Free download: If wages fell during a recession

You can download my full paper “If Wages Fell During a Recession” with Dan Houser from the Journal of Economic Behavior and Organization (only free until September 24, 2022).

There is a simulated recession in our experiment. We ask what happens if employers cut wages in response. Although nominal wage cuts are rare in the outside world, some of our lab subjects cut the wages of their “employee”. Employees retaliated against nominal wage cuts by shirking, such that the employers probably would have been better off keeping wages rigid.

We also tried the same thing with an inflation shock that allowed the employer to institute a real wage cut without a nominal wage cut. The reaction to that real wage cut was muted compared to the retaliation against the obvious nominal wage cut.

Inflation was implemented after 3 rounds of the same wage to create a reference point.

I blogged about the experiment previously, so I won’t go into more detail here.

The Great Recession happened when I was an undergraduate. As I started my career in research, the issue of employment and recessions seemed like THE problem to work on. The economy of 2022 is so different from the years that inspired this experiment! Below I’ll highlight current events and work from others on this topic.

Inflation used to be something Americans could almost ignore, and now it’s at the highest level I have seen in my lifetime. Suddenly, people are so mad about inflation that politicians named their bill the Inflation Reduction Act just to make it popular.  

The EWED crew has made lots of good posts on inflation. Although job openings and (nominal) wage increases are noticeable right now, Jeremy explored whether inflation has wiped out apparent wage growth.

More recently, the WSJ reports that real wages are down because inflation is so high. “Wage gains haven’t kept pace with inflation. Private-sector wages and salaries declined 3.1% in the second quarter from a year earlier, when accounting for inflation.”

Firms in 2022 did not just sit back and let real wages get eroded exactly proportional to inflation. But it is also not the case that Americans got a raise of 9% to exactly offset inflation. According to our experiment, there would be outrage if workers were experiencing a nominal wage cut in proportion to the real wage cut they are getting right now.

The high inflation combined with a hot job market makes this current economy hard to compare to anything in our recent history. Brian at Price Theory explained that inflation pressure is coming from both supply and demand factors.

Joey has a nice graph on inflation composition.

Did anyone see this coming? Watch Jim Doti of Chapman University predict high inflation based on the money supply in his forecast back in July 2021.

Lastly, our experiment on wage cuts has been cited in these papers:

Intentions rather than money illusion – Why nominal changes induce real effects

Economic stability promotes gift-exchange in the workplace

Wage bargaining in a matching market: Experimental evidence

Can reference points explain wage rigidity? Experimental evidence

Shocking gift exchange

CFTC Orders PredictIt Shut Down- Can Political Betting Survive?

Political betting has long been in a legal grey area. It seems that the Commodities Futures Trading Commission wants to make everything black and white, but at least for now it has simply made everything murkier.

PredictIt is the largest political betting site in the US; if you want to know who is likely to win an upcoming election, its the best place to find a quick answer. Prediction markets have two great virtues- they are usually right about what’s going to happen, and if they aren’t you can bet, making money and improving their accuracy at the same time.

PredictIt has operated since 2014 under a “no-action letter” from the CFTC. Effectively, the regulators told them “we’re not saying what you’re doing is definitely legal, but we know about it and have no plans to shut you down as long as you stick to the limits described in this letter”. But last week the CFTC withdrew their letter and ordered PredictIt to shut down by February 2023.

My first question was, why? Why shut them down now after 8 years when all their operations seem to be working as usual? The CFTC said only that “DMO has determined that Victoria University has not operated its market in compliance with the terms of the letter and as a result has withdrawn it”, but did not specify which of the terms PredictIt violated, leaving us to speculate. Did the scale simply get too big? Did they advertise too heavily? Did Victoria University, the official operator, let too much be handled by a for-profit subcontractor? Did some of their markets stray too far from the “binary option contracts concerning political election outcomes and economic indicators” they were authorized for?

PredictIt hasn’t been much clearer about what happened, simply putting a notice on their site. Their CEO did an interview on the Star Spangled Gamblers podcast where he said there was no one thing that triggered the CFTC but did mention “scope” as a concern- which I interpret to mean that they offered some types of markets the CFTC didn’t like, perhaps markets like “how many times will Donald Trump tweet this month”.

The other big question here is about PredictIt’s competitors. In 2021 it seemed like we were entering a golden age of real-money prediction markets, with crypto-based PolyMarket and economics-focused Kalshi joining PredictIt. I looked forward to seeing this competition play out in the marketplace, but it now seems like we’re headed toward a Kalshi-only monopoly where they win not by offering the product users like best, but by having the best relationship with regulators. Polymarket had offered markets without even a no-action letter, based on the crypto ethos of “better to ask forgiveness than permission”; this January the CFTC hit them with a $1.5 million fine and ordered them to stop serving US customers.

If the CFTC doesn’t reverse their decision to shut down PredictIt, then February 2023 will see a Kalshi monopoly. This has led to speculation that Kalshi is behind the attack on PredictIt; their cofounder issued this not-quite-a-denial. But it certainly looks bad for the CFTC that they are effectively giving a monopoly to the company that hires the most ex-CFTC members.

For now you can still bet on PredictIt or Kalshi (or even Polymarket if you’re outside the US). If you’d like to petition the CFTC about PredictIt you can do so here. It might actually work; while the CFTC’s recent actions certainly look cronyistic, they’ve been reasonable compared to other regulators. They’re giving PredictIt no fines and several months to wind down, and even Polymarket gets to keep serving non-US customers from US soil. I’d likely make different decisions if I were at CFTC but the ideal solution here is a change in the law itself, as we’ve seen recently in sports betting. Prediction markets are impressive generators and aggregators of information, and politics and policy are at least as valuable an application as sports. To go meta, suppose we want to know- will PredictIt survive past February? There’s a prediction market for that, and its currently saying they’ve got a 20% chance.

The “Textbook Definition” of a Recession

Three weeks I wrote a blog post about how economists define a recession. I pretty quickly brushed aside the “two consecutive quarters of declining GDP,” since this is not the definition that NBER uses. But since that post (and thanks to a similar blog post from the White House the day after mine), there has been an ongoing debate among economists on social media about how we define recessions. And some economists and others in the media have insisted that the “two quarters” rule is a useful rule of thumb that is often used in textbooks.

It is absolutely true that you can find this “two quarters” rule mentioned in some economics textbooks. Occasionally, it is even part of the definition of a recession. But to try and move this debate forward, I collected as many examples as I could find from recent introductory economics textbooks. I tried to stick with the most recent editions to see what current thinking on the topic is among textbook authors, though I will also say a little bit about a few older editions after showing the results of my search.

Undoubtedly, I have missed a few principles textbooks (there are a lot of them!) so if you have a recent edition that I didn’t include, please share it and I’ll update the post accordingly. I also tried to stick with textbooks published in the last decade, though I made an exception for Samuelson and Nordhaus (2010) since Samuelson is so important to the history of principles textbooks (and his definition has changed, which I’ll discuss below).

But here’s my data on the 17 recent principles textbooks that I’ve found so far (send me more if you have them!). Thanks to Ninos Malek for gathering many of these textbooks and to my Twitter followers for some pointers too.

Continue reading

Aging Populations = Inevitable Slow GDP Growth?

Last month Eric Basmajian published “Why Demographics Matter More Than Anything (For The Long Term)” on the financial site Seeking Alpha. He predicts that that the developed world plus China face a future of low economic growth (regardless of policy machinations) due simply to demographics. His key points:

Demographics are the most important factor for long-term analysis.

The young and old age cohorts negatively impact economic growth.

The prime-age population (25-64) drives the bulk of economic activity.

The world’s major economies are suffering from lower population growth and an older population.

Over the long run, the world’s major economies will have worse economic growth, which will negatively impact pro-cyclical asset prices (like stocks).

I will paste in some of his supporting charts. First, the labor force is more or less proportional to the 25-64 age cohort (U.S. data shown) :

…and GDP growth trends with labor force growth:

Also, on the consumption side, that is highest with the 25-54 age group:

And so,

Younger people are a drag on economic growth and older people are a drag on economic growth… The prime-age population is the segment that drives economic activity, so if the share of population that is 25-54 is shrinking, which it is, then you’re going to have more people that are a negative force than a positive force:

Once the working-age population growth flips negative, an economy is doomed…. Working age population growth in Japan flipped negative in the 1990s, and they moved to negative interest rates, QE, and they have never been able to stop. The economy is too weak.

After 2009, the working-age population in Europe flipped negative, and they moved to negative rates and QE, and they haven’t been able to stop. Even now, as the US is raising rates, Europe is struggling to catch up and has already abandoned most of its tightening plans.

In 2015, China’s working-age population flipped negative, and they’ve had problems ever since. They devalued their currency in 2015 and tried one more time to inflate a property bubble, but it didn’t work, and now they’re having to manage the deflation of an asset bubble that the population cannot support.

The US is in better shape than everyone else, but we’re not looking at robust growth levels in this prime-age population.

In conclusion, “ The real growth rate in most developed nations is collapsing because of those two factors, worsening demographics, and increased debt burdens.    In the US, as a result of the demographic trends I just outlined plus a rising debt burden, real GDP per capita can barely sustain 1% increases over the long run compared to 2.5% in the 60s, 70s, and 80s.”

That is pretty much where Basmajian leaves it. No actionable advice (besides subscribing to his financial newsletter). What isn’t addressed is whether productivity (production per worker) can somehow be accelerated. Also, one of his charts (which I did not copy here) showed a big trend down in 25-64 age fraction in the US population in the 1950’s-1960’s (as hangover from the Depression?), and yet these were decades of strong GDP growth. So these demographic trends are not the whole story, but his analysis is sobering.

The underrated genius of great athletes

I’ve been a sports fan for as long as I can remember, but there are a handful of athletes I’ve manage to form deep admiration for despite never having the opportunity to watch them while they were still actively playing. The two at the very top are Bill Russell (basketball) and Johan Cruyff (soccer). Russell passed away last week at the age of 88. He was an important man whose deep committment to the Civil Rights Movement we are still growing in appreciation of, but I want to talk about his genius.

I mean genius in a far more literal sense than what we typically mean when referring to brilliant athletes or (ugh) “sports IQ”. What Bill Russell did on the basketball court was no less genius than what might be admired in chess or physics. I really believe that. There are a handful of team sports (basketball, soccer, hockey, etc) where the game involves enough independent agents interacting that real-time prediction elevates to a level of complexity that success within the game demands that a player either

  1. Dominate through one or more overwhelming attributes
  2. Wait for randomness to grant you an opportuntity to contribute
  3. Forecast events to maximally pursue opportunities to succeed

A teenager playing basketball with younger, smaller children can dominate absent any particular insight into the game. Similarly, someone who has practiced shooting 15 foot jumpshots or knows how to skate can contribute to a game simply by repeatedly going to a handful of positions and waiting for the game to presnt an opportunity. Neither, however, is remotely sufficient to come within a mile of sports played at the highest amateur levels, let alone sports played professionally. The very greatest athletes in professional sports come to dominate their respective games through their possession of both overwhelming attributes (both natural and acquired) and genius for pattern-recognition and real-time, within-game forecasting. We spend far too much time goggling over former and, in doing so, subtly denigrating the brilliance of the latter.

Bill Russell saw the patterns at play within a basketball game. When he played defense he knew where the ball was going, what the relevant player’s options would be, and how he could not only deny them the chance to score, but to deny them in a specific manner that would lead to his own team scoring in the subsequent transition. When Johan Cruyff played soccer, he could make as many as 6 or 7 consequent moves into open spaces, each creating different options for his teammates that would eventually lead to a goal scoring opportunity emergent from the series of micro-interactions created by the space and gravity of his own actions. These moments were neither clairovoyance or instinct. Their dominance was a product of intelligence in the purest sense.

Stop calling it “Sports IQ”

Instead of saying Lebron or Sidney Crosby is a genius, people instead often remark that they have a high basketball or hockey IQ. It drives me crazy. Don’t get me wrong, I’m glad we’ve evolved from saying someone has great sport-specific “instincts”, which imply they are not even intellect-adjacent, but I don’t think we even need the sports-genre qualifiers. No one ever talks about a chemist or economist having field-specific intelligence, we just say they’re smart. You watch any fast-moving sport played at the highest level for a couple years, and you will come to appreciate that players are accomplishing feats of analysis under duress that are nothing short of incredible intellectual feats.

Funny enough, I think one of the contributors to our growing appreciation of the intellectual side of sports is video games. We knew chess players were really smart, but when we started programming computers to analyze millions of moves per second and it still took years for them to consistently beat the best humans, it probably raised our esteem for what chess players had achieved at the highest level.

Similarly, there is something about playing a game and controlling multiple players, transforming a team into a perfect hive mind of coordination granted by the top-down omniscience of their controlling deity that you appreciate what how perfect play might look. You participate in simulated perfection only to then subject yourself to the humbling limits of reality. Suddenly you are constrained by the information limitatons of a first-person view within the chaos of separate minds, moving at top speed (possibly on ice), while being hunted by an aggressive opposition (sometimes carrying lumber). And then it dawns on you that professionals produce an order within this caucophy of sweaty chaos that is only otherwise observable in a video game.

There are no doubt a host of reasons for this bias against crediting athletes with genius. First, racism. Nothing much more to say there other than, yeah, just straight-up racism and the penchant within sports commentary to de-intellectualize sports with the ascendancy of Black athletes in the 20th century.

Racism aside, I think there is also a particular bias against genius when it manifests in something hyper-specialized, particularly when the screening mechanisms are so intense that most of what gets observed is at the one in a million level (i.e. the 99.9999th percentile). At the highest level, professional sports are executed in a manner almost unrecognizable to how most observers might themselves have played or even observed first-hand, to the point of becoming unfamiliar, alien, and most importantly, unachievable. If something is intellectually unachievable, that may lower the relative estimation and status of the observer’s own intelligence. If, instead, what the athlete is demonstrating an innate proficiency for the specific physical task at hand, that’s just random, an anomaly made only relevant because of the peculiar game they play.

I’ll close with a manifestation of intelligence in sports that isn’t based in pattern-recognition or external complexity, but rather internal complexity. Simone Biles, in case you are not aware, is the greatest gymnast in history. To deny her standing in history is your prerogative, but even as a non-expert in gymnastics, allow me to assure you that you would be wrong. A moment that stuck with me in revealing her brilliance, ironically, was when she pulled herself from the previous Olympics. During the floor exercises and vault, Biles creates speed, vertical lift, shape, and multiple dimensions of body rotation. To organize a singular force diagram of her a physicist would require significant computational assistance or a whole bunch of math. Not only does Biles do this, she does it while her body is running, jumping, and rotating. She essentially tracks the problem in real time. What was amazing is that in the recent Olympics, still competing at an age considered well post-prime for gymnastics, she understood that she was not managing the physics problem with the reliablitiy and accuracy sufficient to perform her own routine. Could she have managed a set of simpler routines? Probably, but simpler ones had never been practiced. She confronted a dilemma: she had practiced routines that up until that moment no one else in the world could do but her, only now even she could not do them without presenting significant danger to herself and, in turn, no real help to her team. So what did she do on global television?

She withdrew from the Olympics. Which might be the single greatest moment of self-possession I’ve ever seen from an athlete. That probably counts as emotional intelligence, but I don’t actually know how that works and is a different post anyway.

There is real genius in professional sports. It’s time we started crediting them for it.

Secret Fun Tech People

If you are trying to pick a career, it would help to know what the daily experience is like in various professions.

A friend of mine recently quit her old job and did a coding bootcamp. She worked hard, went through interviews and is now working in tech. She was correct in expecting that coding is more interesting and provides more opportunity than her old job.

She is not at a FAANG or grinding at a startup. She got hired in a remote position that requires an understanding of code. She’s starting at the bottom of the hierarchy in her 30’s, as someone with no experience.

Now that she has started work in the industry, she reported to me that, “I don’t think I could have predicted that the people would be this much fun.”

She is genuinely enjoying tech culture. She texts me obscure tech jokes now as if it’s an SNL skit that I would enjoy. (e.g. https://www.youtube.com/watch?v=kHW58D-_O64 somewhat obscure YouTube channel) Her previous job was boring, and she never told me a positive thing about it. She is happy, not just with her financial return on investment but with her community.

If you read much about tech policy, you have heard about harassment in the workplace, especially for women. This is indeed an important issue. I’m not presenting my anecdote to imply that everything is fine everywhere. If people are trying to make important life decisions, then this is worth discussing.

One factor that might make people not want to learn to code is that they are afraid the work would be isolating and boring. It can be, but there is also a community aspect that can be positive.

I polled my Twitter friends and got this result (small, biased sample, albeit, and I suspect it’s mostly men who answered):

No one disputed that tech folk can be fun, although some people wanted to qualify the statement by saying that different companies have different cultures.

John Vandevier (@JohnVandivier) sent me a blog he wrote about a study on tech culture. “Analyzing ‘Resetting Tech Culture’ by Accenture and Girls Who Code” The study shows that the world is complex. Lots of women are happy in tech. At the same time there are people who face harassment. There is good news and bad news. Offenders should stop offending. There are also good opportunities out there for people who train for tech.

When I shared the story about my friend’s good news, it was mostly ignored on Twitter. Good news does not drive engagement. Happy people are not interesting and so no one hears about them. Tech is not the right choice for everyone, and some people have been mistreated at tech companies, but on the margin a few more people should probably go for it.

Here’s something to balance out my rosy report about all the laughing and LOLing among coders. Last year I had a miserable long day of coding. I wrote up a diary entry about how much I hated that day. I’m not trying to get sympathy for myself. I wanted to capture a modern experience that is shared by many.

Coding can be hard and frustrating and lonely. The jokes are funny because the pain is real.

Boutique Science

Science keeps getting bigger- more researchers, more funding, and of course more publications. Scientific progress is much harder to measure, but there are good arguments that it’s roughly flat over time. This implies that productivity per researcher is plummeting.

Source

There’s been a lively debate about what drives this falling productivity- is it that the easy discoveries got made first, leaving only harder ones for today’s scientists? Or is something else tanking scientific productivity, like the bureaucratic way we organize scientific research today?

A recent paper, “Slowed canonical progress in large fields of science“, suggests that the growth in the number of researchers and publications could itself be part of the problem. Comparing scientific fields over time, they find that:

When the number of papers published per year in a scientific field grows large, citations flow disproportionately to already well-cited papers; the list of most-cited papers ossifies; new papers are unlikely to ever become highly cited, and when they do, it is not through a gradual, cumulative process of attention gathering; and newly published papers become unlikely to disrupt existing work. These findings suggest that the progress of large scientific fields may be slowed, trapped in existing canon.

What is driving this? They argue:

First, when many papers are published within a short period of time, scholars are forced to resort to heuristics to make continued sense of the field. Rather than encountering and considering intriguing new ideas each on their own merits, cognitively overloaded reviewers and readers process new work only in relationship to existing exemplars. A novel idea that does not fit within extant schemas will be less likely to be published, read, or cited. Faced with this dynamic, authors are pushed to frame their work firmly in relationship to well-known papers, which serve as “intellectual badges” identifying how the new work is to be understood, and discouraged from working on too-novel ideas that cannot be easily related to existing canon. The probabilities of a breakthrough novel idea being produced, published, and widely read all decline, and indeed, the publication of each new paper adds disproportionately to the citations for the already most-cited papers.

Second, if the arrival rate of new ideas is too fast, competition among new ideas may prevent any of the new ideas from becoming known and accepted field wide.

Supposing they are correct, it’s not totally clear what to do. At the biggest level we could fund fewer researchers in large fields, or push more fields to be like economics, where the quality of each researcher’s publications is valued much more than the quantity. But what can an individual researcher do differently? One idea is “boutique science” or “hipster science”, trying to find the smallest or newest field you could reasonably attach yourself to.

Another idea is that the role of generalists and synthesizers is becoming more valuable, as Tyler Cowen often says and David Esptein applies to science in his book Range. When papers are coming out faster than anyone can read, we need more people to sift through them and explain which few are actually important and which are forgettable or wrong. There are lots of ways to do this- review articles, meta-analysis, replication at scale, and of course blogs. But the junk pile is going to keep growing, so we’ll need new and better ways of finding the hidden gems.

Mom Life and Dad Life

Earlier this week my co-blogger Mike had a really great post on work-from-home, and how we might turn former workspaces into new home spaces. It’s a really great idea, and an excellent example of a “second best” solution to the housing shortage.

I’d like to talk about a related but very different topic, which is the things we do in our homes. And for many working couples, that thing is raising children (and generally, keeping up the house).

If you spend much time on Twitter or Instagram, you’ve probably run across the account “Mom Life Comics.” It’s a very popular Instagram account, and lately some of the comics have been shared widely on Twitter (sometimes sympathetically, sometimes mockingly). The running theme of the topic, in short, is that moms carry much more of the “load” than dads do, both the physical load of doing stuff, and what’s sometimes called the “mental load” as well.

There’s a reason the comic is striking a chord with women: just ask any young mom today, especially a young mom that is also working. They have all felt this way at some point, and some of them probably feel this way all the time.

The idea is nothing new, of course. Sociologists have been using the term “invisible work” since at least the 1980s to describe the unseen, unpaid work that women do around the home. But the concept has, of course, been around for much longer. But how has the balance of work changed over time?

Continue reading

On the Spreading of Monkeypox

New York City has become the second major U.S. city after San  Francisco to declare a state of emergency due to the rise of monkeypox cases: “New York City is currently the epicenter of the outbreak, and we estimate that approximately 150,000 New Yorkers may currently be at risk for monkeypox exposure.”

With the country and the world still feeling the economic/social/personal effects of one pandemic, is there another one on the way? I don’t know, having no special training in epidemiology, but have tried to peruse some reliable sources to find out what I could, and share this information for your examination. I will paste in a general page from a UC Davis article, then conclude with a CDC snip on transmission details.

It seems that monkeypox typically takes pretty close physical contact (especially with skin, body fluids, or e.g. towels/clothing)  to spread, with having multiple romantic partners being a high risk factor. This is the opposite of COVID transmission, where just being in the same room puts you at high risk. However, as with COVID, someone can be contagious in the early stages before they show obvious symptoms. Based on all this, my guess is that monkeypox will not spread in the general population very much, but it will spread significantly in some groups and locales. But that is just my guess.

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

From UC Davis “Monkeypox: What you need to know about this rare virus:

What are the signs and symptoms of monkeypox? At what point is it infectious?

Monkeypox starts with fever, then general body aches, malaise, and muscle aches. with the first symptoms are similar to influenza. Those usually precede the development of a rash. You have probably seen photos of the rash. It’s really hard to miss. It starts as macules, which are flat lesions. Then it forms a firm nodule. From there, it becomes a blister, then a pustule (a blister containing pus) and then it scabs over.

According to the Centers for Disease Control and Prevention (CDC), the incubation period (The time from infection to symptoms) for monkeypox is usually 7 to 14 days, but it can range from 5 to 21 days.

People may be contagious at the early signs of fever and stay infectious through the rash until the skin scabs and heals over.

How is it transmitted?

Monkeypox is transmitted through close person-to-person contact with lesions, body fluids and respiratory droplets, and through contaminated materials such as clothing or bedding.  [[ see more on transmission below]]

Can you die from monkeypox? 

Most people with monkeypox will recover on their own. But 5% of people with monkeypox die. It appears that the current strain causes less severe disease. The mortality rate is about 1% with the current strain….

What are the treatments for monkeypox? Is there a vaccine for monkeypox?

The smallpox vaccine has some cross protection against monkeypox. The vaccine is being made available through public health for people who have had contact with confirmed or suspected cases of monkeypox. If the vaccine is given within four days of exposure, it protects about 85% of the time. Even if the vaccine is given up to two weeks after exposure, it may modify the disease, making it less severe. 

In addition, there are some antivirals and immunoglobulins that are available to treat monkeypox.

Is there a way to test for monkeypox?

If a health care provider suspects that a patient has been exposed to monkeypox, they can get a sample of a lesion and send it to the state for testing. If it turns out positive, the result will be confirmed at the CDC.

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From CDC “How It Spreads”:

Monkeypox spreads in a few ways.

  • Monkeypox can spread to anyone through close, personal, often skin-to-skin contact, including:
    • Direct contact with monkeypox rash, scabs, or body fluids from a person with monkeypox.
    • Touching objects, fabrics (clothing, bedding, or towels), and surfaces that have been used by someone with monkeypox.
    • Contact with respiratory secretions.
  • This direct contact can happen during intimate contact, including:
    • Oral, anal, and vaginal sex or touching the genitals  or anus of a person with monkeypox.
    • Hugging, massage, and kissing.
    • Prolonged face-to-face contact.
    • Touching fabrics and objects during sex that were used by a person with monkeypox and that have not been disinfected, such as bedding, towels, fetish gear, and sex toys.
  • A pregnant person can spread the virus to their fetus through the placenta.

A person with monkeypox can spread it to others from the time symptoms start until the rash has fully healed and a fresh layer of skin has formed. The illness typically lasts 2-4 weeks.

Converting office space and why second-best solutions are what move the world forward

Subscribing to the “Housing Theory of Everything” is to confront the fact that a problem can 1) be important, 2) effect (nearly) everyone, 3) have an obvious and welfare improving policy solution, and 4) still be politically stuck. Whether it’s classic prisoner’s dilemmas or a more subtle transitional gains traps, the reality is that building housing has proven incredibly difficult because there is a group whose wealth is overly concentrated in the stock of housing they own (i.e. nearly every homeowner in the US) and who have every incentive to fight to prevent new housing from being built because restricting housing supply increase the value of their propterty.

That’s it. That’s the whole story, everything else is bookkeeping and tactical anecdotes. So how do we solve this problem? One way is to motivate large swaths of voters to push for reform, but there’s only the entire body of political theory and history telling you that’s easier said than done when the opposition is concentrated and organized.

The thing is, building more housing is the “first best” solution- it’s not the only solution. Should we increase the housing stock, lower prices, make the average person wealthier and more economically secure, reduce homelessness, and spend all of eternity celebrating the victory of common sense in the halls of Valhalla? Yes, of course. But that “first best” solution isn’t available in a lot of places (see the previous two paragraphs). Besides beating our heads against the wall in the hopes of victory one spoonful of brick at a time, what else can we do? We can go looking for second-best solutions, particularly ones where the political opposition is softer and less organized.

Converting office space to residential housing is a near Platonic-ideal second best solution. Why? Because it produces more housing, albeit with the costs of conversion and likely subperfect design. What makes it a dream second-best solution for our dilemma, however, is all of the opposition mechanisms it dodges:

  1. There’s nothing remotely historic about most of these buildings.
  2. The are structured in such a way that lend themselve to high-density housing (i.e. apartment and condo towers).
  3. They’re predominantly in relatively dense urban and edge-city areas.
  4. Whatever views or skylines they are obstructing are already obstructed!
  5. There’s a built in interest group to push for the conversion (i.e. the building owners).
  6. There’s no pre-existing tenant or tenants who’s losses can be highlighted at the expense of everyone else’s gain.

First and second best categorization are always a little squishy because they depend on what you include in the costs and benefits. Building new housing from scratch might seem obviously the best possible outcome, but once you factor in the political costs of zoning and approval, there’s going to be a lot of locales where building conversion is the far lower cost option. I’m very much team “work from home” and this is just one more reason you should join our merry band of robe and slipper-types. Hollow out the offices, convert the buildings to housing, and watch the urban landscape transform from gray and glass offices to a utopia of urban singles skipping from brunch to brunch until their kids are born and their metabolisms slows down.

Now, to be clear, there is no political free lunch here. There will still be costly and difficult re-zoning obstacles in lots of places. Plenty of these building will need to be brought up to code. The locations may not be ideal relative to schools. But those costs and concerns are trifles when considered in the context that the median income in half of US cities is insufficient to rent a two bedroom apartment for less than 30% of gross income.

Democracy is messy and there’s no changing that. While it makes for bad sloganeering and will never insulate you from getting slagged on twitter, the reality is that second best solutions are what move peaceful societies forward. We have a lot of coalitions to keep happy, they all want something for themselves, and nothing is free. We have to work with what we got.

And what we got is a bunch of office buildings that nobody wants to work in anymore. Let’s live in them!

…and then work from home in them? In what used to be the offices we didn’t want to work in?

Yes.