Mailbag column!

So there is a reason why people do this. They are so easy to write. I think I will do this more in the future.

I also want to preface this column by asking everyone who enjoys this to consider taking out a paid subscription. I hope to keep my blog unaffordable and just trust that everyone will pay what they feel is their appreciation. Now, on to the questions!

OH Murphy asks the following four questions:

Have you ever thought about the relationship between video/board game design and mechanism design in economics? Social deduction games in particular seem almost like reverse revelation mechanisms.

No, I’ve never thought about this. This is an interesting way to think about it. I wonder if there’s a way within a game to make it optimal to reveal someone’s true state. Imagine playing werewolf or mafia, where you have private information about who you are and are trying to guess who the bad guys are. Is there a way to force everyone to reveal their true preferences? By the way, werewolf and mafia are the same game with slightly different names. Two players are secretly chosen to be werewolves and decide which villager to kill at night; all players vote on who to kill during the day. If werewolves specifically care about winning, and not their team, you can say to someone you think is a werewolf: “choose to kill your teammate – if it’s a werewolf, you win with a small chance – if it’s not, we’ll kill you”. If you’re certain that one person is a werewolf, that should increase your chances of winning. Maybe I misunderstand the question, but that’s just where my mind goes.

What do you think are the most interesting results at the intersection of computer science and economics?

Much of the stuff about algorithms and auction efficiency in near-optimal auctions is basically the same as theoretical computer science (I think). Tim Roughgarden’s book “20 Lectures in Algorithmic Game Theory” is absolutely fantastic here, extremely clean, clear and precise. My ability to answer what lies at the intersection of computer science and economics is limited by my almost total ignorance of computer science; I know rudimentary python and little else.

If AI matters, then AI is going to be a huge deal in data collection. When humans use satellite datasets, it’s all machine learning — you tell it what you’re looking for, and then it can characterize terabytes of data. Sentiment analysis lets you do what would have taken an army of research assistants months to collect. Optical character recognition lets you use archives that were previously difficult to manipulate.

Since I am not planning to do a Masters/PhD in Economics, I would like to hear from you what you think are the most important topics missing from Bachelor’s programs (that I would have missed)?

I don’t really know! My problem is that I can’t remember where I actually learned things — I don’t generally learn all that much from the classes themselves, they’re mostly there to point out the limits of the literature. Worse still, I didn’t even take introductory economics in college, but took it as an AP class in high school. From what I’ve gathered from tutoring AP economics (which may not overlap with what’s taught in college), there’s probably not enough talk about growth and too much talk about very Keynesian business cycle management. That’s all well and good, but there’s a point of emphasis. I can’t really pontificate, though, because it’s been so long since I’ve been familiar with it. Check back after I’ve taught an introductory class.

What do you think of the quality of economics as presented in fiction? Can you name a few particularly good or bad examples?

It’s really bad in general, although it’s hard to think of examples. Most fiction is written by people who can’t do math and have no sense of scale. So most of them are very hostile to business and production.

Have you ever read Hoot? Growing up is realizing that yes, it’s just a few owls, and that people who can eat pancakes are good.

The sleeping aristocrat asks: Why doesn’t the labor market clean up like other markets do, in the absence of active monetary policy?

Uhhhhhhh… wage stickiness. Don’t look at it too closely.

In all seriousness, wages have two problems. First, the labor market is typically the most regulated market there is, and second, wages are burdened with a moral significance that other prices do not. Your wage affects your dignity and worth as a person. So people are less likely to accept a cut. Furthermore, because you are building up a lot of firm-specific capital, refusing to take a wage cut may be the optimal negotiating strategy.

I think the main reason is how deeply regulated labor markets are. Can you imagine product market unemployment insurance? “Oh, you didn’t sell your product because your asking price was too high, so we’re going to give you money for that?” Isn’t that a crazy idea in any context other than labor markets. And the reason you have frictional unemployment is simply because it’s very expensive to get rid of bad workers — if firing was really cheap and easy, you’d have a lower unemployment rate. So I think most of our inflexibility is created, not natural.

Of course, while I can cite massive wage regulation for the Great Depression (this 2009 Lee Ohanian article provides compelling evidence that labor market regulation was indeed responsible for the Great Depression – the unregulated sectors, like agriculture, saw virtually no unemployment, while the sectors that saw massive government intervention in an attempt to prop up wages saw massive unemployment), I can’t give you the same answer for the Long Depression of the 1870s! It seems reasonable to me that wage and price adjustments simply took longer then than they do now – information was much more expensive. So a recession caused by a fall in aggregate demand is explainable as a result of price rigidity, not just wage rigidity.

OH Murphy asks again: Why don’t companies with rating systems (Yelp, Uber, etc.) often limit stars and the like so that they appear more credible?

The question is: why do rating systems converge to a perfect score or not? Doesn’t that make it impossible to say when quality is exceptional? I think the answer is pretty simple: it converges to all perfect scores, for merely adequate services, when people don’t care about exceptional experiences. When people do, the rating system makes sense and deviates up and down.

So in restaurants, Yelp has meaningful star ratings because you care about good experiences. Uber doesn’t, because people don’t care about exceptional service. They only care about avoiding

Sam Harsimony asks: If a monetary authority has to adhere to a particular algorithm for its decision-making, which algorithm is best?

What strange ideas have you heard about dealing with patents and intellectual property?

I really don’t want them to commit to an algorithm. I want them to commit to certain targets – specifically nominal GDP targeting. Any specific algorithm would need to update its parameters regularly. Think about it this way – pure money supply targeting, à la Friedman or other early monetarists, depends on a given and constant velocity of money. If the velocity of money changes, then your algorithm will lead to undesirable results.

Please read my recent article on Kremer patent auctions.

Arya Biss asks: What is the topic of the PhD you are working on?

I have no idea. I’m just going to follow the papers and probably merge three of them that have at least a weak connection to each other. I’m not sure how attached I am to economic history. I feel like I have a strong tendency to cover a lot of things, rather than delving deeply into just one. I really like blogging for this reason – I can cover the breadth of my knowledge, rather than spending months trying to compile a better dataset.