job market

Bundling the stakes to recalibrate ourselves

PNRJ cognitive science, heuristics and biases, personal , , , , , , , , , ,

Mar 31 JDN 2460402

In a previous post I reflected on how our minds evolved for an environment of immediate return: An immediate threat with high chance of success and life-or-death stakes. But the world we live in is one of delayed return: delayed consequences with low chance of success and minimal stakes.

We evolved for a world where you need to either jump that ravine right now or you’ll die; but we live in a world where you’ll submit a hundred job applications before finally getting a good offer.

Thus, our anxiety system is miscalibrated for our modern world, and this miscalibration causes us to have deep, chronic anxiety which is pathological, instead of brief, intense anxiety that would protect us from harm.

I had an idea for how we might try to jury-rig this system and recalibrate ourselves:

Bundle the stakes.

Consider job applications.

The obvious way to think about it is to consider each application, and decide whether it’s worth the effort.

Any particular job application in today’s market probably costs you 30 minutes, but you won’t hear back for 2 weeks, and you have maybe a 2% chance of success. But if you fail, all you lost was that 30 minutes. This is the exact opposite of what our brains evolved to handle.

So now suppose if you think of it in terms of sending 100 job applications.

That will cost you 30 times 100 minutes = 50 hours. You still won’t hear back for weeks, but you’ve spent weeks, so that won’t feel as strange. And your chances of success after 100 applications are something like 1-(0.98)^100 = 87%.

Even losing 50 hours over a few weeks is not the disaster that falling down a ravine is. But it still feels a lot more reasonable to be anxious about that than to be anxious about losing 30 minutes.

More importantly, we have radically changed the chances of success.

Each individual application will almost certainly fail, but all 100 together will probably succeed.

If we were optimally rational, these two methods would lead to the same outcomes, by a rather deep mathematical law, the linearity of expectation:
E[nX] = n E[X]

Thus, the expected utility of doing something n times is precisely n times the expected utility of doing it once (all other things equal); and so, it doesn’t matter which way you look at it.

But of course we aren’t perfectly rational. We don’t actually respond to the expected utility. It’s still not entirely clear how we do assess probability in our minds (prospect theory seems to be onto something, but it’s computationally harder than rational probability, which means it makes absolutely no sense to evolve it).

If instead we are trying to match up our decisions with a much simpler heuristic that evolved for things like jumping over ravines, our representation of probability may be very simple indeed, something like “definitely”, “probably”, “maybe”, “probably not”, “definitely not”. (This is essentially my categorical prospect theory, which, like the stochastic overload model, is a half-baked theory that I haven’t published and at this point probably never will.)

2% chance of success is solidly “probably not” (or maybe something even stronger, like “almost definitely not”). Then, outcomes that are in that category are presumably weighted pretty low, because they generally don’t happen. Unless they are really good or really bad, it’s probably safest to ignore them—and in this case, they are neither.

But 87% chance of success is a clear “probably”; and outcomes in that category deserve our attention, even if their stakes aren’t especially high. And in fact, by bundling them, we have even made the stakes a bit higher—likely making the outcome a bit more salient.

The goal is to change “this will never work” to “this is going to work”.

For an individual application, there’s really no way to do that (without self-delusion); maybe you can make the odds a little better than 2%, but you surely can’t make them so high they deserve to go all the way up to “probably”. (At best you might manage a “maybe”, if you’ve got the right contacts or something.)

But for the whole set of 100 applications, this is in fact the correct assessment. It will probably work. And if 100 doesn’t, 150 might; if 150 doesn’t, 200 might. At no point do you need to delude yourself into over-estimating the odds, because the actual odds are in your favor.

This isn’t perfect, though.

There’s a glaring problem with this technique that I still can’t resolve: It feels overwhelming.

Doing one job application is really not that big a deal. It accomplishes very little, but also costs very little.

Doing 100 job applications is an enormous undertaking that will take up most of your time for multiple weeks.

So if you are feeling demotivated, asking you to bundle the stakes is asking you to take on a huge, overwhelming task that surely feels utterly beyond you.

Also, when it comes to this particular example, I even managed to do 100 job applications and still get a pretty bad outcome: My only offer was Edinburgh, and I ended up being miserable there. I have reason to believe that these were exceptional circumstances (due to COVID), but it has still been hard to shake the feeling of helplessness I learned from that ordeal.

Maybe there’s some additional reframing that can help here. If so, I haven’t found it yet.

But maybe stakes bundling can help you, or someone out there, even if it can’t help me.

A prouder year for America, and for me

PNRJ current events, personal, politics, public policy , , , ,

Jul 4 JDN 2459380

Living under Trump from 2017 to 2020, it was difficult to be patriotic. How can we be proud of a country that would put a man like that in charge? And then there was the COVID pandemic, which initially the US handled terribly—largely because of the aforementioned Trump.

But then Biden took office, and almost immediately things started to improve. This is a testament to how important policy can be—and how different the Democrats and Republicans have become.

The US now has one of the best rates of COVID vaccination in the world (though lately progress seems to be stalling and other countries are catching up). Daily cases in the US are now the lowest they have been since March 2020. Even real GDP is almost back up to its pre-pandemic level (even per-capita), and the surge of inflation we got as things began to re-open already seems to be subsiding.

I can actually celebrate the 4th of July with some enthusiasm this year, whereas the last four years involved continually reminding myself that I was celebrating the liberating values of America’s founding, not the current terrible state of its government. Of course our government policy still retains many significant flaws—but it isn’t the utter embarrassment it was just a year ago.

This may be my last 4th of July to celebrate for the next few years, as I will soon be moving to Scotland (more on that in a moment).

2020 was a very bad year, but even halfway through it’s clear that 2021 is going to be a lot better.

This was true for just about everyone. I was no exception.

The direct effects of the pandemic on me were relatively minor.

Transitioning to remote work was even easier than I expected it to be; in fact I was even able to run experiments online using the same research subject pool as we’d previously used for the lab. I not only didn’t suffer any financial hardship from the lockdowns, I ended up better off because of the relief payments (and the freezing of student loan payments as well as the ludicrous stock boom, which I managed to buy in near the trough of). Ordering groceries online for delivery is so convenient I’m tempted to continue it after the pandemic is over (though it does cost more).

I was careful and/or fortunate enough not to get sick (now that I am fully vaccinated, my future risk is negligible), as were most of my friends and family. I am not close to anyone who died from the virus, though I do have some second-order links to some who died (grandparents of a couple of my friends, the thesis advisor of one of my co-authors).

It was other things, that really made 2020 a miserable year for me. Some of them were indirect effects of the pandemic, and some may not even have been related.

For me, 2020 was a year full of disappointments. It was the year I nearly finished my dissertation and went on the job market, applying for over one hundred jobs—and got zero offers. It was the year I was scheduled to present at an international conference—which was then canceled. It was the year my papers were rejected by multiple journals. It was the year I was scheduled to be married—and then we were forced to postpone the wedding.

But now, in 2021, several of these situations are already improving. We will be married on October 9, and most (though assuredly not all) of the preparations for the wedding are now done. My dissertation is now done except for some formalities. After over a year of searching and applying to over two hundred postings in all, I finally found a job, a postdoc position at the University of Edinburgh. (A postdoc isn’t ideal, but on the other hand, Edinburgh is more prestigious than I thought I’d be able to get.) I still haven’t managed to publish any papers, but I no longer feel as desperate a need to do so now that I’m not scrambling to find a job. Now of course we have to plan for a move overseas, though fortunately the university will reimburse our costs for the visa and most of the moving expenses.

Of course, 2021 isn’t over—neither is the COVID pandemic. But already it looks like it’s going to be a lot better than 2020.

Selectivity is a terrible measure of quality

PNRJ market failure, science and academia , , , , , , , , ,

May 23 JDN 2459358

How do we decide which universities and research journals are the best? There are a vast number of ways we could go about this—and there are in fact many different ranking systems out there, though only a handful are widely used. But one primary criterion which seems to be among the most frequently used is selectivity.

Selectivity is a very simple measure: What proportion of people who try to get in, actually get in? For universities this is admission rates for applicants; for journals it is acceptance rates for submitted papers.

The top-rated journals in economics have acceptance rates of 1-7%. The most prestigious universities have acceptance rates of 4-10%. So a reasonable ballpark is to assume a 95% chance of not getting accepted in either case. Of course, some applicants are more or less qualified, and some papers are more or less publishable; but my guess is that most applicants are qualified and most submitted papers are publishable. So these low acceptance rates mean refusing huge numbers of qualified people.


Selectivity is an objective, numeric score that can be easily generated and compared, and is relatively difficult to fake. This may accouunt for its widespread appeal. And it surely has some correlation with genuine quality: Lots of people are likely to apply to a school because it is good, and lots of people are likely to submit to a journal because it is good.

But look a little bit closer, and it becomes clear that selectivity is really a terrible measure of quality.


One, it is extremely self-fulfilling. Once a school or a journal becomes prestigious, more people will try to get in there, and that will inflate its selectivity rating. Harvard is extremely selective because Harvard is famous and high-rated. Why is Harvard so high-rated? Well, in part because Harvard is extremely selective.

Two, it incentivizes restricting the number of applicants accepted.

Ivy League schools have vast endowments, and could easily afford to expand their capacity, thus employing more faculty and educating more students. But that would require reducing their acceptance rates and hence jeopardizing their precious selectivity ratings. If the goal is to give as many people as possible the highest quality education, then selectivity is a deeply perverse incentive: It specifically incentivizes not educating too many students.

Similarly, most journals include something in their rejection letters about “limited space”, which in the age of all-digital journals is utter nonsense. Journals could choose to publish ten, twenty, fifty times as many papers as they currently do—or half, or a tenth. They could publish everything that gets submitted, or only publish one paper a year. It’s an entirely arbitrary decision with no real constraints. They choose what proportion of papers to publish entirely based primarily on three factors that have absolutely nothing to do with limited space: One, they want to publish enough papers to make it seem like they are putting out regular content; two, they want to make sure they publish anything that will turn out to be a major discovery (though they honestly seem systematically bad at predicting that); and three, they want to publish as few papers as possible within those constraints to maximize their selectivity.

To be clear, I’m not saying that journals should publish everything that gets submitted. Actually I think too many papers already get published—indeed, too many get written. The incentives in academia are to publish as many papers in top journals as possible, rather than to actually do the most rigorous and ground-breaking research. The best research often involves spending long periods of time making very little visible progress, and it does not lend itself to putting out regular publications to impress tenure committees and grant agencies.

The number of scientific papers published each year has grown at about 5% per year since 1900. The number of peer-reviewed journals has grown at an increasing rate, from about 3% per year for most of the 20th century to over 6% now. These are far in excess of population growth, technological advancement, or even GDP growth; this many scientific papers is obviously unsustainable. There are now 300 times as many scientific papers published per year as there were in 1900—while the world population has only increased by about 5-fold during that time. Yes, the number of scientists has also increased—but not that fast. About 8 million people are scientists, publishing an average of 2 million articles per year—one per scientist every four years. But the number of scientist jobs grows at just over 1%—basically tracking population growth or the job market in general. If papers published continue to grow at 5% while the number of scientists increases at 1%, then in 100 years each scientist will have to publish 48 times as many papers as today, or about 1 every month.


So the problem with research journals isn’t so much that journals aren’t accepting enough papers, as that too many people are submitting papers. Of course the real problem is that universities have outsourced their hiring decisions to journal editors. Rather than actually evaluating whether someone is a good teacher or a good researcher (or accepting that they can’t and hiring randomly), universities have trusted in the arbitrary decisions of research journals to decide whom they should hire.

But selectivity as a measure of quality means that journals have no reason not to support this system; they get their prestige precisely from the fact that scientists are so pressured to publish papers. The more papers get submitted, the better the journals look for rejecting them.

Another way of looking at all this is to think about what the process of acceptance or rejection entails. It is inherently a process of asymmetric information.

If we had perfect information, what would the acceptance rate of any school or journal be? 100%, regardless of quality. Only the applicants who knew they would get accepted would apply. So the total number of admitted students and accepted papers would be exactly the same, but all the acceptance rates would rise to 100%.

Perhaps that’s not realistic; but what if the application criteria were stricter? For instance, instead of asking you your GPA and SAT score, Harvard’s form could simply say: “Anyone with a GPA less than 4.0 or an SAT score less than 1500 need not apply.” That’s practically true anyway. But Harvard doesn’t have an incentive to say it out loud, because then applicants who know they can’t meet that standard won’t bother applying, and Harvard’s precious selectivity number will go down. (These are far from sufficient, by the way; I was valedictorian and had a 1590 on my SAT and still didn’t get in.)

There are other criteria they’d probably be even less willing to emphasize, but are no less significant: “If your family income is $20,000 or less, there is a 95% chance we won’t accept you.” “Other things equal, your odds of getting in are much better if you’re Black than if you’re Asian.”

For journals it might be more difficult to express the criteria clearly, but they could certainly do more than they do. Journals could more strictly delineate what kind of papers they publish: This one only for pure theory, that one only for empirical data, this one only for experimental results. They could choose more specific content niches rather than literally dozens of journals all being ostensibly about “economics in general” (the American Economic Review, the Quarterly Journal of Economics, the Journal of Political Economy, the Review of Economic Studies, the European Economic Review, the International Economic Review, Economic Inquiry… these are just the most prestigious). No doubt there would still have to be some sort of submission process and some rejections—but if they really wanted to reduce the number of submissions they could easily do so. The fact is, they want to have a large number of submissions that they can reject.

What this means is that rather than being a measure of quality, selectivity is primarily a measure of opaque criteria. It’s possible to imagine a world where nearly every school and every journal accept less than 1% of applicants; this would occur if the criteria for acceptance were simply utterly unknown and everyone had to try hundreds of places before getting accepted.


Indeed, that’s not too dissimilar to how things currently work in the job market or the fiction publishing market. The average job opening receives a staggering 250 applications. In a given year, a typical literary agent receives 5000 submissions and accepts 10 clients—so about one in every 500.

For fiction writing I find this somewhat forgivable, if regrettable; the quality of a novel is a very difficult thing to assess, and to a large degree inherently subjective. I honestly have no idea what sort of submission guidelines one could put on an agency page to explain to authors what distinguishes a good novel from a bad one (or, not quite the same thing, a successful one from an unsuccessful one).

Indeed, it’s all the worse because a substantial proportion of authors don’t even follow the guidelines that they do include! The most common complaint I hear from agents and editors at writing conferences is authors not following their submission guidelines—such basic problems as submitting content from the wrong genre, not formatting it correctly, having really egregious grammatical errors. Quite frankly I wish they’d shut up about it, because I wanted to hear what would actually improve my chances of getting published, not listen to them rant about the thousands of people who can’t bother to follow directions. (And I’m pretty sure that those people aren’t likely to go to writing conferences and listen to agents give panel discussions.)

But for the job market? It’s really not that hard to tell who is qualified for most jobs. If it isn’t something highly specialized, most people could probably do it, perhaps with a bit of training. If it is something highly specialized, you can restrict your search to people who already have the relevant education or training. In any case, having experience in that industry is obviously a plus. Beyond that, it gets much harder to assess quality—but also much less necessary. Basically anyone with an advanced degree in the relevant subject or a few years of experience at that job will probably do fine, and you’re wasting effort by trying to narrow the field further. If it is very hard to tell which candidate is better, that usually means that the candidates really aren’t that different.

To my knowledge, not a lot of employers or fiction publishers pride themselves on their selectivity. Indeed, many fiction publishers have a policy of simply refusing unsolicited submissions, relying upon literary agents to pre-filter their submissions for them. (Indeed, even many agents refuse unsolicited submissions—which raises the question: What is a debut author supposed to do?) This is good, for if they did—if Penguin Random House (or whatever that ludicrous all-absorbing conglomerate is calling itself these days; ah, what was it like in that bygone era, when anti-trust enforcement was actually a thing?) decided to start priding itself on its selectivity of 0.05% or whatever—then the already massively congested fiction industry would probably grind to a complete halt.

This means that by ranking schools and journals based on their selectivity, we are partly incentivizing quality, but mostly incentivizing opacity. The primary incentive is for them to attract as many applicants as possible, even knowing full well that they will reject most of these applicants. They don’t want to be too clear about what they will accept or reject, because that might discourage unqualified applicants from trying and thus reduce their selectivity rate. In terms of overall welfare, every rejected application is wasted human effort—but in terms of the institution’s selectivity rating, it’s a point in their favor.

On the quality of matches

PNRJ mathematical models, personal, public policy , , , , , , , ,

Apr 11 JDN 2459316

Many situations in the real world involve matching people to other people: Dating, job hunting, college admissions, publishing, organ donation.

Alvin Roth won his Nobel Prize for his work on matching algorithms. I have nothing to contribute to improving his algorithm; what baffles me is that we don’t use it more often. It would probably feel too impersonal to use it for dating; but why don’t we use it for job hunting or college admissions? (We do use it for organ donation, and that has saved thousands of lives.)

In this post I will be looking at matching in a somewhat different way. Using a simple model, I’m going to illustrate some of the reasons why it is so painful and frustrating to try to match and keep getting rejected.

Suppose we have two sets of people on either side of a matching market: X and Y. I’ll denote an arbitrarily chosen person in X as x, and an arbitrarily chosen person in Y as y. There’s no reason the two sets can’t have overlap or even be the same set, but making them different sets makes the model as general as possible.

Each person in X wants to match with a person in Y, and vice-versa. But they don’t merely want to accept any possible match; they have preferences over which matches would be better or worse.

In general, we could say that people have some kind of utility function: Ux:Y->R and Uy:X->R that maps from possible match partners to the utility of such a match. But that gets very complicated very fast, because it raises the question of when you should keep searching, and when you should stop searching and accept what you have. (There’s a whole literature of search theory on this.)

For now let’s take the simplest possible case, and just say that there are some matches each person will accept, and some they will reject. This can be seen as a special case where the utility functions Ux and Uy always yield a result of 1 (accept) or 0 (reject).

This defines a set of acceptable partners for each person: A(x) is the set of partners x will accept: {y in Y|Ux(y) = 1} and A(y) is the set of partners y will accept: {x in X|Uy(x) = 1}

Then, the set of mutual matches than x can actually get is the set of ys that x wants, which also want x back: M(x) = {y in A(x)|x in A(y)}

Whereas, the set of mutual matches that y can actually get is the set of xs that y wants, which also want y back: M(y) = {x in A(y)|y in A(x)}

This relation is mutual by construction: If x is in M(y), then y is in M(x).

But this does not mean that the sets must be the same size.

For instance, suppose that there are three people in X, x1, x2, x3, and three people in Y, y1, y2, y3.

Let’s say that the acceptable matches are as follows:

A(x1) = {y1, y2, y3}

A(x2) = {y2, y3}

A(x3) = {y2, y3}

A(y1) = {x1,x2,x3}

A(y2) = {x1,x2}

A(y3) = {x1}

This results in the following mutual matches:

M(x1) = {y1, y2, y3}

M(y1) = {x1}

M(x2) = {y2}

M(y2) = {x1, x2}

M(x3) = {}

M(y3) = {x1}

x1 can match with whoever they like; everyone wants to match with them. x2 can match with y2. But x3, despite having the same preferences as x2, and being desired by y3, can’t find any mutual matches at all, because the one person who wants them is a person they don’t want.

y1 can only match with x1, but the same is true of y3. So they will be fighting over x1. As long as y2 doesn’t also try to fight over x1, x2 and y2 will be happy together. Yet x3 will remain alone.

Note that the number of mutual matches has no obvious relation with the number of individually acceptable partners. x2 and x3 had the same number of acceptable partners, but x2 found a mutual match and x3 didn’t. y1 was willing to accept more potential partners than y3, but got the same lone mutual match in the end. y3 was only willing to accept one partner, but will get a shot at x1, the one that everyone wants.

One thing is true: Adding another acceptable partner will never reduce your number of mutual matches, and removing one will never increase it. But often changing your acceptable partners doesn’t have any effect on your mutual matches at all.

Now let’s consider what it must feel like to be x1 versus x3.

For x1, the world is their oyster; they can choose whoever they want and be guaranteed to get a match. Life is easy and simple for them; all they have to do is decide who they want most and that will be it.

For x3, life is an endless string of rejection and despair. Every time they try to reach out to suggest a match with someone, they are rebuffed. They feel hopeless and alone. They feel as though no one would ever actually want them—even though in fact there is someone who wants them, it’s just not someone they were willing to consider.

This is of course a very simple and small-scale model; there are only six people in it, and they each only say yes or no. Yet already I’ve got x1 who feels like a rock star and x3 who feels utterly hopeless if not worthless.

In the real world, there are so many more people in the system that the odds that no one is in your mutual match set are negligible. Almost everyone has someone they can match with. But some people have many more matches than others, and that makes life much easier for the ones with many matches and much harder for the ones with fewer.

Moreover, search costs then become a major problem: Even knowing that in all probability there is a match for you somewhere out there, how do you actually find that person? (And that’s not even getting into the difficulty of recognizing a good match when you see it; in this simple model you know immediately, but in the real world it can take a remarkably long time.)

If we think of the acceptable partner sets as preferences, they may not be within anyone’s control; you want what you want. But if we instead characterize them as decisions, the results are quite different—and I think it’s easy to see them, if nothing else, as the decision of how high to set your standards.

This raises a question: When we are searching and not getting matches, should we lower our standards and add more people to our list of acceptable partners?

This simple model would seem to say that we should always do that—there’s no downside, since the worst that can happen is nothing. And x3 for instance would be much happier if they were willing to lower their standards and accept y1. (Indeed, if they did so, there would be a way to pair everyone off happily: x1 with y3, x2 with y2, and x3 with y1.)

But in the real world, searching is often costly: There is at least the involved, and often a literal application or submission fee; but perhaps worst of all is the crushing pain of rejection. Under those circumstances, adding another acceptable partner who is not a mutual match will actually make you worse off.

That’s pretty much what the job market has been for me for the last six months. I started out with the really good matches: GiveWell, the Oxford Global Priorities Institute, Purdue, Wesleyan, Eastern Michigan University. And after investing considerable effort into getting those applications right, I made it as far as an interview at all those places—but no further.

So I extended my search, applying to dozens more places. I’ve now applied to over 100 positions. I knew that most of them were not good matches, because there simply weren’t that many good matches to be found. And the result of all those 100 applications has been precisely 0 interviews. Lowering my standards accomplished absolutely nothing. I knew going in that these places were not a good fit for me—and it looks like they all agreed.

It’s possible that lowering my standards in some different way might have worked, but even this is not clear: I’ve already been willing to accept much lower salaries than a PhD in economics ought to entitle, and included positions in my search that are only for a year or two with no job security, and applied to far-flung locales across the globe that I don’t know if I’d really be willing to move to.

Honestly at this point I’ve only been using the following criteria: (1) At least vaguely related to my field (otherwise they wouldn’t want me anyway), (2) a higher salary than I currently get as a grad student (otherwise why bother?), (3) a geographic location where homosexuality is not literally illegal and an institution that doesn’t actively discriminate against LGBT employees (this rules out more than you’d think—there are at least three good postings I didn’t apply to on these grounds), (4) in a region that speaks a language I have at least some basic knowledge of (i.e. preferably English, but also allowing Spanish, French, German, or Japanese) (5) working conditions that don’t involve working more than 40 hours per week (which has severely detrimental health effects, even ignoring my disability which would compound the effects), and (6) not working for a company that is implicated in large-scale criminal activity (as a remarkable number of major banks have in fact been implicated). I don’t feel like these are unreasonably high standards, and yet so far I have failed to land a match.

What’s more, the entire process has been emotionally devastating. While others seem to be suffering from pandemic burnout, I don’t think I’ve made it that far; I think I’d be just as burnt out even if there were no pandemic, simply from how brutal the job market has been.

Why does rejection hurt so much? Why does being turned down for a date, or a job, or a publication feel so utterly soul-crushing? When I started putting together this model I had hoped that thinking of it in terms of match-sets might actually help reduce that feeling, but instead what happened is that it offered me a way of partly explaining that feeling (much as I did in my post on Bayesian Impostor Syndrome).

What is the feeling of rejection? It is the feeling of expending search effort to find someone in your acceptable partner set—and then learning that you were not in their acceptable partner set, and thus you have failed to make a mutual match.

I said earlier that x1 feels like a rock star and x3 feels hopeless. This is because being present in someone else’s acceptable partner set is a sign of status—the more people who consider you an acceptable partner, the more you are “worth” in some sense. And when it’s something as important as a romantic partner or a career, that sense of “worth” is difficult to circumscribe into a particular domain; it begins to bleed outward into a sense of your overall self-worth as a human being.

Being wanted by someone you don’t want makes you feel superior, like they are “beneath” you; but wanting someone who doesn’t want you makes you feel inferior, like they are “above” you. And when you are applying for jobs in a market with a Beveridge Curve as skewed as ours, or trying to get a paper or a book published in a world flooded with submissions, you end up with a lot more cases of feeling inferior than cases of feeling superior. In fact, I even applied for a few jobs that I felt were “beneath” my level—they didn’t take me either, perhaps because they felt I was overqualified.

In such circumstances, it’s hard not to feel like I am the problem, like there is something wrong with me. Sometimes I can convince myself that I’m not doing anything wrong and the market is just exceptionally brutal this year. But I really have no clear way of distinguishing that hypothesis from the much darker possibility that I have done something terribly wrong that I cannot correct and will continue in this miserable and soul-crushing fruitless search for months or even years to come. Indeed, I’m not even sure it’s actually any better to know that you did everything right and still failed; that just makes you helpless instead of defective. It might be good for my self-worth to know that I did everything right; but it wouldn’t change the fact that I’m in a miserable situation I can’t get out of. If I knew I were doing something wrong, maybe I could actually fix that mistake in the future and get a better outcome.

As it is, I guess all I can do is wait for more opportunities and keep trying.

What would a better job market look like?

PNRJ personal, public policy , , , , , , , , ,

Sep 13 JDN 2459106

I probably don’t need to tell you this, but getting a job is really hard. Indeed, much harder than it seems like it ought to be.

Having all but completed my PhD, I am now entering the job market. The job market for economists is quite different from the job market most people deal with, and these differences highlight some potential opportunities for improving job matching in our whole economy—which, since employment is such a large part of our lives, could have wide-ranging benefits for our society.

The most obvious difference is that the job market for economists is centralized: Job postings are made through the American Economic Association listing of Job Openings for Economists (often abbrievated AEA JOE); in a typical year about 4,000 jobs are posted there. All of them have approximately the same application deadline, near the end of the year. Then, after applying to various positions, applicants get interviewed in rapid succession, all at the annual AEA conference. Then there is a matching system, where applicants get to send two “signals” indicating their top choices and then offers are made.

This year of course is different, because of COVID-19. The conference has been canceled, with all of its presentations moved online; interviews will also be conducted online. Perhaps more worrying, the number of postings has been greatly reduced, and based on past trends may be less than half of the usual number. (The number of applicants may also be reduced, but it seems unlikely to drop as much as the number of postings does.)

There are a number of flaws in even this system. First, it’s too focused on academia; very few private-sector positions use the AEA JOE system, and almost no government positions do. So those of us who are not so sure we want to stay in academia forever end up needing to deal with both this system and the conventional system in parallel. Second, I don’t understand why they use this signaling system and not a deferred-acceptance matching algorithm. I should be able to indicate more about my preferences than simply what my top two choices are—particularly when most applicants apply to over 100 positions. Third, it isn’t quite standardized enough—some positions do have earlier deadlines or different application materials, so you can’t simply put together one application packet and send it to everyone at once.

Still, it’s quite obvious that this system is superior to the decentralized job market that most people deal with. Indeed, this becomes particularly obvious when one is participating in both markets at once, as I am. The decentralized market has a wide range of deadlines, where upon seeing an application you may need to submit to it within that week, or you may have several months to respond. Nearly all applications require a resume, but different institutions will expect different content on it. Different applications may require different materials: Cover letters, references, writing samples, and transcripts are all things that some firms will want and others won’t.

Also, this is just my impression from a relatively small sample, but I feel like the AEA JOE listings are more realistic, in the following sense: They don’t all demand huge amounts of prior experience, and those that do ask for prior experience are either high-level positions where that’s totally reasonable, or are willing to substitute education for experience. For private-sector job openings you basically have to subtract three years from whatever amount of experience they say they require, because otherwise you’d never have anywhere you could apply to. (Federal government jobs are a weird case here; they all say they require a lot of experience at a specific government pay grade, but from talking with those who have dealt with the system before, they are apparently willing to make lots of substitutions—private-sector jobs, education, and even hobbies can sometimes substitute.)

I think this may be because the decentralized market has to some extent unraveled. The job market is the epitome of a matching market; unraveling in a matching market occurs when there is fierce competition for a small number of good candidates or, conversely, a small number of good openings. Each firm has the incentive to make a binding offer earlier than the others, with a short deadline so that candidates don’t have time to shop around. As firms compete with each other, they start making deadlines earlier and earlier until candidates feel like they are in a complete crapshoot: An offer made on Monday might be gone by Friday, and you have no way of knowing if you should accept it now or wait for a better one to come along. This is a Tragedy of the Commons: Given what other firms are doing, each firm benefits from making an earlier binding offer. But once they all make early offers, that benefit disappears and the result just makes the whole system less efficient.

The centralization of the AEA JOE market prevents this from happening: Everyone has common deadlines and does their interviews at the same time. Each institution may be tempted to try to break out of the constraints of the centralized market, but they know that if they do, they will be punished by receiving fewer applicants.

The fact that the centralized market is more efficient is likely a large part of why economics PhDs have the lowest unemployment rate of any PhD graduates and nearly the lowest unemployment rate of any job sector whatsoever. In some sense we should expect this: If anyone understands how to make employment work, it should be economists. Noah Smith wrote in 2013 (and I suppose I took it to heart): “If you get a PhD, get an economics PhD.” I think PhD graduates are the right comparison group here: If we looked at the population as a whole, employment rates and salaries for economists look amazing, but that isn’t really fair since it’s so much harder to become an economist than it is to get most other jobs. But I don’t think it’s particularly easier to get a PhD in physics or biochemistry than to get one in economics, and yet economists still have a lower unemployment rate than physicists or biochemists. (Though it’s worth noting that any PhD—yes, even in the humanities—will give you a far lower risk of unemployment than the general population.) The fact that we have AEA JOE and they don’t may be a major factor here.


So, here’s my question: Why don’t we do this in more job markets? It would be straightforward enough to do this for all PhD graduates, at least—actually my understanding is that some other disciplines do have centralized markets similar to the one in economics, but I’m not sure how common this is.

The federal government could relatively easily centralize its own job market as well; maybe not for positions that need to be urgently filled, but anything that can wait several months would be worth putting into a centralized system that has deadlines once or twice a year.

But what about the private sector, which after all is where most people work? Could we centralize that system as well?

It’s worth noting the additional challenges that immediately arise: Many positions need to be filled immediately, and centralization would make that impossible. There are thousands of firms that would need to be coordinated (there are at least 100,000 firms in the US with 100 or more employees). There are millions of different jobs to be filled, requiring a variety of different skills. In an average month over 5 million jobs are filled in the United States.

Most people want a job near where they live, so part of the solution might be to centralize only jobs within a certain region, such as a particular metro area. But if we are limited to open positions of a particular type within a particular city, there might not be enough openings at any given time to be worth centralizing. And what about applicants who don’t care so much about geography? Should they be applying separately to each regional market?

Yet even with all this in mind, I think some degree of centralization would be feasible and worthwhile. If nothing else, I think standardizing deadlines and application materials could make a significant difference—it’s far easier to apply to many places if they all use the same application and accept them at the same time.

Another option would be to institute widespread active labor market policies, which are a big part of why #ScandinaviaIsBetter. Denmark especially invests heavily in such programs, which provide training and job matching for unemployed citizens. It is no coincidence that Denmark has kept their unemployment rate under 7% even through the worst of the Great Recession. The US unemployment rate fluctuates wildly with the business cycle, while most of Europe has steadier but higher unemployment. Indeed, the lowest unemployment rates in France over the last 30 years have exceeded the highest rates in Denmark over the same period. Denmark spends a lot on their active labor market programs, but I think they’re getting their money’s worth.

Such a change would make our labor markets more efficient, matching people to jobs that fit them better, increasing productivity and likely decreasing turnover. Wages probably wouldn’t change much, but working in a better job for the same wage is still a major improvement in your life. Indeed, job satisfaction is one of the strongest predictors of life satisfaction, which isn’t too surprising given how much of our lives we spend at work.

My first AEA conference

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Jan 13 JDN 2458497

The last couple of weeks have been a bit of a whirlwind for me. I submitted a grant proposal, I have another, much more complicated proposal due next week, I submitted a paper to a journal, and somewhere in there I went to the AEA conference for the first time.

Going to the conference made it quite clear that the race and gender disparities in economics are quite real: The vast majority of the attendees were middle-aged White males, all wearing one of either two outfits: Sportcoat and khakis, or suit and tie. (And almost all of the suits were grey or black and almost all of the shirts were white or pastel. Had you photographed in greyscale you’d only notice because the hotel carpets looked wrong.) In an upcoming post I’ll go into more detail about this problem, what seems to be causing it, and what might be done to fix it.

But for now I just want to talk about the conference itself, and moreover, the idea of having conferences—is this really the best way to organize ourselves as a profession?

One thing I really do like about the AEA conference is actually something that separates it from other professions: The job market for economics PhDs is a very formalized matching system designed to be efficient and minimize opportunities for bias. It should be a model for other job markets. All the interviews are conducted in rapid succession, at the conference itself, so that candidates can interview for positions all over the country or even abroad.

I wasn’t on the job market yet, but I will be in a few years. I wanted to see what it’s like before I have to run that gauntlet myself.

But then again, why did we need face-to-face interviews at all? What do they actually tell us?

It honestly seems like a face-to-face interview is optimized to maximize opportunities for discrimination. Do you know them personally? Nepotism opportunity. Are they male or female? Sexism opportunity. Are they in good health? Ableism opportunity. Do they seem gay, or mention a same-sex partner? Homophobia opportunity. Is their gender expression normative? Transphobia opportunity. How old are they? Ageism opportunity. Are they White? Racism opportunity. Do they have an accent? Nationalism opportunity. Do they wear fancy clothes? Classism opportunity. There are other forms of bias we don’t even have simple names for: Do they look pregnant? Do they wear a wedding band? Are they physically attractive? Are they tall?

You can construct your resume review system to not include any of this information, by excluding names, pictures, and personal information. But you literally can’t exclude all of this information from a face-to-face interview, and this is the only hiring mechanism that suffers from this fundamental flaw.

If it were really about proving your ability to do the job, they could send you a take-home exam (a lot of tech companies actually do this): Here’s a small sample project similar to what we want you to do, and a reasonable deadline in which to do it. Do it, and we’ll see if it’s good enough.

If they want to offer an opportunity for you to ask or answer specific questions, that could be done via text chat—which could be on the one hand end-to-end encrypted against eavesdropping and on the other hand leave a clear paper trail in case they try to ask you anything they shouldn’t. If they start asking about your sexual interests in the digital interview, you don’t just feel awkward and wonder if you should take the job: You have something to show in court.

Even if they’re interested in things like your social skills and presentation style, those aren’t measured well by interviews anyway. And they probably shouldn’t even be as relevant to hiring as they are.

With that in mind, maybe bringing all the PhD graduates in economics in the entire United States into one hotel for three days isn’t actually necessary. Maybe all these face-to-face interviews aren’t actually all that great, because their small potential benefits are outweighed by their enormous potential biases.

The rest of the conference is more like other academic conferences, which seems even less useful.

The conference format seems like a strange sort of formality, a ritual that we go through. It’s clearly not the optimal way to present ongoing research—though perhaps it’s better than publishing papers in journals, which is our current gold standard. A whole bunch of different people give you brief, superficial presentations of their research, which may be only tangentially related to anything you’re interested in, and you barely even have time to think about it before they go on to the next once. Also, seven of these sessions are going on simultaneously, so unless you have a Time Turner, you have to choose which one to go to. And they are often changed at the last minute, so you may not even end up going to the one you thought you were going to.

I was really struck by how little experimental work was presented. I was under the impression that experimental economics was catching on, but despite specifically trying to go to experiment-related sessions (excluding the 8:00 AM session for migraine reasons), I only counted a handful of experiments, most of them in the field rather than the lab. There was a huge amount of theory and applied econometrics. I guess this isn’t too surprising, as those are the two main kinds of research that only cost a researcher’s time. I guess in some sense this is good news for me: It means I don’t have as much competition as I thought.

Instead of gathering papers into sessions where five different people present vaguely-related papers in far too little time, we could use working papers, or better yet a more sophisticated online forum where research could be discussed in real-time before it even gets written into a paper. We could post results as soon as we get them, and instead of conducting one high-stakes anonymous peer review at the time of publication, conduct dozens of little low-stakes peer reviews as the research is ongoing. Discussants could be turned into collaborators.

The most valuable parts of conferences always seem to be the parts that aren’t official sessions: Luncheons, receptions, mixers. There you get to meet other people in the field. And this can be valuable, to be sure. But I fear that the individual gain is far larger than the social gain: Most of the real benefits of networking get dissipated by the competition to be better-connected than the other candidates. The kind of working relationships that seem to be genuinely valuable are the kind formed by working at the same school for several years, not the kind that can be forged by meeting once at a conference reception.

I guess every relationship has to start somewhere, and perhaps more collaborations have started that way than I realize. But it’s also worth asking: Should we really be putting so much weight on relationships? Is that the best way to organize an academic discipline?

“It’s not what you know, it’s who you know” is an accurate adage in many professions, but it seems like research should be where we would want it least to apply. This is supposed to be about advancing human knowledge, not making friends—and certainly not maintaining the old boys’ club.