covid

All posts tagged covid

Mancur Olson’s The Rise and Decline of Nations tells us that in stable times special interest groups tend to slowly create increasingly rigid agreements to cement their income streams. Major wars, and other cataclysms of that size, occasionally sweep away those rigidities, creating conditions under which faster economic growth is possible.

COVID has been much less cataclysmic than what Olson talks about. Yet I see hints that the recent pandemic has had effects that weakly resemble war. I see stronger evidence that the pandemic was a useful trigger for overcoming the effects status quo bias. I’m writing this post to help clarify my thoughts about how significant these effects will be, and how they’ll affect stock markets.

Is the stock market’s impressive performance partly due to expectations that the pandemic caused a lasting increase in profits?

I’ll guess that it explains one quarter of the stock market’s rise. A fair amount of that guess reflects my intuitions about how much can’t be explained by other factors. That approach is at least as error-prone as estimating individual pandemic effects. So please interpret this post as mostly groping around in the dark.

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Last month, I conceded defeat in my bet (with Robin Hanson) that US COVID-19 deaths would be less than 250,000.

My biggest mistake was thinking voters would care about results, and unite against a common enemy as they did in WWII. I should have been more aware of the tendency to treat natural deaths as more acceptable than deaths due to a hostile agent. Robin clearly did better at evaluating this.

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From The problem with rapid Covid testing, Mayank Gupta writes:

The absolute number of false positives would rise dramatically under slightly inaccurate, broad surveillance testing. At least initially, the number of people going to the doctor to ask what to do would also rise. One can imagine if doctors truly flubbed and didn’t know how to advise patients accurately, a lot of individual patients would lose trust in the medical system (testing, doctors, or both). The consequence of this would be more resistance to health public policy measures in the future.

For a reminder of why rapid testing is valuable, see Alex Tabarrok. Note also the evidence from the NBA that people who need useful tests can be more innovative than the medical system.

This seems like the tip of an important iceberg.

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Lots of people want to frame the 2020 US election as a fight between the left and the right, with the wrong side being near an extreme on that spectrum. They are deceiving you.

The U.S. is facing some extremism, but it has little connection to the extremes of the left-right spectrum [1]. The latest dangers are extreme mainly on a very different spectrum. A spectrum on which Trump and the woke are very similar.

How to describe this spectrum? I’m unsure what the best description is, so I’ll list some that capture important components of it:

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Lots of people have been asking recently why the stock market appears unconnected with the economy.

There are several factors that contribute to that impression.

First, stock market indexes are imperfect measures of the whole stock market. Well-known indexes such as the S&P500 are higher than pre-pandemic, but the average stock is down something like 10% over the same time period. The difference is due to some well-known stocks such as Apple and Amazon, which have unusually large weights in the S&P500.

See this Colby Davis post for some relevant charts, and for some good arguments against buying large growth stocks today.

Stock markets react to the foreseeable future, whereas the daily news, and most politicians, prefer to focus attention on the recent past. People who focus on the recent past see a US that’s barely able to decide whether to fight COVID-19, whereas the market sees vaccines and/or good treatments enabling business to return to normal within a year.

Stock markets don’t try to reflect the costs associated with death, chronic fatigue, domestic violence, etc. Too many people want the market to be either a perfect indicator of how well we’re doing, or to dismiss it as worthless. Sorry, but imperfect indicators are all we have.

Plenty of influential people have been exaggerating the harm caused by the pandemic, in order to manipulate the average person into taking the pandemic seriously. As far as I can tell, this backfired, and contributed to the anti-mask backlash. It also contributed to stocks being underpriced in the spring, so parts of the stock market rebound have simply been reactions to the growing evidence that most large companies are recovering.

The vaccine news has been persistently good, except for the opposition from big pharma and their friends at the FDA to making vaccines available as soon as possible.

Another modest factor is that many companies dramatically reduced their capital expenditure plans starting around March and April. That will reduce production capacities for the next year or two, thus making shortages of goods a bit more common than usual. This should prop up profit margins. But I haven’t noticed much connection between the most relevant industries and rising stock prices.

Why is there such a large divergence between the S&P500 and the average stock?

Investors have developed a somewhat unusual degree of preference for well-known companies whose long-term growth prospects seem safe.

I guessed last year that this would be a rerun of the Nifty Fifty. I still see important similarities in investor attitudes, but I see enough divergences in patterns of stock prices that I’m guessing we’ll get something in between the broad, gradual peak of the Nifty Fifty and a standard bubble (i.e. with a well-defined peak followed by a clear reversal within months).

Remember that high volatility is somewhat correlated with being in a bubble. We’ve recently seen Zoom Video Communications rise 40% in one day, and Salesforce rise 26%, in response to good earnings reports. That’s a $50 billion one-day gain for Salesforce. It reminds me of the volatility in PetroChina in 2007 (PetroChina has declined 87% since then). There was also that $173 billion rise in Apple after it’s latest earnings report, but that was a mere 10.5% rise.

Some of the divergence is due to small retailers losing business to Amazon, and to small restaurant chains losing business to fast food chains.

The bubble is a bit broader than just tech stocks – Home Depot and Chipotle are well above their pre-pandemic levels, by much larger amounts than can be explained by any near-term changes in their profits.

Incumbent politicians have been trying to buy votes by shoveling money to influential companies and people. There’s been some speculation that that’s biased toward large companies. It seems likely that large companies are better able to take advantage of those deals, because they’re more likely to employ someone with expertise at dealing with the government than is, say, a barbershop.

But I don’t see how that explains more than 1% of the stock market divergence. Stocks like Apple and Tesla have risen much more than can be explained by any change in this year’s profits. Any sane explanation of those soaring stocks has to involve increased optimism about profits that they’ll be making 5 to 10 years from now.

Large companies have better access to banks. Large companies typically have someone who is an expert at dealing with banks, and they have the accounting competence to make it easy for banks to figure out how much they can safely lend to the company. In contrast, a family-owned business will be slower to figure out how to borrow money, and therefore is more likely to go out of business due to unusual problems such as a pandemic. That might explain a fair amount of the divergence between the S&P500 and what you hear by word of mouth, but it explains little of the divergence between the S&P500 and the publicly traded companies that are too small for the S&P500.

I’ve only done a little selling recently, and I’ve been mostly avoiding large companies for many years. I’m guessing that Thursday’s tech stock crash wasn’t the end of the bubble. Bubbles tend to continue expanding until the average investor gets tired of hearing pundits say that we’re in a bubble. That suggests the peak is at least a month away, and I could imagine it being more than a year away.

I’ve been brainstorming about what might happen with this year’s election. Here’s one of the more interesting (but not likely) scenarios that I’ve imagined:

Most parts of the US are experiencing their second or third wave of the pandemic. Two of the more heavily funded vaccine trials have just been declared to be failures. No US or European company expects to have a vaccine ready for FDA approval before December. Prediction markets say Trump’s chances of re-election have dropped to 20%.

China announces on October 27 that Sinopharm Group has a COVID-19 vaccine that meets China’s safety and effectiveness standards, and can deliver about 1 million doses by November 2.

China offers to allocate half of this initial batch of vaccines to the US, on the grounds that the US is a relatively needy country, and Donald Trump is currently a close friend of China.

Why, I hear you wonder, would China treat Trump as a friend?

China is strong enough that the government can afford to tolerate Trump’s annoying trade wars, and they maybe even see some benefit from reducing China’s somewhat excessive dependence on the US. It’s important to keep in mind that democracy is one of the larger threats to the Beijing regime (h/t scholars-stage). 2020 has proven to be a good year to mount a campaign to demonstrate that socialism with Chinese characteristics is superior to US-style democracy. I’ll leave as an exercise for my readers to figure out how many ways the idea of democracy could be discredited by a Trump re-election.

Why would a Chinese company be faster than US/European companies at producing a vaccine? My initial guess was differences in regulatory red tape would favor China, but my attempt to find evidence for such a pattern turned up nothing. My current guess is that differences between countries in who volunteers for a vaccine trial will have important effects on how quickly the control groups get infected. Trials in some countries may attract only people who are sufficiently risk-averse that the control groups are slower than expected at getting infected. Please don’t assume that I have any useful expertise here; I’m mostly just guessing.

I also wondered whether willingness to do human challenge trials would determine who verifies their vaccine first. I have vague intuitions that China is more likely than other countries to try that, but I haven’t found evidence to confirm those intuitions.

How would voters react to this scenario? My best guess is that the election would be surprisingly close, but Trump would still lose.

The stock market would rise due to the vaccine benefits. There would be no good way to infer the market’s opinion about the election until the results are announced. I’m still confused as to how this scenario should affect my investment strategies.

P.S. – China and Sinopharm aren’t willing to predict when they’ll able to submit the forms needed for FDA approval, and ask that the US consider the approval of China’s NMPA to be good enough evidence of the vaccine’s safety and effectiveness. How does the FDA react?

Book review: Black Death at the Golden Gate: The Race to Save America from the Bubonic Plague, by David K. Randall.

Imagine a story about an epidemic that reached San Francisco, after devastating parts of China. A few cases are detected, there’s uncertainty about how long it’s been spreading undetected, and a small number of worried public health officials try to mobilize the city to stop an imminent explosion of disease. Nobody knows how fast it’s spreading, and experts only have weak guesses about the mechanism of transmission. News media and politicians react by trying to suppress those nasty rumors which threaten the city’s economy.

Sounds too familiar?

The story is about a bubonic plague outbreak that started in 1900. It happens shortly after the dawn of the Great Sanitary Awakening, when the germ theory of disease is fairly controversial. A few experts in the new-fangled field of bacteriology have advanced the radical new claim that rats have some sort of connection to the spread of the plague, and one has proposed that the connection involves fleas transmitting the infection through bites. But the evidence isn’t yet strong enough to widely displace the standard hypothesis that the disease is caused by filth.

There was a vaccine for the bubonic plague, which maybe helped a bit. It was only 50% effective, the benefits lasted about 6 months, and the side effects sound like cruel and unusual punishment. It was controversial and often resisted, much like the compulsory smallpox vaccinations of the time.

Yet the plague didn’t seem to know that it was supposed to grow at exponential rates. That left an eerie sense of mystery about how the plague could linger for years, with people continuing to disagree about whether it existed.

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New infections have been declining at an almost adequate pace (10% per week?) in most parts of the US, and probably the rest of the developed world.

The overall reported new cases look more discouraging, for two reasons.

One reason is the increase in testing. I estimate that two months ago, a bit less than 10% of new infections were being confirmed by tests, and I estimate that now it’s above 20%, maybe getting close to 25%. That means that if the new infection rate were unchanged, we’d be seeing a roughly 10% per week increase in reported cases.

Nearly all parts of the country have done a good deal better than that.

I estimate the change in new infections since the early April peak by multiplying the early April confirmed daily cases by 10 or 12, and the June ones by 4 or 5, and I get a current rate that’s about 1/4 to 1/3 of the peak.

The bad news is that there are some heavily populated areas for which the trend doesn’t look very good over the past few weeks. When the rate of new infections remains constant in some areas, but declines at exponential rates in others, the exponential declines stop affecting the total numbers before too long. E.g. much of California has suppressed the pandemic, but a few cities, such as Los Angeles and Oakland, are enough to keep the state’s total count of new infections steady.

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In this post, I make some conjectures about U.S. economic growth over the next year or two.

Many people expect a depression, due to the current high unemployment numbers. But depressions aren’t caused by unemployment – that’s a symptom, with little predictive power.

The main cause of poor economic growth has been an inability to alter wages so that the supply of and demand for labor are in balance. That typically means deflation, or a large, unexpected decline in the inflation rate, combined with sticky wages.

So I’ll mostly focus on guessing whether we’ll have inflation or deflation.

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Elon Musk has gotten some well-deserved flack for predicting (in March) close to zero new infections in the U.S. by now.

Yet the focus on national or statewide infections has obscured a curious phenomenon: if he’d just predicted infections in Santa Clara county, he’d have been partly right – new cases peaked on April 10 at 83, were down to 23 on April 27, and appear to have dropped more since then (reporting may be incomplete for more recent days). (Santa Clara county roughly coincides with Silicon Valley; Tesla’s plants are a few miles from the Santa Clara county border, technically in Alameda county, but in most senses Tesla’s plants are part of Silicon Valley, which I’ll treat as a city, even though it’s more a city-less suburb).

Meanwhile, the statewide totals fail to show a trend of doing much more than stabilizing the rate of new cases. A good deal of that is due to Los Angeles.

What’s different between LA and Silicon Valley that would explain this difference?

It’s probably not much due to differences in government policy. California is using a mix of statewide rules and county rules, which makes it tricky to say whether there are policy differences. My impression is that most differences between county policies have relatively minor effects. I guessed that the most important difference would be in when they required facemasks use. Yet it looks like LA required facemasks on April 17, in synchrony with most of the bay area. But Santa Clara county differed by strongly urging, but not requiring, facemasks.

Maybe the reason that Santa Clara county didn’t create a formal facemask rule is that residents were sufficiently quick to adopt them that there was less need than in other counties? That fits my intuitions fairly well.

The LA area has been in the news for having crowded beaches. Outdoor activity in warm, sunny weather seems relatively low risk, but I doubt that the people on those beaches carefully evaluated the effects of ventilation, sun, and temperature on their risk, so it’s likely that the crowded beaches are at least a symptom of attitudes which cause the spread of infections.

I can see from Ohio that there are significant regional differences in people’s willingness to wear facemasks. I’m surprised that Ohio voters won’t put up with a rule to make them wear masks in order to enter stores. (Ohio’s Governor DeWine deserves much better constituents than he’s currently stuck with. Here in Berkeley, I get the impression that a majority decided that we needed to follow that rule before our government got around to announcing it).

Another relevant difference is that Silicon Valley workers switched to working from home more readily than most other places. This is likely a moderate factor, but I’d have expected a peak before April 10 if it explained more than half of Silicon Valley’s success.

Another influence might be blood types: type A blood creates higher risk of COVID-19, while type O lowers risk. Judging from the blood type differences between the U.S. and China, the large Chinese population in Silicon Valley ought to lower risk a bit.

LA’s apparently steady number of new cases can’t be very stable. People’s willingness to take precautions will decline at some point if herd immunity looks inevitable. Pushing in the other direction: increasing numbers of people will become immune, reducing the virus’ ability to spread. It seems almost impossible for these forces to balance out.

Robin Hanson sees a world polarized between regions that prevent infections and regions that get something like herd immunity. I expect that many regions, such as LA, will end up at various places in between, with maybe 10% of the population becoming immune. Since the people most likely get infected and to spread the virus will be over-represented in that fraction, it will put a sizable dent in R, enough to enable significant periods of suppression.

Robin expects that variance in R will be harmful. Zvi counters that variance is not bad, given sufficiently effective travel restrictions.

I mostly agree with Zvi here. The cost of restricting nearly all travel to commuting distance or less from home is much lower than the cost of the current drastic restrictions, so voters will typically demand a shift in that direction. My main concern is that these travel restrictions are getting lumped in with “lockdown measures” such as stay at home, and shut down “nonessential” businesses. That means that pressure to reopen activities that ought to be reopened could become pressure to remove most travel restrictions.

How many politicians will see beyond simple categories such as lockdowns versus reopening the economy, to pick and choose between the good and bad pieces of lockdowns? My impression is that at least half of the state and local politicians are on track to doing so, and have enough power to sidestep whatever problems exist at the federal level.

I sympathize with Musk’s desire to reopen Tesla plants, and it’s somewhat plausible that now is the right time for that. But I’m reluctant to side with him until he alters his tweets to be more narrowly targeted on specific, arguably safe, changes. I don’t want the world polarized between openers and closers.