they self-reported, a significant number of them would probably be wrong.

The other reason we don't know the number of cases or active cases is that reported cases may be overstated due to cases being reported on the basis of a diagnosis of symptoms, without a positive test. Since there aren't enough tests yet for every suspected case, health care providers are reserving the tests for those with the most severe symptoms. (You may recall my story from the last post about my friend who was diagnosed based on symptoms, and sent home to self-quarantine.) Some of those diagnosed but untested cases may have been flu, a cold or some other virus.

So some cases are probably being over-reported, and vast numbers are under-reported. We won't really know the number of cases until every man, woman and child in this country has had the antibody test to see whether they've ever had the virus. That won't happen this year. So if and when there is a second season, we won't know if the people who've had it contracted it this year or next. We will never have reliable case-count data for this season.

We do know that a group of researchers from MIT have been testing sewage from 10 U.S. cities for traces of the virus. And the amounts they've found suggest far more cases than have been reported. Far more, as in a multiple of about 258 times. That could mean that more than a third of the U.S. population has had the virus, which would further indicate a recovery rate of more than 99.99%, making the mortality rate a fraction of what's been estimated.

What about deaths? Well, Dr. Birx admitted that they are erring on the side of citing COVID-19 as primary cause of death (PCOD), even when there are one or more co-morbidities, and regardless of the patient's age. We know that 83% of coronavirus deaths in Italy are among patients over the age of 70, and the majority of them had three co-morbidity factors. Three.

If you're over 70 and have three co-morbidity factors already, your chances of surviving anything - COVID-19, the flu, or a common cold - are dicey, I would think.

So if somebody has heart disease, and they die of a heart attack, but test positive for COVID-19, PCOD is COVID-19. Heart disease may or may not be listed as an underlying cause of death (UCOD).

That first death in Kansas on March 12? It was a gentleman in his 70s living in a long-term care facility who had a "heart condition."

The COVID diagnosis on that patient was made post-mortem. The patient had already died from the heart condition, then COVID was diagnosed and listed as PCOD.

The deaths reported by generally reliable sites such as worldometers.info, and reported in the news media and in public health briefings, are far higher than the number of cases reported on the CDC's own website - about four times as high. According to the CDC site, the reason is that the more widely reported death count includes cases that are "presumptive positives," meaning that a lab has recorded a positive test but the CDC has not confirmed it by submitting the completed death certificate to the National Center for Health Statistics (NCHS) and processing it for reporting purposes. In other words, there's a lag due to government bureaucracy.

The CDC's statistics by age clearly show that the risk of death is infinitesimally small for anyone under the age of 55 - and I am rather generously defining "infinitesimally" as less than .001% of the population of Americans under the age of 55. If you're 55-65, it's still only .0012%. Even for those over the age of 84, the death rate is .0176%. Those numbers will, of course, increase, as there will be more deaths. But even if we account for the fact that the more widely reported deaths are four times what the CDC reports as "confirmed," the death rate for those over the age of 85 is less than .07%. The overall mortality rate for people in that age group, from all causes, is over 38%. Suffice it to say that, if I'm lucky enough to celebrate my 85th birthday, I won't be buying unripe bananas.

Here's the link to the CDC site I'm referring to, for your own perusal. Note in particular the footnotes, the table broken down by age, and pay special attention to the Technical Notes at the bottom related to cause of death reporting: https://www.cdc.gov/nchs/nvss/vsrr/COVID19/index.htm.

Now, back to the model. What if we hit the 60,000 or so deaths it currently projects? That would put the death rate for those 85 and older at .26%, if the distribution by age holds statistically. If we hit the 95,000 cases the model originally projected? It would be .41%. To even get to a 1% rate, there would have to be more than 231,000 total deaths in the U.S., a 14-fold increase.

The final data point we need for accurate modeling is recoveries. And the reporting there is laughable. I've written previously about the lag in confirming a recovery (at least 14 days) and confirming a death (as little as two days, typically no more than five). That's not the issue.

Recoveries are, by and large, simply not being reported. Originally they were reported by state for the U.S., but on the worldometers.info site, but after several days, they took that column out of their table altogether. (I don't attribute this to some conspiracy theory. I'll explain below.)

Recoveries are, however, reported at the country level. In most countries, the ratio of recoveries to deaths is steadily climbing. In Italy and Spain, it was up from April 8 to April 9 by 0.1 (to 1.6 and 3.4, respectively). In Germany, it was up from 16.5 to 18.9. The most reliable "mature" data is probably from South Korea, where the outbreak hit early and now appears to be largely contained. There, the ratio of recoveries to deaths is 34.2. In the U.S., the ratio isn't advancing at all. The UK updates cases and deaths daily, but hasn't updated recoveries for two weeks.

This isn't because there aren't recoveries in the U.S. the UK and the states. It's because they aren't being reported.

Why aren't recoveries being reported? Again, I don't see a conspiracy here. I see a lack of data, and a lack of reliability of any data there is. Since we have no idea how many cases are out there, any reported recoveries will be vastly understated. All of those people who likely had it in December and January and didn't know it have recovered, but they'll never be counted until we've all had that antibody test.

Let's recap: we have no idea how many cases there have been, or how many active cases there are now. We have no idea how many deaths are directly and solely attributable to COVID-19. And we have no idea how many people have recovered. If we ever have reliable case and death data, recoveries are easy: cases minus deaths. That won't happen soon.

So the model is starving from a lack of sufficient data, and it's malnourished from poor quality data. Imagine trying to survive on one moldy Ho-Ho a day, and you understand the model's extreme weakness.

A final point about the model: many media outlets and people in general are pointing to the sharp decline in projected deaths as evidence that social distancing is working. To be polite, those people are being stupid. They believe what they hear and read, without going to the source. They haven't looked at the model's website. If they had, they would see, at the top of the page, in large letters, these words:

COVID-19 projections assuming full social distancing through May 2020

Assuming full social distancing through May. No re-opening of businesses and churches. No eating out. Limits on the number of shoppers in a store at any time. Grocery store aisles marked one-way. One customer per cart. "Non-essential" items like electronics being removed from store shelves. People ticketed for being too close to each other. For another seven weeks.

(For what it's worth, I don't see that happening. Nor, if it doesn't happen, do I see the death toll rising to the horror movie levels that the models were projecting "if we do nothing." I expect some reasonable happy medium that carries no more risk than the risk of the flu. We'll see.)

So it's clear that the decline in the model's projected deaths is not at all related to some notion that social distancing is "working." (I'm sure it is - if we followed these guidelines every flu season, we'd hardly see any flu cases or deaths either. But a hell of a lot of Americans aren't working, which also bears a cost.) In any event, the decline in projected deaths is solely attributable to new data, which is still insufficient and not reliable enough to produce realistic projections. It has nothing to do with social distancing's effectiveness. So stop kidding yourselves regarding that myth.

**Note: in the April 10 model update, projected total U.S. deaths went up by about 1,000, further debunking the myth that the projections are influenced by the "success" of social distancing.**

The upshot of all of this is that any decisions made on the basis of these models are foolhardy. Much of the American economy has been destroyed, hopefully not permanently. The stock market has been moving higher this week. Other countries are beginning to ease mitigation measures, including allowing some "non-essential" businesses to re-open, with some social distancing requirements. I've even seen a couple of local restaurant locations that initially closed, re-open for curbside and delivery service. As Red said near the end of The Shawshank Redemption, "I hope."

However, here are some real numbers for you: 30,000 U.S. restaurants have closed permanently. That number is projected to hit 110,000 by the end of May if this continues. That's about 10% of all U.S. restaurants - a much higher "mortality rate" than COVID-19's. U.S. restaurants employed more than 15 million people before this shutdown. And 70% of U.S. restaurants are single-unit operations. I won't even mention the downstream effects on farmers. Or the similar decimation of the hotel, leisure and airline industries. And the government can't spend our way out of this, because it's our money that is ultimately being spent.

The fact is that ALL businesses are essential to the people who own them and work for them.

A couple of final points. The panic that has, in part, led us to where we are today was fueled in no small part by the news media's reprehensibly sensationalist and inaccurate reporting. Sadly, they will never be held to account. They never are.

But another group used a different medium - social media - to foment panic. They posted wildly inaccurate articles based on bad math. When called on it, they tried to sanctimoniously scold those who tried to be a voice of calm reason. That group can, and should, be held to account, by their friends. They and the news media should apologize to everyone who lost a job, experienced investment losses, and had to go to several different stores just to find items that were heretofore commonly available. The overreaction was born of panic that led local governments - cities, counties, and states, not the federal government - to issue mandatory shut-down orders.

I'm all for saving lives, but there may have been better ways to go about this. There will be plenty of time for recriminations, Monday-morning quarterbacking, and situation analysis later. But perhaps the concept behind a familiar phrase should be broadened, and applied to local government, the news media, and the panic-mongers:

First, do no harm.

Are You Part of the Solution, or Part of the Problem?

I was walking my two mini Schnauzers yesterday, on their leashes. Why on leashes? Well, first, they're flight risks. I haven't trained them to walk with me. So it's for their own safety, so they don't run out into the street, and so they aren't a nuisance to someone else.

And why haven't I trained them to just stay with me when we walk? I haven't needed to, because the city in which I live has a leash law. So they only go off-leash in our fenced backyard or at the dog park.

When we see other dogs on our walks, my guys go nuts. Charlie and Topher are our fourth and fifth mini Schnauzers, and if we've learned anything about the breed, it is this: you can't train the Schnauzer out of them. They are yappy. They bark at other dogs (but they love to play with them). They bark at people (but they love people too - complete strangers are family to them). They bark at cars driving by. They bark at parked cars. Every situation is Threatcon Orange with a mini Schnauzer.

So on our walk yesterday, Charlie did his business, and I turned around to pull out a bag to pick it up. I looked down the street and saw a couple walking in the street, with what appeared to be a Great Dane on a leash, and what looked like a large Lab mix. The Great Dane was very well-behaved, but the Lab, which was off-leash (the woman was holding its leash, folded up), was scampering around.

My dogs barked, of course, and the Lab took off - it charged us. Now, this may have been a perfectly docile, playful dog that just wanted to come check out some new buddies. But I had no way of knowing whether that was true. I have a friend whose dog was killed by another dog that attacked it, and a relative's dog was nearly killed by another dog. My own Topher needed staples to close a wound inflicted by another dog at a local dog park (and the tough little guy didn't even act like he felt it).

I stepped in front of my dogs, between them and the Lab, and yelled to the people, "HE NEEDS TO BE ON A LEASH!" The lab ran back to them, and the woman clipped the leash on. I proceeded to clean up after Charlie while restraining him and Topher, who were now frantic. People don't understand that dogs behave very differently when they are restrained, but are in proximity to another dog that is not. They go into protection mode and can become aggressive, even if normally they are not.

As the couple passed, the man offered a feeble "Sorry." I didn't respond - what was I going to say, "That's okay"? Because it isn't.

They walked around the corner, past a cul-de-sac, and to the end of the block. They crossed the street, and then -

The woman took the leash off the Lab again. And it went scampering into people's front yards and out into the street.

Now I had intended to go to the corner, turn left, go up the street and then around another block, and then head home. But I didn't know whether they'd be going that direction when we got around the block. So instead, I turned the opposite direction and went back down the street. I didn't just want to take my dogs around our block, because it was a pretty nice day, so I walked on past the street that leads back to our house, and to the corner by the entrance to our neighborhood. Then we turned around to come back.

We got to the street that leads to our house, and turned to go home - and there they were again, coming our way. And the Lab was off-leash. I just looked at them, shook my head, and went back around the block to go home another way.

There was another situation recently in our neighborhood. Our HOA has a Facebook page. A woman posted that she had had a basketball goal permanently installed, set into concrete, in the island of their cul-de-sac (the island is city property, not the HOA's or any homeowner's). Someone called to complain, and some city workers came and said they were going to take it out. The workers also went around the neighborhood and saw several free-standing basketball goals in the cul-de-sacs - in the streets, not on the islands, where they're in the way of trash and recycling trucks and snow plows in the winter. The workers said those had to go, too. The woman actually said that the workers should have better things to do during a crisis, and that the complainant - not she - had put those workers at risk by making them come out to her block.

You'd have thought they had come and seized people's houses, judging from the hue and cry that went up on Facebook. (This is what happens when people are under a stay-at-home order and have time on their hands.) The woman threatened to "out" the person she suspected of calling in the complaint. She refused to believe that it was against a city ordinance to install a goal on city property, even after someone posted a link to the ordinance. One guy demanded to know who the complainant was, and tried to see whether the city would give him the name. I envisioned a scene like the one in Young Frankenstein, in which the villagers are hunting for the monster with torches and pitchforks. Another guy said they should get all the local news stations to come out and film the city workers taking down the goal (which would only make public what idiots these people are).

Someone else lamented that the poor kids' childhoods would be ruined because they didn't have a basketball goal, which begs the question: if you don't want your kids scarred for life, why not put up a goal next to your own driveway, like your responsible neighbors do? Oh, that would be be inconvenient for you? Any more inconvenient than those of us who live in the cul-de-sacs having to dodge all the kids who use it as their personal playground, bike track and skate park, while their parents are inside watching TV?

After the episode with the dogs yesterday, I began to think about the relationship between these two incidents, and to see them in a broader context. And I realized why the coronavirus is not being contained more rapidly, and why our economy is being decimated by mandated stay-at-home orders:

Too many of our fellow Americans are selfish. They think the laws don't exist for them. They don't understand that their non-compliance with the laws only works if the rest of us comply - in fact, they are relying on us to comply so that chaos doesn't ensue.

Leash law? To heck with it. I'll walk my dog off-leash if I want to - but I can really only do that if everyone else has their dog on a leash, so the whole street isn't filled with a pack of dogs chasing each other about, and so that my dog doesn't run off after another dog and get lost, or hit by a car.

An ordinance against installing a basketball goal on city property? I'll install one if I bloody well please, ordinance be damned. But that only works if everyone else in the cul-de-sac obeys the ordinance. Otherwise, there would be trampolines, jungle gyms, skate ramps and who knows what else in the middle of the street.

So if you're one of those who believe the rules don't apply to you, understand that your non-compliance depends on my, and everyone else's, compliance.

How does that apply to the present situation? Simple. As the virus began to spread within communities, the doctors that lead the Task Force to fight it implored people to stay at home as much as possible, and to maintain social distance and practice good hygiene.

Some of us did. But too many others decided the rules didn't apply to them. High school kids hung out together at parks. College kids went on Spring break and partied on the beaches in large crowds. Adults had their friends over for coronavirus parties, or went to bars. We were asked to use our judgment, but too many of us proved themselves incapable.

Stay-at-home order? Social distancing guidelines? I can only violate those if nearly everyone else doesn't.

And people got sick. And they made other people sick. And some people along that chain died.

So the government had to force compliance. They closed the beaches. They closed the bars. They closed the restaurant dining rooms. They closed most retail shops. All to save us from the stupidity of the minority.

And hundreds of thousands of people lost their jobs. That number will soon be in the millions. Businesses that closed temporarily may ultimately fail. People's retirement savings have been decimated.

It's time for an attitude check. We are Americans. We won't hand ourselves over to a totalitarian regime like today's China, or Iran, without a fight. But that cannot translate into an attitude that we don't need to follow rules or laws. We may be a Democratic Republic, but we are still a nation of laws. You can't drive 60 mph through a neighborhood where kids are playing outside. You can't just walk into someone's house and take their toilet paper.

This attitude of, "Aw, heck, I can go ahead and do that, it's not gonna hurt anything," is in effect a parsing of the rules into those that we know need to be followed, and those we think it's okay for us to ignore (again, counting on others not ignoring them so that we can get away with it). The problem with that is that you might think ignoring a leash law or a city property ordinance is okay to ignore. Somebody else might think that, with fewer cars on the road right now, the speed limit doesn't matter. Well, the Kansas City, Missouri Police Department has issued tickets for speeds as high as 125 mph recently. From March 16-30, injury accidents were up 43% vs. the same period a year ago.

Laws, rules, and even guidelines exist for a reason. Some of them may seem stupid, and we may not like them. But if we ignore one, then we're headed down a slippery slope. You may think a leash law is stupid. So your kid sees you walking your dog off-leash, and might think, "If mom can do that, why can't I go to the park and hang out with my friends?" or "Why can't I go on Spring break?" Your neighbor might see the basketball goal you put up on city property and say, "What the heck - I'm having my buddies over for a party."

If you believe the rules don't apply to you, know this: that attitude has resulted in more people getting sick and dying than would otherwise be the case. It has resulted in significant parts of our economy shutting down, in some cases unnecessarily if not for you. It has cost people their jobs and their retirement savings. It has forced all of us to eventually pay the piper, for the trillions of dollars of government spending that your attitude, your selfishness, has required.

So check that attitude. And make sure you're part of the solution, and not part of the problem.

They're Trying to Scare You. This Time, Let Them.

Okay, calm down - I'm not talking about the media, and I'm not talking about economic or financial data. I'll explain a little later in this post who I'm talking about, how they're trying to scare you, and why you should let them.

But first, let's introduce the topic of this post: models.

Not runway or swimsuit models. (I know, you're disappointed.) But mathematical models. Don't worry, I'm not going to get technical. And - long post alert. So if you know how mathematical models work, or don't care, scroll down to the dashed line and read from there.

Now first, a caveat: I am not an epidemiologist, or an infectious diseases specialist. I'm not a doctor, and I don't play one on TV.

However, I do know mathematical models, and I know data. I look at numbers in a spreadsheet and see them graphically. Data is my drug. I've never built a pandemic model, but I've built econometric models, and I've built stochastic mortgage prepayment models. So I know a bit about modeling data. (This explains why I'm not popular.)

Here's a truth: all models are wrong. Let's consider a model that predicts the price of a bond. Not to bore you, but such a model will be based on the projected interest and principal cash flows of that bond, discounted at current and projected interest rates. The theoretical, or modeled, price of the bond is the sum of the discounted principal and interest cash flows. Don't worry if you don't understand that; it's not important for this discussion.

When clients used to ask me about the modeled price of a bond, I would reply, "No model ever bought a bond." The real market value of a bond is what the next buyer will pay you for it. Thus the modeled price isn't the market price. So what value do models bring, and how are they built?

The value proposition of models is that they may give us some idea of what is likely to come. The key words are italicized. How well do they do that?

The first thing to consider is that data informs models. Let me say that again: data informs models. By the same token, a model is only as good as the data that informs it.

Let's look at mortgage prepayment models as an example, because I know them intimately (don't worry, I'm not going to bore you with a lot of math). Bear with me through this: it will help you understand how the COVID models work.

Mortgage prepayment models forecast how likely a group of mortgage loans is to pay off early, based on the mortgage interest rate, prevailing interest rates, how long the mortgage has been outstanding, geography, and other factors. They do this using historical data that captures those variables. The data informs the model.

This is important to mortgage lenders and investors in pools of mortgages in forecasting cash flows. If rates fall, more people will refinance (i.e., prepay the entire mortgage), and I'll get my money back sooner than expected - then I'll have to re-lend or re-invest it at now-lower rates.

Back in 1990, we thought the mortgage prepayment models were pretty darn reliable. Then, in 1993 the Fed cut interest rates to what was then an all-time low, and in the following year, they raised rates by about 3%, effectively doubling them. We called that a "whipsaw." As a result, a lot of people refinanced their mortgages at record-low rates in 1993, so prepayments exceeded what the models - informed by historical data - projected. (Prepayments happen for a number of reasons, but the biggest driver is refinancing when rates have fallen.)

Then, when rates rose sharply the next year, people prepaid their mortgages more slowly than the models - again informed by historical data - projected. So cash flows slowed as people just paid the minimum monthly mortgage payment, instead of further reducing principal. Why should I pay early on a 5% (at that time) mortgage, when the prevailing rate is 8%, especially when I get to write off my mortgage interest on my taxes, so that my after-tax mortgage rate is even less? Now, lenders and investors had to wait longer to receive the amount of prepaid principal they expected, when they'd rather have had it right away to reinvest at higher rates.

Thus the prepayment models totally missed the mark in the 1993-94 whipsaw. The data from previous periods that had been incorporated into the models to inform them didn't capture how readily people would refinance a mortgage if rates fell by a certain amount. That's because people used to want more of an incentive to refinance than they want now. And that's because the process of getting a mortgage is more streamlined today, and the fees are lower. Thus mortgages that we thought wouldn't prepay in 1993 did refinance, and mortgages that we thought would prepay in 1994 didn't. Investors took a beating by betting wrong based on the models, which were based on historical data.

The silver lining is that after that whipsaw, all of the actual prepayment data from 1993-94 was fed into the models, making them more robust. Data informs models. With more data capturing more unique circumstances, including changes in borrower behavior, the model is made more robust.

So, in 2005, we thought the mortgage prepayment models were really good. But we'd never seen subprime mortgage loans before. Housing bubbles had always been local; by 2007 they were widespread. Credit ratings on packaged mortgage loan pools were being gamed.

And the housing market came crashing down. Mortgage defaults reached unprecedented levels. Borrower behavior was different, too. My Dad, a Depression kid and a WWII vet, taught me that the last thing you ever miss a payment on is your mortgage, because that's your family's home. By 2008, the family home was seen as an investment, and if the value of that investment had fallen to less than the mortgage balance, you initiated a "strategic default." In other words, you walked away from the property and defaulted, even if that meant you wrecked your credit in the process. Unwise, yes. But it became common.

Those shifts destroyed the efficacy of the prepayment models. Drastic changes in borrower behavior, interest rates, mortgage structures, default protections, and other factors resulted in the actual prepayment experience being vastly different than what the models projected. A lot of people just walked away from their homes, as described above. A lot of other people had to default because they lost their jobs. (A default and subsequent foreclosure counts as a prepayment, because the loan goes away at that point - due to charge-off by the lender, not payoff by the borrower).

Once again, the models, informed by historical data, did not capture how high prepayments would go on a mortgage with a given interest rate. But as before, after the carnage, all of that data was plugged into the prepayment models to inform them, and thus today they are more robust than ever before (though they're still not prepared for the next thing we haven't seen yet). We have more data, covering a far wider range of scenarios. Data informs models.

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Okay, so what does all of this have to do with the current situation? Well, you've probably heard Drs. Fauci and Birx refer to a coronavirus model that predicts when the curve will peak and flatten, and how many deaths are likely. That model was developed by researchers at the University of Washington's Institute for Health Metrics and Evaluation (IHME), which was funded by Bill Gates (thank you, Bill).

You may have also heard that, in a matter of just a few days, total U.S. deaths projected by the model jumped by about 10,000. You may have wondered why the big change - is the virus more deadly than previously thought? More contagious? Are containment measures failing?

None of those are reasons for the change in the model. The reason is that more data was fed into the model, and that changed the model's forecasts. What kind of data does the model incorporate? We'll get to that in a minute.

Since the COVID-19 outbreak got serious, I've been following the actual data (cases, deaths, recoveries) daily. I track it by each state, and by a number of countries (China, S. Korea, Iran, several European countries, and the U.S.). I'm looking at cases and deaths per capita, because denominators matter. I'm looking at maturity of the outbreak in those locations (time since first death, because time since first reported case isn't always available). I'm looking at the ratio of recoveries to deaths, which is encouraging. It improves with maturity. The problem is that it's clear from the data that in some locations (including the U.S. and the U.K.) recoveries are under-reported, and they aren't reported at all for the individual states.

Sidebar: lest you think this is morbid, please know that, as with the unemployment numbers, I recognize that each data point is a human life, someone who is sick, or a loved one lost. In fact, that's why I'm writing this post. Read on.

Also, since the IHME model data was made public, I have been tracking that as well, and comparing it to the actual data. I update it every few days, because it changes. As noted above, total deaths jumped from the first time the model's projection of them was mentioned to just a few days later. Since then, they are down by a few hundred. I'm tracking the model's projections for total deaths, when the curve peaks, when it flattens (no more new deaths, or several days with just one per day), per capita data (you know why), days to curve peak, days to curve flattening, and actual to projected deaths. I'm looking at this for the U.S. and state by state. And for the days to curve peak or flattening, I'm looking at averages, minimums, and maximums.

Let's first get to the question of what kind of data the model uses, then we'll look at some examples from the model. It doesn't use some scientific chemical formula related to a possible treatment or vaccine. It just uses math - complex math, but math. The variables it considers are number of cases to date, number of deaths to date, whether there are mandated containment measures, and how stringent they are. (In states that have strong stay-at-home orders, the curve is expected to peak and flatten sooner. That's what Drs. Fauci and Birx have been telling us.) At the state level it is also influenced by the population of the individual state. Finally, the model appears to be considering the assumption that this virus is seasonal.

Some quick examples. For the U.S., using April 1 model data and an April 2 date, projected days to the curve peaking are 13 - therefore the projected peak for the country as a whole is April 15. The minimum is 8 days, in NY and NJ, where the outbreak and containment measures started early. The maximum is 56 days, in MO, where the governor has yet to issue a stay-at-home order. In my home state of KS, the curve peaks in 30 days. And the curve flattens for the U.S. as a whole in 60 days (June 1). The earliest state to flatten is Delaware, in 27 days. The latest are MO and VA, 104 days (this may be why the VA governor recently issued a stay-at-home order that doesn't expire until June 10). In KS, it's 64 days (June 5).

So, how and why have the model's projections been changing? I first started analyzing the model's projections on Monday, March 30. I updated the data on Wednesday, April 1. In those two days, the projected total U.S. deaths jumped by about 10,000. In some states they doubled. In others, they fell by half. Also, in some states, the time to the curve peaking or flattening extended by as much as two weeks. In other states, the time shortened by as much as two weeks.

Why the big changes? Data. Data informs models. The model was initially way off, because the data was sparse. As more data comes in - more cases, more deaths, more mandated containment measures - the model's predictive value will increase. So it will continue to change. My guess is that the time to peak/flattening will remain more or less constant for the U.S. as a whole - about April 15 to peak, and late May to flatten. This is consistent with the season for influenza and other coronaviruses such as the common cold.

However, I expect the projected total deaths will come down, and I don't think the actual data will reach the 93,000 or so currently projected. Before I explain why, I'll repeat my disclaimer: I'm not an epidemiologist, an infectious disease specialist, or a doctor. I'm just a data guy.

Why are we seeing big increases in the number of cases? More testing. There are probably a lot of people who've been sick this winter that had COVID-19 and didn't know it, because their symptoms were mild. Francis Suarez, the mayor of Miami, tested positive, and posted daily videos on his Twitter account describing his symptoms, which were mild. The CDC reports that 95% of cases are mild. In Iceland, which has tested 5% of its total population, 50% of cases had no symptoms at all. I know someone who presented with symptoms, was diagnosed without a test, and sent home to self-quarantine. Maybe he had COVID, maybe he had something else with similar mild symptoms. We're not going to know this until we have a test to see if people have ever had it - which is coming.

As the U.S. is now testing more than 100,000 people every day, we're going to see more cases, so the case total will go up. The IHME model doesn't project total cases, at least not that I've been able to find, but it probably incorporates them. So why are projected deaths going up?

Let's go back to my friend who presented with symptoms. Why did they diagnose him without testing him? Because his symptoms were mild, he's under 60, and in good health. And even though we're testing very large numbers of people daily, there still aren't enough tests to test everyone who has symptoms. The U.S. has tested 1.3 million people, but that's less than half a percent of the population (because people are failing to follow the guidelines, not because the government response is failing). So they're reserving the tests for the most at-risk population - the elderly, those with complicating health issues, and those who present with severe symptoms.

The sad reality is that a larger proportion of those people will die after they're diagnosed, usually due to those complicating health issues, but brought on by the virus. Remember the CDC said that 95% of cases are mild? Of closed cases, 80% have recovered.

So just as more testing leads to more diagnosed cases, which feeds into the model, more testing of critical or at-risk cases will lead to more deaths, which will also be fed into the model. And that's why, based purely on math and data alone, I don't think we'll see 93,000 deaths in the U.S. I hope and pray we don't.

Now, here's the point of this post, beyond helping you understand how the model works, what feeds it, and why it changes daily as more data informs it.

Drs. Birx and Fauci cite the model projections to keep people following the guidelines and social distancing. It's critical. If we stop doing it because we think the model numbers are too high, then we will indeed reach the model numbers - remember, part of the data informing the model is the presence of mandated mitigation strategies.

So even though I think that, based on math and data, the projected numbers may be on the high side, I'm not hanging out with friends and family. I'm still washing my hands thoroughly and frequently. We have a routine for handling groceries and take-out food and we're following it to the letter. I wipe down everything I touch in my car every time I come home from the grocery store. I use a credit card at Target instead of their Red Card or another debit card so I don't have to touch the PIN pad. We all need to do that, to do our part.

Looking to the future, here's my fear. Let's say total deaths in the U.S. come in much, much lower than 90,000. (I'm not saying they will - I think they'll be lower; I can't even guess by how much.) You know how every time there's a hurricane approaching the U.S., and officials issue evacuation orders, and lots of people ignore them, thinking, "They said Hurricane XYZ was gonna be really bad, and it wasn't, so I'm not going to let them scare me into leaving my house"? Then, when it is bad, first responders get overwhelmed having to evacuate those people using boats or helicopters, when the people could have just driven to safety had they heeded the warnings?

You guessed it. If the total casualties from this seasonal round of the coronavirus are far below the modeled projections, the next season - and there will be one, whether it's in the fall or next spring or both - people will take the social distancing guidelines lightly. They'll ignore the hygiene protocols. They'll sneeze into the air, or on their hands and then touch public surfaces like airplane seatbacks and handrails and shopping carts. And when there's a vaccine available, they won't get it because they don't think the risk warrants getting jabbed in the arm.

And they may be okay. But they'll pass the virus to me. To you. To your child, or your grandparent. And the health care system will be overwhelmed, unless the government shuts down the economy again because people are too selfish to do what they need to do to protect other people.

The model isn't bad. It's well-constructed by people who know what they're doing. But any model is only as good as the data, and even though the numbers reported sound tragically staggering, it's not enough data as a percent of the U.S. population for the model to be considered robust yet. Its predictive value improves every day as new data is added, and the projections are going to continue to change.

So the doctors on the task force aren't misrepresenting the model or the projections. They are pointing to those numbers because they want to make sure we continue to comply with the guidelines, to avoid ever reaching those numbers. I don't know whether they believe the numbers will reach the model projections if we all do our part. But they are trying to scare you, and for good reason.

Let them. We need to be scared - scared of what would happen if we drop our guard. If not for ourselves, for our grandparents, our parents, our kids, our nurses and doctors, our police and firefighters, our grocery store clerks, our restaurant take-out workers - all of the people who are on the front line, every day, putting themselves at risk so people's health is cared for, we're safe, we have food. Be scared for them. They're counting on you.

They're Going to Try to Scare You. Don't Let Them. Part II.

Okay, so that was a big number - 3.3 million initial claims filed for unemployment insurance (Initial Jobless Claims, the topic of my last post) were filed the week ended March 21. If you look at a graph of claims historically, it looks like ... well, it looks like the face of El Capitan, as I predicted in that post.

The reporting the morning of the release was nothing short of comical. Some "business news" network's talking heads couldn't even get the terminology right. One network anchor wondered why the stock market was up so much after such a bad number. (Answer A: The Senate finally got their thumbs out of their backsides and passed the stimulus bill. Answer B: The House moved up the timing of their vote on the package and Speaker Pelosi indicated that it would pass. Answer C: The market had already priced in a really bad claims number. Estimates ranged from 1.5 million to more than 4 million.)

One national cable network anchor started an interview with one of their sister "business news" network's anchors by saying, "Even though this number was expected, it's still a shocker, right?" Yeah, I'm always shocked by news I expected. And the "business news" anchor then proceeded to conflate initial claims with ongoing (continued) claims in saying "this number" is only going to go higher next week because this is just the first time these people filed for benefits.

FYI, that "business news" anchor has a degree in Art History.

One local news channel - I won't name names but their initials are KSHB - seemed to conflate jobless claims with the unemployment rate: "The Labor Department saw an additional 3 million people seeking unemployment claims last week — the highest increase of unemployment claims the Labor Department has recorded since it began measuring seasonal unemployment. It also marked the highest level of insured unemployment since April 2018, when the unemployment rate was at 3.9 percent."

For one thing, unemployment claims are not "seasonal unemployment." And today's number wasn't due to seasonal factors (okay, so the virus is almost certainly seasonal, but I mean like seasonal layoffs in auto manufacturing as they shut down plants to re-tool). It was event-driven. And it isn't "insured unemployment," it's the number of people who filed claims to receive unemployment insurance benefits. Continued claims would be more akin to "insured unemployment." And finally, it's not the same as the unemployment rate.

But wait, it gets better: "According to figures released Thursday morning, 3.2 million people sought unemployment between March 14 and March 21." News flash: nobody seeks unemployment. Well, maybe those about to retire.

Okay, enough about the media dunderheads. To clarify things, let's look at the unemployment rate. And I chose this topic not only because of the dim-witted reporting, but because -

They're going to try to scare you again, this time when the next unemployment rate is released.

By definition, the unemployment rate is the number of unemployed, divided by the Civilian Labor Force. It is based on a survey of U.S. households. It is released on the first Friday of each month by the Bureau of Labor Statistics (with rare exceptions for Good Friday), and it is the unemployment rate as defined above, as of the end of the previous month. So on Friday, April 3, the March unemployment rate will be released. It will be considerably higher than the February rate of 3.5%, which was released on Friday, March 6.

And the media will try to scare you with it. Once again because they do not understand it in context, and they think it's their job to scare you. So a little history is in order.

The February 2020 unemployment rate of 3.5% - the same rate reported in September, November and December 2019 - is the lowest since 1969. So as was the case with initial jobless claims before the March 26 release, the unemployment rate recently has been at historically low levels, the lowest seen in a very, very long time.

More historical context: The record low unemployment rate was 2.5%, recorded in May and June of 1953 (at which point in time the Curmudgeon would not yet be unleashed on the world for more than five years). Thus the February 2020 rate is just a point above the record low. The record high of 10.8% was reached in November and December of 1982. The peak of the Great Recession was 10.0%, reached in October 2009.

And as is the case with Initial Jobless Claims, cyclical peaks in the Unemployment Rate tend to coincide with the ends of recessions - in fact, with the Unemployment Rate, it has been the case in every recession since the end of WWII that it has peaked after the recession officially ended. Usually just after, which again means that when the unemployment rate peaks, the worst is behind us.

Now that we've looked at record highs and lows, let's look at some trend data. But before we do, here's what the media will try to scare you with on Friday, April 3, when the March unemployment rate is released:

It's possible that it will be over 5%. And if not in March, it certainly could be in April.

So the headlines will read:
"Highest unemployment rate since (depends on how high it is; 6% would be 2014)!"
"Biggest one-month jump in the unemployment rate since (again depends on how high it is; 6% would be the biggest one-month jump ever, while 5.8% would be the biggest one-month jump since 1949)!"
And again, depending on how high the number is, "Unemployment rate nearly doubles!!!"

Okay. The average unemployment rate, going back to when the data was first recorded in 1948, was 5.73%. It has been below 5% only about 37% of the time, and of those 318 occurrences, 53 have come in the last 54 months. When Janet Yellen was Fed Chair (a dark time in economic history), her target unemployment rate was 5%. That was considered "good" - in fact, good enough to start raising interest rates to stave off inflation resulting from rising wages.

"Full employment" has long been considered to be 6%. (Technically speaking, "full employment" is one-half of the Fed's dual policy mandate: to promote stable prices, i.e. maintain low inflation, and to promote "full employment", which basically means "get as many people working as you can, but don't worry about the small percentage that might be more or less unemployable." Plus, there is friction in the number in that people in the survey may be in and out of a job at a given point in time, even in a good year.)

The unemployment rate has been above 6% about a third of the time, and we haven't been in recession a third of the time since 1948 - in fact, we've only been in recession about 14% of the months since January of that year. So the unemployment rate has been above 6% through about 20% of the non-recessionary months since just after WWII.

So understand that while an unemployment rate of 5% or 6% isn't as good a situation as we had before this virus hit, it's actually pretty good historically. And even if it goes higher, it's not likely to for long. Unless, again, you defy the scientists - the qualified ones like Drs. Fauci and Birx, not the self-described ones on Facebook - and believe that this thing isn't going to prove to be seasonal, and that this is going to last many months. But if that's the case, you're reading the tin-foil-hat posts on Facebook, not this, so I'm guessing that if you're still with me, you're okay.

Expect a number above 5%, and don't be "shocked" by what you expect. And know that in context, it's not the Great Depression. Far from it. In fact, most of the recessions during which unemployment peaked below 8% have been relatively mild, and short in duration.

Think of it this way: it's like the folks who have only been looking at mortgage rates since the Great Recession, and they think that a mortgage rate above 5% is "high." Historically speaking, it is not. My first mortgage carried a rate of 10.5%. It has only been during this very recent (since 2009) period of extreme and unprecedented accommodation by the Fed that mortgage rates have been below 5%. They've been as high as 18.6% - and people still bought homes. So just as sub-5% mortgage rates are not "normal," sub-5% unemployment is not "normal."

One final note: lest anyone be offended by my comments about the media: I have nothing against people with majors in Art History, Theology, or Interdisciplinary Studies. Heck, I once had an investment sales rep who worked for me who had a History degree.

Okay, bad example. I fired him.

My issue is when those folks join the media and try to apply their economic ignorance and innumeracy to the economy and the markets, and they sensationalize things to try to scare people, because if you're terrified, you're riveted, and they can sell more of their their sponsors' crap.

But look on the bright side: if you're a newly-minted grad with a degree in Etruscan Civilations facing six figures of student loans from an Ivy League college you couldn't afford because going to juco to be a paralegal was beneath you, you may be in luck: there's a promising future ahead of you. In "business journalism."

They're Going to Try to Scare You Again. Don't Let Them. Part III.

Two posts ago, I addressed how the media would try to use what would be a significant increase in Initial Jobless Claims to try to scare you. I talked about the results of that release in my last post, which addressed the unemployment rate. This post will focus on Continued Claims, which I introduced in the post about Initial Claims.

But first - the Curmudgeon would like to offer his humble thanks to all who shared the link to the post on Initial Claims. It was the most-read post in the history of this blog. So keep the shares coming, and maybe we can keep a lot more people from being scared (for the record, I don't make a cent from doing this). And if you liked the most recent post, feel free to share it too.

As a reminder, Continued Claims is the aggregate number of people receiving unemployment insurance benefits in a given week. There is a one-week lag in reporting the number vs. Initial Jobless Claims. Thus the Continued Claims number that was released on Thursday, March 26 showed total claimants as of the week ended March 14, whereas the Initial Claims number released that morning was for first-time filings as of the week ended March 21.

Now, it stands to reason that if initial claims spike, the following week's continued claims will jump. You may recall that initial claims captures people who file for the first time, then the next week they fall out of that total and move into the continued claims number, where they stay until they no longer qualify for benefits (found a job, no longer looking, benefits expired, etc.).

The move from initial to continued claims is not a precise additive transition. For example, initial claims for the week ended Mar. 7 were 211,000. The increase in continued claims from the week ended Mar. 7 to that ended Mar. 14 was 101,000, not 211,000. This could be due to some of the first-time filers going back to work (unlikely), some of the previously reported continued claimants going back to work (thus there were people moving into and out of the continued claims pool), or delays between filing and receiving benefits.

So, the continued claims total reported on Thursday, April 9 for the week ended Mar. 28 is unlikely to show an increase equal to the 3.3 million initial filers that we saw in the initial claims release for the week ended Mar. 21, which came out last Thursday and was the topic of that original post. But there will be a very large increase, easily over 1 million. The most recent reading was just over 1.8 million, so the number will increase by more than half, and could certainly double, or worse.

And the media is going to try to scare you with it.

The headlines this time will be:
"Highest continuing jobless claims since (probably 2012 or 2013, depending on the number)!"
"Biggest one-week increase in continuing claims in history!" (It will be.)
"Continuing jobless claims doubled in just one week (if the number does double)!"

And, as they always do because they are woefully under-qualified to report on economic data, they will muck up the terminology they use and the ways they abuse the data to the point that my Curmudgeonly head explodes, and I find myself standing in front of the television and cussing at it. (I'm doing a lot of that these days - my dogs are very confused.)

However, you should not be fearful as a result of the number, for several reasons. The first is that you now understand what it means, and you expect the large increase, just as you expect another increase the following week, and further increases in the weeks after that until initial claims subside and people go back to work, at which point the continued claims number will begin to fall. And, unlike the media simpletons, you are not shocked by that which you expected.

Second, let's look at historical highs, lows and trends for perspective, as we have done in the last two posts. The most recent continued claims total of 1.8 million as of the week ended Mar. 14 was the first reading above the 1.8 million mark since April 14 of last year. Things weren't so bad back then, right? So even though the last reading was up by 101,000 from the prior week, that shouldn't alarm us - historically, 1.8 million continued claims is a very low level.

How low? Well, in October of last year, continued claims were the lowest since 1973 at just under 1.65 million - pretty darn close to where we are today. The average from that low to the most recent reading is right at 1.7 million. So a trend between 1.65 million and 1.8 million is about as low as we're gonna get.

And if we go back to the early days of this data series, we find that the record low was 988,000 in May 1969. (The U.S. population was more than a third smaller, too - remember, denominators matter.)

Now let's look at highs. The record for continued claims was 6.635 million in May 2009, just after the end of the Great Recession. (By the way, for anyone unfamiliar with what I'm referring to when I reference the Great Recession, it's a name commonly given the most recent recession, from 2008-09, which was brought on by the housing collapse and subsequent financial crisis.)  Next highest was just after the 1981-82 recession, in November 1982, at about 4.7 million. And the third highest total followed the 1973-75 recession, at about 4.64 million in May 1975. No other recession since the claims data was first recorded in 1967 has seen continued claims reach 4 million.

What's significant about these highs and lows? First - and this is very important in terms of mitigating your risk of the numbers scaring you, so pay close attention - these spikes resulted from the three longest recessions in the history of the data. The Great Recession lasted 18 months, the longest downturn since the Great Depression of the 1930s. The recessions of 1973-75 and 1981-82 lasted 16 months each. All three of these recessions were followed by slow recoveries. The next-longest recession since WWII lasted 11 months, and the average duration of recessions since WWII, excluding the three noted above, has been nine months.

Generally speaking, shorter recessions see shorter peaks in continued claims. However, it does not hold true that the shorter the recession, the lower the peak in continued claims. So if we see a peak above 4 million, as I strongly believe we will, do NOT assume that means we're in for a 16-month downturn. The 1980 recession lasted only six months, yet continued claims peaked shortly after it ended at slightly more than 3.9 million. I do not anticipate a prolonged downturn or a slow recovery, for reasons I have explained in previous posts.

Second, denominators matter (okay, so I'm sounding like a skipping record - younger readers will need to google that reference). So let's divide each of the three most severe peaks by the Civilian Labor Force at the time.

To recap, the peak of the Great Recession was 6.635 million continued claims. As a percent of the Civilian Labor Force at that time, continued claims were 4.29%. At the 1982 peak, continued claims were 4.19% of the labor force. And at the 1975 peak, they were 4.90% of the labor force - higher than in 1982 or 2009.

So, to exceed those levels on a percentage of the labor force basis, we'd have to see continued claims of more than 8 million - exceeding the most recent reading by more than 6 million. And yet, even if that happens, it's not the most significant indicator. Why?

Because the duration of unemployment matters most. Weeks unemployed peaked in July 2011 at 40.7. (That was 25 months after the recession ended.) The recovery from that recession was the slowest ever, due in part to fiscal policy and the causal factors of the recession. That record was far and away higher than any previous duration of unemployment. After the 1982 recession, weeks unemployed peaked at about 17. No other recession even came close. And as I've noted, I don't expect the duration of unemployment to reach the "worst-since" levels we've seen previously, for a variety of fundamental reasons.

Finally, the peak in continued claims always occurs after a recession officially ends, as is the case with the unemployment rate. So when that peak arrives, the worst is over.

The consensus forecast for the initial jobless claims release scheduled for Thursday, April 2 (for the week ended March 28) is around 3.5 million, which would exceed last week's record level by about 200,000, and set a new record. I'll go out on a limb and say that'll probably be the highest level we'll see, though there will still be new filings each week well in excess of the 210,000 or so trend that we were seeing before the pandemic response shut things down. So we probably will see a peak above 8 million continued claims, and the peak will probably occur within a few weeks. But that still doesn't foretell a prolonged downturn.

The duration of unemployment this time should be short. If we look at the model data from the University of Washington's Institute for Health Metrics and Evaluation (the same model that has been referenced by Drs. Fauci and Birx - and more on models in a subsequent post), we see that in most states, the curve flattens within a couple of months. The modelers and experts seem to agree that this virus is likely to be seasonal. So if we assume that things will open back up within a couple of months, either on a rolling basis state-by-state based on curve flattening projections, or on a wider basis, then the duration of unemployment is likely to peak at a few weeks over and above the levels we were seeing before the virus hit.

A caveat about that. Those pre-pandemic levels were already at about 21 weeks, which was above the peaks that followed every downturn before the Great Recession. These levels were unusual given how strong the labor market was through February, and resulted from the government's response to the Great Recession. As part of that response, the amount of time someone could draw unemployment was extended to account for the extremely slow job growth at the beginning of the recovery.

However, that amount of time was never reduced in many large states when things normalized, and thus more recently was well beyond the time it would have taken to find a job in the prevailing labor market. So many people had a disincentive to work, if they could live off their unemployment checks. As a result, if the duration of unemployment resulting solely from the pandemic is about 11 to 16 weeks - as the curve data suggest - adding that to the "base" levels we saw in February could result in a reported duration of unemployment of more than 30 weeks, which would be historically pretty high. And would, of course, be distorted by the media to try to scare you.

One last point. My numbers could be wrong in terms of the level of the peak in claims or in the unemployment rate. Those peaks could be much higher. It doesn't matter this time - what's critical is the duration of unemployment. As noted before, there will (and has already been) a sharp, cliff-like spike in the unemployment metrics, but every indication from the medical data is that the duration will not be as long as seen in many recessions.

Remember that when the media tries to scare you with things like a recent St. Louis Fed blog post (which is already being sensationalized) that estimated that 47 million jobs would be lost in the second quarter and the unemployment rate could hit 32%, a record high. Even that blog post acknowledged that it is not the level but the duration of unemployment that is important in this environment. And their estimate also assumes that no part of the economy opens back up until after Q2, which contradicts the data regarding when the curve flattens throughout the U.S.

Stay calm, stay safe and stay sane.

They're Going to Try to Scare You. Don't Let Them.

In case you've been living under a rock for the last several weeks ...

Okay, wait. For many of us, it feels like that. Let me start over.

In case you haven't watched or read the news or been on social media for the last several weeks, I have some important information for you:

The news media believes that their job is to scare the bejeebers out of you, make you an anxious wreck, increase your blood pressure, and paralyze you with abject fear.

Yes, it's despicable. It is beneath contempt, and if they were capable of empathy, they would be ashamed. But they're not.

So on Thursday morning, March 26, 2020, in the middle of this coronavirus pandemic and the ensuing government-mandated shutdown of our economy, when they've already made you anxious enough -

They are going to try their best to scare the crap out of you. Don't let them. Read on, and you'll be far more informed than they will ever be.

Every Thursday morning, the U.S. Employment and Training Administration (ETA), a division of the Department of Labor, releases Initial Jobless Claims for the week ended the prior Saturday. So on Thursday, March 26, they will release the number for the week ended Saturday, March 21.

And it's going to be a bad number. The media is going to go into a frenetic Chicken Little dance over it.

Because they have no idea what the number means. It doesn't mean what they think it means. It doesn't mean what you think it means. Let me explain.

Initial Jobless Claims is a point-in-time number. It means something that week, then it means nothing the next week. It is a trend number, in that it is only important in context of the trend. And as the Curmudgeon has long been fond of pointing out, one week doth not a trend make.

The claims number represents the number of Americans who filed for unemployment insurance benefits in a given week. If those Americans are still unemployed the following week, they fall out of the number, because at that point, their claims are no longer initial. Another way to label it is "first-time unemployment filings." And there can only be one first time.

So it isn't cumulative (we have a couple of other numbers to capture that, but more on them later). For perspective, initial claims peaked at 665,000 in March 2009. The record was 695,000 in October 1982. This is incredibly significant - stay with me.

First, as my alter ego has been posting on social media pretty consistently lately, when it comes to statistics, the denominator matters. For example, the number of COVID-19 cases in the U.S. is north of 50,000 - third in the world behind China and Italy. However, dividing the number of cases by a country's population, we find that the number of U.S. cases is .0054%, or 54 per 1 million population. China's is 59 per 1 million, South Korea's is 176 per 1 million, and Italy's, tragically, is 1,144 per 1 million, as of this writing (the evening of March 24).

In the case of initial jobless claims, we have to look at some denominator - either the Civilian Labor Force (those unemployed plus those employed), or the All Employees (Nonfarm) published by the Bureau of Labor Statistics. I prefer the former as it is more comprehensive.

So in March 2009 during the Great Recession, when initial claims peaked at 665,000, the number was 0.43% of the Civilian Labor Force (CLF). And at the peak of 695,000 in October 1982, when the U.S. population was much smaller, that number was .63% of the CLF. Get it? Larger numerator, smaller denominator. (And remember those dates - we'll come back to them.)

Now, that data would tell you that the recession of 1981-1982, which lasted 16 months and saw a trough in GDP growth of -6.69% year-over-year, was worse than the Great Recession of 2007-2009, which lasted 18 months and saw a GDP trough of -8.45%. The unemployment rate peaked in 1982 at 10.8%, vs. 10.0% in 2009.

As someone who lived through both recessions, I can tell you that '81-82 was bad. It was nearly a double-dip recession, coming on the heels of a much shorter and milder downturn in 1980. It was driven by extremely high energy prices, high inflation and high interest rates.

But 2007-09 was worse. It was driven by the most massive housing bubble the U.S., and the rest of the world, have ever seen. The fallout was Armageddon-like. And bubble-driven recessions tend to be more severe and far-reaching than those driven by other factors.

Back to jobless claims, and back to the present. For the week ended Feb. 1, 2020, initial claims totaled 201,000 - the lowest level since Nov. 1969 (when the Curmudgeon was but a lad, not the senior citizen he is today). That's a long time. The upshot is that claims of late have been near all-time historic lows.

So last Thursday, when the ETA reported that claims had increased 70,000, from 211,000 the week before to 281,000, the media's collective hair, real or otherwise, caught fire. At this point, they use their own irrelevant comparisons and misleading numerators to try and scare you, the unwitting:

• "The highest total since September 2017!"
• "The largest one-week jump in claims since 2012!"
• "The largest one-week percentage jump in claims since 1992!"

And the number on Thursday, March 26, 2020 will be worse. Much worse. But again, it doesn't mean what you or they think it means. Here's why.

Back to initial claims being a point-in-time number. For it to have meaning, we'd have to sum weekly initial claims over the entire duration of a downturn. Let's look at the Great Recession.

Because claims fluctuate weekly due to seasonal and other factors, it's hard to pinpoint when they started to rise due to the Great Recession, so we'll just pick the week of Jan. 26, 2008, when claims rose from 321,000 to 366,000 (the Jan. 19 number was the low for 2008, and claims averaged about 321,000 for all of 2007). If we start there, and total claims through the week before they fell back below 366,000 - Feb. 4, 2012 - we get a cumulative total of about 98 million initial claims.

Which is a meaningless number. Why? Well over the four-plus years from Jan. 2008 to Feb. 2012, some people who filed initial claims went back to work. Some may have given up and stopped looking for work, waiting for the job market to improve further, so they didn't qualify to receive benefits anymore.

Fortunately, we have a couple of more meaningful numbers related to unemployment insurance claims: Continued Claims and Weeks Unemployed. Let's look at those.

Continued Claims - also released each week by the ETA - counts those who are past the first week of filing claims for unemployment insurance. It works like this: let's say that on Monday, having lost my job, I file for unemployment insurance for the first time. That gets counted in Initial Jobless Claims for the week ended next Saturday, which will be reported the following Thursday morning. With me so far?

Okay. After that, I will no longer be counted among the initial claims, because, as noted previously, there is only one first time. However, if I remain unemployed and am continuing to file for unemployment insurance benefits, I will now be counted - for the first time - in the Continued Claims data series. And I will continue to be counted in that series until I no longer meet the requirements to file for benefits, or until I stop filing for benefits - in other words, until I stop looking for work, my benefits expire (currently 26 weeks in most states, but that's sure to get extended in the current situation, as it does in every other downturn), I get a job, or I just stop filing for some reason.

Continued Claims peaked at 6.6 million in May 2009, which was a record. But it's far less than the cumulative initial claims during that downturn of 98 million. Far, far, far, FAR less. Because the vast majority of those people had gone back to work. So don't let the Initial Claims number scare you.

Now, as for Weeks Unemployed, or the duration of unemployment. This measures the average number of weeks that those drawing unemployment insurance benefits remain on the rolls. So it is influenced by the extension of benefits by federal or state legislators during a downturn, as well as individual states' baseline benefits expirations. Generally, in a downturn, benefits will be extended, as noted above. This series peaked in July 2011 - more than two years after the end of the Great Recession - at nearly 41 weeks. It was the slowest of the employment data metrics to normalize. It fell to just under 20 weeks in July 2019, which was about where it was in 2005. This speaks to just how ugly the bubble-driven Great Recession was, and how long it took to recover from it. (The baseline trend on this metric has risen over successive downturns, as government increasingly extends benefits, providing a disincentive for people to go back to work for some jobs.)

One other point about Initial Claims - remember when I said we'd get back to the peak dates from 2009 and 1982? The Great Recession ended less than two months after the peak in Initial Claims. And the '82 recession ended within four weeks of that year's peak. In fact, every recession since 1975 has ended within weeks of that downturn's peak in Initial Claims. So if we see a big spike in the next release, and maybe another two or three after that before we hit a peak, you can rest assured the worst is pretty much over, at least for the economy.

So what does all of this mean? First, it means that whatever number we see on Thursday, March 26, 2020, it doesn't tell us much. We need to know how long this thing lasts, and how we recover from it, and that will only be evident in the Continued Claims and Weeks Unemployed data.

Second, we have every reason to expect - not hope - that this time will be different. In most recessions, and especially in bubble-driven recessions, initial claims follow a pattern that, if you graph it, looks like the Matterhorn: they begin rising, they rise sharply, they peak, they begin to decline, and then at some point they level off.

There's a reason for that, and let's use the Great Recession as our lesson. First, the builders who overbuilt, the developers who over-developed, the subprime mortgage lenders who made bad loans, and the title companies who closed those loans, shed jobs. Then, the restaurants those people used to eat at, the bars where they hung out, the hotels where they had their conferences, all saw sharp declines in business, and they laid people off.

Then, those people - who had taken out subprime mortgages - stopped paying on them, and more lenders failed. The Wall Street firms that securitized and bought those mortgages in mass quantities suffered losses. The insurance companies that issued credit guarantees on the bonds that securitized the subprime mortgages ran out of money to insure the losses, and they failed. And all the restaurants, bars, movie theatres, car dealerships, jewelry stores, clothing stores, and every other piece of the economy that depended on those jobs - they all suffered. The second- and third- and fourth-order effects were catastrophic.

This time, there was no asset bubble, so this thing won't develop over time. There will likely be no second- or beyond-order effects. The first few weeks of initial claims numbers should be the worst of it, at least if we get the economy opened back up again in relatively short order (and by that I mean by summer, when seasonality suggests that this thing should have run its course for this year, and we'll just have to see what happens next year - I'm sorry, I'm not one of those doomsday theorists who says this coronavirus will be unlike ANY OTHER the world has ever seen in that it won't be seasonal). In other words, instead of looking like the Matterhorn, with a steep upward slope toward a peak, followed by a steep downward slope back to the norm, it's more likely to look like a cliff - like El Capitan in Yosemite, but with a sharper downward slope like the Matterhorn after the peak is reached.

And why do I believe that the recovery from that peak will be sharp - even sharper, in fact, than the recoveries from the last two recessions?

Again, those recessions were driven by asset bubbles, both of which were fueled by excessive accommodation (read: too-low interest rates) by the Fed - first, the dot-com bubble in 2000-01, driven by the Fed cutting rates in 1998; and the housing bubble of 2007-09, fueled by low rates in response to the dot-com bubble.

Those recessions led to the kind of business failures I noted above. In the dot-com bubble's aftermath, there were far fewer tech firms, because they had overbuilt the tech sector on cheap money, and many of those companies never came back to pick up the slack. In the case of the housing bubble, a lot of those builders and developers never came back, because we had already overbuilt housing capacity relative to demand. The subprime lenders never came back, because we've learned our lesson about subprime lending and we're not going back there - nobody wants to take out the loans, no lender wants to hold the loans, and nobody wants to securitize the loans, or buy the securitized product. Everything else - restaurants, hotels, cruise lines, casinos, retailers - all recovered.

This time, the government, in the interest of saving lives, which is the right thing to focus on, forced those businesses to shut down. This time, there is no moral hazard. This time, the business impact is through no fault of the business sector. The government mandated this, and that's why the government is bailing companies and workers out. True, we must be careful to avoid perverse incentives. But the moral hazard of 2009 isn't there today.

So where does that leave us in terms of the rebound from the effects of this pandemic on our economy?

From my own little corner of the world, my clients have postponed some of my on-site visits, but not much else has changed. We may do things via webex in the interim. But when the dust settles, they will want me on-site again. So I will need plane tickets and hotel rooms and rental cars. I will have to eat out while I'm on the road. Multiply that from my tiny sector of the economy to far larger sectors, and you will see a significant rebound in travel.

Plus, I'm going to want to go on vacation again - aren't you? We already have our next cruise booked. I'm going to want to eat in restaurants, and go to movies, and go to bars (okay, so I don't go to bars much). People are going to want to take their kids to zoos and museums that are closed. Schools are going to re-open, even if not until next year. The Olympics will take place. The NFL, MLB, NBA, NHL and college sports seasons will resume.

The demand is there. Things just have to open back up, which they will in due course. There will be spring after this winter, as there always is.

So the bottom line is, when the media tries to freak you out over Thursday's initial jobless claims number, don't let them. Now, you know better. You know that number better than they do if you've read this far. Godspeed, be safe, be sane, and be calm.

Bonus reading: lest you doubt my assertion that the media pundits don't know jack about economics, consider this:

• Ali Velshi, NBC/MSNBC's senior economic and business correspondent, and formerly CNN's Chief Business Correspondent. Has a degree in ... religious studies.
• Richard Quest, CNN's Business at Large Editor. Has a degree in ... law.
• Kelly Evans, CNBC co-anchor of Power Lunch. Has a degree in ... journalism.

'Nuff said, folks. You stand as good a chance of understanding this stuff as the media does.

'Twas the Night Before New Years'

‘Twas the night before New Years’, two thousand and twenty

The year past had brought us news items a-plenty.

What could we say about twenty-nineteen

Except, "Gee, what a wild one the last year has been."

The Democrats started the new year in power,

Having reclaimed the House. It was Pelosi’s hour.

Caressing her gavel, she vowed at each turn

To oppose the Republicans, and her heart burned

With desire for impeachment, though she claimed it must be

Bipartisan; then along came AOC.

Apparently owning the party’s left base,

She persuaded the Speaker to move with no case.

“Impeach!” cried the left wing, “Accuse him of treason!

If that doesn’t work, let's just make up a reason!

He’s mean; we don’t like him; he’s orange – that hair!

Impeach ‘cause winning in ’16 he did dare!”

So the Intel Committee, chaired by Adam Schiff

Held their secret hearings, secured in a SCIF.

No transcripts released; we the people weren’t able

To learn what was happening at that SCIF table.

Schiff, meanwhile, leaked out the bits that looked damning

To a media ready to take up the shamming.

When finally hearings were held in plain sight,

Schiff tried to paint the Prez in a bad light.

But in spite of made-up rules that favored the Dems,

Their hearings failed to reveal any new gems

That would bolster their case to impeach and remove,

Let alone give them any high crimes they could prove.

So, the best they could do was “Abuse and Obstruction

Of Congress,” who just seemed hell-bent on destruction

Of due process, fairness, and our Constitution –

How sad that our legislative institution

Could stoop to such lows, in a desperate quest

From their opposition, all power to wrest.

This is how partisan we have become;

Just thinking about it makes moderates glum.

Now, on to the Senate – but wait, Nancy balked!

As in a most strange turn, of fairness she talked.

But she has no leverage left, as we see;

In the Senate, the Dems aren’t the majority.

Meantime hints of new Articles started to rise,

Leaving voters to wonder, “What’s up with these guys?

Do they not think we see that they’re grasping at straws?

That their case for impeachment is so full of flaws?”

So while all of this nonsense is being conducted,

No new legislation is being constructed.

The Dems gained in ’16 on talk of health care,

But since, all their promises have gone nowhere.

Their primary field started with more than 20

Candidates vying to spend people's money,

But one by one, out of the race they did fall

(In the end, if we're lucky, there'll be none at all).

One of the hopefuls was Eric Swalwell,

But it seemed just a week before his campaign fell.
These days he's supporting his pal Adam Schiff
In another gambit doomed to fall off a cliff.

Next out was Beto - "Hell, yes," he once vowed,
But he couldn't keep pace with the rest of the crowd
In spite of his stunts and his gesticulations.
Guess it takes more than skateboarding to lead a nation.

DiBlasio followed soon after O'Rourke,
But he isn't even liked back in New York.
Harris was next, hoist with her own petard,
Though she, in denial, played the gender card.

So who will it be? Bernie? Spartacus? Liz?
Bloomberg or Steyer? Or Yang, the math whiz?
Or will it be Klobuchar, or Mayor Pete?
Or can Joe Biden help the Dems stave off defeat?

Whoever winds up at the top of the pile,
When they debate Donald Trump, don't touch that dial!
For whoever the winner from this crowded bunch is,
Will need to be able to take verbal punches.

Meanwhile, new trade deals are now getting done,

Thus the stock market’s been on a heck of a run.

A new budget deal was agreed to by all,

Including some funding for Trump’s border wall.

(Why fund the wall if you’re going to impeach?

Are you not confident in your plot’s reach?

Are you protecting your red-state comrades

From election results that will likely be bad?)

The numbers show strength in the economy,

So it's unlikely that a recession we'll see,

At least 'til November, then it just depends

On voters, and how the election night ends.

So - what to expect? What will this new year bring?

To be sure, a lot more partisan bickering.

More posturing from both the left and the right;

Relief from our divide is nowhere in sight.

But where does that start? Well, with you and with me.

Can we re-learn to respectfully disagree?

To accept other views without intolerance?

Or must we maintain such a divisive stance?

I hope that we can, but if we cannot,

Our differences still needn’t leave us distraught.

We’ve survived political divides before,

So I’m sure that we’re able to survive one more.

Just remember: we have more that keeps us united

Than those things that may serve to make us divided.

So let me express to all folks, red or blue

A happy and prosperous New Year to you!