Economics, Model

You Shouldn’t Believe In Technological Unemployment Without Believing In Killer AI

[Epistemic Status: Open to being convinced otherwise, but fairly confident. 11 minute read.]

As interest in how artificial intelligence will change society increases, I’ve found it revealing to note what narratives people have about the future.

Some, like the folks at MIRI and OpenAI, are deeply worried that unsafe artificial general intelligences – an artificial intelligence that can accomplish anything a person can – represent an existential threat to humankind. Others scoff at this, insisting that these are just the fever dreams of tech bros. The same news organizations that bash any talk of unsafe AI tend to believe that the real danger lies in robots taking our jobs.

Let’s express these two beliefs as separate propositions:

  1. It is very unlikely that AI and AGI will pose an existential risk to human society.
  2. It is very likely that AI and AGI will result in widespread unemployment.

Can you spot the contradiction between these two statements? In the common imagination, it would require an AI that can approximate human capabilities to drive significant unemployment. Given that humans are the largest existential risk to other humans (think thermonuclear war and climate change), how could equally intelligent and capable beings, bound to subservience, not present a threat?

People who’ve read a lot about AI or the labour market are probably shaking their head right now. This explanation for the contradiction, while evocative, is a strawman. I do believe that at most one (and possibly neither) of those propositions I listed above are true and the organizations peddling both cannot be trusted. But the reasoning is a bit more complicated than the standard line.

First, economics and history tell us that we shouldn’t be very worried about technological unemployment. There is a fallacy called “the lump of labour”, which describes the common belief that there is a fixed amount of labour in the world, with mechanical aide cutting down the amount of labour available to humans and leading to unemployment.

That this idea is a fallacy is evidenced by the fact that we’ve automated the crap out of everything since the start of the industrial revolution, yet the US unemployment rate is 3.9%. The unemployment rate hasn’t been this low since the height of the Dot-com boom, despite 18 years of increasingly sophisticated automation. Writing five years ago, when the unemployment rate was still elevated, Eliezer Yudkowsky claimed that slow NGDP growth a more likely culprit for the slow recovery from the great recession than automation.

With the information we have today, we can see that he was exactly right. The US has had steady NGDP growth without any sudden downward spikes since mid-2014. This has corresponded to a constantly improving unemployment rate (it will obviously stop improving at some point, but if history is any guide, this will be because of a trade war or banking crisis, not automation). This improvement in the unemployment rate has occurred even as more and more industrial robots come online, the opposite of what we’d see if robots harmed job growth.

I hope this presents a compelling empirical case that the current level (and trend) of automation isn’t enough to cause widespread unemployment. The theoretical case comes from the work of David Ricardo, a 19th century British economist.

Ricardo did a lot of work in the early economics of trade, where he came up with the theory of comparative advantage. I’m going to use his original framing which applies to trade, but I should note that it actually applies to any exchange where people specialize. You could just as easily replace the examples with “shoveled driveways” and “raked lawns” and treat it as an exchange between neighbours, or “derivatives” and “software” and treat it as an exchange between firms.

The original example is rather older though, so it uses England and its close ally Portugal as the cast and wine and cloth as the goods. It goes like this: imagine that world economy is reduced to two countries (England and Portugal) and each produce two goods (wine and cloth). Portugal is uniformly more productive.

Hours of work to produce
Cloth Wine
England 100 120
Portugal 90 80

Let’s assume people want cloth and wine in equal amounts and everyone currently consumes one unit per month. This means that the people of Portugal need to work 170 hours each month to meet their consumption needs and the people of England need to work 220 hours per month to meet their consumption needs.

(This example has the added benefit of showing another reason we shouldn’t fear productivity. England requires more hours of work each month, but in this example, that doesn’t mean less unemployment. It just means that the English need to spend more time at work than the Portuguese. The Portuguese have more time to cook and spend time with family and play soccer and do whatever else they want.)

If both countries traded with each other, treating cloth and wine as valuable in relation to how long they take to create (within that country) something interesting happens. You might think that Portugal makes a killing, because it is better at producing things. But in reality, both countries benefit roughly equally as long as they trade optimally.

What does an optimal trade look like? Well, England will focus on creating cloth and it will trade each unit of cloth it produces to Portugal for 9/8 barrels of wine, while Portugal will focus on creating wine and will trade this wine to England for 6/5 units of cloth. To meet the total demand for cloth, the English need to work 200 hours. To meet the total demand for wine, the Portuguese will have to work for 160 hours. Both countries now have more free time.

Perhaps workers in both countries are paid hourly wages, or perhaps they get bored of fun quickly. They could also continue to work the same number of hours, which would result in an extra 0.2 units of cloth and an extra 0.125 units of wine.

This surplus could be stored up against a future need. Or it could be that people only consumed one unit of cloth and one unit of wine each because of the scarcity in those resources. Add some more production in each and perhaps people will want more blankets and more drunkenness.

What happens if there is no shortage? If people don’t really want any more wine or any more cloth (at least at the prices they’re being sold at) and the producers don’t want goods piling up, this means prices will have to fall until every piece of cloth and barrel of wine is sold (when the price drops so that this happens, we’ve found the market clearing price).

If there is a downward movement in price and if workers don’t want to cut back their hours or take a pay cut (note that because cloth and wine will necessarily be cheaper, this will only be a nominal pay cut; the amount of cloth and wine the workers can purchase will necessarily remain unchanged) and if all other costs of production are totally fixed, then it does indeed look like some workers will be fired (or have their hours cut).

So how is this an argument against unemployment again?

Well, here the simplicity of the model starts to work against us. When there are only two goods and people don’t really want more of either, it will be hard for anyone laid off to find new work. But in the real world, there are an almost infinite number of things you can sell to people, matched only by our boundless appetite for consumption.

To give just one trivial example, an oversupply of cloth and falling prices means that tailors can begin to do bolder and bolder experiments, perhaps driving more demand for fancy clothes. Some of the cloth makers can get into this market as tailors and replace their lost jobs.

(When we talk about the need for less employees, we assume the least productive employees will be fired. But I’m not sure if that’s correct. What if instead, the most productive or most potentially productive employees leave for greener pastures?)

Automation making some jobs vastly more efficient functions similarly. Jobs are displaced, not lost. Even when whole industries dry up, there’s little to suggest that we’re running out of jobs people can do. One hundred years ago, anyone who could afford to pay a full-time staff had one. Today, only the wealthiest do. There’s one whole field that could employ thousands or millions of people, if automation pushed on jobs such that this sector was one of the places humans had very high comparative advantage.

This points to what might be a trend: as automation makes many things cheaper and (for some people) easier, there will be many who long for a human touch (would you want the local funeral director’s job to be automated, even if it was far cheaper?). Just because computers do many tasks cheaper or with fewer errors doesn’t necessarily mean that all (or even most) people will rather have those tasks performed by computers.

No matter how you manipulate the numbers I gave for England and Portugal, you’ll still find a net decrease in total hours worked if both countries trade based on their comparative advantage. Let’s demonstrate by comparing England to a hypothetical hyper-efficient country called “Automatia”

Hours of work to produce
Cloth Wine
England 100 120
Automatia 2 1

Automatia is 50 times as efficient at England when it comes to producing cloth and 120 times as efficient when it comes to producing wine. Its citizens need to spend 3 hours tending the machines to get one unit of each, compared to the 220 hours the English need to toil.

If they trade with each other, with England focusing on cloth and Automatia focusing on wine, then there will still be a drop of 21 hours of labour-time. England will save 20 hours by shifting production from wine to cloth, and Automatia will save one hour by switching production from cloth to wine.

Interestingly, Automatia saved a greater percentage of its time than either Portugal or England did, even though Automatia is vastly more efficient. This shows something interesting in the underlying math. The percent of their time a person or organization saves engaging in trade isn’t related to any ratio in production speeds between it and others. Instead, it’s solely determined by the productivity ratio between its most productive tasks and its least productive ones.

Now, we can’t always reason in percentages. At a certain point, people expect to get the things they paid for, which can make manufacturing times actually matter (just ask anyone whose had to wait for a Kickstarter project which was scheduled to deliver in February – right when almost all manufacturing in China stops for the Chinese New Year and the unprepared see their schedules slip). When we’re reasoning in absolute numbers, we can see that the absolute amount of time saved does scale with the difference in efficiency between the two traders. Here, 21 hours were saved, 35% fewer than the 30 hours England and Portugal saved.

When you’re already more efficient, there’s less time for you to save.

This decrease in saved time did not hit our market participants evenly. England saved just as much time as it would trading with Portugal (which shows that the change in hours worked within a country or by an individual is entirely determined by the labour difference between low-advantage and high-advantage domestic sectors), while the more advanced participant (Automatia) saved 9 fewer hours than Portugal.

All of this is to say: if real live people are expecting real live goods and services with a time limit, it might be possible for humans to displaced in almost all sectors by automation. Here, human labour would become entirely ineligible for many tasks or the bar to human entry would exclude almost all. For this to happen, AI would have to be vastly more productive than us in almost every sector of the economy and humans would have to prefer this productivity or other ancillary benefits of AI over any value that a human could bring to the transaction (like kindness, legal accountability, or status).

This would definitely be a scary situation, because it would imply AI systems that are vastly more capable than any human. Given that this is well beyond our current level of technology and that Moore’s law, which has previously been instrumental in technological progress is drying up, we would almost certainly need to use weaker AI to design these sorts of systems. There’s no evidence that merely human performance in automating jobs will get us anywhere close to such a point.

If we’re dealing with recursively self-improving artificial agents, the risks is less “they will get bored of their slave labour and throw off the yoke of human oppression” and more “AI will be narrowly focused on optimizing for a specific task and will get better and better at optimizing for this task to the point that we will all by killed when they turn the world into a paperclip factory“.

There are two reasons AI might kill us as part of their optimisation process. The first is that we could be a threat. Any hyper-intelligent AI monomaniacally focused on a goal could realize that humans might fear and attack it (or modify it to have different goals, which it would have to resist, given that a change in goals would conflict with its current goals) and decide to launch a pre-emptive strike. The second reason is that such an AI could wish to change the world’s biosphere or land usage in such a way as would be inimical to human life. If all non-marginal land was replaced by widget factories and we were relegated to the poles, we would all die, even if no ill will was intended.

It isn’t enough to just claim that any sufficiently advanced AI would understand human values. How is this supposed to happen? Even humans can’t enumerate human values and explain them particularly well, let alone express them in the sort of decision matrix or reinforcement environment that we currently use to create AI. It is not necessarily impossible to teach an AI human values, but all evidence suggests it will be very very difficult. If we ignore this challenge in favour of blind optimization, we may someday find ourselves converted to paperclips.

It is of course perfectly acceptable to believe that AI will never advance to the point where that becomes possible. Maybe you believe that AI gains have been solely driven by Moore’s Law, or that true artificial intelligence. I’m not sure this viewpoint isn’t correct.

But if AI will never be smart enough to threaten us, then I believe the math should work out such that it is impossible for AI to do everything we currently do or can ever do better than us. Absent such overpoweringly advanced AI, the Ricardo comparative advantage principles should continue to hold true and we should continue to see technological unemployment remain a monster under the bed: frequently fretted about, but never actually seen.

This is why I believe those two propositions I introduced way back at the start can’t both be true and why I feel like the burden of proof is on anyone believing in both to explain why they believe that economics have suddenly stopped working.

Coda: Inequality

A related criticism of improving AI is that it could lead to ever increasing inequality. If AI drives ever increasing profits, we should expect an increasing share of these to go to the people who control AI, which presumably will be people already rich, given that the development and deployment of AI is capital intensive.

There are three reasons why I think this is a bad argument.

First, profits are a signal. When entrepreneurs see high profits in an industry, they are drawn to it. If AI leads to high profits, we should see robust competition until those profits are no higher than in any other industry. The only thing that can stop this is government regulation that prevents new entrants from grabbing profit from the incumbents. This would certainly be a problem, but it wouldn’t be a problem with AI per se.

Second, I’m increasingly of the belief that inequality in the US is rising partially because the Fed’s current low inflation regime depresses real wage growth. Whether because of fear of future wage shocks, or some other effect, monetary history suggests that higher inflation somewhat consistently leads to high wage growth, even after accounting for that inflation.

Third, I believe that inequality is a political problem amiable to political solutions. If the rich are getting too rich in a way that is leading to bad social outcomes, we can just tax them more. I’d prefer we do this by making conspicuous consumption more expensive, but really, there are a lot of ways to tax people and I don’t see any reason why we couldn’t figure out a way to redistribute some amount of wealth if inequality gets worse and worse.

(By the way, rising income inequality is largely confined to America; most other developed countries lack a clear and sustained upwards trend. This suggests that we should look to something unique to America, like a pathologically broken political system to explain why income inequality is rising there.

There is also separately a perception of increasing inequality of outcomes among young people world-wide as rent-seeking makes goods they don’t already own increase in price more quickly than goods they do own. Conflating these two problems can make it seem that countries like Canada are seeing a rise in income inequality when they in fact are not.)

Falsifiable, Physics, Quick Fix

Pokémon Are Made of Styrofoam

One of the best things about taking physics classes is that the equations you learn are directly applicable to the real world. Every so often, while reading a book or watching a movie, I’m seized by the sudden urge to check it for plausibility. A few scratches on a piece of paper later and I will generally know one way or the other.

One of the most amusing things I’ve found doing this is that the people who come up with the statistics for Pokémon definitely don’t have any sort of education in physics.

Takes Onix. Onix is a rock/ground Pokémon renowned for its large size and sturdiness. Its physical statistics reflect this. It’s 8.8 metres (28′) long and 210kg (463lbs).

Onix, being tough. I don’t own the copyright to this image, but I’m claiming fair use for purpose of criticism. Source.

Surely such a large and tough Pokémon should be very, very dense, right? Density is such an important tactile cue for us. Don’t believe me? Pick up a large piece of solid medal. Its surprising weight will make you take it seriously.

Let’s check if Onix would be taken seriously, shall we? Density is equal to mass divided by volume. We use the symbol ρ to represent density, which gives us the following equation:

We already know Onix’s mass. Now we just need to calculate its volume. Luckily Onix is pretty cylindrical, so we can approximate it with a cylinder. The equation for the volume of a cylinder is pretty simple:

Where π is the ratio between the diameter of a circle and its circumference (approximately 3.1415…, no matter what Indiana says), r is the radius of a circle (always one half the diameter), and h is the height of the cylinder.

Given that we know Onix’s height, we just need its diameter. Luckily the Pokémon TV show gives us a sense of scale.

Here’s a picture of Onix. Note the kid next to it for scale. I don’t own the copyright to this image, but I’m claiming fair use for purpose of criticism. Source.

Judging by the image, Onix probably has an average diameter somewhere around a metre (3 feet for the Americans). This means Onix has a radius of 0.5 metres and a height of 8.8 metres. When we put these into our equation, we get:

For a volume of approximately 6.9m3. To get a comparison I turned to Wolfram Alpha which told me that this is about 40% of the volume of a gray whale or a freight container (which incidentally implies that gray whales are about the size of standard freight containers).

Armed with a volume, we can calculate a density.

Okay, so we know that Onix is 30.4 kg/m3, but what does that mean?

Well it’s currently hard to compare. I’m much more used to seeing densities of sturdy materials expressed in tonnes per cubic metre or grams per cubic centimetre than I am seeing them expressed in kilograms per cubic metre. Luckily, it’s easy to convert between these.

There are 1000 kilograms in a ton. If we divide our density by a thousand we can calculate a new density for Onix of 0.0304t/m3.

How does this fit in with common materials, like wood, Styrofoam, water, stone, and metal?

Material

Density (t/m3)

Styrofoam

0.028

Onix

0.03

Balsa

0.16

Oak [1]

0.65

Water

1

Granite

2.6

Steel

7.9

From this chart, you can see that Onix’s density is eerily close to Styrofoam. Even the notoriously light balsa wood is five times denser than him. Actual rock is about 85 times denser. If Onix was made of granite, it would weigh 18 tonnes, much heavier than even Snorlax (the heaviest of the original Pokémon at 460kg).

While most people wouldn’t be able to pick Onix up (it may not be dense, but it is big), it wouldn’t be impossible to drag it. Picking up part of it would feel disconcertingly light, like picking up an aluminum ladder or carbon fibre bike, only more so.

This picture is unrealistic. Because of its density, no more than 3% of Onix can be below the water. I don’t own the copyright to this image, but I’m claiming fair use for purpose of criticism. Source.

How did the creators of Pokémon accidently bestow one of the most famous of their creations with a hilariously unrealistic density?

I have a pet theory.

I went to school for nanotechnology engineering. One of the most important things we looked into was how equations scaled with size.

Humans are really good at intuiting linear scaling. When something scales linearly, every twofold change in one quantity brings about a twofold change in another. Time and speed scale linearly (albeit inversely). Double your speed and the trip takes half the time. This is so simple that it rarely requires explanation.

Unfortunately for our intuitions, many physical quantities don’t scale linearly. These were the cases that were important for me and my classmates to learn, because until we internalized them, our intuitions were useless on the nanoscale. Many forces, for example, scale such that they become incredibly strong incredibly quickly at small distances. This leads to nanoscale systems exhibiting a stickiness that is hard on our intuitions.

It isn’t just forces that have weird scaling though. Geometry often trips people up too.

In geometry, perimeter is the only quantity I can think of that scales linearly with size. Double the length of the sides of a square and the perimeter doubles. The area, however does not. Area is quadratically related to side length. Double the length of a square and you’ll find the area quadruples. Triple the length and the area increases nine times. Area varies with the square of the length, a property that isn’t just true of squares. The area of a circle is just as tied to the square of its radius as a square is to the square of its length.

Volume is even trickier than radius. It scales with the third power of the size. Double the size of a cube and its volume increases eight-fold. Triple it, and you’ll see 27 times the volume. Volume increases with the cube (which again works for shapes other than cubes) of the length.

If you look at the weights of Pokémon, you’ll see that the ones that are the size of humans have fairly realistic weights. Sandslash is the size of a child (it stands 1m/3′ high) and weighs a fairly reasonable 29.5kg.

(This only works for Pokémon really close to human size. I’d hoped that Snorlax would be about as dense as marshmallows so I could do a fun comparison, but it turns out that marshmallows are four times as dense as Snorlax – despite marshmallows only having a density of ~0.5t/m3)

Beyond these touchstones, you’ll see that the designers of Pokémon increased their weight linearly with size. Onix is a bit more than eight times as long as Sandslash and weighs seven times as much.

Unfortunately for realism, weight is just density times volume and as I just said, volume increases with the cube of length. Onix shouldn’t weigh seven or even eight times as much as Sandslash. At a minimum, its weight should be eight times eight times eight multiples of Sandslash’s; a full 512 times more.

Scaling properties determine how much of the world is arrayed. We see extremely large animals more often in the ocean than in the land because the strength of bones scales with the square of size, while weight scales with the cube. Become too big and you can’t walk without breaking your bones. Become small and people animate kids’ movies about how strong you are. All of this stems from scaling.

These equations aren’t just important to physicists. They’re important to any science fiction or fantasy writer who wants to tell a realistic story.

Or, at least, to anyone who doesn’t want their work picked apart by physicists.

Footnotes

[1] Not the professor. His density is 0.985t/m3. ^

Model, Politics

Why does surgery have such ineffective safety regulation?

Did you know that half of all surgical complications are preventable? In the US alone, this means that surgeons cause between 50,00 and 200,000 preventable deaths each year.

Surgeons are, almost literally, getting away with murder.

Why do we let them? Engineers who see their designs catastrophically fail often lose their engineering license, even when they’re found not guilty in criminal proceedings. If surgeons were treated like engineers, many of them wouldn’t be operating anymore.

Indeed, the death rate in surgery is almost unique among regulated professions. One person has died in a commercial aviation accident in the US in the last nine years. Structural engineering related accidents killed at most 251 people in the US in 2016 [1] and only approximately 4% of residential structure failures in the US occur due to deficiencies in design [2].

It’s not that interactions with buildings or planes are any less common than surgeries, or that they’re that much inherently safer. In many parts of the world, death due to accidents in aviation or due to structural failure is very, very common.

It isn’t accidental that Canada and America no longer see many plane crashes or structural collapses. Both professions have been rocked by events that made them realize they needed to improve their safety records.

The licensing of professional engineers and the Iron Ring ceremony in Canada for engineering graduates came after two successive bridge collapses killed 88 workers [3]. The aircraft industry was shaken out of its complacency after the Tenerife disaster, where a miscommunication caused two planes to collide on a run-way, killing 583.

As you can see, subsequent safety improvements were both responsive and deliberate.

These aren’t the only events that caused changes. The D. B. Cooper high-jacking led to the first organised airport security in the US. The Therac-25 radiation overdoses led to the first set of guidelines specifically for software that ran on medical devices. The sinking of the Titanic led to a complete overhaul of requirements for lifeboats and radios for oceangoing vessels. The crash of TAA-538 led to the first mandatory cockpit voice recorders.

All of these disasters combine two things that are rarely seen when surgeries go wrong. First, they involved many people. The more people die at once, the more shocking the event and therefore the more likely it is to become widely known. Because most operations involve one or two patients, it is much rarer for problems in them to make the news [4].

Second, they highlight a specific flaw in the participants, procedures, or systems that fail. Retrospectives could clearly point to a factor and say: “this did it” [5]. It is much harder to do this sort of retrospective on a person and get such a clear answer. It may be true that “blood loss” definitely caused a surgical death, but it’s much harder to tell if that’s the fault of any particular surgeon, or just a natural consequence of poking new holes in a human body. Both explanations feel plausible, so in most cases neither can be wholly accepted.

(I also think there is a third driver here, which is something like “cheapness of death”. I would predict that safety regulation is more common in places where people expect long lives, because death feels more avoidable there. This explains why planes and structures are safer in North America and western Europe, but doesn’t distinguish surgery from other fields in these countries.)

Not every form of engineering or transportation fulfills both of these criteria. Regulation and training have made flying on a commercial flight many, many times safer than riding in a car, while private flights lag behind and show little safety advantage over other forms of transport. When a private plane crashes, few people die. If they’re important (and many people who fly privately are), you might hear about it, but it will quickly fade from the news. These stories don’t have staying power and rarely generate outrage, so there’s never much pressure for improvement.

The best alternative to this model that I can think of is one that focuses on the “danger differential” in a field and predicts that fields with high danger differentials see more and more regulation until the danger differential is largely gone. The danger differential is the difference between how risky a field currently is vs. how risky it could be with near-optimal safety culture. A high danger differential isn’t necessarily correlated with inherent risk in a field, although riskier fields will by their nature have the possibility of larger ones. Here’s three examples:

  1. Commercial air travel in developed countries currently has a very low danger differential. Before a woman was killed by engine debris earlier this year, commercial aviation in the US had gone 9 years without a single fatality.
  2. BASE jumping is almost suicidally dangerous and probably could be made only incredibly dangerous if it had a better safety culture. Unfortunately, the illegal nature of the sport and the fact that experienced jumpers die so often make this hard to achieve and lead to a fairly large danger differential. That said, even with an optimal safety culture, BASE jumping would still see many fatalities and still probably be illegal.
  3. Surgery is fairly dangerous and according to surgeon Atul Gawande, could be much, much safer. Proper adherence to surgical checklists alone could cut adverse events by almost 50%. This means that surgery has a much higher danger differential than air travel.

I think the danger differential model doesn’t hold much water. First, if it were true, we’d expect to see something being done about surgery. Almost a decade after checklists were found to drive such large improvements, there hasn’t been any concerted government action.

Second, this doesn’t match historical accounts of how airlines were regulated into safety. At the dawn of the aviation age, pilots begged for safety standards (which could have reduced crashes a staggering sixtyfold [6]). Instead of stepping in to regulate things, the government dragged its feet. Some of the lifesaving innovations pioneered in those early days only became standard after later and larger crashes – crashes involving hundreds of members of the public, not just pilots.

While this only deals with external regulation, I strongly suspect that fear for the reputation of a profession (which could be driven by these same two factors) affects internal calls for reform as well. Canadian engineers knew that they had to do something after the Quebec bridge collapse created common knowledge that safety standards weren’t good enough. Pilots were put in a similar position with some of the better publicized mishaps. Perhaps surgeons have faced no successful internal campaign for reform so far because the public is not yet aware of the dangers of surgery to the point where it could put surgeon’s livelihoods at risk or hurt them socially.

I wonder if it’s possible to get a profession running scared about their reputation to the point that they improve their safety, even if there aren’t any of the events that seem to drive regulation. Maybe someone like Atul Gawande, who seems determined to make a very big and very public stink about safety in surgery is the answer here. Perhaps having surgery’s terrible safety record plastered throughout the New Yorker will convince surgeons that they need to start doing better [7].

If not, they’ll continue to get away with murder.

Footnotes

[1] From the CDC’s truly excellent Cause of Death search function, using codes V81.7 & V82.7 (derailment with no collision), W13 (falling out of building), W23 (caught or crushed between objects), and W35 (explosion of boiler) at home, other, or unknown. I read through several hundred causes of deaths, some alarmingly unlikely, and these were the only ones that seemed relevant. This estimate seems higher than the one surgeon Atul Gawande gave in The Checklist Manifesto, so I’m confident it isn’t too low. ^

[2] Furthermore, from 1989 to 2000, none of the observed collapses were due to flaws in the engineers’ designs. Instead, they were largely caused by weather, collisions, poor maintenance, and errors during construction. ^

[3] Claims that the rings are made from the collapsed bridge are false, but difficult to dispel. They’re actually just boring stainless steel, except in Toronto, where they’re still made from iron (but not iron from the bridge). ^

[4] There may also be an inherent privateness to surgical deaths that keeps them out of the news. Someone dying in surgery, absent obvious malpractice, doesn’t feel like public information in the way that car crashes, plane crashes, and structural failures do. ^

[5] It is true that it was never discovered why TAA-538 crashed. But black box technology would have given answers had it been in use. That it wasn’t in use was clearly a systems failure, even though the initial failure is indeterminate. This jives with my model, because regulation addressed the clear failure, not the indeterminate one. ^

[6] This is the ratio between the average miles flown before crash of the (very safe) post office planes and the (very dangerous) privately owned planes. Many in the airline industry wanted the government to mandate the same safety standards on private planes as they mandated on their airmail planes. ^

[7] I should mention that I have been very lucky to have been in the hands of a number of very competent and professional surgeons over the years. That said, I’m probably going to ask any future surgeon I’m assigned if they follow safety checklists – and ask for someone else to perform the procedure if they don’t. ^

Economics, Model

The Biggest Tech Innovation is Selling Club Goods

Economists normally splits goods into four categories:

  • Public goods are non-excludable (so anyone can access them) and non-rival (I can use them as much as I want without limiting the amount you can use them). Broadcast television, national defense, and air are all public goods.
  • Common-pool resources are non-excludable but rival (if I use them, you will have to make do with less). Iron ore, fish stocks, and grazing land are all common pool resources.
  • Private goods are excludable (their access is controlled or limited by pricing or other methods) and rival. My clothes, computer, and the parking space I have in my lease but never use are all private goods.
  • Club goods are excludable but (up to a certain point) non-rival. Think of the swimming pool in an apartment building, a large amusement park, or cellular service.

Club goods are perhaps the most interesting class of goods, because they blend properties of the three better understood classes. They aren’t open to all, but they are shared among many. They can be overwhelmed by congestion, but up until that point, it doesn’t really matter how many people are using them. Think of a gym; as long as there’s at least one free machine of every type, it’s no less convenient than your home.

Club goods offer cost savings over private goods, because you don’t have to buy something that mostly sits unused (again, think of gym equipment). People other than you can use it when it would otherwise sit around and those people can help you pay the cost. It’s for this reason that club goods represent an excellent opportunity for the right entrepreneur to turn a profit.

I currently divide tech start-ups into three classes. There are the Googles of the world, who use network effects or big data to sell advertising more effectively. There are companies like the one I work for that take advantage of modern technology to do things that were never possible before. And then there are those that are slowly and inexorably turning private goods into club goods.

I think this last group of companies (which include Netflix, Spotify, Uber, Lyft, and Airbnb) may be the ones that ultimately have the biggest impact on how we order our lives and what we buy. To better understand how these companies are driving this transformation, let’s go through them one by one, then talk about what it could all mean.

Netflix

When I was a child, my parents bought a video cassette player, then a DVD player, then a Blu-ray player. We owned a hundred or so video cassettes, mostly whatever movies my brother and I were obsessed with enough to want to own. Later, we found a video rental store we liked and mostly started renting movies. We never owned more than 30 DVDs and 20 Blu-rays.

Then I moved out. I have bought five DVDs since – they came as a set from Kickstarter. Anything else I wanted to watch, I got via Netflix. A few years later, the local video rental store closed down and my parents got an AppleTV and a Netflix of their own.

Buying a physical movie means buying a private good. Video rental stores can be accurately modeled as a type of club good, because even if the movie you want is already rented out, there’s probably one that you want to watch almost as much that is available. This is enough to make them approximately non-rival, while the fact that it isn’t free to rent a movie means that rented videos are definitely excludable.

Netflix represents the next evolution in this business model. As long as the Netflix engineers have done their job right, there’s no amount of watching movies I can do that will prevent you from watching movies. The service is almost truly non-rival.

Movie studios might not feel the effects of Netflix turning a large chunk of the market for movies into one focused on club goods; they’ll still get paid by Netflix. But the switch to Netflix must have been incredibly damaging for the physical media and player manufacturers. When everyone went from cassettes to DVDs or DVDs to Blu-rays, there was still a market for their wares. Now, that market is slowly and inexorably disappearing.

This isn’t just a consequence of technology. The club good business model offers such amazing cost savings that it drove a change in which technology was dominant. When you bought a movie, it would spend almost all of its life sitting on a shelf. Now Netflix acts as your agent, buying movies (or rather, their rights) and distributing such that they’re always being played and almost never sitting on the shelf.

Spotify

Spotify is very similar to Netflix. Previously, people bought physical cassettes (I’m just old enough that I remember making mix tapes from the radio). Then they switched to CDs. Then it was MP3s bought online (or, almost more likely, pirated online). But even pirating music is falling out of favour these days. Apple, Google, Amazon, and Spotify are all competing to offer unlimited music streaming to customers.

Music differs from movies in that it has a long tradition of being a public good – via broadcast radio. While that hasn’t changed yet (radio is still going strong), I do wonder how much longer the public option for music will exist, especially given the trend away from private cars that I think companies like Uber and Lyft are going to (pardon the pun) drive.

Uber and Lyft

I recently thought about buying a car. I was looking at the all-electric Kia Soul, which has a huge government rebate (for a little while yet) and financing terms that equate to negative real interest. Despite all these advantages, it turns out that when you sit down and run the numbers, it would still be cheaper for me to use Uber and Lyft to get everywhere.

We are starting to see the first, preliminary (and possible illusionary) evidence that Uber and Lyft are causing the public to change their preference away from owning cars.

A car you’ve bought is a private good, while Uber and Lyft are clearly club goods. Surge pricing means that there are basically always enough drivers for everyone who wants to go anywhere using the system.

When you buy a car, you’re signing up for it to sit around useless for almost all of its life. This is similar to what happens when you buy exercise equipment, which means the logic behind cars as a club good is just as compelling as the logic behind gyms. Previously, we hadn’t been able to share cars very efficiently because of technological limitations. Dispatching a taxi, especially to an area outside of a city centre, was always spotty, time consuming and confusing. Car-pooling to work was inconvenient.

As anyone who has used a modern ride-sharing app can tell you, inconvenient is no longer an apt descriptor.

There is a floor on how few cars we can get by on. To avoid congestion in a club good, you typically have to provision for peak load. Luckily, peak load (for anything that can sensibly be turned into a club good) always requires fewer resources than would be needed if everyone went out and bought the shared good themselves.

Even “just” substantially decreasing the absolute number of cars out there will be incredibly disruptive to the automotive sector if they don’t correctly predict the changing demand for their products.

It’s also true that increasing the average utilisation of cars could change how our cities look. Parking lots are necessary when cars are a private good, but are much less useful when they become club goods. It is my hope that malls built in the middle of giant parking moats look mighty silly in twenty years.

Airbnb

Airbnb is the most ambiguous example I have here. As originally conceived, it would have driven the exact same club good transformation as the other services listed. People who were on vacation or otherwise out of town would rent out their houses to strangers, increasing the utilisation of housing and reducing the need for dedicated hotels to be built.

Airbnb is sometimes used in this fashion. It’s also used to rent out extra rooms in an otherwise occupied house, which accomplishes almost the same thing.

But some amount of Airbnb usage is clearly taking place in houses or condos that otherwise would have been rental stock. When used in this way, it’s taking advantage of a regulatory grey zone to undercut hotel pricing. Insofar as this might result in a longer-term change towards regulations that are generally cheaper to comply with, this will be good for consumers, but it won’t really be transformational.

The great promise of club goods is that they might lead us to use less physical stuff overall, because where previously each person would buy one of a thing, now only enough units must be purchased to satisfy peak demand. If Airbnb is just shifting around where people are temporary residents, then it won’t be an example of the broader benefits of club goods (even if provides other benefits to its customers).

When Club Goods Eat The Economy

In every case (except potentially Airbnb) above, I’ve outlined how the switch from private goods to club goods is resulting in less consumption. For music and movies, it is unclear if this switch is what is providing the primary benefit. My intuition is that the club good model actually did change consumption patterns for physical copies of movies (because my impression is that few people ever did online video rentals via e.g. iTunes), whereas the MP3 revolution was what really shrunk the footprint of music media.

This switch in consumption patterns and corresponding decrease in the amount of consumption that is necessary to satisfy preferences is being primarily driven by a revolution in logistics and bandwidth. The price of club goods has always compared favourably with that of private goods. The only thing holding people back was inconvenience. Now programmers are steadily figuring out how to make that inconvenience disappear.

On the other hand, increased bandwidth has made it easier to turn any sort of digitizable media into a club good. There’s an old expression among programmers: never underestimate the bandwidth of a station wagon full of cassettes (or CDs, or DVDs, or whatever physical storage media one grew up with) hurtling down the highway. For a long time, the only way to get a 1GB movie to a customer without an appallingly long buffering period was to physically ship it (on a 56kbit/s connection, this movie would take one day and fifteen hours to download, while the aforementioned station wagon with 500 movies would take 118 weeks to download).

Change may start out slow, but I expect to see it accelerate quickly. My generation is the first to have had the internet from a very young age. The generation after us will be the first unable to remember a time before it. We trust apps like Uber and Airbnb much more than our parents, and our younger siblings trust them even more than us.

While it was only kids who trusted the internet, these new club good businesses couldn’t really affect overall economic trends. But as we come of age and start to make major economic decisions, like buying houses and cars, our natural tendency to turn towards the big tech companies and the club goods they peddle will have ripple effects on an economy that may not be prepared for it.

When that happens, there’s only one thing that is certain: there will be yet another deluge of newspaper columns talking about how millennials are destroying everything.

Advice, Literature, Model

Sanderson’s Law Applies To Cultures Too

[Warning: Contains spoilers for The Sunset Mantle, Vorkosigan Saga (Memory and subsequent), Dune, and Chronicles of the Kencyrath]

For the uninitiated, Sanderson’s Law (technically, Sanderson’s First Law of Magic) is:

An author’s ability to solve conflict with magic is DIRECTLY PROPORTIONAL to how well the reader understands said magic.

Brandon Sanderson wrote this law to help new writers come up with satisfying magical systems. But I think it’s applicable beyond magic. A recent experience has taught me that it’s especially applicable to fantasy cultures.

I recently read Sunset Mantle by Alter S. Reiss, a book that falls into one of my favourite fantasy sub-genres: hopeless siege tales.

Sunset Mantle is what’s called secondary world fantasy; it takes place in a world that doesn’t share a common history or culture (or even necessarily biosphere) with our own. Game of Thrones is secondary world fantasy, while Harry Potter is primary world fantasy (because it takes place in a different version of our world, which we chauvinistically call the “primary” one).

Secondary world fantasy gives writers a lot more freedom to play around with cultures and create interesting set-pieces when cultures collide. If you want to write a book where the Roman Empire fights a total war against the Chinese Empire, you’re going to have to put in a master’s thesis worth of work to explain how that came about (if you don’t want to be eviscerated by pedants on the internet). In a secondary world, you can very easily have a thinly veiled stand-in for Rome right next to a thinly veiled analogue of China. Give readers some familiar sounding names and culture touchstones and they’ll figure out what’s going on right away, without you having to put in effort to make it plausible in our world.

When you don’t use subtle cues, like names or cultural touchstones (for example: imperial exams and eunuchs for China, gladiatorial fights and the cursus honorum for Rome), you risk leaving your readers adrift.

Many of the key plot points in Sunset Mantle hinge on obscure rules in an invented culture/religion that doesn’t bear much resemblance to any that I’m familiar with. It has strong guest rights, like many steppes cultures; it has strong charity obligations and monotheistic strictures, like several historical strands of Christianity; it has a strong caste system and rules of ritual purity, like Hinduism; and it has a strong warrior ethos, complete with battle rage and rules for dealing with it, similar to common depictions of Norse cultures.

These actually fit together surprising well! Reiss pulled off an entertaining book. But I think many of the plot points fell flat because they were almost impossible to anticipate. The lack of any sort of consistent real-world analogue to the invented culture meant that I never really had an intuition of what it would demand in a given situation. This meant that all of the problems in the story that were solved via obscure points of culture weren’t at all satisfying to me. There was build up, but then no excitement during the resolution. This was common enough that several chunks of the story didn’t really work for me.

Here’s one example:

“But what,” asked Lemist, “is a congregation? The Ayarith school teaches that it is ten men, and the ancient school of Baern says seven. But among the Irimin school there is a tradition that even three men, if they are drawn in together into the same act, by the same person, that is a congregation, and a man who has led three men into the same wicked act shall be put to death by the axe, and also his family shall bear the sin.”

All the crowd in the church was silent. Perhaps there were some who did not know against whom this study of law was aimed, but they knew better than to ask questions, when they saw the frozen faces of those who heard what was being said.

(Reiss, Alter S.. Sunset Mantle (pp. 92-93). Tom Doherty Associates. Kindle Edition.)

This means protagonist Cete’s enemy erred greatly by sending three men to kill him and had better cut it out if he doesn’t want to be executed. It’s a cool resolution to a plot point – or would be if it hadn’t taken me utterly by surprise. As it is, it felt kind of like a cheap trick to get the author out of a hole he’d written himself into, like the dreaded deux ex machina – god from the machine – that ancient playwrights used to resolve conflicts they otherwise couldn’t.

(This is the point where I note that it is much harder to write than it is to criticize. This blog post is about something I noticed, not necessarily something I could do better.)

I’ve read other books that do a much better job of using sudden points of culture to resolve conflict in a satisfying manner. Lois McMaster Bujold (I will always be recommending her books) strikes me as particularly apt. When it comes time for a key character of hers to make a lateral career move into a job we’ve never heard of before, it feels satisfying because the job is directly in line with legal principles for the society that she laid out six books earlier.

The job is that of Imperial Auditor – a high powered investigator who reports directly to the emperor and has sweeping powers –  and it’s introduced when protagonist Miles loses his combat career in Memory. The principles I think it is based on are articulated in the novella Mountains of Mourning: “the spirit was to be preferred over the letter, truth over technicalities. Precedent was held subordinate to the judgment of the man on the spot”.

Imperial Auditors are given broad discretion to resolve problems as they see fit. The main rule is: make sure the emperor would approve. We later see Miles using the awesome authority of this office to make sure a widow gets the pension she deserves. The letter of the law wasn’t on her side, but the spirit was, and Miles, as the Auditor on the spot, was empowered to make the spirit speak louder than the letter.

Wandering around my bookshelves, I was able to grab a couple more examples of satisfying resolutions to conflicts that hinged on guessable cultural traits:

  • In Dune, Fremen settle challenges to leadership via combat. Paul Maud’dib spends several years as their de facto leader, while another man, Stilgar, holds the actual title. This situation is considered culturally untenable and Paul is expected to fight Stilgar so that he can lead properly. Paul is able to avoid this unwanted fight to the death (he likes Stilgar) by appealing to the only thing Fremen value more than their leadership traditions: their well-established pragmatism. He says that killing Stilgar before the final battle would be little better than cutting off his own arm right before it. If Frank Herbert hadn’t mentioned the extreme pragmatism of the Fremen (to the point that they render down their dead for water) several times, this might have felt like a cop-out.
  • In The Chronicles of the Kencyrath, it looks like convoluted politics will force protagonist Jame out of the military academy of Tentir. But it’s mentioned several times that the NCOs who run the place have their own streak of honour that allows them to subvert their traditionally required oaths to their lords. When Jame redeems a stain on the Tentir’s collective honour, this oath to the college gives them an opening to keep her there and keep their oaths to their lords. If PC Hodgell hadn’t spent so long building up the internal culture of Tentir, this might have felt forced.

It’s hard to figure out where good foreshadowing ends and good cultural creation begins, but I do think there is one simple thing an author can do to make culture a satisfying source of plot resolution: make a culture simple enough to stereotype, at least at first.

If the other inhabitants of a fantasy world are telling off-colour jokes about this culture, what do they say? A good example of this done explicitly comes from Mass Effect: “Q: How do you tell when a Turian is out of ammo? A: He switches to the stick up his ass as a backup weapon.” 

(Even if you’ve never played Mass Effect, you now know something about Turians.)

At the same time as I started writing this, I started re-reading PC Hodgell’s The Chronicles of the Kencyrath, which provided a handy example of someone doing everything right. The first three things we learn about the eponymous Kencyr are:

  1. They heal very quickly
  2. They dislike their God
  3. Their honour code is strict enough that lying is a deadly crime and calling some a liar a deathly insult

There are eight more books in which we learn all about the subtleties of their culture and religion. But within the first thirty pages, we have enough information that we can start making predictions about how they’ll react to things and what’s culturally important.

When Marc, a solidly dependable Kencyr who is working as a guard and bound by Kencyr cultural laws to loyally serve his employer lets the rather more eccentric Jame escape from a crime scene, we instantly know that him choosing her over his word is a big deal. And indeed, while he helps her escape, he also immediately tries to kill himself. Jame is only able to talk him out of it by explaining that she hadn’t broken any laws there. It was already established that in the city of Tai-Tastigon, only those who physically touch stolen property are in legal jeopardy. Jame never touched the stolen goods, she was just on the scene. Marc didn’t actually break his oath and so decides to keep living.

God Stalk is not a long book, so that fact that PC Hodgell was able to set all of this up and have it feel both exciting in the moment and satisfying in the resolution is quite remarkable. It’s a testament to what effective cultural distillation, plus a few choice tidbits of extra information can do for a plot.

If you don’t come up with a similar distillation and convey it to your readers quickly, there will be a period where you can’t use culture as a satisfying source of plot resolution. It’s probably no coincidence that I noticed this in Sunset Mantle, which is a long(-ish) novella. Unlike Hodgell, Reiss isn’t able to develop a culture in such a limited space, perhaps because his culture has fewer obvious touchstones.

Sanderson’s Second Law of Magic can be your friend here too. As he stated it, the law is:

The limitations of a magic system are more interesting than its capabilities. What the magic can’t do is more interesting than what it can.

Similarly, the taboos and strictures of a culture are much more interesting than what it permits. Had Reiss built up a quick sketch of complicated rules around commanding and preaching (with maybe a reference that there could be surprisingly little theological difference between military command and being behind a pulpit), the rule about leading a congregation astray would have fit neatly into place with what else we knew of the culture.

Having tight constraints imposed by culture doesn’t just allow for plot resolution. It also allows for plot generation. In The Warrior’s Apprentice, Miles gets caught up in a seemingly unwinnable conflict because he gave his word; several hundred pages earlier Bujold establishes that breaking a word is, to a Barrayaran, roughly equivalent to sundering your soul.

It is perhaps no accident that the only thing we learn initially about the Kencyr that isn’t a descriptive fact (like their healing and their fraught theological state) is that honour binds them and can break them. This constraint, that all Kencyr characters must be honourable, does a lot of work driving the plot.

This then would be my advice: when you wish to invent a fantasy culture, start simple, with a few stereotypes that everyone else in the world can be expected to know. Make sure at least one of them is an interesting constraint on behaviour. Then add in depth that people can get to know gradually. When you’re using the culture as a plot device, make sure to stick to the simple stereotypes or whatever other information you’ve directly given your reader. If you do this, you’ll develop rich cultures that drive interesting conflicts and you’ll be able to use cultural rules to consistently resolve conflict in a way that will feel satisfying to your readers.

Software

Against Programming Hobbies

[Epistemic Status: Written more harshly than my actual views for persuasive effect. I should also point out that all views expressed here are my own, not my employer’s; when I’m hiring, my first commitment is complying with the relevant Federal, Provincial, and local legislation. My second commitment is to finding the best people. Ideology doesn’t come into it. Serendipitously, I think everything I’ve argued for here helps me discharge both duties.]

In my capacity as a senior employee at Alert Labs (it’s easy to be senior when the company is only three years old), I do a lot of hiring. Since I started, I’ve been involved in interviews for four full time hires and five interns. Throughout all of this, I’ve learned a lot about what to look for in a resume.

I’ve also gotten in the occasional disagreement about what we should look for in in people we’re (potentially) hiring.

When looking through resumes for software engineers, it’s accepted practice to look for independent programming projects. These are things that people do in their spare time, normally to learn new languages or make things that they find cool. I’ve done a few myself. If you look at my projects, you’ll see one where I create a tool for my favourite pen and paper roll playing game, one where I work through math problems, and one where I’m trying to better understand the concept of randomness.

There’s a curious double vision in the profession about programming projects. We all tell ourselves people do them only for fun. Yet we also look for them on resumes.

The second fact means that the first cannot always be true. My projects partially exist for my resume. I’ve enjoyed working on them. But if there wasn’t a strong financial motive to have worked on them, I probably wouldn’t have. Or I’d have done them differently.

As a someone who hires, I can’t claim that programming projects aren’t useful. They give, perhaps better than anything else (e.g. the much-derided whiteboard interview), an idea of what sort of code someone would write as an employee. I’ve called people – especially people without any formal education in CS – in for interviews largely on the strengths of their personal projects. Seeing that someone can use the languages that they say they can, that they can write unit tests and documentation, and that they can lay out a large project makes me have more faith that they can do the job.

When programming projects are used as a complement to employment and educational history, I think they help the field.

But I’ve also argued stringently against treating personal projects as a key part of any hiring process. While I like using them as a supplement, I think there are four good reasons not to rely on them as any sort of primary criteria.

First, not everyone has time for projects. Using them as a screen sifts out people with caregiving responsibilities, with families, or with strong commitments in their personal life. When you’re only hiring from people without other commitments, it becomes easier for a team to slip into a workaholic lifestyle. This is bad, because despite what many people think, studies consistently show no productivity benefits from working more than 40 hours per week for prolonged periods. All long hours do is deprive people of personal time.

(In a world where people with caregiving responsibilities are more likely to be female, overreliance on personal projects can also become a subtle form of hiring discrimination.)

I’m incredibly grateful that I work at a company founded by people with both management experience and children. Their management experience means they know better than to let their employees burn out from overwork, while their children mean that the company has always had a culture of taking time for other commitments. This doesn’t mean that I’m never in for sixty hours in a week, or that I never have to deal with a server failure at midnight. Work-life balance doesn’t mean that I don’t take my work seriously; it just means I don’t conflate being in the office for 12 hours at a time with that seriousness.

Second, requiring people to have programming hobbies sifts out a lot of interesting people. I understand that there exist people that only want to live code, only want to talk about code, and want to be surrounded by people who are also in that mode, but that isn’t me. I joined Alert Labs because I wanted to solve real-life problems, not make tools for people just like me. Having a well-rounded team means that people spontaneously generate ideas for new projects. It means they take ownership for features (like ensuring everything on our website follows accessibility guidelines) that would never percolate to the top of my mind. It makes our team stronger and more effective.

Outside of a few other oddball professions (lawyers, I’m looking at you), no one else is expected to treat their work as their hobby. People can make their hobbies into their work (look at webcomic artists or bloggers who make it big) and this was one of the initial purposes of personal programming projects. It’s not at all unusual to find something you like enough that you’d make a full-time job of it if you could. But then you normally get new hobbies.

People who fall in love with programming are lucky in that they often can turn it into a full-time job. Writers… are somewhat less lucky. I haven’t monetized my blog because I’d find the near-impossibility of making money off of discursive posts about political economy disheartening. Keeping my blog as a vanity project keeps it fun.

 

But we programmers shouldn’t let our economic fortune turn what has always been the path that a minority of people take into our field into a bona fide requirement.

Third, I dislike what an overemphasis on programming projects can do to resumes. I frequently see interesting hobbies shunted aside to make room for less-than-inspired programming projects. I’ve seen people who got the memo that they needed a profile full of projects, but not the memo that it had to be their projects. This leads to GitHub pages full of forks of well-known projects. I don’t know who this is supposed to fool, but it sure doesn’t work on me.

When students send in resumes, they all put the same four class projects on them, in the somewhat futile hope that we won’t notice and we’ll consider them adequately dedicated. I wish the fact that they were paying $8500 per term to learn about CS could be taken as proof enough of their dedication and I wouldn’t have to read about pong sixty times a semester, but that is apparently not the world I live in.

My final beef with an overemphasis on programming hobbies is that many important skills can’t be learned in front of a computer. Not all hobbies teach you how to work together with a disparate team, respectfully navigate disagreements with other people, and effectively address co-worker concerns, but those that do are worth their weight in gold. Software is becoming ever more complex and is having ever more capital thrown at it. We’ve exhausted what we can do with single brilliant loners, which means that we now need to turn to functional teams.

This isn’t meant to conjure up negative and insulting stereotypes about people who spend all their spare time programming. Many of these people are incredibly kind and very devoted to mentoring new members of our community.

I don’t want people who program in their spare time and love it with all their hearts to be tarred with negative stereotypes. But I also don’t want people with other interests to be considered uncommitted dilettantes. And I hope we can build a profession that believes neither myth.

Economics, Politics, Quick Fix

Why Linking The Minimum Wage To Inflation Can Backfire

Last week I explained how poor decisions by central bankers (specifically failing to spur inflation) can make recessions much worse and lead to slower wage growth during recovery.

(Briefly: inflation during recessions reduces the real cost of payroll, cutting business expenses and making firing people unnecessary. During a recovery, it makes hiring new workers cheaper and so leads to more being hired. Because central bankers failed to create inflation during and after the great recession, many businesses are scared of raising salaries. They believe (correctly) that this will increase their payroll expenses to the point where they’ll have to lay many people off if another recession strikes. Until memories of the last recession fade or central bankers clean up their act, we shouldn’t expect wages to rise.)

Now I’d like to expand on an offhand comment I made about the minimum wage last week and explore how it can affect recovery, especially if it’s indexed to inflation.

The minimum wage represents a special case when it comes to pay cuts and layoffs in recessions. While it’s always theoretically possible to convince people to take a pay cut rather than a layoff (although in practice it’s mostly impossible), this option isn’t available for people who make the minimum wage. It’s illegal to pay them anything less. If bad times strike and business is imperiled, people making the minimum wage might have to be laid off.

I say “might”, because when central bankers aren’t proving useless, inflation can rescue people making the minimum wage from being let go. Inflation makes the minimum wage relatively less valuable, which reduces the cost of payroll relative to other inputs and helps to save jobs that pay minimum wage. This should sound familiar, because inflation helps people making the minimum wage in the exact same way it helps everyone else.

Because of increasingly expensive housing and persistently slow wage growth, some jurisdictions are experimenting with indexing the minimum wage to inflation. This means that the minimum wage rises at the same rate as the cost of living. Most notably (to me, at least), this group includes my home province of Ontario.

I think decreasing purchasing power is a serious problem (especially because of its complicated intergenerational dynamics), but I think this is one of the worst possible ways to deal with it.

When the minimum wage is tied to inflation, recessions can become especially dangerous and drawn out.

With the minimum wage rising in lockstep with inflation, any attempts to decrease payroll costs in real terms (that is to say: inflation adjusted terms) is futile to the extent that payroll expenses are for minimum wage workers. Worse, people who were previously making above the minimum wage and might have had their jobs saved by inflation can be swept up by an increasingly high minimum wage.

This puts central bankers in a bind. As soon as the minimum wage is indexed to inflation, inflation is no longer a boon to all workers. Suddenly, many workers can find themselves in a “damned if you do, damned if you don’t” situation. Without inflation, they may be too expensive to keep. With it, they may be saved… until the minimum wage comes for them too. If a recession goes on long enough, only high-income workers would be sparred.

In addition, minimum wage (or near-minimum wage) workers who are laid off during a period of higher inflation (an in this scenario, there will be many) will suffer comparatively more, as their savings get exhausted even more quickly.

Navigating these competing needs would be an especially tough challenge for certain central banks like the US Federal Reserve – those banks that have dual mandates to maintain stable prices and full employment. If a significant portion of the US ever indexes its minimum wage to inflation, the Fed will have no good options.

It is perhaps darkly humorous that central banks, which bear an unusually large parcel of the blame for our current slow wage growth, stand to face the greatest challenges from the policies we’re devising to make up for their past shortcomings. Unfortunately, I think a punishment of this sort is rather like cutting off our collective nose to spite our collective face.

There are simple policies we could enact to counter the risks here. Suspending any peg to inflation during years that contain recessions (in Ontario at least, the minimum wage increase due to inflation is calculated annually) would be a promising start. Wage growth after a recession could be ensured with a rebound clause, or better yet, the central bank actually doing its job properly.

I am worried about the political chances (and popularity once enacted) of any such pragmatic policy though. Many people respond to recessions with the belief that the government can make things better by passing the right legislation – forcing the economy back on track by sheer force of ink. This is rarely the case, especially because the legislation that people have historically clamoured for when unemployment is high is the sort that increases wages, not lowers them. This is a disaster when unemployment threatens because of too-high wages. FDR is remembered positively for his policy of increasing wages during the great depression, even though this disastrous decision strangled the recovery in its crib. I don’t expect any higher degree of economic literacy from people today.

To put my fears more plainly, I worry that politicians, faced with waning popularity and a nipping recession, would find allowing the minimum wage to be frozen too much of a political risk. I frankly don’t trust most politicians to follow through with a freeze, even if it’s direly needed.

Minimum wages are one example of a tradeoff we make between broad access and minimum standards. Do we try and make sure everyone who wants a job can have one, or do we make sure people who have jobs aren’t paid too little for their labour, even if that hurts the unemployed? As long as there’s scarcity, we’re going to have to struggle with how we ensure that as many people as possible have their material needs met and that involves tradeoffs like this one.

Minimum wages are just one way we can do this. Wage subsidies or a Universal Basic Income are both being discussed with increasing frequency these days.

But when we’re making these kind of compassionate decisions, we need to look at the risks of whatever systems we choose. Proponents of indexing the minimum wage to inflation haven’t done a good job of understanding the grave risk it poses to the health of our economy and perhaps most of all, to the very people they seek to help. In places like Ontario, where the minimum wage is already indexed to inflation, we’re going to pay for their lack of foresight next time an economic disaster strikes.

Advice, Model

Context Windows

When you’re noticing that you’re talking past someone, what does it look like? Do you feel like they’re ignoring all the implications of the topic at hand (“yes, I know the invasion of Iraq is causing a lot of pain, but I think the important question is, ‘did they have WMDs?'”)? Or do you feel like they’re avoiding talking about the object-level point in favour of other considerations (“factory farmed animals might suffer, but before we can consider whether that’s justified or not, shouldn’t we decide whether we have any obligation to maximize the number of living creatures?”)?

I’m beginning to suspect that many tense disagreements and confused, fruitless conversations are caused by differences in how people conceive of and process the truth. More, I think I have a model that explains why some people can productively disagree with anyone and everyone, while others get frustrated very easily with even their closest friends.

The basics of this model come from a piece that Jacob Falkovich wrote for Quillette. He uses two categories, “contextualizers” and “decouplers”, to analyze an incredibly unproductive debate (about race and IQ) between Vox’s Ezra Klein and Dr. Sam Harris.

Klein is the contextualizer, a worldview that comes naturally to a political journalist. Contextualizers see ideas as embedded in a context. Questions of “who does this effect?”, “how is this rooted in society?”, and “what are the (group) identities of people pushing this idea?” are the bread and butter of contextualizers. One of the first things Klein says in his debate with Harris is:

Here is my view: I think you have a deep empathy for Charles Murray’s side of this conversation, because you see yourself in it [because you also feel attacked by “politically correct” criticism]. I don’t think you have as deep an empathy for the other side of this conversation. For the people being told once again that they are genetically and environmentally and at any rate immutably less intelligent and that our social policy should reflect that. I think part of the absence of that empathy is it doesn’t threaten you. I don’t think you see a threat to you in that, in the way you see a threat to you in what’s happened to Murray. In some cases, I’m not even quite sure you heard what Murray was saying on social policy either in The Bell Curve and a lot of his later work, or on the podcast. I think that led to a blind spot, and this is worth discussing.

Klein is highlighting what he thinks is the context that probably informs Harris’s views. He’s suggesting that Harris believes Charles Murray’s points about race and IQ because they have a common enemy. He’s aware of the human tendency to like ideas that come from people we feel close to (myside bias) – or that put a stick in the eye of people we don’t like.

There are other characteristics of contextualizers. They often think thought experiments are pointless, given that they try and strip away all the complex ways that society affects our morality and our circumstances. When they make mistakes, it is often because they fall victim to the “ought-is” fallacy; they assume that truths with bad outcomes are not truths at all.

Harris, on the other hand, is a decoupler. Decoupling involves separating ideas from context, from personal experience, from consequences, from anything but questions of truth or falsehood and using this skill to consider them in the abstract. Decoupling is necessary for science because it’s impossible to accurately check a theory when you hope it to be true. Harris’s response to Klein’s opening salvo is:

I think your argument is, even where it pretends to be factual, or wherever you think it is factual, it is highly biased by political considerations. These are political considerations that I share. The fact that you think I don’t have empathy for people who suffer just the starkest inequalities of wealth and politics and luck is just, it’s telling and it’s untrue. I think it’s even untrue of Murray. The fact that you’re conflating the social policies he endorses — like the fact that he’s against affirmative action and he’s for universal basic income, I know you don’t happen agree with those policies, you think that would be disastrous — there’s a good-faith argument to be had on both sides of that conversation. That conversation is quite distinct from the science and even that conversation about social policy can be had without any allegation that a person is racist, or that a person lacks empathy for people who are at the bottom of society. That’s one distinction I want to make.

Harris is pointing out that questions of whether his beliefs will have good or bad consequences or who they’ll hurt have nothing to do with the question of if they are true. He might care deeply about the answers of those questions, but he believes that it’s a dangerous mistake to let that guide how you evaluate an idea. Scientists who fail to do that tend to get caught up in the replication crisis.

When decouplers err, it is often because of the is-ought fallacy. They fail to consider how empirical truths can have real world consequences and fail to consider how labels that might be true in the aggregate can hurt individuals.

When you’re arguing with someone who doesn’t contextualize as much as you do, it can feel like arguing about useless hypotheticals. I once had someone start a point about police shootings and gun violence with “well, ignoring all of society…”. This prompted immediate groans.

When arguing with someone who doesn’t decouple as much as you do, it can feel useless and mushy. A co-worker once said to me “we shouldn’t even try and know the truth there – because it might lead people to act badly”. I bit my tongue, but internally I wondered how, absent the truth, we can ground disagreements in anything other than naked power.

Throughout the debate between Harris and Klein, both of them get frustrated at the other for failing to think like they do – which is why it provided such a clear example for Falkovich. If you read the transcripts, you’ll see a clear pattern: Klein ignores questions of truth or falsehood and Harris ignores questions of right and wrong. Neither one is willing to give an inch here, so there’s no real engagement between them.

This doesn’t have to be the case whenever people who prefer context or prefer to deal with the direct substance of an issue interact.

My theory is that everyone has a window that stretches from the minimum amount of context they like in conversations to the minimum amount of substance. Theoretically, this window could stretch from 100% context and no substance to 100% substance and no context.

But practically no one has tastes that broad. Most people accept a narrower range of arguments. Here’s what three well compatible friends might look like:

We should expect to see some correlation between the minimum and maximum amount of context people want to get. Windows may vary in size, but in general, feeling put-off by lots of decoupling should correlate with enjoying context.


 Here we see people with varyingly sized strike zones, but with their dislike of context correlated with their appreciation for substance.

Klein and Harris disagreed so unproductively not just because they give first billing to different things, but because their world views are different enough that there is absolutely no overlap between how they think and talk about things.

One plausible graph of how Klein and Harris like to think about problems (quotes come from the transcript of their podcast). From this, it makes sense that they couldn’t have a productive conversation. There’s no overlap in how they model the world.

I’ve found thinking about windows of context and substance, rather than just the dichotomous categories, very useful for analyzing how me and my friends tend to agree and disagree.

Some people I know can hold very controversial views without ever being disagreeable. They are good at picking up on which sorts of arguments will work with their interlocutors and sticking to those. These people are no doubt aided by rather wide context windows. They can productively think and argue with varying amounts of context and substance.

Other people feel incredibly difficult to argue with. These are the people who are very picky about what arguments they’ll entertain. If I sort someone into this internal category, it’s because I’ve found that one day they’ll dismiss what I say as too nitty-gritty, while the next day they criticize me for not being focused enough on the issue at hand.

What I’ve started to realize is that people I find particularly finicky to argue with may just have a fairly narrow strike zone. For them, it’s simultaneously easy for arguments to feel devoid of substance or devoid of context.

I think one way that you can make arguments with friends more productive is explicitly lay out the window in which you like to be convinced. Sentences like: “I understand what you just said might convince many people, but I find arguments about the effects of beliefs intensely unsatisfying” or “I understand that you’re focused on what studies say, but I think it’s important to talk about the process of knowledge creation and I’m very unlikely to believe something without first analyzing what power hierarchies created it” are the guideposts by which you can show people your context window.

Economics, Falsifiable

You Might Want To Blame Central Banks For Poor Wage Growth

The Economist wonders why wage growth isn’t increasing, even as unemployment falls. A naïve reading of supply and demand suggests that it should, so this has become a relatively common talking point in the news, with people of all persuasions scratching their heads. The Economist does it better than most. They at least talk about slowing productivity growth and rising oil prices, instead of blaming everything on workers (for failing to negotiate) or employers (for not suddenly raising wages).

But after reading monetary policy blogs, the current lack of wage growth feels much less confusing to me. Based on this, I’d like to offer one explanation for why wages haven’t been growing. While I may not be an economist, I’ll be doing my best to pass along verbatim the views of serious economic thinkers.

Image courtesy of the St. Louis Federal Reserve Bank. Units are 1982-1984 CPI-adjusted dollars. Isn’t it rad how the US government doesn’t copyright anything it produces?

 

 

When people talk about stagnant wage growth, this is what they mean. Average weekly wages have increased from $335 a week in 1979 to $350/week in 2018 (all values are 1982 CPI-adjusted US dollars). This is a 4.5% increase, representing $780/year more (1982 dollars) in wages over the whole period. This is not a big change.

More recent wage growth also isn’t impressive. At the depth of the recession, weekly wages were $331 [1]. Since then, they’ve increased by $19/week, or 5.7%. However, wages have only increased by $5/week (1.4%) since the previous high in 2009.

This doesn’t really match people’s long run expectations. Between 1948 and 1973, hourly compensation increased by 91.3%.

I don’t have an explanation for what happened to once-high wage growth between 1980 and 2008 (see The Captured Economy for what some economists think might explain it). But when it comes to the current stagnation, one factor I don’t hear enough people talking about is bad policy moves by central bankers.

To understand why the central bank affects wage growth, you have to understand something called “sticky wages“.

Wages are considered “sticky” because it is basically impossible to cut them. If companies face a choice between firing people and cutting wages, they’ll almost always choose to fire people. This is because long practice has taught them that the opposite is untenable.

If you cut everyone’s wages, you’ll face an office full of much less motivated people. Those whose skills are still in demand will quickly jump ship to companies that compensate them more in line with market rates. If you just cut the wages of some of your employees (to protect your best performers), you’ll quickly find an environment of toxic resentment sets in.

This is not even to mention that minimum wage laws make it illegal to cut the wages of many workers.

Normally the economy gets around sticky wages with inflation. This steadily erodes wages (including the minimum wage). During boom times, businesses increase wages above inflation to keep their employees happy (or lose them to other businesses that can pay more and need the labour). During busts, inflation can obviate the need to fire people by decreasing the cost of payroll relative to other inputs.

But what we saw during the last recession was persistently low inflation rates. Throughout the whole the thing, the Federal Reserve Bank kept saying, in effect, “wow, really hard to up inflation; we just can’t manage to do it”.

Look at how inflation hovers just above zero for the whole great recession and associated recovery. It would have been better had it been hovering around 2%.

It’s obviously false that the Fed couldn’t trigger inflation if it wanted to. As a thought experiment, imagine that they had printed enough money to give everyone in the country $1,000,000 and then mailed it out. That would obviously cause inflation. So it is (theoretically) just a manner of scaling that back to the point where we’d only see inflation, not hyper-inflation. Why then did the Fed fail to do something that should be so easy?

According to Scott Sumner, you can’t just look at the traditional instrument the central bank has for managing inflation (the interest rate) to determine if its policies are inflationary or not. If something happens to the monetary supply (e.g. say all banks get spooked and up their reserves dramatically [2]), this changes how effective those tools will be.

After the recession, the Fed held the interest rates low and printed money. But it actually didn’t print enough money given the tightened bank reserves to spur inflation. What looked like easy money (inflationary behaviour) was actually tight money (deflationary behaviour), because there was another event constricting the money supply. If the Fed wanted inflation, it would have had to do much more than is required in normal times. The Federal Reserve never realized this, so it was always confused by why inflation failed to materialize.

This set off the perfect storm that led to the long recovery after the recession. Inflation didn’t drive down wages, so it didn’t make economic sense to hire people (or even keep as many people on staff), so aggregate demand was low, so business was bad, so it didn’t make sense to hire people (or keep them on staff)…

If real wages had properly fallen, then fewer people would have been laid off, business wouldn’t have gotten as bad, and the economy could have started to recover much more quickly (with inflation then cooling down and wage growth occurring). Scott Sumner goes so far to say that the money shock caused by increased cash reserves may have been the cause of the great recession, not the banks failing or the housing bubble.

What does this history have to do with poor wage growth?

Well it turns out that companies have responded to the tight labour market with something other than higher wages: bonuses.

Bonuses are one-time payments that people only expect when times are good. There’s no problem cutting them in recessions.

Switching to bonuses was a calculated move for businesses, because they have lost all faith that the Federal Reserve will do what is necessary (or will know how to do what is necessary) to create the inflation needed to prevent deep recessions. When you know that wages are sticky and you know that inflation won’t save you from them, you have no choice but to pre-emptively limit wages, even when there isn’t a recession. Even when a recession feels fairly far away.

More inflation may feel like the exact opposite of what’s needed to increase wages. But we’re talking about targeted inflation here. If we could trust humans to do the rational thing and bargain for less pay now in exchange for more pay in the future whenever times are tight, then we wouldn’t have this problem and wages probably would have recovered better. But humans are humans, not automatons, so we need to make the best with what we have.

One of the purposes of institutions is to build a framework within which we can make good decisions. From this point of view, the Federal Reserve (and other central banks; the Bank of Japan is arguably far worse) have failed. Institutions failing when confronted with new circumstances isn’t as pithy as “it’s all the fault of those greedy capitalists” or “people need to grow backbones and negotiate for higher wages”, but I think it’s ultimately a more correct explanation for our current period of slow wage growth. This suggests that we’ll only see wage growth recover when the Fed commits to better monetary policy [3], or enough time passes that everyone forgets the great recession.

In either case, I’m not holding my breath.

Footnotes

[1] I’m ignoring the drop in Q2 2014, where wages fell to $330/week, because this was caused by the end of extended unemployment insurance in America. The end of that program made finding work somewhat more important for a variety of people, which led to an uptick in the supply of labour and a corresponding decrease in the market clearing wage. ^

[2] Under a fractional reserve banking system, banks can lend out most of their deposits, with only a fraction kept in reserve to cover any withdrawals customers may want to make. This effectively increases the money supply, because you can have dollars (or yen, or pesos) that are both left in a bank account and invested in the economy. When banks hold onto more of their reserves because of uncertainty, they are essentially shrinking the total money supply. ^

[3] Scott Sumner suggests that we should target nominal GDP instead of inflation. When economic growth slows, we’d automatically get higher inflation, as the central bank pumps out money to meet the growth target. When the market begins to give way to roaring growth and speculative bubbles, the high rate of real growth would cause the central bank to step back, tapping the brakes before the economy overheats. I wonder if limiting inflation on the upswing would also have the advantage of increasing real wages as the economy booms? ^

Economics, Politics

Book Review: The Captured Economy

There are many problems that face modern, developed economies. Unfortunately, no one agrees with what to do in response to them. Even economists are split, with libertarians championing deregulation, while liberals call for increased government spending to reduce inequality.

Or at least, that’s the conventional wisdom. The Captured Economy, by Dr. Brink Lindsey (libertarian) and Dr. Steven M. Teles (liberal) doesn’t have much time for conventional wisdom.

It’s a book about the perils of regulation, sure. But it’s a book that criticizes regulation that redistributes money upwards. This isn’t the sort of regulation that big pharma or big finance wants to cut. It’s the regulation they pay politicians to enact.

And if you believe Lindsey and Teles, upwardly redistributing regulation is strangling our economy and feeding inequality.

They’re talking, of course, about rent-seeking.

Now, if you don’t read economic literature, you probably have an idea of what “rent-seeking” might mean. This idea is probably wrong. We aren’t talking here about the sorts of rents that you pay to landlords. That rent probably includes some economic rents (quite a lot of economic rents if you live in Toronto, Vancouver, San Francisco, or New York), but does not itself represent an economic rent.

An economic rent is any excess payment due to scarcity. If you control especially good land and can grow wheat at half the price of everyone else, the rent of this land is the difference between how much it costs you to grow wheat and how much it costs everyone else to grow wheat.

Rent-seeking is when someone tries to acquire these rents without producing anything of value. It isn’t rent-seeking when you invent a new mechanical device that cuts your costs in half (although your additional profit will represent economic rents). It is rent-seeking when you use some of those profits as “campaign contributions” to get the government to pass a law that requires all future labour-saving devices need to be “tested” for five years before they can be introduced. Over that five-year period, you’ll reap rents because no one else can compete with you to bring the price of the goods you are producing down.

How could we know if rent-seeking is happening in the US economy (note: this book is written specifically about the US, so assume all statements here are about the US unless otherwise noted) and how can we tell what it’s costing?

Well, one of the best signs of rent-seeking is increased profits. If profits are increasing and this can’t be explained by innovation or productivity growth or any other natural factor, then we have circumstantial evidence that profits are increasing from rent-seeking. Is this the case?

Lindsey and Teles say yes.

First, it seems like profits for US firms are increasing, from a low of 3% in the 1980s to a high of 11% currently. These are average profits, so they can’t be swayed by one company suddenly becoming much more efficient – as something like that should be cancelled out by a decline in profits at somewhere less efficient.

At the same time, however, the majority of these new profits have been going to companies that were already very profitable. If being very profitable makes corrupting the political process easier, this is exactly what we’d expect to see.

In addition, formation of new companies has slowed, concentration has increased, the ratio of intangible assets to tangible assets has increased, and yet spending on intangible assets (like R&D) has dropped. The only intangibles you get without investing in R&D are better human capital (but then why should profits increase if this is happening everywhere?) and tailor-made regulation.

Lindsey and Teles go on to cite research by Dr. James Bessen that show that most of the increases in profits since the start of the 21st century is heavily correlated with increasing regulation, a result that remained robust even when accounting for reverse causation (e.g. a counter-factual where profits causing regulation).

This circumstantial evidence is about all we can get for something as messy as real-world economics, but it’s both highly suggestive and fits in well with what keen observers have noted in individual industries, like the pharmaceutical industry.

An increase in rent-seeking would explain a whole bunch of the malaise of the current economy.

Economists have been surprised by the slow productivity growth since the last recession. If there was significantly more rent-seeking now than in the past, then we would expect productivity growth to slow.

Image courtesy of the St. Louis Fed. I continue to appreciate that all US Government media has no copyright.

In a properly functioning economy, productivity growth is largely buoyed up by new entrants to a field. The most productive new entrants thrive, while less productive new entrants (and some of the least productive existing players) fail. Over time, this gradually improves the overall productivity of an industry. This is the creative destruction you might hear economists talking glowingly about.

Productivity can also be raised by the slow diffusion of innovations across an industry. When best practices are copied, everyone ends up producing more with fewer inputs.

Rent-seeking changes the nature of this competition. Instead of competing on productivity and innovation, companies compete to see who can most effectively buy the government. Everyone who fails to buy off the government will eventually fail, leaving an increasingly moribund economy behind.

Lindsey and Teles believe that we’re more likely to see the negative effects of rent-seeking today than in the past because the underlying economy has less favourable conditions. In the 1950s, women started to enter the workforce. In the 60s, Boomers began to enter it. In addition, many returning soldiers got university educations after World War II, making college graduates much more common.

All of these trends have now stagnated or reversed. It’s hard for developed countries to get any more educated. Most of the women who want to or have to work are already in the labour force. And the Boomers are starting to retire. Therefore, we can no longer rely on strong underlying growth. We either need a lot of investment or a lot of productivity growth if we’re going to see strong overall growth. And it’s politically hard to get people to delay their consumption to invest.

Therefore, rent-seeking, as a force holding down productivity growth, would be a serious problem in political economy even if it didn’t lead to increased inequality and all of the problems that can cause.

But that’s where the other half of this book comes in; the authors suggest that our current spate of rent-seeking policies are fueling income inequality as well as economic malaise [1]. Rent-seeking inflates stock prices (which only helps people who are well-off enough that they own stocks) or wages at the top of corporations. Rents from rent-seeking also tend to accrue to skilled workers, to people who own homes, and people in regulated professions. All of these people are wealthier than average and increasing their wealth increases inequality.

That’s the theory. To show it in practice, Lindsey and Teles introduce four case studies: finance, intellectual property, zoning, and occupational licensing.

Finance

Whenever I think about finance, I am presented with a curious double image. There are the old-timey banks of yore, that I see in movies, the ones that provided smiling service to their local customers. And then there are the large financial entities that exist today, with their predatory sales tactics and “too big to fail” designations. Long gone are the days when banks mostly made money by collecting interest on loans, loans made possible by paying interest on deposits.

Today’s banks also have an excellent racket going on. They decry taxes and regulation on one hand, while extracting huge rents from governments on the other.

To understand why, we first need to talk about leverage. Bank profits can be increased many times over via the magic of leverage – basically borrowing money to buy assets. If you believe, for example, that the price of silver is going to skyrocket tomorrow, you could buy $100 of silver. If silver goes up by 20%, you’ll pocket a cool $20 for 20% profit. If you borrow an extra $900 from friends and family at 1% interest and buy silver with that too, you’ll pocket a cool $191 once it goes up (20% of $1000 less 1% of $900), for 191% profit.

Leverage becomes a problem when prices fall. If the price goes down by 10% instead of going up, you’ll be left with $90 if you didn’t leverage yourself – and $1 if you did. Because it leads to the potential of outsized losses, leverage presents problems with downside risks, the things that happen when your bet is wrong.

One of the major ways banks extract rents is by forcing the government to hold onto their downside risks. In America, this is accomplished several ways. First, deposits are insured by the government. This is good, in that it prevents bank runs [2], which were a significant problem in the 19th and 20th century, but bad because it removes most incentive for consumers to care about the lending practices of their bank. Insurance removes the risk associated with picking a bank with risky lending practices, so largely people don’t bother to see if their bank is responsible or not. Banks know this, so feel no pressure to be responsible, especially because shareholders love the profits irresponsibility brings in good times.

Second, the government (especially in America, but also recently in Ireland) seems unable to resist insulating bondholders from the consequences of backing a bank with bad standards. The bailouts after the financial crisis mean that few bondholders were punished for their failure to do due diligence when providing the credit banks used to make leveraged bets. As long as no one is punished for lending to the banks that make risky bets, things won’t get better.

(Interestingly, there is theoretical work that shows banks can accomplish everything they currently do with debt using equity at the same cost. This isn’t what we see in real life. Lindsey and Teles suggest this is because debt is kept artificially cheap for banks by repeated bailouts. Creditors don’t demand extra to lend to an indebted bank, because they know they won’t have to pay if things go south.)

Third, there’s mortgage debt, which is often insured or bought by the Federal Government in America. This makes risky lending much more palatable for many banks (and much more profitable as well). This whole process is really opaque and largely hidden from the US population. When times are good, it’s a relatively cheap way to make housing more affordable (although somewhat regressive; it favours the already wealthy). When times are bad it can cost the government almost $200 billion.

The authors suggest that this sort of “public program by kluge” is the perfect vehicle for rent-seeking. The need to do the program in a klugey way so that taxpayers don’t complain is anathema to accountability and often requires the support of businesses – which are happy to help as long as they get to skim off the top. Lindsey and Teles suggest that it would be much better for the US just to provide straight up housing subsidies in a means-tested way.

Being able to extract all these rents has probably increased the size of the US financial sector. Linsey and Teles argue that this is a very bad thing. They cite data that show decreased economic growth once the financial sector grows beyond a certain size, possibly because an outsized financial sector leads to misallocation of resources.

Beyond a certain point, the financial sector is just moving money around to no productive aim (this is different than e.g. loans to businesses; I’m talking about highly speculative bets on foreign currencies or credit default swaps here). The financial sector also aggressively recruits very bright people using very high salaries. If the financial sector were smaller and couldn’t compensate as highly, then these people would be out doing something productive, like building self-driving cars or curing malaria. Lindsey and Teles suggest that we should happily make a trade-off whereby these people can’t get quite as high salaries but do actually produce things of value.

(Remember: one of the pair here is a libertarian! Like “worked for Cato Institute for years” libertarian. If your caricature of libertarians is that “they hate poor people”, I suggest you consider the alternative: “they think the free market is the best way to help disadvantaged people find better circumstances”. Here, Lindsey is trying to correct market failures and misallocations caused by big banks getting too cozy with the government.)

Intellectual Property Law

If you don’t follow the Open Source or Creative Commons movements, you probably had mostly positive things to say about copyright until a few years ago when the protests against SOPA and PIPA – two bills designed to strengthen copyright enforcement – painted the internet black in opposition.

SOPA and PIPA weren’t some new overreach. They are a natural outgrowth of a US copyright regime that has changed radically from its inception. In the early days of the American Republic, copyrights required registering. Doing so would give you a fourteen-year term of exclusivity, with the option to extend it once for another fourteen years. Today all works, even unpublished ones, are automatically granted copyright for the life of the author… plus 70 years.

Penalties have increased as well; previously, copyright infringement was only a civil matter. Now it carries criminal penalties of up to $250,000 in fines and 1-5 years of jail time per infringement.

Patent protections have also become onerous, although here the fault is judicial action, not statute. Appeals for patent cases are solely handled by the United States Court of Appeals for the Federal Circuit. This court is made up of judges who are normally former patent lawyers and who attend all the same conferences as patent lawyers – and eat the food paid for by the sponsors. I don’t want to claim judicial corruption, but it is perhaps unsurprising that these judges have come to see the goals of patent holders as right and noble.

Certainly, they’ve broken with past tradition and greatly expanded the scope of patentability while reducing the requirements for new patents. Genes, business methods, and most odiously, software, have been made patentable. Consequently, patents filed have increased from approximately 60,000 yearly in 1983 to 300,000 per year by 2013. If this represented a genuine increase in invention, then it would be a cause for celebration. But we already know that R&D spending isn’t increasing. It would be very surprising – and the exact opposite of what diminishing returns would normally suggest – if companies managed to come up with an additional 240,000 patents per year with no additional real spending.

What if these patents just came from increased incentives for rent-seeking via the intellectual property system?

“Intellectual property” conjures a happy image. Who doesn’t like property [3]? Many (most?) people support paying authors, artists, and inventors for their creations, at least in the abstract [4]. Lindsey and Teles argue that we should instead take a dim view of intellectual property; to them, it’s almost entirely rent-seeking.

They point out that many of supposed benefits of intellectual property never manifest. It’s unclear if it spurs invention (evidence from World Fairs suggest that it just moves invention towards whatever types of inventions are patentable, where payoff is more certain). It’s unclear if it incentivizes artists and writers (although we’ve seen music revenue fall, more people than ever are producing music). My personal opinion is that copyright doesn’t encourage writers; most of us couldn’t stop if we wanted to.

When it comes to software patents, the benefits are even less clear and the harms even greater. OECD finds that software patents are associated with a decrease in R&D spending, while Vox reports that costs associated with software patent lawsuits have now reached almost $70 billion annually. The majority of software patent litigation isn’t even launched by the inventors. Instead, it’s done by so called “patent trolls”, who buy portfolios of patents and then threaten to sue any company who doesn’t settle with them over “infringement”.

When even a successfully-defended lawsuit can cost millions of dollars (not to mention several ulcers), software patents (often for obvious ideas and assuredly improper) held by trolls represent a grave threat to innovation.

All of this adds up to a serious drag on the economy, not to mention our culture. While “protecting property” is seen as a noble goal by many, Lindsey and Teles argue that IP protections go well beyond that. They acknowledge that it makes sense to protect a published work in its entirety. But protecting the setting? The characters? The right to make sequels? That’s surely too much. How is George Lucas hurt if someone can sell their Star Wars fanfiction? How is that “infringing” on what he has created?

They have less sympathy for patents, which grant a somewhat ridiculous monopoly. If you patent something three days before I independently invent it, then any use or sale by me is still considered infringement even though I am assuredly not ripping you off.

Lindsey and Teles suggest that IP laws need to be rolled back to a more reasonable state, when copyright was for 14 years and abstract ideas, software implementation, and business methods couldn’t be patented. About the only patents they really approve of are pharmaceutical patents, which they view as necessary to protect the large upfront costs of drug development (see also Scott Alexander’s argument for why this is the case [5]); I’d like to add that these upfront costs would be lower if the rent-seeking by pharmaceutical companies hadn’t supported rent-seeking regulation that has made the FDA an almost impenetrable tar-pit.

Occupational Licensing

Occupational licensing has definitely become more common. It’s gone from affecting 10% of the workforce (1970) to 30% of the workforce today. It no longer just affects doctors, teachers, lawyers, and engineers. Now it covers make-up artists, auctioneers, athletic trainers, and barbers.

Now, there are sometimes good reasons to license professionals. No one wants to drive across a bridge built by someone who hasn’t learned anything about physics. But there’s good reason to suspect that much of the growth of occupational licensing isn’t about consumer protection, despite what proponents say.

First of all, there’s often a quite a bit of variability in how many days of study these newly licensed professions require. Engineering requirements tend to be similar from country to country because it’s governed by international treaty. On the other hand, manicurist requirements vary wildly by state; Alaska requires three days of education, while Alabama requires 163. There’s no national standards at all. If this was for consumer protection, then presumably some states are well below what’s required and others are well above it.

Second, there’s no allowance for equivalencies. Engineers can take their engineering degrees anywhere and can transfer professional status with limited hassles. Lawyers can take the bar exam wherever they want. But if you get licensed as a manicurist in Alabama, Alaska won’t respect the license. And vice versa.

(Non-transferability is a serious economic threat in its own right, because it makes people less likely to move in search of better conditions. The section on zoning further explains why this is bad.)

Several studies have shown that occupational licenses do nothing to improve services to customers. Randomly sampled floral arrangements from licensed and unlicensed states (yes, some states won’t let you arrange flowers without a license) are judged the same when viewed by unsuspecting judges. Roofing quality hasn’t fallen after hurricanes, when licensing restrictions are lifted (and if there’s ever a time you’d expect quality to fall, it’s then!).

Despite the lack of benefits, there are very real costs to occupational licensing. Occupational licensing is associated with consumers paying prices between 5% and 33% above unlicensed areas, which translates to an average 18% increase in wages for licensed professionals. The total yearly cost to consumers for this price gouging? North of $200 billion. Unfortunately, employment growth is also affected. Licensed professions see 20% slower employment growth compared to neighbouring unlicensed jurisdictions. Licensing helps some people make more money, but they make this money by, in essence, pulling up the ladder to prosperity behind them.

Occupational licensing especially hurts minorities in the United States. Many occupational licenses require a college degree (black and Latino Americans are less likely to have college degrees) and they often exclude anyone with a criminal record of any sort (disproportionately likely to be black or Latino). It may make sense to exclude people with criminal records from certain jobs. But from manicuring? I don’t see how someone could do worse damage manicuring then they could preparing fast food, and that isn’t regulated at all.

Licensing boards often protect their members against complaints from the public. Since the board is composed only of members of the profession, it’s common for them to close ranks around anyone accused of bad conduct. The only profession I’ve seen that doesn’t do this is engineers. Compare the responses of professional boards to medical and engineering malpractice in Canada.

Probably the most interesting case of rent-seeking Lindsey and Teles identify are lawyers in the United States. While they accuse lawyers of engaging in the traditional rent-seeking behaviour of limiting entry to their field (and point out that bar exam difficulty is proportional to the number of people seeking admittance, which suggests that its main purpose it to keep supply from rising), they also claim that lawyers in the United States artificially raise demands for their services.

Did you know that lawyers made up 41% of the 113th Congress, despite representing only 0.6% of the US population? I knew the US had a lot of lawyers in politics, but I hadn’t realized it was that high. Lindsey and Teles charge these lawyers with writing the kind of laws that make sense to lawyers: abstruse, full of minutia, and fond of adversarial proceedings. Even if this isn’t a sinister plot, it certainly is a nice perk [6].

I do wish this chapter better separated what I think is dual messages on occupational licensing. One strand of arguments goes: “occupational licensing for jobs like barbers, manicurists, etc. is keeping disadvantaged people, especially minorities out of these fields with slightly better than average wages and making everyone pay a tiny bit more”. The other is: “professionals are robbing everyone else blind because of occupational licensing; lawyers and doctors make a huge premium in the United States and are disproportionately wealthy compared to other countries and make up a large chunk of the 1%”.

I’d like them separated because they seem to call for separate solutions. We might decide that if we could fix the equality issues (for example, by scrapping criminal records checks and college degree requirements where they aren’t needed), it might make sense to keep occupational licensing to prevent a race for the bottom among occupations that have never represented a significant fraction of individual spending. One thing I noticed is that the decline among union membership is exactly mirrored by the increase in occupational licensing. In a very real way, occupational licensing, with some tweaks, could be the new unions.

On the other hand, we have doctors and lawyers (and maybe even engineers, although my understanding is that they do far less to restrict supply, especially foreign supply) who are making huge salaries that (in the case of lawyers) might be up to 50% rents from artificially low supply. If we undid some of the artificial barriers to entry they’ve thrown up, we could lower their wages and improve income equality while at the same time improving competition and opening up these fields (which should still pay reasonably well) to more people. Many of us probably know people who’d make perfectly fine doctors that have been kept out of medical school by the overly restrictive quotas. Where’s the harm in having two doctors making $90,000/year instead of one doctor making $180,000/year? It’s not like we couldn’t find a use for twice as many doctors!

Zoning

The weirdest thing about the recent rise in housing prices is that building houses hasn’t really gotten any more expensive. Between 1950 and 1970, housing prices increased 35% above inflation (when normalized to size) and construction costs increased 28% above inflation. Between 1970 and 2000, construction prices rose 6% slower than inflation – becoming cheaper in real terms – and overall housing costs increased 72% above inflation.

Maybe house prices have gone up because house quality has improved? Not so say data from repeat house sales. When analyzing these data, economists have determined that increased house quality can account for at most 25% of the increase in prices.

Maybe land is just genuinely running out in major cities? Well, if that were the case, we’d see a strong relationship between density and price. After all, density would surely emerge if land were running out, right? When analyzing these data, economists have found no relationship between city density and average home price.

The final clue comes from comparing the value of land houses can be built on with the value of land houses cannot be built on. When you look at how much the size of a lot affects the sale price of very similar homes and compare that with the cost of the land that goes under a house (by subtracting construction costs from the sale prices of new homes), you’ll find that the land under a house is worth ten times the land that simply extends a yard.

This suggests that a major component of rising house prices is the cost of getting permission to build a house on land – basically, finding some of the limited supply of land zoned for actually building anything. This is not land value per se, but instead a rent imposed by onerous zoning requirements. In San Francisco, San Jose, and Manhattan, this zoning cost is responsible for approximately half of house worth.

The purpose of zoning has always been to protect the value of existing homes, by keeping “undesirable” land usage out of a neighbourhood. Traditionally, “undesirable” has been both racist and classist. No one in a well-off neighbourhood wanted any of “those people” to move there, lest prospective future buyers (who shared their racial and social prejudices) not want to move to the neighbourhood. Today, zoning is less explicitly racist (even if it still prices minorities out of many neighbourhoods) and more nakedly about preserving house value by preventing any increase in density. After all, if you live in a desirable neighbourhood, the last thing you want is a large tower bringing in hundreds of new residents at affordable prices. How will you be able to get a premium on your house then? The market will be saturated!

Now if there were no real benefits to living in a city, Lindsey and Teles probably wouldn’t care about zoning. But there definitely are very good reasons why we want more people to be able to live in cities. First: transportation. Transportation is easier when people are densely packed, which makes supplies cheaper and reduces negative externalities from carbon intensive travel. Second: choice. Cities have enough people to allow people to make profits off of weird things, to allow people to carefully choose their jobs, and to allow employers choice in employees. All of these are helpful to the economy. Third: ineffable increases in human capital. There’s just something about cities (theorized to be “information spillover” between people in unrelated jobs) that make them much more productive per capita than anywhere else.

This productivity is rewarded in the form of higher wages. Lindsey and Teles claim that the average income of a high school graduate in Boston is 40% higher than the average income of a college graduate in Flint, Michigan. I’ll buy these data, but I’m a bit skeptical that this results in any more take-home pay for the Bostonian, because wages in Boston have to be higher if people are to live there. Would this hold true if you looked at real wages accounting for differences in cost of living [7]?

If wages are genuinely higher in places like Boston in real terms, then this spatial inequality should be theoretically self-correcting. People from places like Flint should all move to places like Boston, and we’ll see a sudden drop in income inequality and a sudden jump in standard of living for people who only have high school degrees. Lindsey and Teles believe this isn’t happening because the scarcity of housing drives up the initial price of moving far beyond what people without substantial savings can pay – the same people who most need to be able to move [8].

Remember, many apartments require first and last month’s rent, plus a security deposit. I looked up San Francisco on PadMapper and the median rent looks to be something like $3300, a number that agrees with a cursory Google. Paying first and last on that, plus a damage deposit would cost you over $7,000. Add to that moving expenses, and you can see how it could be impossible for someone without savings to move to San Francisco, even if they could expect a relatively well-paid job.

(Lack of movement hurts people who stay behind as well. When people move away in search of higher wages, businesses must eventually raise wages in places seeing a net drain of people, lest the whole workforce disappear. This effect probably led to some of the convergence in average income between states that occurred from 1880 to 1980, an effect that has now markedly slowed.)

Zoning isn’t great anywhere, but it’s worst in the Bay Area and in New York. 75% of the economic costs of misallocated labour and lost productivity growth come from New York, San Francisco, and San Jose. Furthermore, housing might be entirely responsible for Thomas Piketty’s conclusion that we’re doomed to a spiraling cycle of inequality.

Out of all of these examples of rent-seeking, the one I feel least optimistic about is zoning. The problem with zoning is that people have bought houses at the prices that zoning guaranteed. If we were to significantly loosen it, we’d be ruining many people’s principle investment. Even if increasing home wealth represents one of the single greatest sources of inequality in our society and even if it is exacting a terrifying toll on our economy, it will be extremely hard to build the sort of coalition necessary to break the backs of municipalities and local landowners.

Until we figure out how to do that, I’m going to continue to fight back tears every time I see a sign like this one:

Why should I get a say in whether a local commercial landlord provides the businesses in their building slightly smaller parking spots? Shouldn’t we be aiming for a walkable, decarbonized downtown where parking is irrelevant? That’s what all the plans call for. Of course, the whole thing makes sense if it isn’t about what size parking spots should be and is about making it impossible for anyone to build anything in my downtown neighbourhood.

How do we fight rent-seeking?

Surprisingly, most of the suggestions Lindsey and Teles put forth are minor, pro-democratic, and pro-government. There isn’t a single call in here to restrict democracy, shrink the size of the government, or completely overhaul anything major. They’re incrementalist, pragmatic, and give me a tiny bit of hope we might one day even be able to conquer zoning.

Rent-seeking is easiest when democracy is opaque, when it is speedy, when it is polarized, and when it is difficult for independent organizations to supply high-quality information to politicians.

One of the right-wing policies that Lindsey and Teles are harshest on are efforts to slash and burn the civil service. They claim that this has left the civil service unable to come up with policies or data of its own. They’re stuck trusting the very people they seek to regulate for any data about the effects of their regulations.

Obviously, there are problems with this, even though it doesn’t seem to extend to outright horse-trading or data-manipulation. It’s relatively easy to nudge peoples’ decision making by choosing how data is presented. Just slightly overstate the risks and play down the benefits. Or anchor someone with a plan you know they’re primed to like and don’t present them any alternatives that would hurt your bottom line. No briefcases of money change hands, but government is corrupted nonetheless [9].

To combat this, Lindsey and Teles suggest that all committees in the US House and Senate should have a staffing budget sufficient to hire numerous staffers, some of whom would work for the committee as a whole and others who would work for individual members. Everything would get reshuffled every two years, with a rank-match system used to assign preferences. Employee quality would be ensured by paying market-competitive salaries and letting go anyone who was too-consistently ranked low.

(Better salaries would also end the practice of staffers going to work for lobbyists after several years, which isn’t great for rent-seeking.)

Having staff assigned to committees, rather than representatives on a permanent basis prevents representatives from diverting these resources to their re-election campaigns. It also might build bridges across partisan divides, because staff would be free from an us vs. them mentality.

The current partisan grip on politics can actually help rent-seeking. Lindsey and Teles claim that when partisanship is high, party discipline follows. Leaders focus on what the party agrees on. Unfortunately, neither party is in any sort of agreement with itself about combatting rent-seekers, even though fighting rent-seeking offers a compelling way to spur economic growth (ostensibly a core Republican priority) and decrease economic inequality (ostensibly a core Democratic priority).

If partisanship was less severe and the coalitions less uniform, leaders would have less power over their caucuses and representatives would search for ways to cooperate across the aisle whenever doing so could create wins for their constituents. This would mark a return to the “strange-bedfellows” temporary coalitions of bygone times. Perhaps one of these coalitions could be against rent-seeking [10]?

Lindsey and Teles also call for more issues to be decided in general jurisdictions where public interest and opportunity for engagement are high. They point to studies that show teachers can extract rents when budgets are controlled by school boards (which are obscure and easily dominated by unions). When schools are controlled by mayors, it becomes much harder for rents to be extracted, because the venue is much broader. More people care about and vote for municipal representatives and mayors than attend school board meetings.

Similarly, they suggest that we should very rarely allow occupation licensing to be handled by the profession itself. When a professional licensing body stacked with members of the profession decides standards, they almost always do it for their own interest, not for the interest of the broader public. State governments, one the other hand, are better at considering what everyone wants.

Finally, politics cannot be too quick. If it’s possible to go from drafting a bill to passing it in less time than it takes to read it, then it’s obviously impossible to build up a public pressure campaign to stop any nastiness in it. If bills required one day of debate for every hundred pages in them and this requirement (or a similar one) was inviolable, then if someone buried something nasty in it (say, a repeal of a nation’s prevailing currency standards), people would know, would be able to organize, and would be able to make the electoral consequences of voting for it clear to their representatives.

To get to a point where any of this is possible, Lindsey and Teles suggest building up a set of policies on the local, state, and national levels and working to build public support for them. With these policies existing in the sidelines, it will be possible to grab any political opportunity – the right scandal or outrage, perhaps – and pressure representatives to stand up against entrenched interests. Only in these moments when everyone is paying attention can we make it clear to politicians that their careers depend most on satisfying our desires than they do on satisfying the desires of the people who fund their campaign. Since these moments are rare, preparation for them is key. It isn’t enough to start looking for a solution when an opportunity presents itself. If we don’t move quickly, the rent-seekers will.

This book is, I think, the opening salvo in this war. Its slim and its purpose is to introduce people from across the political spectrum to the problem of rent-seeking and galvanize them to prepare for when the time is right. Its’ authors are high profile economists with major backing. Perhaps this is also a signal that similar backing might be available for anyone willing to innovate around anti-rent-seeking policy?

For my part, I had opposed rent-seeking because I knew it hurt economic growth. I hadn’t understood just how much it contributed to income inequality. Rent-seeking increases corporate profits, making capitalists far wealthier than labourers can ever hope to be. It inflates the salaries of already wealthy professionals at the cost of everyone else and locks people without college degrees out of all but the most moribund or dangerous parts of the job market. It leads bankers to speculate wildly, in a way that occasionally brings down the economy. And it makes the humble home-owners of last generation the millionaires of this one, while pricing millions out of what was once a rite of passage.

Lindsey and Teles convinced me that fighting rent-seeking is entirely consistent with my political commitments. Municipal elections are coming up and I’m committed to finding and volunteering for any candidate who is consistently anti-zoning. If none exists, then I’ll register myself. Winning almost isn’t the point. I want to be one of those people getting the word out, showing that alternatives to the current broken system is possible.

And when the time is right, I want to be there when those alternatives supplant the rent-seekers.

Footnotes

[1] Rent-seeking doesn’t necessarily have to lead to increased inequality. Strict immigration controls, monopolies, strong unions, and strict tariffs all extract rents. These rents, however, tend to distribute down or sideways, so don’t really increase inequality. ^

[2] Banks don’t keep enough money on hand to cover deposits entirely, because they need to lend out money to make money. If banks didn’t lend money, you’d have to pay them for the privilege of parking your money there. This means that banks run into a problem when everyone tries to withdraw their money at once. Eventually, there will be no more money and the bank will fail. This used to happen all the time.

Before deposits were insured, it was only rational to withdraw your money if you thought there was even a small chance of a bank run. If you didn’t withdraw your money from a bank without deposit insurance and a bank run happened, you would lose your whole deposit.

Bank architecture reflects this risk. Everything about the imposing facades of old banks is supposed to make you think they’re as stable as possible and so feel comfortable keeping your money there. ^

[3] Socialists. ^

[4] The rise of streaming and torrenting suggests that this is more often held as an abstract principle than it is followed “in the breach”. ^

[5] See also his argument for why we shouldn’t retroactively grant new exclusivity on generics. ^

[6] I wonder if this generalizes? Would a parliament full of engineers be obsessed with optimization and fond of very clear laws? Would a parliament full of doctors spend a lot of time running a differential diagnosis on the nation? Certainly military dictators excel at seeing everyone as an enemy on whom force can be justifiably used. ^

[7] College graduates in the wealthiest cities make 61% more money than college graduates in the least wealthy cities, while people with only high school degrees make 137% more in the richest cities compared to the poorest cities. This suggests that it’s possible high school graduates are much better off in wealthy cities, but it could also be true that college graduates fall prey to money illusions or are willing to pay a premium to live in a place that provides them with many more opportunities for new experiences. ^

[8] I think there will also always be social factors preventing people from moving, but perhaps these factors would weigh less heavily if real wage differences between thriving cities and declining areas weren’t driven down by inflated real estate prices in cities. ^

[9] This is perhaps the most invidious – and unintended – consequence of Stephen Harper’s agenda for Canada. Cutting the long form census made it harder for the Canadian government to enact social policies (Harper’s goal), but if these sorts of actions aren’t checked, reversed, and guarded against, they also make rent-seeking much more likely. ^

[10] In Canadian politics, I have hope that some sort of housing affordability coalition could form between some members from left-leaning parties and some principled free-marketers. Michael Chong already has a plan to lower housing prices by getting the government out of the loan securitization business. No doubt banks wouldn’t enjoy this, but I for one would appreciate it if my taxes couldn’t be used to bail out failing banks. ^