Online Reading Onpaper

We have six weekly contributors here at EWED and I try to read every single post. I don’t always read them the same day that they are published. Being subscribed is convenient because I can let my count of unread emails accumulate as a reminder of what I’ve yet to read.

Shortly after my fourth child was born over the summer, I understandably got quite behind in my reading. I think that I had as many as twelve unread posts. I would try to catchup on the days that I stayed home with the children. After all, they don’t require constant monitoring and often go do their own thing. Then, without fail, every time that I pull out my phone to catch up on some choice econ content, the kids would get needy. They’d start whining, fighting, or otherwise suddenly start accosting me for one thing or another – even if they were fine just moments before. It’s as if my phone was the signal that I clearly had nothing to do and that I should be interacting with them. Don’t get me wrong, I like interacting with my kids. But, don’t they know that I’m a professional living in the 21st century? Don’t they know that there is a lot of good educational and intellectually stimulating content on my phone and that I am not merely zoning out and wasting my time?

No. They do not.

I began to realize that it didn’t matter what I was doing on my phone, the kids were not happy about it.

I have fond childhood memories of my dad smoking a pipe and reading the newspaper. I remember how he’d cross his legs and I remember how he’d lift me up and down with them. I less well remember my dad playing his Game Boy. That was entertaining for a while, but I remember feeling more socially disconnected from him at those times. Maybe my kids feel the same way. It doesn’t matter to them that I try to read news articles on my phone (the same content as a newspaper). They see me on a 1-player device.

So, one day I printed out about a dozen accumulated EWED blog posts as double-sided and stapled articles on real-life paper.

The kids were copacetic, going about their business. They were fed, watered, changed, and had toys and drawing accoutrement. I sat down with my stack of papers in a prominent rocking chair and started reading. You know what my kids did in response? Not a darn thing! I had found the secret. I couldn’t comment on the posts or share them digitally. But that’s a small price to pay for getting some peaceful reading time. My kids didn’t care that I wasn’t giving them attention. Reading is something they know about. They read or are read to every day. ‘Dad’s reading’ is a totally understandable and sympathetic activity. ‘Dad’s on his phone’ is not a sympathetic activity. After all, they don’t have phones.

They even had a role to play. As I’d finish reading the blog posts, I’d toss the stapled pages across the room. It was their job to throw those away in the garbage can. It became a game where there were these sheets of paper that I cared about, then examined , and then discarded… like yesterday’s news. They’d even argue some over who got to run the next consumed story across the house to the garbage can (sorry fellow bloggers).

If you’re waiting for the other shoe to drop, then I’ve got nothing for you. It turns out that this works for us. My working hypothesis is that kids often don’t want parents to give them attention in particular. Rather, they want to feel a sense of connection by being involved, or sharing experiences. Even if it’s not at the same time. Our kids want to do the things that we do. They love to mimic. My kids are almost never allowed to play games or do nearly anything on our phones. So, me being on my phone in their presence serves to create distance between us. Reading a book or some paper in their presence? That puts us on the same page.

ChatGPT Cites Economics Papers That Do Not Exist

This discovery and the examples provided are by graduate student Will Hickman.

Although many academic researchers don’t enjoy writing literature reviews and would like to have an AI system do the heavy lifting for them, we have found a glaring issue with using ChatGPT in this role. ChatGPT will cite papers that don’t exist. This isn’t an isolated phenomenon – we’ve asked ChatGPT different research questions, and it continually provides false and misleading references. To make matters worse, it will often provide correct references to papers that do exist and mix these in with incorrect references and references to nonexistent papers. In short, beware when using ChatGPT for research.

Below, we’ve shown some examples of the issues we’ve seen with ChatGPT. In the first example, we asked ChatGPT to explain the research in experimental economics on how to elicit attitudes towards risk. While the response itself sounds like a decent answer to our question, the references are nonsense. Kahneman, Knetsch, and Thaler (1990) is not about eliciting risk. “Risk Aversion in the Small and in the Large” was written by John Pratt and was published in 1964. “An Experimental Investigation of Competitive Market Behavior” presumably refers to Vernon Smith’s “An Experimental Study of Competitive Market Behavior”, which had nothing to do with eliciting attitudes towards risk and was not written by Charlie Plott. The reference to Busemeyer and Townsend (1993) appears to be relevant.

Although ChatGPT often cites non-existent and/or irrelevant work, it sometimes gets everything correct. For instance, as shown below, when we asked it to summarize the research in behavioral economics, it gave correct citations for Kahneman and Tversky’s “Prospect Theory” and Thaler and Sunstein’s “Nudge.” ChatGPT doesn’t always just make stuff up. The question is, when does it give good answers and when does it give garbage answers?

Strangely, when confronted, ChatGPT will admit that it cites non-existent papers but will not give a clear answer as to why it cites non-existent papers. Also, as shown below, it will admit that it previously cited non-existent papers, promise to cite real papers, and then cite more non-existent papers. 

We show the results from asking ChatGPT to summarize the research in experimental economics on the relationship between asset perishability and the occurrence of price bubbles. Although the answer it gives sounds coherent, a closer inspection reveals that the conclusions ChatGPT reaches do not align with theoretical predictions. More to our point, neither of the “papers” cited actually exist.  

Immediately after getting this nonsensical answer, we told ChatGPT that neither of the papers it cited exist and asked why it didn’t limit itself to discussing papers that exist. As shown below, it apologized, promised to provide a new summary of the research on asset perishability and price bubbles that only used existing papers, then proceeded to cite two more non-existent papers. 

Tyler has called these errors “hallucinations” of ChatGPT. It might be whimsical in a more artistic pursuit, but we find this form of error concerning. Although there will always be room for improving language models, one thing is very clear: researchers be careful. This is something to keep in mind, also, when serving as a referee or grading student work.

New Survey on Bootcamp Graduates

I have been investigating how to get more talent in the tech industry for a while. There is not a lot of data on precisely how people select into tech and what might cause more people to train for in-demand jobs. Gordon Macrae, in his substack The View, has a recent relevant post Issue #9: Tracking 100 bootcamp graduates from 2015.

Gordon ran his own survey of 100 graduates of coding bootcamps. Coding bootcamps are a fascinating element that help fill in the skills gap. They are not well-understood, and we don’t have much publicly available data of the sort that helps researchers measure the outcomes of a traditional college education.

Here are some of his results from this preliminary survey:

Of this total, 68% of the graduates surveyed in 2022 were doing roles where the bootcamp was necessary for them to work in that role. What I found fascinating, though, was that this figure varied wildly depending on the bootcamp they attended. 

On the lowest end, just 50% of graduates from Bootcamp A were doing jobs in 2022 that required having gone to a bootcamp. Conversely, 90% of Bootcamp D graduates were working in technical roles seven years after graduating.

What is more, the percentage of bootcamp graduates in technical roles at 7 years after graduation has gone done by 15%. The average immediately after graduation was 82% working in a technical role. 

Other resources:

There is more work to be done in this area.

AI Can’t Cure a Flaccid Mind

Many of my classes consist of a large writing component. I’ve designed the courses so that most students write the best paper that they’ll ever write in their life. Recently, I had reason to believe that a student was using AI or a paid service to write their paper. I couldn’t find conclusive evidence that they didn’t write it, but it ended up not mattering much in the end.

Continue reading

Reckless Management Led to BlockFi Crypto Bankruptcy

Since my nontrivial deposits at the cryptocurrency lending firm BlockFi have been blocked (maybe forever) from withdrawal, I keep an eye on news from that front. My main source of information has been missives from BlockFi itself, in which management portrays itself as being very careful with customer funds; it was only the shocking, unforeseeable collapse of the FTX exchange that forced the otherwise sober and responsible BlockFi into its recent bankruptcy. I have believed that view of things, since that is all I knew.

However, Emily Mason at Forbes has poked around behind the scenes, including finding insiders willing to talk (off the record) about less-savory doings within BlockFi. The title of her recent article, BlockFi Employees Warned Of Credit Risks, But Say Executives Dismissed Them, pretty much says it all. The article starts out:

In its bankruptcy filing last week, New Jersey-based BlockFi attempted to paint itself as a responsible lender hit by plummeting crypto prices and the collapse of crypto brokerage FTX and its affiliated trading firm, Alameda.

That is the view I have held up till now. However, Mason then goes on to note:

 But a closer look at the company’s history reveals that its vulnerabilities likely began much earlier with missteps in risk management, including loosened lending standards, a highly concentrated pool of borrowers and unsustainable trading activity.

To keep this blog post short, I will just paste in a few excerpts where she fleshes out her case:

While the company regularly touted a sophisticated risk management team, current and former employees indicate in interviews that risk professionals were dismissed by executives preoccupied with delivering growth to investors. As early as 2020, employees were discouraged from describing risks in written internal communications to avoid liability, a former employee states.

Ouch. Not a good sign.

Until August 2021, BlockFi advertised that loans were typically over-collateralized. But large potential borrowers were often unwilling to meet those requirements, a cease and desist order brought by the Securities and Exchange Commission against BlockFi in February states. The availability of uncollateralized capital from competing companies like Voyager created stiff competition in the lending field.

Under pressure to continue growing and delivering yields, BlockFi began lending to these parties with less collateral than publicly stated without informing customers on the amount of risk involved with interest accounts, according to the SEC order which resulted in a $100 million fine for the company. As a result, BlockFi paused access to its interest accounts in the U.S.

Wait, that is MY money they were messing with. Now I am really annoyed.

In addition to lowering its collateral requirements, BlockFi’s due diligence process had flaws, former borrowers say. Available credit for borrowers was decided based on their assets, but BlockFi and other lenders failed to investigate both the size and quality of potential borrowers’ holdings. Like Voyager and other crypto lenders, BlockFi accepted unaudited balance sheets from hedge funds and proprietary trading firms former borrowers say, leaving room for manipulation on the borrower side.

In the due diligence process, lenders like BlockFi and Voyager did not examine whether borrowers’ balance sheet assets were denominated in dollars or less liquid tokens like FTX-issued FTT.

The revelation that Alameda’s balance sheet was mostly FTT tokens was the news that set off the unraveling of both Alameda and FTX and triggered contagion effects across the industry. In early November, Alameda defaulted on $680 million in loan obligations to BlockFi, according to the bankruptcy filing.

Some BlockFi employees reportedly warned of the shakiness of the parties to whom clients’ finds were being loaned. Management dismissed these concerns because the loans were “collateralized”,  but as noted above, the extent of that collateral was *not* what we clients were told:

An internal team at BlockFi also raised concerns that the borrower pool was too concentrated among a pool of crypto whales, including mega hedge funds Three Arrows Capital and Alameda, another former employee states. Management responded that the loans were collateralized, according to the employee.

This is a very common scenario in finance: In search of profits, management  cuts corners and takes more risks with client funds than they were telling the clients. Maybe Sam Bankman-Fried will up with cell-mates from BlockFi.

Because BlockFi survived the Luna/Terra collapse some months ago and because I believed the steady stream of reassuring pronouncements from BlockFi management, I only withdrew a third of my funds back in the summer. But as it turns out, that withdrawal was apparently bankrolled by a big loan to BlockFi from Bankman-Fried’s FTX; but FTX is now caput.  So the odds of my ever seeing the rest of my funds are slim indeed:

In BlockFi’s bankruptcy filing and in public statements made by its CEO, Zac Prince, the company points to its survival through the collapse of the Terra/Luna ecosystem and subsequent shuttering of Three Arrows Capital as evidence of strong management. But that endurance four months ago was made possible through a $400 million credit line from now-defunct FTX, which allowed the firm to meet panicked withdrawal requests from depositors. When FTX folded in early November, BlockFi lost its lending back stop and could no longer meet fresh waves of withdrawal requests.

One lesson learned: If there is a reasonable chance of a panic, it can pay to be the first to panic, not the last.

Slow Adjustment in Tech Labor for CGO Research

The CGO published a policy paper I wrote with Henry Kronk.

The Slow Adjustment in Tech Labor: Why Do High-Paying Tech Jobs Go Unfilled?

Executive Summary

The United States technology industry continues to struggle to recruit new talent. According to the US Bureau of Labor Statistics, the number of people employed in technology is not increasing quickly. 

Tech jobs pay well and don’t have the drawbacks of some other in-demand jobs, such as the travel schedule of a truck driver or the physically taxing labor required in oil fields.

Tech jobs are sometimes touted as a guarantee of having a comfortable and rewarding career, but the reality is not that simple.

Economics suggests that high wages would eliminate labor shortages, but that’s not the case in tech work. Why?

In this paper, authors Joy Buchanan and Henry Kronk propose a set of factors that have been overlooked and apply broadly to the tech sector. 

Individuals with high-status tech jobs report burnout, anxiety, depression, and other mental health issues at higher rates than the general population. They also have to deal with the constant threat of becoming obsolete. Because technology changes so quickly, they must constantly work to update their skills in order to remain competitive.  

The authors offer several recommendations for tech companies, educators, and policymakers:

  • Political and community leaders can provide more accurate messaging such as communicating clearer expectations about the difficulties of entering the tech workforce. 
  • The tech industry could benefit from improvements in computer education. The authors cite a need for more pre-college exposure to computer occupations as well as a need to add communication skills to computer science curriculums.
  • Teachers, parents, and tech companies can all find ways to inform young people at an age-appropriate level about opportunities. Computer science is abstract and hard to understand. Young people who have some exposure to computer science through a class or camp are more likely to become CS majors in college. 
  • Company leaders can improve their recruitment and development strategies to reflect the labor market realities including paying enough to compensate employees for the mental challenges of demanding technical work and alleviating their own talent shortages by investing in training and education. 
  • Tech companies may be able to attract more women and minorities by improving their scheduling and management practices.

Henry and I examined public data and the existing literature to get a better understanding of the current state of knowledge on this issue. I hope our paper can be helpful, however we partly just highlight how many questions still exist about tech and talent.

My recent paper in Labour Economics, Willingness to be Paid: Who Trains for Tech Jobs?, was designed to add new data to address these questions.

Message To My Students: Don’t Use AI to Cheat (at least not yet)

If you have spent any time on social media in the past week, you’ve probably noticed a lot of people using the new AI program called ChatGPT. Joy blogged about it recently too. It’s a fun thing to play with and often gives you very good (or at least interesting) responses to questions you ask. And it’s blown up on social media, probably because it’s free, responds instantly, and is easy to screenshot.

But as with all things AI, there are numerous concerns that come up, both theoretical and immediately real. One immediately real concern among academics is the possibility of cheating by students on homework, short writing assignments, or take-home exams. I don’t want to diminish these concerns, but I think for now they are overblown. Let me demonstrate by example.

This semester I am teaching an undergraduate course in Economic History. Two of the big topics we cover are the Industrial Revolution and the Great Depression. Specifically, we spend a lot of time discussing the various theories of the causes of these two events. On the exams, students are asked to, more or less, summarize these potential causes and discuss them.

How does ChatGPT do?

On the Industrial Revolution:

And on the Great Depression:

Now, it’s not that these answers are flat out wrong. The answers certainly list theories that have been discussed by at various times, including in the academic literature. But these answers just wouldn’t be very good for my class, primarily because they miss almost all of the theories that we have discussed in class as being likely causes. Moreover, the answers also list theories that we have discussed in class as probably not being correct.

These kinds of errors are especially true of the answer about the Great Depression, which reads like it was taken straight from a high school history textbook, ignoring almost everything economists have said about the topic. The answer for the Industrial Revolution doesn’t make this mistake as much as it misses most of the theories discussed by Koyama and Rubin, which was the main book we used to work through the literature. If a student gave an answer like the AI, it suggests to me that they didn’t even look at the chapter titles in K&R, which provide a roadmap of the main theories.

So, my message to students: don’t try to use this to answer questions in class, at least not right now. The program will certainly improve in the future, and perhaps it will eventually get very good at answering these kinds of academic questions.

But I also have a message to fellow academics: make sure that you are writing questions that aren’t easily answered by an AI. This can be hard to do, especially if you haven’t thought about it deeply, but ultimately thinking in this way should help you to write better exam and homework questions. This approach seems far superior to the one that the AI suggests.

Gambler Ruined: Sam Bankman-Fried’s Bizarre Notions of Risk and the Blow-Up of FTX

The drama continues for Sam Bankman-Fried (SBF), the former head of now-bankrupt crypto exchange FTX. This past week has been giving a series of interviews, in which he (the brilliant master, the White Knight, of the crypto world a mere month ago) is trying to convince us (potential jurors?) that he is too dim-witted to have masterminded a shell game of international wire transfers, and that he had no idea what was happening in the closely-held company of which he was Chief Executive Officer. (For an entertaining take on what We The People think of SBF’s disclaimers, see responses in this thread ttps://twitter.com/SBF_FTX/status/1591989554881658880, especially the video posted by “Not Jim Cramer”). 

The word on the street is that his former partner Caroline Ellison (who he has been implicitly throwing under the bus with his disclaimers of responsibility for the multi-billion dollar transfers from his FTX to her Alameda company) may well be cutting a deal with prosecutors to testify against SBF.  It remains to be seen whether SBF’s monumental political donations will suffice to keep him from doing hard time.

But all that legal drama aside, the SBF saga brings up some interesting issues on risk management. Earlier here on EWED James Bailey  highlighted a revealing exchange between SBF and Tyler Cowen, in which SBF displayed a heedless neglect of the risk of catastrophic outcomes, as long as there is a reasonable chance of great gain:

TC: Ok, but let’s say there’s a game: 51% you double the Earth out somewhere else, 49% it all disappears. And would you keep on playing that game, double or nothing?

SBF: Yeah…take the pure hypothetical… yeah.

TC: So then you keep on playing the game. What’s the chance we’re left with anything? Don’t I just St. Petersburg Paradox you into non-existence?

SBF: No, not necessarily – maybe [we’re] St. Petersburg-paradoxed into an enormously valuable existence. That’s the other option.

Boiled down, the St Petersburg Paradox involves a scenario where you have a 50% chance of winning $2.00, a 25% (1/4) chance of winning $4.00, a 1/8 chance of winning $8.00, and so on without limit. If you add up all the probabilities multiplied by the amount won for each probability, the Expected Value for this scenario is infinite. Therefore it seems like it would be rational, if you were offered a chance to play this game, to stake 100% of your net worth in one shot. However, almost nobody would actually do that; most folks might spend something like $20 or maybe 0.1% of their net worth for a shot at this, since the likely prospect of losing a large amount does not psychologically compensate for the smaller chance of gaining a much, much larger amount. But SBF is not “most folks”.

Victor Haghani recently authored an article on risk management and on SBF’s approach:

Most people derive less and less incremental satisfaction from progressive increases in wealth – or, as economists like to say: most people exhibit diminishing marginal utility of wealth. This naturally leads to risk aversion because a loss hurts more than the equivalent gain feels good. The classic Theory of Choice Under Uncertainty recommends making decisions that maximize Expected Utility, which is the probability-weighted average of all possible utility outcomes.

SBF explained on multiple occasions that his level of risk-aversion was so low that he didn’t need to think about maximizing Expected Utility, but could instead just make his decisions based on maximizing the Expected Value of his wealth directly. So what does this mean in practice? Let’s say you find an investment which has a 1% chance of a 10,000x payoff, but a 99% chance of winding up worth zero. It has a very high expected return, but it’s also very risky. How much of your total wealth would you want to invest in it?

There’s no right or wrong answer; it’s down to your own personal preferences. However, we think most affluent people would invest somewhere between 0.1% and 1% of their wealth in this investment, based on observing other risky choices such people make and surveys we’ve conducted…

SBF on the other hand, making his decision strictly according to his stated preferences, would choose to invest 100% of his wealth in this investment, because it maximizes the Expected Value of his wealth.

Even in a game with a fair 50/50 outcome, a player with finite resources will eventually go broke. This is the “Gambler’s Ruin” concept in statistics. SBF’s outsized penchant for risk took his net worth to something like $30 billion earlier this year, something we more-timid souls will never achieve, but it eventually proved to be his undoing.

Most people have a more or less logarithmic sense of the utility of money – if you only have $1000, the gain or loss of $100 is significant, whereas $100 is lost in the noise for someone whose net worth is over a million dollars. SBF apparently felt that he was playing with such big numbers, that he did not need to worry about big losses, as long as there was a chance at a big, big win. Here is a Twitter Thread  by SBF, from  Dec 10, 2020:

SBF: …What about a wackier bet? How about you only win 10% of the time, but if you do you get paid out 10,000x your bet size?

[So, if you have $100k,] Kelly* suggests you only bet $10k: you’ll almost certainly lose. And if you kept doing this much more than $10k at a time, you’d probably blow out.

…this bet is great Expected Value; you win [more precisely, your Expected Value is] 1,000x your bet size.

…In many cases I think $10k is a reasonable bet. But I, personally, would do more. I’d probably do more like $50k.

Why? Because ultimately my utility function isn’t really logarithmic. It’s closer to linear.

…Kelly tells you that when the backdrop is trillions of dollars, there’s essentially no risk aversion on the scale of thousands or millions.

Put another way: if you’re maximizing EV(log(W+$1,000,000,000,000)) and W is much less than a trillion, this is very similar to just maximizing EV(W).

Does this mean you should be willing to accept a significant chance of failing to do much good sometimes?

Yes, it does. And that’s ok. If it was the right play in EV, sometimes you win and sometimes you lose.

(*The Kelly criterion is a formula that determines the optimal theoretical size for a bet.)

Haghani concludes, “It seems like SBF was essentially telling anyone who was listening that he’d either wind up with all the money in the world, which he’d then redistribute according to his Effective Altruist principles – or, much more likely, he’d die trying.”

( Full disclosure: I have lost an irritating amount of money thanks to SBF’s shenanigans. My BlockFi crypto account is frozen due to fallout from the FTX collapse, with no word on if/when I might see my funds again. )

Introducing Students to Text Mining II

In the Fall of 2020, I blogged about how I introduce students to text mining, as part of a data analytics class.

Could Turing ever have imagined that a human seeking customer service from a bank could chat with a bot? Maybe text mining is a big advance over chess, but it only took about one decade longer for a computer (developed by IBM) to beat a human in Jeopardy. Winning Jeopardy requires the computer to get meaning from a sentence of words. Computers have already moved way beyond playing a game show to natural language processing.

https://economistwritingeveryday.com/2020/11/07/introducing-students-to-text-mining/

I told the students that “chat bots” are getting better and NLP is advancing. By July 2020, OpenAI had released a beta API playground to external developers to play with GPT-3, but I did not sign up to use it myself.

In April of 2022, I added some slides inspired by Alex’s post about the Turing Test that included output from Google’s Pathway Languages Model. According to Alex, “It seems obvious that the computer is reasoning.”

This week in class, I did something that few people could have imagined 5 years ago. I signed into the free new GPTChat function in class and typed in questions from my students.

We started with questions that we assumed would be easy to answer:

Then we were surprised that it answered a question we had thought would be difficult:

And then we asked two questions that prompted the program to hedge, although for different reasons.

It seems like the model is smarter than it lets on. For now, the creators are trying hard not to offend anyone or get in the way of Google’s advertising business. Overall, the quality of the answers are high.

Because of when I was born, I believe that something I have published will make it into the training data for these models. Will that turn out to be more significant than any human readers we can attract?

Of course, GPT can still make mistakes. I’m horrified by this mischaracterization of my tweets:

Protests Erupt Across China Over COVID Policy But Lockdowns Continue: Why?

Headlines in today’s financial news include items like “Clashes in Shanghai as COVID protests flare across China“ from Yahoo Finance. There have been widespread protests this week, which are normally a rarity under the authoritarian regime, and are being suppressed by any means necessary. Apple stock is down about 4% in the past two trading days on fears that iPhone shortages will get worse due to worker unrest in the giant Foxconn factory in Zhengzhou. Wall Street keeps hoping the China will loosen up, since the lockdowns on the world’s second-largest economy are a drag on global markets.

China has pursued a “zero-COVID” policy, of strict mass lockdowns to halt any spread of the virus. Residents have been confined to their apartments for over 3 months in some cases. Whole cities with tens of millions of people have been locked down for months at a time whenever a number of cases are spotted. China’s economic growth has stagnated, and unemployment among young people has risen to 20%, which has helped fuel unrest.  Chinese people are aware that the rest of the world has moved on from mass lockdowns, and may be realizing the futility of thinking that lockdowns can stave off the virus indefinitely.

Given its discomfort with widespread discontent and protests, why does the Chinese government persist in this policy? An article in the Atlantic by Michael Shuman answers that question: “Zero COVID Has Outlived Its Usefulness. Here’s Why China Is Still Enforcing It. “  Back in 2020 when COVID first swept through the world, the strict lockdowns (readily enforced in an authoritarian regime) seemed like a big win for the Chinese leadership:

When the outbreak began in Wuhan in early 2020, the virus was unknown, vaccines were unavailable, and China’s poorly equipped health system could have quickly become overwhelmed by a sweeping pandemic. Yet the policy had a political element from the very beginning as well. The Communist Party is adept at sniffing out threats to its rule, and it quickly identified COVID as one of them. A major public-health crisis, with millions dying, would have raised serious doubts about the regime’s competence, which is, in effect, its sole claim to legitimacy.

Worse, the party could have faced a populace that directly blamed it for the outbreak—with good reason. The Chinese authorities at both the national and local levels botched their initial response to the novel coronavirus, suppressing information about its discovery by a Wuhan doctor and acting far too slowly to contain the initial spread. Sensing its potential vulnerability, the party shifted into anti-COVID overdrive, shutting down large swaths of the country, with the result that it did succeed in snuffing out an epidemic in a matter of weeks, even as it spread to the rest of the world.

That success allowed the Communist Party to transform a potential tragedy into a public-relations triumph. Within weeks of the Wuhan outbreak, China’s propaganda machine was touting the wonders of its virus-busting methods. And as the U.S. and other Western countries struggled to contain the disease, Beijing’s big win became even more valuable as evidence that its authoritarian system was more capable and caring than any democratic one. Beijing and its advocates pointed to rising case and death counts in the U.S. as proof of China’s superiority and American decline.

A number of other countries including Australia and New Zealand also implemented strict (stricter than in the U.S.) lockdown measures in 2020, and, like China, experienced far less impact from the virus in that timeframe than seen in the U.S. However, most of these measures were lifted in 2021. The widespread application of mRNA vaccines like those from Pfizer and Moderna in the West has served to mitigate the severity of the viral infection. Also, some measure of herd immunity has been achieved by the widespread exposure to COVID in the population; antibodies persist for at least eight months after contracting the disease. So, what’s up with China?

China has resisted using Western vaccines, relying instead on homegrown vaccines which are less effective, though they do give some measure of protection.  Also, “The additional layers of high-tech surveillance adopted in the name of pandemic prevention can be used to enhance the tracking and monitoring of the populace more generally,” which is another win for the government. However, the major factor is that the Party, and especially President Xi, cannot afford to loosen up now and risk an embarrassing explosion of cases that would overburden the healthcare system and probably lead to millions of deaths:

The victory of zero COVID was claimed not just as the party’s but as Xi Jinping’s in particular. The State Council, China’s highest governing body, declared in a 2020 white paper that Xi had “taken personal command, planned the response, overseen the general situation and acted decisively, pointing the way forward in the fight against the epidemic.”

This narrative became entrenched. If Beijing loosened up and allowed COVID to run amok, the Chinese system would appear no better than those of loser democracies, and Xi would seem like another failing politician, a mere mortal, not the virus-fighting superhero he was painted as. Zero COVID’s failure would be a disaster for the Communist Party’s veneer of infallibility.

So the leadership insists on zero COVID and damn the consequences.