History according to Polybius

It’s unusual for me to sit down on a weekend morning and read *literally checks notes* Polybius. This was assigned to me for a seminar. Here’s his proposal for the inevitable endless cycle of human leadership structures:

  1. Some humans are still alive. They band together because they are too weak to survive alone.
  2. A strong man who bravely defends the group in his youth becomes a monarch. “Kingship” is the next progression. Polybius assumes that people could consent to be under the leadership of a powerful and noble man.
  3. The king has children. The people venerate the descendants of the good king, but these princes and princesses will be spoiled and selfish. The princes “gave way to their appetites owing to this superabundance…” Thus, kingship becomes tyranny.
  4. Nobles overthrow the tyrants, and so become aristocrats. “But here again when children inherited this position of authority from their fathers, having no experience of misfortune and none at all of civil equality and liberty of speech… they abandon themselves to greed… and… rape…” The aristocracy becomes a corrupt oligarchy.
  5. Democracy emerges when people have “killed or banished the oligarchs” and the people remember being mistreated by kings. How does Polybius think democracy ends? Once again, it’s the new generation and the corruption they indulge in. Where do they end up? “… democracy in its turn is abolished and changes into a rule of force and violence.”
  6. There are two ways to get back to stage 1 monarchy. Life in the decline of a democracy is chaotic enough that people willingly back a strong man to protect them. Alternatively, he presents a “floods, famines, failure of crops… all arts and crafts perish…” scenario. A natural disaster, regardless of what stage in the political cycle humans were at before, will position the survivors to start again at monarchy.
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Overfitting Celebrity Pitches

The Washington Post created a fun infographic of celebrity baseball pitches.

I use this graphic in my Data Analytics class. Students are tempted to draw inferences about individuals from this data set. John Wall and Michael Jordan are great athletes, but in this case they are underperforming Avril Lavigne and George W. Bush. Do we conclude that Sonia Sotomayor missed her calling as an MLB player?

The first lesson here is that we should not assume we can predict where Harrison Ford’s next pitch will go based on observing just one pitch. A single pitch should be considered a random draw from a distribution centered around Ford’s average ability. Any single pitch could be an outlier.

Snoop Dog features twice on this graph. In 2012 he got the ball in the strike zone. Had we only seen that, we would want to conclude that he is a great pitcher. However, in 2016 he was way off to the right. In either case, overconfidence that he is predictably near a single pitch would have been a mistake.

Lastly, I use this graph to illustrate the concept of overfitting (investopedia definition). I suggest a model that is obviously inappropriate. What if we conclude from these data that anyone with the last name of Bieber will not be able to throw the ball in the strike zone? That model surely will not generalize. The problem is that if we test that prediction on the same data we used to train the model, the misclassification rate will be zero. If possible, start with a large data set and set aside some portion of the data for validation, before training a model. Having validation data for assessment is a good way to check that you haven’t modeled the noise in your training set.

The Research Process: Identifying the Ideas that Motivate You

Hello to all the EWED readers! I’m Dr. Darwyyn Deyo, an Assistant Professor of Economics at San José State University and a Visiting Scholar at the Knee Center for the Study of Occupational Regulation. I research law and economics, occupational licensing, and the economics of crime. I would also like to thank Joy for inviting me to write some blog posts this summer! I’ll be writing a series of posts about the curriculum of the research process, from the initial idea to the development of a complete draft. This week, I’m focusing on the mechanics behind choosing that initial idea.

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Does Cohabitation Predict Divorce?

My article, coauthored with Sarah Kerrigan and published last week, tries to answer the question. In short, the answer seems to be yes- cohabitation before marriage is associated with a 4.6 percentage point increase in the rate of marital dissolution. This is in line with much of the previous literature, which notes one big exception- choosing right (or getting lucky) the first time: “cohabitation had a significant negative association with marital stability, except when the cohabitation was with the eventual marriage partner”.

But we found some even more interesting facts while digging through the National Survey of Family Growth.

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Publications as Positional Goods, and the Division of Labor in Academia

My co-blogger Mike Makowsky has a thoughtful post this week about the academic publishing process. I wanted to offer a slightly different perspective on the same topic. But my perspective comes from someone who is not at a research university, and someone who has recently survived the tenure process.

A little background for those not completely familiar with the academic world: schools are usually considered either teaching or research schools. At first this seems confusing: both Clemson (where Makowksy is) and the University of Central Arkansas (where I am) require that faculty engage in both research and teaching. The difference is subtle, but the big hint is that Clemson is considered an “R1” school (the highest research designation) and has a PhD program with many graduate students. At a school like Clemson, research is valued more than teaching. At UCA, teaching is valued more than research. (Much more could be said about the differences, perhaps in a future post.)

We both engage in both teaching and research (as well as service!), but the emphasis is different. For me at UCA, the expectations of which journals I will publish in and how frequently I will publish are lower than at a school like Clemson. At Clemson, some of your publications should be in the Top 5 (or at least Top 10) journals from time-to-time. At UCA, if you published in one of the top journals, the assumption would be that you are probably leaving soon to go to an R1 school

I’m glad both types of schools exist, and my point here is not to disparage either type of school. But the difference is important for thinking about the academic publishing process.

For someone at an R1 school, publications in top journals are positional goods. Makowsky doesn’t say this exactly, but that’s my takeaway from his post. There are only so many spots available in these journals, and they have value because there is only a fixed number available. And since there has been, over the years, a lot more economists doing a lot more research not all of the great papers will end up being published in one of the top journals.

Upshot: there are a lot of great papers being published in Top 50 or even Top 100 journals! Let me pick on myself. As I said, I recently successfully survived the tenure process. My publication record was good enough. You can inspect my publications over at Google Scholar. I’m proud of these publications. I think some of them are really great. But I’m fairly confident that I would never earn tenure at Clemson with these publications. Instead, you need a publication record like Makowsky.

What’s interesting here is that Mike and I occasionally publish in some of the same journals. Public Choice and Constitutional Political Economy jump out to me. These are, in my view, very fine journals. Lots of interesting research is published in these journals. I’m especially proud of this paper in Public Choice. But if someone published only in these two journals and journals like them, they wouldn’t get tenure at an R1 university.

So what do we do with this information?

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The Rise and Fall (?) of Bitcoin Price

Well, it has been a fun party. Here is a chart of Bitcoin prices over the last year or so. Folks that bought in before December were up X4 or more by April. Woo-hoo! But prices have dropped by half in the past two months. Many articles were published over the winter justifying ever greater heights for Bitcoin. It was to be the digital equivalent of gold as a store of value. Also, it is touted as being decentralized and free of government manipulation – – a global, privatized people’s currency. What happened?

Source: Seeking Alpha
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Academic Publishing: How I think we got here

Fabio Ghironi, whom you should be following on twitter already, threaded the #econtwitter needle the other day, managing to write about the growing problems within academic economic publishing without falling victim to the sorts of whining and nihilism that discussions of publishing experiences often degenerate into. Below I’ve included a sample. Do go read the whole thing.

I don’t want to adjudicate the merits and flaws of the economic journal system. I have no idea how it would fare in a benefit-cost analysis or how to improve it, and I’m deeply skeptical of anything that has a whiff of “easy fix” for what is a very complex system of scientific incentives, social benefit, and academic sociology.

Instead, I’d like to discuss how I think we got here. A couple stylized facts about how research in economics has changed over the last 50 years, none of which I expect to be controversial

  1. There are a lot more people writing academic journal articles.
  2. There is a lot more well-executed economic research.
  3. The teams of co-authors on papers/projects have become much larger.
  4. The number of journals whose prestige is commensurate with a tenured position at an elite school has grown slower than the total faculty employed by elite schools.
  5. Economics research has become more expensive and labor intensive.

What is immediately obvious from 1-4 is the journal space squeeze, resulting in journals with vanishingly small acceptance rates. The American Economic Journal: Microeconomics (one of the very top journals that isn’t part of the holy Top-5, hallowed be thy names) managed to go an entire year without accepting a paper! Their editorial team, as any Murphy’s Law aficionado would have predicted, interpreted this as evidence they should publish fewer papers.

[Update: 6/2/21 A reader has pointed out that AEJ:Micro has over the past year managed a more than respectable turnaround time on submissions and eventually accepted 33 papers in 2019, 20 in 2020, yielding acceptance rates of 5 to 9%. Editors Report here]

One of the things that economics has become, and maybe always has been, obsessed with is “super stars”, and not just those who get medals. Within every subfield there are a handful of current researchers who are known to everyone else, whose papers are always top of the list in the best working paper series, who tour the country tirelessly promoting their latest papers. And they are often promoting multiple papers. How is it that they find the time to do so much research?

Well, first and foremost, they are incredibly conscientious, with work ethics bordering on obsessive. But a not distant second is the change in the nature of their jobs. They are not just working at a chalkboard by themselves or analyzing the latest batch of data. They are managing research teams. They are applying for grants that support grad students and post-docs. They are meeting for 3 hours each day with different teams of scholars, some at different institutions. They are coming up with their own ideas and refining the ideas of others, they are guiding the research of apprentices while also collaborating with equally experienced peers. They are the CEOs of miniature research empires.

Let’s assume that for a second that the number of super stars in the field has remained constant (it’s grown, but lets keep it simple). In 1950 the top 5 journals probably could have published every single full research paper written by super stars and still had room to spare. Nowadays I’m not sure the top 5 journals could handle the research output in a given year just from MIT. I don’t think the top 10 journals could handle all of the research from the Boston metropolitan area

Let’s visit the other side of the fence now. If you are a co-editor at one of the 5 elite journals in economics, you are allotted roughly 13 acceptances per year. These are fixed. For these slots you review roughly 200 papers. Let’s say 50 of those papers are trash and 50 are good but below the bar. These you desk reject. Of the remaining 100, another 25 are a bad match for the aesthetic or substantive targets laid out by the editor-in-chief(s). Another 25 are good, but the reviewers are, upon closer inspection, able to identify real problems that will undermine the impact of the paper, ruling it out for an elite journal such as yours.

That leaves you with 25 papers for 13 slots. That might not sound like a problem, but think about the process of elimination you just went through. These are really good papers that make important contributions to the field and you need to reject half of them. The discipline will not accept you flipping a coin. You need reasons to reject some of these papers. Well, let’s look at the co-authors. You don’t want to be a jerk, but you’re both desperate and don’t want to be remembered in your hallway at work as the person who rejected that massively influential paper that reinvented the field. You’d feel bad, but 20 of the papers have at least 1 superstar on them. Sorry, but status is a heuristic for a reason. You still need to reject 7 more.

Let’s go through those referee reports again. Was there anything questionable? Any possible source of bias speculatively hypothesized by a person who spent two days thinking about the paper that the people who worked on it for three years never thought of? Are they relying on econometrics that someone has recently posited might sometimes fail to calculate error terms optimally? Is it a theory without an application? Is it an application without a theory? Are the coefficients too small to be interesting or too large to be believable?

Now, let’s remember the single most important thing: everyone knows this is happening. This is not a secret process and academic researchers have responded accordingly. Superstars have responded by managing bigger teams, producing even more research, adding more and more layers of robustness checks, alternative specification designs, even entirely different research designs serving as papers within papers that put Hamlet to shame. At the same time comparably excellent, but perhaps slightly less famous, authors with outstanding research records are thrilled to work with a star, knowing that it will increase their odds at a top journal. When designing the research they know what is in vogue, what is falling out of favor, and how to shape their papers to fit the ambitions of current editors. Research designs are defensive from the start, anticipating as many angles of attack as possible. When the research is completed, it will go on the presentation circuit for a year or two, subject to the slings and arrows from the pool of economists where your future referees will be drawn from. It is from these comments that your appendix will grow. And grow. And grow. You must anticipate every attack, lest your paper’s shortcomings make the editor’s job easier.

Now try to imagine what the research lives of everyone start to look like. For the bulk of good researchers, this means working on 3-6 projects at all time, with each of those projects stretching out over 3 to 5 years. Even if you land a 2 year post-doc, submitting your tenure packet in the fall of your 6th year means you have 7 total years to get multiple papers through a process accepting less than 3-5% of submissions and, more importantly, less than half of all the objectively outstanding research. At the same time, superstars are stretching themselves impossibly thin, expected to meet impossible expectations and get papers accepted at journals with impossible standards knowing full well the careers of their co-authors depend on those acceptances. A faculty appointment should come with a free clonazepam prescription.

To sum up: academic economics has more star researchers, managing larger teams producing more high-quality papers than there is space in the elite journals which have been forced to invent impossible acceptance criteria to produce the singular output that journal editors absolutely cannot shirk: rejections.

And if you think the easy answer is to just increase the size of journals, you are missing the entire function of journals. Journals no longer function as disseminators of economic science.** Rather, they are criteria for tenure and promotion. There are a finite number of faculty slots and schools need reasons to keep/dismiss/promote/retain/recruit. If the number of elite journal articles published were to change, the prinipal effect would be to shift the threshold for success or failure in tenure and promotion.

Of course, increasing the number of publication slots in historically high prestige journals might still be a good thing. Going back to our editor’s dilemma, if they could accept the entire 12.5% of papers that our editor-under-truth-serum genuinely believes are significant contributions, then everyone’s CV would more accurately reflect the subjectively assessed merit of their work, and less their luck and ability to tirelessly play a zero-sum game. Sure, the high-low prestige bar would inflated upwards, but it would nonetheless increase the signal-to-noise ratio on everyone’s CV.

This, of course, would lower the value of every CV that already includes a Top-5 publication, but such is the struggle of every YIMBY vs NIMBY movement. Increasing the supply of elite journal publications won’t be a Pareto improvement (what is?), but it seems likely to me to be welfare improving. So I lied. I do think I know how to improve the system. Big shocker, an academic who thinks they can solve a complex system in one blog post.

** That role has been entirely usurped by the NBER and their working paper series. Now that I have tenure, I would literally rather receive an email permitting me to distribute my future work as NBER working papers than an acceptance at a Top 5 journal. It’s not even close, actually.

John Duffy Experiments and Crypto

John Duffy and Daniela Puzzello published a paper in 2014 on adopting fiat money. I think of that paper when I hear the ever-more-frequent discussions of crypto currencies around me. To research the topic, I went to John Duffy’s website. There I found a May 2021 working paper about adopting new currencies in which they directly reference crypto. Before explaining that interesting new paper, first I will summarize the 2014 paper “Gift Exchange versus Monetary Exchange.”

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More computer jobs than San Francisco

The U.S. Bureau of Labor Statistics reports Occupational Employment and Wages from May 2020 for

15-0000 Computer and Mathematical Occupations (Major Group). The website contains a few interesting insights.

Where are the computer jobs in the United States? When looking just at total numbers of jobs, three major population centers make it into the top 7 areas: NYC, LA, and Chicago. San Francisco is ahead of Chicago, while San Jose is behind Chicago. In terms of the total number of jobs, the D.C. area is ahead of any West Coast city. Is Silicon Valley not as central as we thought?

Here’s a map of the U.S. that isn’t just another iteration of population density.

When metropolitan areas are ranked by employment in computer occupations per thousand jobs, then New York City no longer makes the top-10 list. San Jose, California reigns at the top, which seems suitable for Silicon Valley. The 2nd ranked area will surprise you: Bloomington, IL. A region of Maryland and Washington D.C. shouldn’t surprise anyone. If you aren’t familiar with Alabama, then would you expect Huntsville to rank above San Francisco in this list?

Huntsville, AL is not a large city, but it is a major hub for government-funded high-tech activity. The small number of people who live there overwhelmingly selected in to take a high-tech job. For an example, I quickly checked a job website to sample in Huntsville. Lockheed Martin is hiring a “Computer Systems Architect” based in Huntsville.

Anyone familiar with Silicon Valley already knows that the city of San Francisco was not considered core to “the valley”. Even though computer technology seems antithetical to anything “historical”, there is in fact a Silicon Valley Historical Association. They list the cities of the valley, which does include San Francisco. (corrected an error here)

The last item reported on this Census webpage is annual mean wage. For that contest, San Francisco does seem grouped with the San Jose area, at last. The computer jobs that pay the most are in Silicon Valley or next-door SF. Those middle-of-the-country hotspots like Huntsville do not make the top-10 list for highest paid. However, if cost of living is taken into account, some Huntsville IT workers come out ahead.

What Forex says about cheap travel

The 2007-9 Financial Crisis turned Iceland into a major tourist destination, as a newly cheap currency combined with affordable flights and natural beauty. For anyone with plenty of time and a moderate amount of money, chasing the newly-cheap destination seems like a good travel strategy.

Since January 2020, here are the countries where the US dollar has gained the most vs the local currency:

Calculated using https://fx-rate.net/USD/?date_input=2020-01-01
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