Europe Natural Gas Shortage: Factories Shut, Maybe Worse to Come

Scott Buchanan2 Comments

Shut down your old reliable coal and nuclear power plants. Replace them with wind turbines. Count on natural gas fueled power plants to fill in when the breeze stops blowing. Curtail drilling for your own natural gas, and so become dependent on gas supplied by pipeline from Russia or by tankers chugging thousands of miles from the Middle East. What could possibly go wrong?

That is what Europe is discovering now as natural gas prices have quintupled, taking electricity prices up with them. Europe is having a hard time finding enough gas supply to fill up storage facilities to get them through the winter. If consumers are prioritized, widespread industry shutdowns are possible if there is a cold winter. Prices for many things will rachet up, with implications for inflations and in turn for central banks’ response to inflation. (The Fed’s Powell has been talking down the current inflation as merely transitory).

 In the UK, energy companies are going bankrupt because the wholesale price that at which they purchase gas is higher than the government-mandated cap on gas price they can charge consumers. Plants which use natural gas as a feedstock like fertilizer plants are shutting down, which impacts farmers. Carbon dioxide is a byproduct of some of these operations, and the resulting shortage of CO2 is affecting meat-packers who use it in their operations. Indeed, a food producer has warned that the Christmas dinner could be “cancelled.” That’s just how bad it is. The Brits are even delaying the shutdown of the country’s largest remaining coal-burning power plant.

Jason Bordoff of the Columbia Climate School and the Center on Global Energy Policy just published a long article giving his perspective on all this. He identifies several contributing factors:

( 1 ) Cold and hot weather affected gas consumption this year. Winter in much of the Northern Hemisphere was unusually cold earlier this year, which boosted gas demand for heating. And then a hot summer consumed more gas to make electricity for air conditioning. 

( 2 ) Other sources of electricity have been hampered. “Wind generation in Europe has been far below average this year due to long periods of less windy weather. …Demand for fossil fuels is set to spike further as Germany takes another three nuclear reactors off the grid this year as part of its nuclear shutdown. Meanwhile, drought conditions in China and South America have led to reduced hydropower output, drawing supplies of globally traded gas into those markets instead.”

( 3 ) The post-COVID economic recovery has boosted industrial demand.

( 4 ) Russia has restricted gas deliveries to Europe though the existing pipeline that runs through Ukraine. (Many observers see this as a pressure tactic to get Europe to switch over to a northern pipeline route, which would then remove the importance of Ukraine for Russian gas marketing, which would then give Russia a freer hand to resume military harassment of that country.) Also, European countries have restricted their own gas production. The Dutch are curtailing the production rate at their big Groningen gas field because local residents fear earthquakes from ground subsidence, and the Brits have restricted fracking of promising gas fields due to public protests.

As might be expected in our interconnected world, the European supply crunch has affected U.S. prices, which are at their highest level in five years. America exports gas via liquified natural gas (LNG) tankers, but U.S. gas supplies so far have not responded much to the price increase. The hostility of the Biden administration and pressure from green-leaning investors has discouraged petroleum companies from expanding drilling.

Meanwhile, California is running its own experiment in green energy  adoption:             

California, for example, is having trouble keeping the lights on as it rapidly scales the use of intermittent solar and wind power. It recently requested an emergency order from the U.S. federal government to maintain system reliability by, among other actions, allowing the state to require certain fossil fuel plants scheduled to retire to stay online and by loosening pollution restrictions. California is also proposing to build several temporary natural gas plants to avoid blackouts, even as the state shuts down the Diablo Canyon nuclear power plant, which produces more zero-carbon electricity than all the state’s wind turbines combined.

Professor Bordoff notes that “Many projections for how quickly and how much clean energy can be scaled are based on stylized models of what is technically and economically possible”, and unsurprisingly calls for policies which mitigate volatility, e.g., “…regulatory and infrastructure policies can facilitate more integration, flexibility, and interconnectedness in the energy system—from power grids to pipelines—so there are more options to pull energy supplies into a market when needed.”

Oh, and this restatement of the obvious:  

Uncertainty about the pace of transition may lead to periodic shortfalls in supply if climate action shutters traditional fossil fuel infrastructure before alternatives can pick up the slack—as may be starting to happen in some places now. And if fossil fuel supply is curbed faster than the pace at which fossil fuel demand falls, shortfalls can result in market crunches that cause prices to spike and exacerbate existing geopolitical risks. In fact, this is what the International Energy Agency just warned is happening in oil markets—a striking contrast to what it said only a few months ago, when it warned that new fossil fuel supplies would not be needed if nations were on track to achieve net-zero emissions by 2050.

Me? After working through  all this material, I’m going to go buy me some shares of ExxonMobil, the largest natural gas producer in the U.S.

Evolutionary Science and Gresham’s Law of Ideas

mdmakowsky1 Comment

So there’s a book that said something really dumb:

And by cursory inspection of excerpts and reviews, it is chock full of all kinds of silly ideas that experienced what I can only imagine to be a frictionless path from the authors’ minds to publication. I don’t really care about this book or the specific ways in which it is is bad. And I don’t really care about the authors, who appear to be mediocre self-styled evolutionary scientists whose major claims to fame appear to be favoring ivermectin over vaccines and supporting themselves financially by levying a lawsuit against Evergreen State College.

What I care about is evolutionary biology and psychology as subfields. The core idea is that the evolutionary framework of persistent adoption and adaptation of traits under unrelenting selective pressures can be a useful modeling framework for generating theories of social, economic, biological, and psychological phenomena. Evolutionary selection is a good idea, one of the most powerful in intellectual history! But to me, an outsider economist with a long-ago acquired undergraduate degree in biology, the subfields seems to be suffocating under the weight of ad hoc theories generated in volume by marginal practitioners and non-scientists. Why? What’s wrong with evolutionary sciences? Here’s a couple thoughts.

1) There’s nothing wrong. Saying something is wrong with the subfields is like watching The Shining and thinking “There’s something wrong with axes”. This is just a bad book with bad ideas thought up by authors with minimal right to claim the mantle of evolutionary science.

That’s a totally reasonable response but I’m in no mood to leave well enough alone.

2) There’s a perverse selective pressure within evolutionary sciences where the worst ideas rise to the level of public dissemination. The culling forces of the popular press select along dimensions that are not merely orthogonal to good science, they are actively selecting against it. Put in the language of my own field, publishing bad ideas seems to be more profitable than publishing good ones.

That’s pretty big claim, and one for which I have no real proof, just tacit intuition and a small number of anecdotes. Sorting through the reviews of the Heying & Weinstein book, I thought of the brief phenomenon that was “Sex at Dawn” a decade ago. It, similarly, sold a breathless explanation of human behavior, specifically promiscuity. Emphasis on the world sold. “Sex at Dawn” proved that you could be scientifically hollow and still sell a boatload of copies. For those who are curious, here’s a review by an evolutionary psychologist that doesn’t hold nearly the grudge that I do. He politely sifts through the major claims, weaving through the silliness to find the handful of specific claims, and proceeds to debunk them. Other reviewers were considerably less kind (including those at Oxford Press, who rejected it for publication).

So why are these and similar books, so successful?

I’ve long suspected that there is a Gresham’s Law of Popular Science at work. Simply put, bad ideas are less costly to generate than good ones, so they are more plentiful. For the non-expert consumer of popular science, this raises the costs of search probability that a randomly encountered book is bunk. What I believe to be more problematic, though, is that bad ideas are often less costly to consume. Spoon-fed as common sense writ magnificent and powerful, pseudoscientific books get a foothold in our mind first through the scarcity of our time and attention only to then grow roots in our ego. Easily consumed during rare moments of relaxed reading, they then show us ideas that give us explanatory access to life, the universe, and everything. Why struggle through caveated niche explorations when someone else has distilled the complexity of a modern life well-lived to something that is as flexible in its flattery as a horoscope and often conveniently enumerated?

Does this happen within economics? Of course it does. It happens in every scientific field. But that is why scientific fields evolve intellectual immune systems, and often very aggressive ones at that. The entire field of “Statistics” essentially exists as the custodian of the scientific method. But there are little details that matter, too.

Take, for example, the core concept of “maximization” in economics. Sure, it gets abused, but at the end of the day it’s pretty tough to get very far with an ad hoc utility/profit/wealth maximizing model in economics that produces useful predictions. Why is that? Well, a big reason is that we’ve left out a very important word. Economists deal almost exclusively in constrained maximization. Absent constraints, nearly every maximizing model amounts to little more than a tautology. It’s requirement for maximization under constraints, both components transparently introduced, that gives a model it’s power. When I observe meritless pop evolutionary science books, mostly what I’m seeing is unconstrained just so stories that work backwards from a conclusion they believe there is a book-purchasing audience for. There are selective pressures, but where are the resource constraints? There are groups but where are the rivals they are competing with? There is this evolutionary path, but why not the other paths?

So what should evolutionary sciences do? Well, first of all, I don’t know. But if I had to guess, the answer is nothing. Nothing but do the thing a proper science always does. Do the work, push the good ideas, kill the bad ones, and trust that the custodians of the scientific method will do their jobs. And so will the editors. And the hiring committees. And the critics. Sure, a couple folks will pay a couple years mortgage, but a bit of financial and status injustice are a small price to pay while we keep the scientific mission moving forward. At least until we’re all crabs.

Clemens and Strain on Large and Small Minimum Wage Changes

Joy Buchanan2 Comments

In my Labor Economics class, I do a lecture on empirical work and the minimum wage, starting with Card & Kreuger (1993). I’m going to quickly tack on the new working paper by Clemens & Strain “The Heterogeneous Effects of Large and Small Minimum Wage Changes: Evidence over the Short and Medium Run Using a Pre-Analysis Plan”.

The results, as summarized in the second half of their abstract are:

relatively large minimum wage increases reduced employment rates among low-skilled individuals by just over 2.5 percentage points. Our estimates of the effects of relatively small minimum wage increases vary across data sets and specifications but are, on average, both economically and statistically indistinguishable from zero. We estimate that medium-run effects exceed short-run effects and that the elasticity of employment with respect to the minimum wage is substantially more negative for large minimum wage increases than for small increases.

The variation in the data comes from choices by states to raise the minimum wage.

A number of states legislated and began to enact minimum wage changes that varied substantially in their magnitude. … The past decade thus provided a suitable opportunity to study the medium-run effects of both moderate minimum wage changes and historically large minimum wage changes.

We divide states into four groups designed to track several plausibly relevant differences in their minimum wage regimes. The first group consists of states that enacted no minimum wage changes between January 2013 and the later years of our sample. The second group consists of states that enacted minimum wage changes due to prior legislation that calls for indexing the minimum wage for inflation. The third and fourth groups consist of states that have enacted minimum wage changes through relatively recent legislation. We divide the latter set of states into two groups based on the size of their minimum wage changes and based on how early in our sample they passed the underlying legislation.

The “large” increase group includes states that enacted considerable change. New York and California “have legislated pathways to a $15 minimum wage, the full increase to which firms are responding exceed 60 log points in total.” Data comes from the American Community Survey (ACS) and the Current Population Survey (CPS).

Continue reading →

Human Capital and Filepaths

Joy Buchanan3 Comments

Someone wrote a story about my life. It’s a report from The Verge called “File Not Found: A generation that grew up with Google is forcing professors to rethink their lesson plans”.

When I started teaching an advanced data analytics class to undergraduates in 2017, I noticed that some of them did not know how to locate files on a PC. Something that is unavoidable in data analytics is getting software to access data from a storage device. It’s not “programming” nor is it “predictive analytics”, but you can’t get far without it. You need to know what directory to point the software to, meaning that you need to know what directory contains the data file.

As the article says

the concept of file folders and directories, essential to previous generations’ understanding of computers, is gibberish to many modern students. It’s the idea that a modern computer doesn’t just save a file in an infinite expanse; it saves it in the “Downloads” folder, the “Desktop” folder, or the “Documents” folder, all of which live within “This PC,” and each of which might have folders nested within them, too. It’s an idea that’s likely intuitive to any computer user who remembers the floppy disk.

I am a long-time PC user. Navigating File Explorer is about as instinctive as drinking a glass of water for me. The so-called digital natives of Gen Z have been glued to mobile device screens that shield them from learning anything about computers.

Not everyone needs to know how computers work. I myself only know the layer that I was forced to learn.

My Dad, to whom I owe so much, kept a Commodore 64 in a closet in our house. About once a year, he would try to entice me into learning how to use it. I remember screwing up my 9-year-old eyes and trying to care. Care, I could not. It’s hard to force yourself to do extra work without a clear goal. The Verge article explains

But it may also be that in an age where every conceivable user interface includes a search function, young people have never needed folders or directories for the tasks they do. The first internet search engines were used around 1990, but features like Windows Search and Spotlight on macOS are both products of the early 2000s. Most of 2017’s college freshmen were born in the very late ‘90s. They were in elementary school when the iPhone debuted; they’re around the same age as Google. While many of today’s professors grew up without search functions on their phones and computers, today’s students increasingly don’t remember a world without them.

One area in which I do minimum archiving is my email. I rely heavily on the search function. I could spend time creating email folders, but I’m not going to put in the time unless I’m forced to.

Here’s where the “problem” lies:

The primary issue is that the code researchers write, run at the command line, needs to be told exactly how to access the files it’s working with — it can’t search for those files on its own. Some programming languages have search functions, but they’re difficult to implement and not commonly used. It’s in the programming lessons where STEM professors, across fields, are encountering problems.

Regardless of source, the consequence is clear. STEM educators are increasingly taking on dual roles: those of instructors not only in their field of expertise but in computer fundamentals as well.

Personally, I don’t mind taking on that dual role. I didn’t learn to program until I really wanted to. The only reason I wanted to was that I had discovered economics. I wanted to be able to participate in social science research. Let these STEM or business courses be the motivation for students to learn to use computers as tools instead of just for entertainment.

Allen Downey wrote a great blog on this topic back in 2018 that is more practical for teachers than the Verge report. He argues that learning to program will be harder for the 20-year-olds of today than it was for “us” (old people as defined by entering college before 2016). He recommends a few practical strategies, while acknowledging that there is “pain” somewhere along the process. He thinks it is sometimes appropriate to delay that pain by using browser-based programming interfaces, in the beginning.

I gave my students a break from pain this week with a little in-browser game that you can play at https://www.brainpop.com/games/blocklymaze/ They got 10 minutes to forget about file paths, and then it was back to the hard work.

I have found that a lot of students need individual attention for this step – the finding a file in their hard drive. I only have to do that once per student. Students pick the system up quickly. File Explorer is a pretty user-friendly mechanism. Everyone just has to have a first time. Sometimes, Zoomers just need a real person who cares about them to come along and say, “The file you downloaded exists on this machine.”

One way around this problem is to reference data that lives on the internet instead of in a local machine. If you are working through the examples in Scott Cunningham’s new book Causal Inference, here’s a piece of the code he provides to import data from his public repository into R.

full_path <- paste(https://raw.github.com/scunning1975/mixtape/master/, df, sep=“”)

df <- read_dta(full_path)

The nice thing about referencing data that is freely available online is that the same line of code will work on every machine as long as the student is connected to the internet.

As more and more of life moves into the cloud, technologists might increasingly be pointing programs to a web address instead of the /Downloads folder on their local machine. Nevertheless, the kids need to have a better sense of where files are stored. He or she who can understand file architecture is going to get paid a lot more than their peers who only know who to poke and scroll on a smartphone.

There is a future scenario in which AI does most of the programming for us. When AI can fetch files for us, then File Explorer may seem obsolete. But I worry about a world in which fewer and fewer humans know where their information is stored.

Avoiding Intertemporal Idiosyncratic Risk

Zachary Bartsch1 Comment

Hopefully by this time we all know about index funds. The idea is that by investing in a large, diversified portfolio, one can enjoy the average return across many assets and avoid their individual risk. Because assets are imperfectly correlated, they don’t always go up and down at the same time or in the same magnitude. The result is that one can avoid idiosyncratic risk – the risk that is specific to individual assets. It’s almost like a free lunch. A major caveat is that there is no way to diversify away the systemic risk – the risk that is common across all assets in the portfolio.

We can avoid the idiosyncratic risk among assets. But, we can also avoid idiosyncratic risk among times. Each moment has its own specific risks that are peculiar to it. Many people think of investing as a matter of timing the market. However, people who try to time the market are actively adopting the specific risks that are associated with the instant of their transaction. This idea seems obvious now that I’m writing it down. But I had a real-world investing experience that– though embarrassing in hindsight – taught me a heuristic for avoiding overconfidence and also drilled into my head the idea of diversifying across time.

I invested a lot into my preferred index fund this past year. I’d get a chunk of money, then I’d turn around and plow it into the fund. What with the Covid rebound, it was an exciting time. I started paying more attention to the fund’s performance, identifying patterns in variance and the magnitude of the irregularly timed and larger changes. In short, by paying attention and looking for patterns, I was fooling myself into believing that I understood the behavior of the fund price.

And it’s *so* embarrassing in hindsight. I’d see the value rise by $10 and then subsequently fall to a net increase of $5. I noticed it happening several times. I acted on it. I transferred funds to my broker, then waited for the seemingly regular decline. Cha-ching! Man, those premium returns felt good. Success!

Silly me. I thought that I understood something. I got another chunk of change that was destined for investing. I saw the $10 rise of my favorite fund and I placed a limit order, ensuring that I’d be ready when the $5 fall arrived. And I waited. A couple weeks passed. “NBD, cycles are irregular”, I told myself. A month passed. And like a guy waiting at the wrong bus stop, my bus never arrived. All the while, the fund price was ultimately going up. I was wrong about the behavior of the fund. Not only did I fail to enjoy the premium of the extra $5 per share. I also missed what turned out to be a $10 per share gain that I would have had if I had simply thrown in my money in the first place, inattentive to the fund’s performance.

Reevaluation

I hate making bad decisions. I can live with myself when I make the right decision and it doesn’t pan out. But if I set myself up for failure through my own discretion, then it hurts me at a deep level. What was my error? Overconfidence is the answer. But why did it hurt me?

Continue reading →

Covid, Cars, China, Crypto, Corruption

James BaileyLeave a comment

We generally do long “effort posts” on specific topics here, but today I’m mixing things up with 5 quick updates.

  1. Covid My daughter got sent home with a cough Tuesday, which meant I cancelled classes Wednesday to hang out with her until we get a Covid-negative PCR. Last Thursday my son’s public school was closed for Yom Kippur, and I got so focused on hanging out with him I forgot to post here.
  2. Cars My wife bought a new used car last week. We’ve covered here how car prices have jumped up while inventories fell this summer, and the latest numbers show that used car prices are now falling slightly from very high levels while new car prices continue to rise. While actually buying a car, the low inventories stood out even more than the high prices. Several times we saw a promising car online, only to call or visit the dealer and find out it had sold the day before. The new Nissan Leaf sounds like an excellent value at its sticker price, but none were available in Rhode Island, and no blue ones anywhere in New England.
  3. China Scott covered the collapsing Chinese real estate market on Tuesday. I’ll just pass along the takes I’ve seen from Western economists and China-watchers Michael Pettis and Christopher Balding, which is that this is a big deal that will slow Chinese growth for years but is unlikely to precipitate a 2007-style financial crisis. I find Balding’s argument that financial contagion will be limited to be convincing partly because of his actual arguments about quasi-bailouts, and partly because he almost always argues that “things in China are worse than you think”, so if he says “Evergrande isn’t Lehman Brothers” I listen.
  4. Crypto Tuesday I met the co-founder of a new crypto-based prediction market, Melange, which sounds promising. The prediction market space is growing rapidly with PolyMarket and Kalshi joining the older PredictIt.
  5. Corruption Last week the World Bank announced it is discontinuing the Doing Business report/ranking due to apparent corruption; top Bank officials in the middle of raising money from countries including China pushed to raise the rankings of those countries beyond what the data justified. I hope another organization steps up do continue the good parts of the Doing Business report in a more trustworthy way.

Selectivity and Selection Bias: Are Selective Colleges Better?

Jeremy Horpedahl1 Comment

If you have ever been through the process of applying to colleges, you have almost certainly heard the term “selective colleges.” If you haven’t the basic idea is that some colleges are harder to get into, for example as measured by what percentage of applicants are accepted to the school. The assumption of both applicants and schools is that a more selective college is “better” in some sense than a less selective college. But is it?

In a new working paper, Mountjoy and Hickman explore this question in great detail. The short version of their answer: selective colleges don’t seem to matter much, as measured by either completion rates or earnings in the labor market. That’s an interesting result in itself, but understanding how they get to this result is also interesting and an excellent example of how to do social science correctly.

Here’s the problem: when you just look at outcomes such as graduation rates or earnings, selective colleges seem to do better. But most college freshmen could immediately identify the problem with this result: that’s correlation, not causation (and importantly, they probably knew this before stepping onto a college campus). Students that go to more selective colleges have higher abilities, whether as measured by SAT scores or by other traits such as perseverance. It’s a classic selection bias problem. How much value is the college really adding?

Here’s how this paper addresses the problem: by only looking at students that apply to and are accepted to colleges with different selectivity levels, but some choose to go to the less selective colleges. What if we only compare this students (and of course, control for measurable differences in ability)?

Now this approach is not a perfect experiment. Students are not randomly assigned to different colleges. There is still some choice going on. But are the students who choose to attend a less selective college different in some way? The authors try to convince us in a number of ways that they are not really that different. Here’s one thing they point out: “nearly half of the students in our identifying sample choose a less selective college than their most selective option, suggesting this identifying variation is not merely an odd choice confined to a small faction of quirky students.”

Perhaps that alone doesn’t convince you, but let’s proceed for now to the results. This chart on post-college earnings nicely summarizes the results (see Figure 3 in the paper, which also has a very similar chart for completion rates)

Continue reading →

Likely Collapse of Chinese Real Estate Conglomerate Evergrande Roils World Markets

Scott Buchanan2 Comments

Nearly a year ago, on this blog we described the sequence of events that led to the Great Recession ( or “Global Financial Crisis”) of 2008-2009. The underlying problem was real estate-related debt: as inflated housing prices collapsed, many people couldn’t (or wouldn’t) pay their mortgages. Various financial dominos fell, but the one that gets singled out as the single most critical event was  the collapse of Lehman Brothers investment bank on September 15, 2008. The Dow Industrial average fell 504 points that day, and loss of confidence in the financial markets led to a freeze-up in credit, which was/is the lifeblood of business.

The likely bankruptcy of the gigantic Chinese real estate conglomerate Evergrande is being discussed as another possible “Lehman Moment”. It is hard to comprehend just how big this outfit is. It owns more than 1,300 real estate projects across China, directly employs 200,000 people, and is indirectly sustains some 3.8 million jobs. It got that big by borrowing (including selling bonds) and spending enormous amounts of money. The problem now is that it seems like it cannot service its $300 billion debt. Once things like this start to go bad, they often get much worse, quickly. Other parties stop wanting to do business with you, and it all goes downhill. (A famous reply in Ernest Hemingway’s The Sun Also Rises to the question, “How do you go bankrupt?” was “Gradually, and then suddenly”). The market prices on Evergrande’s bonds indicate that the market expects bankruptcy, with bondholders getting only about 25 cents on the dollar.

If this collapse materializes fully, a lot of investors will lose a lot of money, a lot of suppliers of building materials to Evergrande will not get paid and may go broke, and a lot of real estate development in China will freeze up for the time being.  Goldman Sachs estimates a 1-4% hit to China’s GDP, which is huge, and would reverberate across the whole world.

Wall Street seems to have been ignoring this drama, until yesterday (Monday). Blam, stocks fell around 2%, and were still headed south at the end of trading. Is this the start of The Big One? Well, that makes for dramatic commentary, but most observers seem to take a more nuanced approach. First, the all-powerful Chinese government could order the People’s Bank of China to “fix this”. We all now know that central banks have magical powers to create as much money as needed to, e.g., buy all outstanding Evergrande bonds at near-par. On the other hand, the Chinese government lately has been clamping down on speculation. So there may be some sort of compromise, a semi-orderly unwinding, with bondholders feeling some pain, but actual real estate operations being sold off and continuing under some other names.

Wall Street may be more worried about whether the Fed announced on Wednesday that it will take away the punch bowl by tapering of its bond purchases. The last time the Fed did that, in 2018, stocks took a long and hard tumble. Again, a range of outcomes is possible here.

Ironically, all these concerns, as long as they don’t really turn into something serious, may be a bullish indicator for stocks. Stocks are said to “climb a wall of worry”; it is when everyone is totally complacent that is a setup for a crash. Time will tell whether the Evergrande difficulties end up being part of  a bullish wall or a bearish cliff.

An Economic Model of Loneliness and Being Extremely Online

mdmakowsky3 Comments

Bo Burnham is a comedian and musician who, like so many of the artists I enjoy, produces art that I can only describe as extremely specific to him. His newest special on Netflix features a song, “Welcome to the Internet” (some NSFW lyrics), that I liked so much I thought it was worth writing as a formal model.

No, really. Hey, we all need a hobby.

The whole song is a meditation on the overwhelming nature of the internet and is, in my opinion, fantastic. I think if we zero in on two pieces of refrain in the lyrics, we can get some traction in what Burnham believes is the underlying problem, if not outright crisis, that resides within the internet and those that are “extremely online”:

First, the lure:

Could I interest you in everything?
All of the time?
A little bit of everything
All of the time

This is the value-add of the internet and why we can never, and will never, leave it behind willingly. This is also the “cognitive overload” hypothesis of why the internet is bad. Sure, for the infovores of the world there hasn’t been a bigger technological shift since the printing press, but there certainly exists the possibility that most human minds (if any) aren’t built to handle the deluge of information they are drowning in. That’s one theory, but I think that’s the kind of problem that isn’t actually a problem. Some will consume more of the internet, some will consume less, c’est la vie.

It’s in the second half of the refrain, however, that we see the actual problem.

Apathy’s a tragedy
And boredom is a crime
Anything and everything
All of the time

And therein lies the rub. You can’t opt out. But is that true? Well, that depends on who you are and how you live your best life i.e. how you optimize your utility function. So let’s do it. Let’s write down the utility function that lives inside the song. What we’re going to do is this- we’re going to lay out the simple components as natural language, then turn it into formal math, and then bring it back to natural language.

In our Burnhamian mode, people need two things: Private goods like food and shelter and Social Goods like friendship and camraderie. How much Utility you enjoy will always be increasing in both, but the optimal mix will depend on your constraints (wealth, time, accessible population) and the mathematical function determining how much Utility you get from a mix of Private and Social goods i.e. are they additive, multiplicative, or something else. Utility equal to zero is equivalent to death.

Let’s add one last layer of complexity. Let’s say that your Social goods are a function of two kinds of elements: Friends and Clubs. Friends are direct, one-to-one relationships. Clubs are large social groups. We will define and differentiate between the two as such: if you cease to be part of a friendship (whether between 2,3, or 5 people), then that friendship no longer exists in the same form. If you drop out of a club, on the other hand, that club will persist without you.

So what a person has to do is, within their constraints, try to optimize how much of their resources they invest in their Private goods, their Friends, and their Clubs.

The first line is our base model, the second is an expanded version with our two-input model of Social goods. The function we are using is called a Constant Elasticity of Substitution utility function. The key parameter, α, determines how Private and Social goods interact. If α=1, then they are what economists call perfect substitutes. All that matters is how much you have in total of the two inputs, and if you want you could specialize in just one of them. They are perfectly additive. If, on the other hand, α=-∞ (negative infinity), then they are perfect complements, like right and left shoes. There is no point in adding even one more unit of Private goods until you have another unit of Social goods to pair with it. In a sense, they are multiplicative, meaning if either value is zero, then your utility is zero. The value of α will tell us whether the best life requires more of a mix of Social and Private inputs (if they are more complementary), or simply the most of whatever is the easiest to come by (if they are good substitutes for each other).

We’ve nested in our Friends and Clubs production of Social goods as a CES function within the second equation, with a similar story, only here β will determine how much of a mix of Friends and Clubs we want, or whether we can specialize more in one over the other. In the third and last line of the model, we’ve reduced it down to the underlying questions that will tell our story represented by addition and multiplication signs:

Are Private and Social Goods complements (multiplicative) or substitutes (additive) when we internally produce utility? Are Friends and Clubs complements or substitutes when we internally produce our Social goods?

Assumption 1: α= -0.1 Private and Social goods are weak complements. What this means is that there are diminishing returns to Private and Social goods, you need some of both, but you can have less of one or the other and its fine. Let’s just assume wealthy people need other people in their lives to stay sane while, at the same time people with rich social lives and supportive communities still need food and shelter. You can specialize a bit more on one side, depending on what’s available, but you can’t live without at least some of both.

We’re all different in how we build our social lives and, in turn, how we internalize the internet in our lives. I think we can gain some insight into this process by working out the stories in this simple model through our second parameter, β. Let’s consider three broad types of people.

Person Type 1: Friends and Clubs are Strong Substitutes (high β)

These people are either relatively offline (e.g. they still use their phones as phones to make phone calls) or extremely online (e.g. they get a panic attack unless they have 80% battery and a charge pack on their person). These are the people who can become hyper-specialized in new clubs if they are extricated from prior social networks or club settings. This is why cults recruit people who move to new places where they don’t know anyone. This is how your diehard hippie socialist friend grew up to be a conservative evangelical after they moved to the Texas suburbs.

With regards to our original question, people who hyperspecialize in their club and club identity will be constantly contributing grist to the club’s identity: evidence of the necessity of the club and it’s mission, rage at non-members, disappointment in members who aren’t committed enough, and constant vigilance in the monitoring of everyone else’s commitment. They are in it, they are of it, and they are ready to purge.

Apathy’s a tragedy (You must care about everything the club cares about)
And boredom is a crime (All of your time must be allocated to the club)
Anything and everything
All of the time

Type 2: Friends and Clubs are strong complements (low β)

These are the people that I think Burnham’s song is targeted at, for whom he has the most sympathy, and with whom I suspect he would count himself. These are people for whom the internet is the most taxing, the most exhausting to navigate.

Type 2 folks want to have personal friendships and friend groups while still feeling a part of something bigger, whether it’s a community, a political movement, or spiritual affiliation. Type 2 people will have preferences towards one or more social identities manifested as clusters on the internet, but they don’t want to purge people who don’t share those preferences from their circle of friends. Type 2 folks are interested in civil rights and social justice, but they want to diversify their emotional and material resources across their personal relationships and private wellbeing as well.

The deluge of the internet, with its stark images, focus on extreme outcomes, battle cries, and public reputation mauling, are constantly admonishing and shaming Type 2’s. Type 2 people are tired. Perhaps most importantly, the pandemic has been especially hard on Type 2’s. While Type 1 club-specialists have thrived by focusing the totality of their efforts to the online arena, their voices have been tearing the Type 2 social-portfolio diversifiers to shreds.

Type 3: Friends and Clubs are weak complements (middle β)

Type 3 people are a lot like Type 2’s, but it is easier for them to compartmentalize the production of their social goods. Type 3 people are often in clubs, but they are rarely of clubs. They’re not joiners. Whether you’re looking at sacrifice-demanding religious cults or extremely-online political culture warriors, if the social associations of the world demand too much of Type 3 people, they are happy to half-ass their contribution or opt-out entirely. They might be on Twitter or Facebook, but they don’t need to reply to anyone. They might go to church on Sunday with the family, but if the minister tells them their sister is going to hell for their sexual preference, it’s just not that costly to stop going. For them clubs will always remain a luxury good, never a necessity.

To be clear this post is an exercise in building a toy model of something big and complex and important. It’s a gross abstraction and shouldn’t be taken too seriously. The process of formalizing your thinking on a social mechanism, however, is something that I think you should take very seriously. Formal models are useful because there is no hiding what your idea actually is. There’s no “sorry, you misread me” or reliance on obscure jargon. Formal models force you to clarify and reveal your thinking to everyone, including yourself. They can open up new avenues for explorations and even generate empirically testable predictions. Formal models have in many ways been the principal force behind economic imperialism in the social sciences. Not because the math is perfect or all encompassing or even correct. It’s because it’s all out there, ready to be judged and dissected and tested. That transparency makes it a useful.

I don’t know if my interpretation of Bo Burnham’s theory of the internet is correct or even necessarily what he intended it to be. But this is one way we can take it a step forward and see what we can actually learn from it. Which is pretty much all I want to do for the rest of my research life, on every topic, all of the time.

Behavioral Economist at Work

Joy Buchanan3 Comments

A blog post titled “The Death of Behavioral Economics” went viral this summer. The clickbait headline was widely shared. After Scott Alexander debunked it point-by-point on Astral Codex Ten, no one corrected their previous tweets. I recommend Scott’s blog for the technical stuff. For example, there is an important distinction between saying that loss aversion does not exist versus saying that its underlying cause is the Endowment Effect.

The author of the original death post, Hreha, is angry. Here’s how he describes his experience with behavioral economics.

I’ve run studies looking at its impact in the real world—especially in marketing campaigns.

If you read anything about this body of research, you’ll get the idea that losses are such powerful motivators that they’ll turn otherwise uninterested customers into enthusiastic purchasers.

The truth of the matter is that losses and benefits are equally effective in driving conversion. In fact, in many circumstances, losses are actually *worse* at driving results.

Why?

Because loss-focused messaging often comes across as gimmicky and spammy. It makes you, the advertiser, look desperate. It makes you seem untrustworthy, and trust is the foundation of sales, conversion, and retention.

He’s trying to sell things. I wade through ads every day and, to mix metaphors, beat them off like mosquitoes. Knowing how I feel about sales pitches, I don’t envy Hreha’s position.

I don’t know Hreha. From reading his blog post, I get the impression that he believes he was promised certain big returns by economists. He tried some interventions in a business setting and did not get his desired results or did not make as much money as he was expecting.

According to him, he seeks to turn people into “enthusiastic purchasers” by exploiting loss aversion. What would consumers be losing, if you are trying to sell them something new? I’m not in marketing research so I should probably just not try to comment on those specifics. Now, Hreha claims that all behavioral studies are misleading or useless.

The failure to replicate some results is a big deal, for economics and for psychology. I have seen changes within the experimental community and standards have gotten tougher as a result. If scientists knowingly lied about their results or exaggerated their effect sizes, then they have seriously hurt people like Hreha and me. I am angry at a particular pair of researchers who I will not name. I read their paper and designed an extension of it as a graduate student. I put months of my life into this project and risked a good amount of my meager research budget. It didn’t work for me. I thought I knew what was going to happen in the lab, but I was wrong. Those authors should have written a disclaimer into their paper, as follows:

Disclaimer: Remember, most things don’t work.

I didn’t conclude that all of behavioral research is misleading and that all future studies are pointless. I refined my design by getting rid of what those folks had used and eventually I did get a meaningful paper written and published. This process of iteration is a big part of the practice of science.

The fact that you can’t predict what will happen in a controlled setting seems like a bad reason to abandon behavioral economics. It all got started because theories were put to the test and they failed. We can’t just retreat and say that theories shouldn’t get tested anymore.

I remember meeting a professor at a conference who told me that he doesn’t believe in experimental economics. He had tried an experiment once and it hadn’t turned out the way he wanted. He tried once. His failure to predict what happened should have piqued his curiosity!

There is a difference between behavioral economics and experimental economics. I recommend Vernon Smith’s whole book on that topic, which I quoted from yesterday, for those interested.

The reason we run experiments is that you don’t know what will happen until you try. The good justification for shutting down behavioral studies is if we get so good at predicting what interventions will work that the new data ceases to be informative.

Or, what if you think nudges are not working because people are highly sensible and rational? That would also imply that we can predict what they are going to do, at least in simple situations. So, again, the fact that we are not good at predicting what people are going to do is not a reason to stop the studies.

I posted last week about how economists use the word “behavioral” in conversation. Yesterday, I shared a stinging critique of the behavioral scientist community written by the world’s leading experimental researcher long before the clickbait blog.

Today, I will share a behavioral economics success story. There are lots of papers I could point to. I’m going to use one of my own, so that readers could truly ask me anything it. My paper is called “My reference point, not yours”.

I started with a prediction based on previous behavioral literature. My design depended on the fact that in the first stage of the experiment, people would not maximize expected value. You never know until you run the experiment, but I was pretty confident that the behavioral economics literature was a reliable guide.

Some subjects started the experiment with an endowment of $6. Then they could invest to have an equal chance of either doubling their money (earn $12) or getting $1. To maximize expected value, they should take that gamble. Most people would rather hold on to their endowment of $6 than risk experiencing a loss. It’s just $5. Why should the prospect of losing $5 blind them to the expected value calculation? Because most humans exhibit loss aversion.

I was relying on this pattern of behavior in stage 1 of the experiment for the test to be possible in stage 2. The main topic of the paper is whether people can predict what others will do. High endowment people fail to invest in stage 1, so then they predict that most other participants failed to invest. The high endowment people failed to incorporate easily available information about the other participants, which is that starting endowments {1,2,3,4,5,6} were randomly assigned and uniformly distributed. The effect size was large, even when I added in a quiz to test their knowledge that starting endowments are uniformly distributed.

Here’s a chart of my main results.

Investing always maximizes expected value, for everyone. The $1 endowment people think that only a quarter of the other participants fail to invest. The $6 endowment people predict that more than half of other participants fail to invest.

Does this help Mr. Hreha get Americans to buy more stuff at Walmart, for whom he consults? I’m not sure. Sorry.

My results do not directly imply that we need more government interventions or nudge units. One could argue instead that what we need is market competition to help people navigate a complex world. The information contained in prices helps us figure out what strangers want, so we don’t have to try to predict their behavior at all.

Here’s the end of my Conclusion

One way to interpret the results of this experiment is that putting yourself in someone else’s shoes is costly. We often speak of it as a moral obligation, especially to consider the plight of those who are worse off than ourselves. Not only do people usually decline to do this for moral reasons, they fail to do it for money. Additionally, this experiment shows that, if people are prompted to think about a specific past experience that someone else had, then mutual understanding is easier to establish.

I’m attempting to establish general purpose laws of behavior. I’ll end with a quote from Scott Alexander’s reply post.

A thoughtful doctor who tailors treatment to a particular patient sounds better (and is better) than one who says “Depression? Take this one all-purpose depression treatment which is the first thing I saw when I typed ‘depression’ into UpToDate”. But you still need medical journals. Having some idea of general-purpose laws is what gives the people making creative solutions something to build upon.