Dec13 JDN 2459197

This phenomenon has been particularly salient for me the last few months, but I think it’s a common experience for most people in my generation: Getting a job takes an awful lot of work.

Over the past six months, I’ve applied to over 70 different positions and so far gone through 4 interviews (2 by video, 2 by phone). I’ve done about 10 hours of test work. That so far has gotten me no offers, though I have yet to hear from 50 employers. Ahead of me I probably have about another 10 interviews, then perhaps 4 of what would have been flyouts and in-person presentations but instead will be “comprehensive interviews” and presentations conducted online, likely several more hours of test work, and then finally, maybe, if I’m lucky, I’ll get a good offer or two. If I’m unlucky, I won’t, and I’ll have to stick around for another year and do all this over again next year.

Aside from the limitations imposed by the pandemic, this is basically standard practice for PhD graduates. And this is only the most extreme end of a continuum of intensive job search efforts, for which even applying to be a cashier at Target requires a formal application, references, and a personality test.

This wasn’t how things used to be. Just a couple of generations ago, low-wage employers would more or less hire you on the spot, with perhaps a resume or a cursory interview. More prestigious employers would almost always require a CV with references and an interview, but it more or less stopped there. I discussed in an earlier post how much of the difference actually seems to come from our chronic labor surplus.

Is all of this extra effort worthwhile? Are we actually fitting people to better jobs this way? Even if the matches are better, are they enough better to justify all this effort?

It is a commonly-held notion among economists that competition in markets is good, that it increases efficiency and improves outcomes. I think that this is often, perhaps usually, the case. But the labor market has become so intensely competitive, particularly for high-paying positions, that the costs of this competitive effort likely outweigh the benefits.

How could this happen? Shouldn’t the free market correct for such an imbalance? Not necessarily. Here is a simple formal model of how this sort of intensive competition can result in significant waste.

Note that this post is about a formal mathematical model, so it’s going to use a lot of algebra. If you are uninterested in such things, you can read the next two paragraphs and then skip to the conclusions at the end.

The overall argument is straightforward: If candidates are similar in skill level, a complicated application process can make sense from a firm’s perspective, but be harmful from society’s perspective, due to the great cost to the applicants. This can happen because the difficult application process imposes an externality on the workers who don’t get the job.

All right, here is where the algebra begins.

I’ve included each equation as both formatted text and LaTeX.

Consider a competition between two applicants, X and Z.

They are each asked to complete a series of tasks in an application process. The amount of effort X puts into the application is x, and the amount of effort Z puts into the application is z. Let’s say each additional bit of effort has a fixed cost, normalized to 1.

Let’s say that their skills are similar, but not identical; this seems quite realistic. X has skill level hx, and Z has skill level hz.

Getting hired has a payoff for each worker of V. This includes all the expected benefits of the salary, benefits, and working conditions. I’ll assume that these are essentially the same for both workers, which also seems realistic.

The benefit to the employer is proportional to the worker’s skill, so letting h be the skill level of the actually hired worker, the benefit of hiring that worker is hY. The reason they are requiring this application process is precisely because they want to get the worker with the highest h. Let’s say that this application process has a cost to implement, c.

Who will get hired? Well, presumably whoever does better on the application. The skill level will amplify the quality of their output, let’s say proportionally to the effort they put in; so X’s expected quality will be hxx and Z’s expected output will be hzz.

Let’s also say there’s a certain amount of error in the process; maybe the more-qualified candidate will sleep badly the day of the interview, or make a glaring and embarrassing typo on their CV. And quite likely the quality of application output isn’t perfectly correlated with the quality of actual output once hired. To capture all this, let’s say that having more skill and putting in more effort only increases your probability of getting the job, rather than actually guaranteeing it.

In particular, let’s say that the probability of X getting hired is P[X] = hxx/(hxx + hzz).

\[ P[X] = \frac{h_x}{h_x x + h_z z} \]

This results in a contest function, a type of model that I’ve discussed in some earlier posts in a rather different context.

The expected payoff for worker X is:

E[Ux] = hxx/(hxx + hzz) V – x

\[ E[U_x] = \frac{h_x x}{h_x x + h_z z} V – x \]

Maximizing this with respect to the choice of effort x (which is all that X can control at this point) yields:

hxhzz V = (hxx + hzz)2

\[ h_x h_z x V = (h_x x + h_z z)^2 \]

A similar maximization for worker Z yields:

hxhzx V = (hxx + hzz)2

\[ h_x h_z z V = (h_x x + h_z z)^2 \]

It follows that x=z, i.e. X and Z will exert equal efforts in Nash equilibrium. Their probability of success will then be contingent entirely on their skill levels:

P[X] = hx/(hx + hz).

\[ P[X] = \frac{h_x}{h_x + h_y} \]

Substituting that back in, we can solve for the actual amount of effort:

hxhzx V = (hx + hz)2x2

\[h_x h_z x V = (h_x + h_z)^2 x^2 \]

x = hxhzV/(hx + hz)2

\[ x = \frac{h_x h_z}{h_x + h_z} V \]

Now let’s see what that gives for the expected payoffs of the firm and the workers. This is worker X’s expected payoff:

E[Ux] = hx/(hx + hz) V – hxhzV/(hx + hz)2 = (hx/(hx + hz))2 V

\[ E[U_x] = \frac{h_x}{h_x + h_z} V – \frac{h_x h_z}{(h_x + h_z)^2} V = \left( \frac{h_x}{h_x + h_z}\right)^2 V \]

Worker Z’s expected payoff is the same, with hx and hz exchanged:

E[Uz] = (hz/(hx + hz))2 V

\[ E[U_z] = \left( \frac{h_z}{h_x + h_z}\right)^2 V \]

What about the firm? Their expected payoff is the the probability of hiring X, times the value of hiring X, plus the probability of hiring Z, times the value of hiring Z, all minus the cost c:

E[Uf] = hx/(hx + hz) hx Y + hz/(hx + hz) hz Y – c= (hx2 + hz2)/(hx + hz) Y – c

\[ E[U_f] = \frac{h_x}{h_x + h_z} h_x Y + \frac{h_z}{h_x + h_z} h_z Y – c = \frac{h_x^2 + h_z^2}{h_x + h_z} Y – c\]

To see whether the application process was worthwhile, let’s compare against the alternative of simply flipping a coin and hiring X or Z at random. The probability of getting hired is then 1/2 for each candidate.

Expected payoffs for X and Z are now equal:

E[Ux] = E[Uz] = V/2

\[ E[U_x] = E[U_z] = \frac{V}{2} \]

The expected payoff for the firm can be computed the same as before, but now without the cost c:

E[Uf] = 1/2 hx Y + 1/2 hz Y = (hx + hz)/2 Y

\[ E[U_f] = \frac{1}{2} h_x Y + \frac{1}{2} h_z Y = \frac{h_x + h_z}{2} Y \]

This has a very simple interpretation: The expected value to the firm is just the average quality of the two workers, times the overall value of the job.

Which of these two outcomes is better? Well, that depends on the parameters, of course. But in particular, it depends on the difference between hx and hz.

Consider two extremes: In one case, the two workers are indistinguishable, and hx = hz = h. In that case, the payoffs for the hiring process reduce to the following:

E[Ux] = E[Uz] = V/4

\[ E[U_x] = E[U_z] = \frac{V}{4} \]

E[Uf] = h Y – c

\[ E[U_f] = h Y – c \]

Compare this against the payoffs for hiring randomly:

E[Ux] = E[Uz] = V/2

\[ E[U_x] = E[U_z] = \frac{V}{2} \]

E[Uf] = h Y

\[ E[U_f] = h Y \]

Both the workers and the firm are strictly better off if the firm just hires at random. This makes sense, since the workers have identical skill levels.

Now consider the other extreme, where one worker is far better than the other; in fact, one is nearly worthless, so hz ~ 0. (I can’t do exactly zero because I’d be dividing by zero, but let’s say one is 100 times better or something.)

In that case, the payoffs for the hiring process reduce to the following:

E[Ux] = V

E[Uz] = 0

\[ E[U_x] = V \]

\[ E[U_z] = 0 \]

X will definitely get the job, so X is much better off.

E[Uf] = hx Y – c

\[ E[U_f] = h_x Y – c \]

If the firm had hired randomly, this would have happened instead:

E[Ux] = E[Uz] = V/2

\[ E[U_x] = E[U_z] = \frac{V}{2} \]

E[Uf] = hY/2

\[ E[U_f] = \frac{h}{2} Y \]

As long as c < hY/2, both the firm and the higher-skill worker are better off in this scenario. (The lower-skill worker is worse off, but that’s not surprising.) The total expected benefit for everyone is also higher in this scenario.

Thus, the difference in skill level between the applicants is vital. If candidates are very different in skill level, in a way that the application process can accurately measure, then a long and costly application process can be beneficial, not only for the firm but also for society as a whole.

In these extreme examples, it was either not worth it for the firm, or worth it for everyone. But there is an intermediate case worth looking at, where the long and costly process can be worth it for the firm, but not for society as a whole. I will call this case hyper-competition—a system that is so competitive it makes society overall worse off.

This inefficient result occurs precisely when:
c < (hx2 + hz2)/(hx + hz) Y – (hx + hz)/2 Y < c + (hx/(hx + hz))2 V + (hz/(hx + hz))2 V

\[ c < \frac{h_x^2 + h_z^2}{h_x + h_z} Y – \frac{h_x + h_z}{2} Y < c + \left( \frac{h_x}{h_x + h_z}\right)^2 V + \left( \frac{h_z}{h_x + h_z}\right)^2 V \]

This simplifies to:

c < (hx – hz)2/(2hx + 2hz) Y < c + (hx2 + hz2)/(hx + hz)2 V

\[ c < \frac{(h_x – h_z)^2}{2 (h_x + h_z)} Y < c + \frac{(h_x^2 + h_z^2)}{(h_x+h_z)^2} V \]

If c is small, then we are interested in the case where:

(hx – hz)2 Y/2 < (hx2 + hz2)/(hx + hz) V

\[ \frac{(h_x – h_z)^2}{2} Y < \frac{h_x^2 + h_z^2}{h_x + h_z} V \]

This is true precisely when the difference hx – hz is small compared to the overall size of hx or hz—that is, precisely when candidates are highly skilled but similar. This is pretty clearly the typical case in the real world. If the candidates were obviously different, you wouldn’t need a competitive process.

For instance, suppose that hx = 10 and hz = 8, while V = 180, Y = 20 and c = 1.

Then, if we hire randomly, these are the expected payoffs:

E[Uf] = (hx + hz)/2 Y = 180

E[Ux] = E[Uz] = V/2 = 90

If we use the complicated hiring process, these are the expected payoffs:

E[Ux] = (hx/(hx + hz))2 V = 55.5

E[Uz] = (hz/(hx + hz))2 V = 35.5

E[Uf] = (hx2 + hz2)/(hx + hz) Y – c = 181

The firm gets a net benefit of 1, quite small; while the workers face a far larger total expected loss of 90. And these candidates aren’t that similar: One is 25% better than the other. Yet because the effort expended in applying was so large, even this improvement in quality wasn’t worth it from society’s perspective.

This conclude’s the algebra for today, if you’ve been skipping it.

In this model I’ve only considered the case of exactly two applicants, but this can be generalized to more applicants, and the effect only gets stronger: Seemingly-large differences in each worker’s skill level can be outweighed by the massive cost of making so many people work so hard to apply and get nothing to show for it.

Thus, hyper-competition can exist despite apparently large differences in skill. Indeed, it is precisely the typical real-world scenario with many applicants who are similar that we expect to see the greatest inefficiencies. In the absence of intervention, we should expect markets to get this wrong.

Of course, we don’t actually want employers to hire randomly, right? We want people who are actually qualified for their jobs. Yes, of course; but you can probably assess that with nothing more than a resume and maybe a short interview. Most employers are not actually trying to find qualified candidates; they are trying to sift through a long list of qualified candidates to find the one that they think is best qualified. And my suspicion is that most of them honestly don’t have good methods of determining that.

This means that it could be an improvement for society to simply ban long hiring processes like these—indeed, perhaps ban job interviews altogether, as I can hardly think of a more efficient mechanism for allowing employers to discriminate based on race, gender, age, or disability than a job interview. Just collect a resume from each applicant, remove the ones that are unqualified, and then roll a die to decide which one you hire.

This would probably make the fit of workers to their jobs somewhat worse than the current system. But most jobs are learned primarily through experience anyway, so once someone has been in a job for a few years it may not matter much who was hired originally. And whatever cost we might pay in less efficient job matches could be made up several times over by the much faster, cheaper, easier, and less stressful process of applying for jobs.

Indeed, think for a moment of how much worse it feels being turned down for a job after a lengthy and costly application process that is designed to assess your merit (but may or may not actually do so particularly well), as opposed to simply finding out that you lost a high-stakes die roll. Employers could even send out letters saying one of two things: “You were rejected as unqualifed for this position.” versus “You were qualified, but you did not have the highest die roll.” Applying for jobs already feels like a crapshoot; maybe it should literally be one.

People would still have to apply for a lot of jobs—actually, they’d probably end up applying for more, because the lower cost of applying would attract more applicants. But since the cost is so much lower, it would still almost certainly be easier to do a job search than it is in the current system. In fact, it could largely be automated: simply post your resume on a central server and the system matches you with employers’ requirements and then randomly generates offers. Employers and prospective employees could fill out a series of forms just once indicating what they were looking for, and then the system could do the rest.

What I find most interesting about this policy idea is that it is in an important sense anti-meritocratic. We are in fact reducing the rewards for high levels of skill—at least a little bit—in order to improve society overall and especially for those with less skill. This is exactly the kind of policy proposal that I had hoped to see from a book like The Meritocracy Trap, but never found there. Perhaps it’s too radical? But the book was all about how we need fundamental, radical change—and then its actual suggestions were simple, obvious, and almost uncontroversial.

Note that this simplified process would not eliminate the incentives to get major, verifiable qualifications like college degrees or years of work experience. In fact, it would focus the incentives so that only those things matter, instead of whatever idiosyncratic or even capricious preferences HR agents might have. There would be no more talk of “culture fit” or “feeling right for the job”, just: “What is their highest degree? How many years have they worked in this industry?” I suppose this is credentialism, but in a world of asymmetric information, I think credentialism may be our only viable alternative to nepotism.

Of course, it’s too late for me. But perhaps future generations may benefit from this wisdom.

My first AEA conference

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.

Why is it so hard to get a job?

JDN 2457411

The United States is slowly dragging itself out of the Second Depression.

Unemployment fell from almost 10% to about 5%.

Core inflation has been kept between 0% and 2% most of the time.

Overall inflation has been within a reasonable range:


Real GDP has returned to its normal growth trend, though with a permanent loss of output relative to what would have happened without the Great Recession.


Consumption spending is also back on trend, tracking GDP quite precisely.

The Federal Reserve even raised the federal funds interest rate above the zero lower bound, signaling a return to normal monetary policy. (As I argued previously, I’m pretty sure that was their main goal actually.)

Employment remains well below the pre-recession peak, but is now beginning to trend upward once more.

The only thing that hasn’t recovered is labor force participation, which continues to decline. This is how we can have unemployment go back to normal while employment remains depressed; people leave the labor force by retiring, going back to school, or simply giving up looking for work. By the formal definition, someone is only unemployed if they are actively seeking work. No, this is not new, and it is certainly not Obama rigging the numbers. This is how we have measured unemployment for decades.

Actually, it’s kind of the opposite: Since the Clinton administration we’ve also kept track of “broad unemployment”, which includes people who’ve given up looking for work or people who have some work but are trying to find more. But we can’t directly compare it to anything that happened before 1994, because the BLS didn’t keep track of it before then. All we can do is estimate based on what we did measure. Based on such estimation, it is likely that broad unemployment in the Great Depression may have gotten as high as 50%. (I’ve found that one of the best-fitting models is actually one of the simplest; assume that broad unemployment is 1.8 times narrow unemployment. This fits much better than you might think.)

So, yes, we muddle our way through, and the economy eventually heals itself. We could have brought the economy back much sooner if we had better fiscal policy, but at least our monetary policy was good enough that we were spared the worst.

But I think most of us—especially in my generation—recognize that it is still really hard to get a job. Overall GDP is back to normal, and even unemployment looks all right; but why are so many people still out of work?

I have a hypothesis about this: I think a major part of why it is so hard to recover from recessions is that our system of hiring is terrible.

Contrary to popular belief, layoffs do not actually substantially increase during recessions. Quits are substantially reduced, because people are afraid to leave current jobs when they aren’t sure of getting new ones. As a result, rates of job separation actually go down in a recession. Job separation does predict recessions, but not in the way most people think. One of the things that made the Great Recession different from other recessions is that most layoffs were permanent, instead of temporary—but we’re still not sure exactly why.

Here, let me show you some graphs from the BLS.

This graph shows job openings from 2005 to 2015:


This graph shows hires from 2005 to 2015:


Both of those show the pattern you’d expect, with openings and hires plummeting in the Great Recession.

But check out this graph, of job separations from 2005 to 2015:


Same pattern!

Unemployment in the Second Depression wasn’t caused by a lot of people losing jobs. It was caused by a lot of people not getting jobs—either after losing previous ones, or after graduating from school. There weren’t enough openings, and even when there were openings there weren’t enough hires.

Part of the problem is obviously just the business cycle itself. Spending drops because of a financial crisis, then businesses stop hiring people because they don’t project enough sales to justify it; then spending drops even further because people don’t have jobs, and we get caught in a vicious cycle.

But we are now recovering from the cyclical downturn; spending and GDP are back to their normal trend. Yet the jobs never came back. Something is wrong with our hiring system.

So what’s wrong with our hiring system? Probably a lot of things, but here’s one that’s been particularly bothering me for a long time.
As any job search advisor will tell you, networking is essential for career success.

There are so many different places you can hear this advice, it honestly gets tiring.

But stop and think for a moment about what that means. One of the most important determinants of what job you will get is… what people you know?

It’s not what you are best at doing, as it would be if the economy were optimally efficient.
It’s not even what you have credentials for, as we might expect as a second-best solution.

It’s not even how much money you already have, though that certainly is a major factor as well.

It’s what people you know.

Now, I realize, this is not entirely beyond your control. If you actively participate in your community, attend conferences in your field, and so on, you can establish new contacts and expand your network. A major part of the benefit of going to a good college is actually the people you meet there.

But a good portion of your social network is more or less beyond your control, and above all, says almost nothing about your actual qualifications for any particular job.

There are certain jobs, such as marketing, that actually directly relate to your ability to establish rapport and build weak relationships rapidly. These are a tiny minority. (Actually, most of them are the sort of job that I’m not even sure needs to exist.)

For the vast majority of jobs, your social skills are a tiny, almost irrelevant part of the actual skill set needed to do the job well. This is true of jobs from writing science fiction to teaching calculus, from diagnosing cancer to flying airliners, from cleaning up garbage to designing spacecraft. Social skills are rarely harmful, and even often provide some benefit, but if you need a quantum physicist, you should choose the recluse who can write down the Dirac equation by heart over the well-connected community leader who doesn’t know what an integral is.

At the very least, it strains credibility to suggest that social skills are so important for every job in the world that they should be one of the defining factors in who gets hired. And make no mistake: Networking is as beneficial for landing a job at a local bowling alley as it is for becoming Chair of the Federal Reserve. Indeed, for many entry-level positions networking is literally all that matters, while advanced positions at least exclude candidates who don’t have certain necessary credentials, and then make the decision based upon who knows whom.

Yet, if networking is so inefficient, why do we keep using it?

I can think of a couple reasons.

The first reason is that this is how we’ve always done it. Indeed, networking strongly pre-dates capitalism or even money; in ancient tribal societies there were certainly jobs to assign people to: who will gather berries, who will build the huts, who will lead the hunt. But there were no colleges, no certifications, no resumes—there was only your position in the social structure of the tribe. I think most people simply automatically default to a networking-based system without even thinking about it; it’s just the instinctual System 1 heuristic.

One of the few things I really liked about Debt: The First 5000 Years was the discussion of how similar the behavior of modern CEOs is to that of ancient tribal chieftans, for reasons that make absolutely no sense in terms of neoclassical economic efficiency—but perfect sense in light of human evolution. I wish Graeber had spent more time on that, instead of many of these long digressions about international debt policy that he clearly does not understand.

But there is a second reason as well, a better reason, a reason that we can’t simply give up on networking entirely.

The problem is that many important skills are very difficult to measure.

College degrees do a decent job of assessing our raw IQ, our willingness to persevere on difficult tasks, and our knowledge of the basic facts of a discipline (as well as a fantastic job of assessing our ability to pass standardized tests!). But when you think about the skills that really make a good physicist, a good economist, a good anthropologist, a good lawyer, or a good doctor—they really aren’t captured by any of the quantitative metrics that a college degree provides. Your capacity for creative problem-solving, your willingness to treat others with respect and dignity; these things don’t appear in a GPA.

This is especially true in research: The degree tells how good you are at doing the parts of the discipline that have already been done—but what we really want to know is how good you’ll be at doing the parts that haven’t been done yet.

Nor are skills precisely aligned with the content of a resume; the best predictor of doing something well may in fact be whether you have done so in the past—but how can you get experience if you can’t get a job without experience?

These so-called “soft skills” are difficult to measure—but not impossible. Basically the only reliable measurement mechanisms we have require knowing and working with someone for a long span of time. You can’t read it off a resume, you can’t see it in an interview (interviews are actually a horribly biased hiring mechanism, particularly biased against women). In effect, the only way to really know if someone will be good at a job is to work with them at that job for awhile.

There’s a fundamental information problem here I’ve never quite been able to resolve. It pops up in a few other contexts as well: How do you know whether a novel is worth reading without reading the novel? How do you know whether a film is worth watching without watching the film? When the information about the quality of something can only be determined by paying the cost of purchasing it, there is basically no way of assessing the quality of things before we purchase them.

Networking is an attempt to get around this problem. To decide whether to read a novel, ask someone who has read it. To decide whether to watch a film, ask someone who has watched it. To decide whether to hire someone, ask someone who has worked with them.

The problem is that this is such a weak measure that it’s not much better than no measure at all. I often wonder what would happen if businesses were required to hire people based entirely on resumes, with no interviews, no recommendation letters, and any personal contacts treated as conflicts of interest rather than useful networking opportunities—a world where the only thing we use to decide whether to hire someone is their documented qualifications. Could it herald a golden age of new economic efficiency and job fulfillment? Or would it result in widespread incompetence and catastrophic collapse? I honestly cannot say.