Killing EJMR

For the last couple of weeks, the online economics community has been discussing and reacting to rampant misogyny on a website, Economics Job Market Rumors. A Justin Wolfers post to The Upshot at the New York Times reported on research by Alice Wu that laid bare the ugly, shocking language used to describe women on the website.

I recommend this post by Emily Eisner, Fiona Burlig and Aluma Dembo for a brief overview of recent research on gender inequality and discrimination in economics. Beatrice Cherrier’s post on the topic is rich and thoughtful.

The context of this discussion is that women are unacceptably underrepresented at all levels of the economics profession (source):

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Our profession, our work, and our image suffer from being male-dominated. EJMR is both a disease unto itself, and a symptom of a sick discipline.

Killing EJMR

One: the supply of bullying and bile on this anonymous forum must be stopped. A minimally moderated website dominated by lowest forms of vulgar misogyny cannot continue to be a significant institution in economics. And no, I do not want to hear it that EJMR is “just another” facet of tantrum-and-harassment masculinity on the internet. Don’t even say that. It makes you look like you are grasping for excuses.

Two: the sexist culture of the profession must be changed. Even if EJMR as it is now is mercifully destroyed, the rot is deeper. Smarter people than me have been fighting for women in economics for decades. We all must promote a culture that allows all people to succeed. This means confronting and shutting down “locker room talk” in any setting, including private conversation. It means reflecting on the structure of our institutions, from our classes to our schools to our professional associations, to promote diversity. It means mentoring women at all stages.

In no way am I looking to deflect from, minimize, or excuse these top, difficult priorities. I view this as an urgent crisis.

But three: I think, though, that there is one more thing we could all be more conscious of: what can we do better to reduce the demand for EJMR, or whatever comes next?

The EJMR website has been an open and significant part of the experience of graduate students in economics for many years. It is anonymous and extremely lightly moderated, and it is known for sourness, cruelty, and bullying. As with any online community, there is a core user base who either enjoy participating in the vulgarity or are willing to overlook it. However, the site is also widely used by young economists desperate for scraps of information on the gauntlet of the academic job market.

A narrative is emerging in which there is undeniable value to EJMR that helps to explain its persistence as an institution in the economics profession. It’s a place, this narrative goes, where valuable and mostly accurate information flows that young economists want.

Has a school called to schedule interviews yet? What type of candidate are they looking for? Has a job offer gone out? Who to? Are they going to take it?

What journal should I submit to? Why haven’t I heard about my submission yet? Is my dissertation idea garbage? What kind of research are people laughing at?

The academic job market is an intensely stressful experience. It is hard to surrender agency over where you would like to live and work, to navigate dual career concerns with partners, to fly around the country on a shoestring budget, to have one’s work and worth judged over and again, to compete against hundreds of other talented and deserving people, to fear the derailment of a career before it can even properly begin. It is overwhelming.

I want to reject the narrative that EJMR is an inevitable, valuable salve for the understandable neuroses of the young academic. I think that there are concrete steps that individuals and institutions in the economics profession can take to mitigate the need for something like EJMR, not just clean it up.

Superstars and insecurity

EJMR, like so much else in the profession, caters to the elite. Its tone is dominated by the concerns and perspective of the “top schools” and their students. It belittles “low ranked” students and schools. It devours the perceived weak and shrouds itself in the excuse of “the market”. Like a person who treats waitstaff as subhuman, it is a callous manifestation of the insecurity of the wannabe who feels that they must display their superiority by belittling others.

Of course people want to gossip about the “stars” of the market and to know where the “best” research is coming from. Page Six prints gossip about celebrities, not little people. Let us leave aside for a moment that “best” is located in a Catch-22 of “top school” path dependence. We could agree, maybe, that a little luck and a little path dependence do not undermine the achievements of the top economists. Excellence is rewarded. But that’s all a question for another day.

Here’s a funny thing, though. As Trevon Logan pointed out on Twitter, the imprimatur of Berkeley, Harvard, and the New York Times has helped to elevate this story to the attention of the profession at large.

 

EJMR itself could not distract the attention of the profession’s most powerful until it was graced with the formal attention of the elite. It is by the top schools, for the top schools, of the top schools. The vast majority of graduate students desperate for help and reassurance must go begging for scraps at a table of people who will mock them for their perceived shortcomings. It is vulgar in the extreme.

In this it is not alone. For example: there are many “guides” to the job market out there for graduate students. They include such concerns as how to politely turn down an interview when you simply cannot fit any more into your busy schedule. They are not helpful to a student who is ill with worry that their handful of interviews will not convert to a job, who will give a job talk to three people in a broken conference room rather than a shiny hall of power to a faculty of famous faces. The guides become useless and scary.

Edited to add (8/31/17): In my haste to make a case for reform, I made unfair generalizations about job market guides. In particular I was remiss not to acknowledge that John Cawley’s guide is one that has helped countless students over the years (myself included) and indeed addresses many of the concerns that I have raised in this post. This is an example of the kind of document that would be complemented by the kind of real-time and in-person information that I have suggested in my proposals would undermine EJMR. I apologize to John and to others like him who give up their time to provide information and advice on the job market process.

For example: insane paper turnaround times on submitted research favor the students of top schools. If each rejection takes most of a year—conservatively—and if you do not have elite mentorship and an elite network, mistakes will happen and be exceptionally costly. Here is the order in which you submit to journals, they say. They are survivors. They are there to advise you because they hit those journals. Their work is surely excellent, and they also managed to place it well. If you are a little less lucky, or a little less brilliant, where will their advice lead?

The profession has no mechanisms to help the average student.

Almost no graduate students can usefully call on the direct experience of the faculty around them. Each Ph.D.-granting institution hires fewer new faculty than it graduates. The bucket overflows. Students will do worse than their advisors. It is in this context that EJMR thrives. Students see how it is. They are desperate for help. They find it, poisoned by insecure hatefulness, in an anonymous forum that in a tragic twist of fate exhibits the very same elite bias that drove them to it in the first place.

What can we do?

1. Formalize interview information reporting through Job Openings for Economists

This is the most obvious way that the AEA can undercut EJMR. I appreciate that the incentive for schools to report when they have made calls or offered interviews is not clear cut. Too bad.

A more radical approach here would truly centralize interview offers on a clearinghouse schedule, but I accept that a centralized mechanism like this is not going to happen in economics.

2. Establish formal cross-school, cross-rank mentorship networks

Students need help and support that their own school’s faculty cannot adequately provide. We must have institutions that connect students with the economists that they will become, not the economists that they are shamed for being unlike.

This is probably awkward on both sides. No-one wants to admit that they are not a top dog. That means some bravery, humility, and discretion is required.

3. Formalize practical information on journal policies and characteristics

If we were starting with a blank slate, I would imagine most economists would have plenty of ideas for how to design research dissemination—submitting, refereeing, editing, publishing.

Given that we’re not starting over, we need a living database of relevant characteristics of as many journals as we can corral. Turnaround times, journal policies, fees, readership, citations, even the distribution of authors’ affiliations.

The Committee for the Status of Women in the Economics Profession has an excellent document on navigating the research publication process. This provides a great template for the kind of concerns we need to address. The more concrete we can make the advice, the better.

Treat the disease

There are two traps here. One is that we succeed in reforming or replacing EJMR without having an impact on the sexist and racist culture of economics. There may even be a risk of backlash as that certain type of Internet Man resents being prevented from being hateful.

The other is that we achieve a minor miracle in affecting true, even if slight, change on a profession that is overdue for it, but that we miss an opportunity to implement complementary positive reforms.

We can take this opportunity to support young economists whose mental and physical wellbeing suffers under the pressures of our job market and early career concerns. A tiny fraction of graduating economists can choose their own adventure. The vast majority can hope, at best, to get a decent job in a decent place, to uproot their life and their family and their support network, again: to survive.

Let’s all commit to helping each other.

A foot in the door, citation counts, the academic Overton window

What does it take to build a case for a policy? One interpretation of the increasingly fashionable Overton window is to redefine the limits of the acceptable by systematically injecting extreme positions into the discourse, to the point where the previously-extreme becomes normal. What’s important is to get a foot in the door.

As a practical matter, academic citations are one currency of discourse that can be used and abused in this way. Once an idea has a foot in the door, legitimized by publication in an established, well-regarded outlet, academic culture requires that it will inevitably be cited. The problem of distinguishing positive from negative citations is well known. Exhaustive citation of prior literature is the standard practice, and understandably, since for those with institutional access to academic journals, the cost is low and the benefits—engaging with other researchers, appeasing referees, demonstrating expertise—are quite high.

(On that note, check out this recent Journal of Economic Perspectives article for some I-can’t-believe-this-is-necessary-but-it-definitely-is tips on how to be a good referee. I’ve said before that explicit separation of powers would go a long way—editors make publication decisions, referees review without being asked for a recommendation.)

The influence of an idea becomes a self-fulfilling prophecy, then, as citation counts grow. The more subtle stage is when these metrics of influence escape the academic sphere and are used as evidence in external advocacy. Michael Waldman’s book on the Second Amendment makes a case that the strategy of the NRA proceeded from a “foot in the door” of law review articles promoting a particular interpretation, which generated debate and citations, providing a self-perpetuating corpus of material for lawyers and judges to cite in practice.

It’s a Catch-22 and not an easy one. Of course we want to encourage a broad range of ideas and arguments, but it is very difficult to engage with arguments without validating them. The squeaky, motivated wheel gets the grease of scarce academic attention, and so not only enters the discourse but crowds out other efforts.  The tactic generates loaded questions and a false dichotomy that moves the rhetorical ground underneath our feet independently of whatever argument we had originally wanted to advance. But this is nothing new. What is special to academia is the we must at this point cite the prior art, and citations are our currency. The institution validates all ideas in the same way, no matter what the average researcher truly thinks of them.

I think few academics would openly advocate for a world in which scholarly communications are passed through politicized filters. Yet that is the world we have. Everything is political, as they say, and so the decision to publish an argument inescapably exercises power far beyond the author and the potential readers.

In the context of economics, so often close to the ground of policy debates but whose intent and bias are so often misunderstood in the popular press, I think surveys of the economics profession are an undervalued weapon. These certainly exist but I think they deserve more resources, prestige, and promotion to act as ballast against a drift towards controversy, misconception, and squeaky wheels. Get them into our top journals and grant money for their thoughtful design and execution. Our areas of consensus should be as clichéd as the old “97% of climate scientists agree” line that I’m sure we have all come across at some time. People should grow bored of how often economists agree.

Rethinking the first year graduate economics program

The job market for economists is revving up again, and I’m thinking about the gauntlet of graduate education that the rookie economists have just survived to get to this point. I want to raise a few questions—typical academic navel-gazing about “the state of the field”. Basically my message is that I think the time has come to retire and replace the first year graduate economics “canon”. Hopefully I can justify myself with some (leading) questions.

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Who should pay for higher education?

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Details might change, but the really big question in higher education funding is always the same: public funding versus private funding. I’m not breaking any new ground here and I’m certainly not advocating anything radical, but it’s on my mind today with “debt-free college” very much in the discussion on the first day of the Democratic National Convention. Every so often I like to refresh my memory on the fundamentals and reaffirm why, on balance, I favor the availability of ambitious, quality, zero-tuition higher education.

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What economists do: the one model we use

Economics is jargon-heavy, like, well, everything. Economists, I can attest, are especially fond of that academic disease of using their jargon in everyday conversation, a kind of subconscious economic imperialism. Nevertheless, there’s a difference between specialist knowledge (with its public face, jargon) and the concepts on which a body of knowledge is built.

How could we distill economics to its base? This is a slightly different goal than defining the principles of economics; here we want to identify the actual applicable results that derive from the principles and recur again and again in the whole discipline. As a first pass at this problem, let’s look at the model we use in economics. That’s model, singular.

Modern economics is built on the formalized statement of the definition of the subject: some entity has objectives, and limited resources with which to achieve them. These become the objective function and the constraints. Now the entity could be anything: a firm, a person, a government, a group of people.

The objective function might be expressed in math, but it doesn’t have to be. It reflects, obviously, the objectives of the entity, and these could be anything, but will typically have to be a simplified version of what an entity really wants or needs. This is one place where the abstraction from reality might have to be made, although we can imagine, as a thought experiment, the omniscient modeler who is not subject to this particular problem.

The constraints can, again, be put into math but doesn’t have to be. It is the expression of scarcity; constraints can reflect anything that poses a challenge to our entity’s achieving its goal. The abstraction is often necessary here, too: time, money, social convention, technology are just some of the possible constraints.

Now this is really a very simple idea, but all economics uses this as its base. We are concerned with the allocation of scarce resources in the quest to satisfy objectives, and this is the model of that. The math angle comes in to make this model give tangible and quantifiable results; we could make arguments without the math, but using it often helps to make things clear. The challenge for the modeler is to write the model cleverly, making simplifications enough to make the problem understandable, but not too many to make it irrelevant.

Theoretical economists are explicitly writing this model, over and over, and the illustrative economic models we present in teaching students use this model exclusively. Empirical economists use it too, not just writing it down, but in subtle ways: perhaps the empiricist can identify a precise moment or situation in which the constraints on a particular entity’s problem changed, offering them a useful opportunity to see how choices change in the face of the discrete change in conditions.

This model gives rise to the marginalist result. It says that our entity will allocate resources to a particular use while the benefit of doing so exceeds the cost of doing so, and that the point at which the entity stops will be characterized by the balance of cost and benefit at that particular point. If the decision was changed slightly in either direction, the results would be less satisfying, either because the cost would exceed the benefit, or because the benefit would exceed the cost. That might sound complicated, but it is a familiar idea from everyday life.

In the jargon, the result is that marginal benefit equals marginal cost in the solution to this model of an entity facing a resource allocation model. That simply means that from the “solution”, there is no change that would be a better result for the objective function. Not to say that the solution is a perfect prediction or description; the solution is to the model, not the actual problem faced by the entity. Perhaps the loaded meaning of the word solution is a hindrance here. Much debate has been staged over the extent of predictive or descriptive accuracy of this economic model.

Where it gets a bit complicated is at the point where we’re unsatisfied with the objectives or constraints we are using in our models. Benefit and cost are pretty easy to think about when our abstraction acknowledges only money, for example, but what if my entity’s objective function included the wellbeing of others, the environment, the amount of time spent in the sun, the quality of life? These assumptions are not just more difficult to incorporate, they are more difficult to interpret.

Where does all this fit in with the themes I’ve touched on before? The debate about rationality, economic man and realism informs directly the objective function. For example, the behavioral school could be characterized as seeking to come up with a more realistic, tractable objective function. The economics-as-money fallacy crops up in both the objective and constraints, yet how we denominate these things doesn’t change the model one bit. The debate about math in economics informs the language in which the model is presented; non-mathematical economists still use the model, even if they don’t write all these components down as mathematical relationships.

This is the model of economics. Two examples to show what’s going on.

First: how would a subsidy on the wage of low-income workers, such as the Earned Income Tax Credit, affect the number of people who work and how much they work? The common treatment of this problem in basic economics is to imagine a person who has a desire for money and for time, and to illustrate the wage subsidy as affecting the constraint, in this case the rate at which our person can earn money by giving up their time to work. We can then ask how this change in the constraint affects the hypothetical best choice of our simple person, and even ask how the person’s relative desire for time and money affect this answer.

Theorists can try to answer this question by investigating the model itself; empiricists might look to the data to try to identify the change in actual choices observed when the subsidy is introduced. [Aside: as it turns out, weird things happen with the EITC during the income range when it’s gradually phased out.]

Second: how would a person choose to react to another person they perceive to be unfair? Our person might have an objective function that includes preference for money and fairness, and their constraint could include the social norms for fairness as well as their money budget. This example is well-studied in experimental economics, where our person can decide to sacrifice some common good in order to punish a person who was greedy.

No matter what field of economics one works in, this is the one model most dominant in all of the work. It is, of course, very flexible (in fact, infinitely flexible, as I argued before), and I have stated it in the most general terms. Nevertheless, when economists argue, it is worth remembering that we all play the same game. It might just be the language, but it is a language that can seem daunting and restrictive to outsiders or students. I don’t believe that is the case: the beauty of this most fundamental model is its simplicity and its malleability. To distill economics into its essence, this is the place to start.

Is economics vocational?

Being that we have not the faintest idea why people choose to take economics courses, this will be a difficult question to answer: is economics vocational? What exactly would an economics education prepare you for?

My stereotypical economics major wants to be an investment banker or something of the sort (again, pure prejudice, since no evidence exists). I argued a while ago that maybe – maybe – the sub-discipline of finance could possibly be considered vocational for those types. Economics courses will be of no practical help, although I suppose the civics that passes for Econ 101 might help with terminology. My advice: go to a school that will let you major in business.

So: what would an economics education prepare you for? To be more explicit: “if I major in economics, what will I be able to do, or be better at, that I couldn’t otherwise have done, or done so well?” Some suggestions, and justifications.

Statistician or data analyst. Econometrics is usually a requirement for all economics majors. Since the computing revolution, economists have lovingly embraced statistical analysis as a way to coax the relationships in the real world out of data. Theoretical and practical data preparation and analysis will be practiced in econometrics courses, and any course falling under the foul name of “applied economics”.

Policy wonk. Economics can inform argument and debate about policy. This is especially true of economics courses that straddle positivist analysis and normative debate, such as public economics. The purely esoteric economics courses might not be the ideal ones to make this point.

Philosopher. Economics contains a lot of points of philosophical debate. “Welfare economics”, which tries to discuss and provide metrics for normative goal-setting, is a particularly rich field for flights of fancy. The realness of economics does not take it out of the philosophical world; it’s elusiveness holds it in.

Applied mathematician. I’m very doubtful about this one, but here it is anyway. Economics can’t teach you math. Plus, economists are like the chimps jumping up and down to reach the fruit when we could just ask the giraffe – it feels like any mathematical or technical problem we have would be immeasurably simpler for a mathematician or computer scientist to solve than it is for the economist. Nevertheless, it may well be the case that studying economics could make a person better at applying mathematical methods to the tangible.

Academic economist. Our courses are taught with the same positivist motivation demanded in the research conducted by academic economists. The “applied” courses accomplish this for the type of researcher who does data work, and the theoretical courses accomplish it for the type of researcher who does, well, theory work. I’m worried by the lack of diversity in the models and applications we present – it doesn’t reflect the range and power of economics – but nevertheless, the method we present is, for better or worse, the same as the method we use.

Historian / person-of-the-world. No idea what I should be calling this, but an economics education should (should) include some history of thought and history of economic policy. One of my favorite college courses was one where we took one simple, flexible model of a country’s economy – really simple, just pictures and words – and used it to debate the economic history of the 20th century. Whatever that type of knowledge-for-its-own-sake is called or is useful for, I’m throwing it in this list.

I’ve kind of exhausted my ideas. Now, at least here in American colleges – or maybe just this American college, though I suspect not – “academic economist” gets far, far, far, far too much play. Far more than anything else on my list or anything else that could be on the list. I would be utterly astonished if the non-existent evidence on why people take economics courses showed that they all wanted to do write academic articles in economics. Astonished and miserable. Yet, here we are, in a situation where economics courses are most commonly run without philosophy, history, politics, debate.

Academics do economic science in a vacuum without these things. It has to be that way, because we want to isolate facts as well as we can isolate them. That doesn’t mean that we should be teaching economics that way. It could be so rich. Yes, the science can be interesting, but so can the history of the world, the intellectual foundations of the discipline, the policy debate built on the evidence. I’d hate to think we’re robbing our students of these things.

Perhaps the answer, then, is that economics is not fundamentally vocational. Aside from my pet issue of economics-abused-as-civics, we have a problem with economics teaching if it is trying to pretend to prepare people for something specific. It can surely help a person develop skills, but at least as important, and probably more interesting for the average student, is teaching economics as an intellectual pursuit for its own sake. We know what would make our courses more interesting (not the same thing as pandering, I hasten to add), more intellectually exciting, yet we pull back. Is it because we believe we’re training all our students to be academics?

Too complicated?

One of the principles of writing economic theory is to create a simplified abstraction of reality. If the theory convincingly isolates an idea, it cannot be too simple; hopefully, the narrower the question, the simpler the theory can be written.

Economists therefore appeal to the “all else equal” assumption a lot. The oft-perceived superiority complex of economists is traceable to our willingness to use the “all else equal” clause to make our questions answerable, theoretically and empirically. If we want to write relevant economic models that investigate the link between A and B, we hold C equal; whether or not C would really be equal or relevant in reality, we can’t isolate the effect we’re interested in if we don’t figure out a way stop it from contaminating the abstraction.

It’s the same principle that underlies the ideal of “controlled experiments” in all science; empirically, if we want to figure out how A and B are related, I need to be careful to avoid finding an effect because a third factor C is involved. For example, there’s an important difference between “people who exercise more have a longer lifespan” and “people who exercise more also eat well, and people who eat well have a longer lifespan”. That’s well understood in statistics and empirics generally; there’s no reason why the same principle is not also needed when we use the theoretical standard of proof rather than the empirical standard of proof.

Why, then, is “economic theory” so amazingly bewildering? With very little exaggeration, we can claim that no great development in the science of economics has used very complicated techniques, even when math was involved, yet even to the technically competent a lot of economics research is very difficult to understand. Of course, if an economist could all find ground-breaking theory that can be represented in two lines, I’m sure she’d write it. Is the reason for the complexity an attempt to make average ideas look better?

Let’s be charitable and assume that’s not the case. I think that once we exclude the “obfuscation motive”, there are two possible reasons why economic theory is technically complex. One might be that the relationships being investigated are broader, that less is held equal, that we’re looking to more nuanced explanations. Another possible reason is, paradoxically, that theory gets more complex as the questions get narrower – the more we assume, the higher the complexity.

Why? Imagine I want to figure out the relationship between a person’s income and the number of hours that person does voluntary work. This is a question that asks about how people allocate a scarce resource, time. I might make an abstraction that says “if all people like both money and helping others, then people with higher incomes will spend more time helping others, while people with lower incomes will spend more time trying to earn extra money.” I might make an abstraction that says “people with more income work more so have less time to volunteer”. What assumptions lead to the first conclusion, and what to the second?

If I wanted to broaden my question, I might start including in my theory labor market conditions, the availability of volunteering opportunities, the peer pressure to volunteer, the social pressure to earn more money to buy a big car, and so on and so forth. That would certainly make my theory more complicated; whether or not it makes it a better theory than the one that kept all that stuff equal and abstracted from it is a matter of preference, but I’m sure it would be more difficult to understand.

The second way to make the theory more “complicated”, at least superficially, might be to keep all the same stuff equal, but to say “imagine the person cares this much about money and this much about volunteering; then someone with this income will volunteer this much”. The abstraction is getting more abstract; we are getting more and more specific about the conditions of our model, and we must use more specific techniques to, in particular, quantify the result.

What do we gain from this quantification, and what do we lose? Perhaps we can look at actual evidence on the link between income and volunteering, and compare it to the quantified prediction, but that only works if all else is equal in our evidence, too. A better justification is that we can get a theoretical idea of how big our effect is. However, as we get more specific we get more abstract; in this example, we’re getting more abstract about preferences, which are themselves unobservable. We’ve gone from “a person cares about money and volunteering” to attaching magnitudes to those cares.

The link between simplicity and usefulness is not just in the realism of the abstraction; it’s also in the procedure itself. Economic theory should be neither too broad or too narrow, but “just right”, whatever that means. Assume too little and we can’t figure out what’s really causing what; assume too much and you rest an entire argument on a special case. What’s the simplest model that explores the relationship I care about, and what’s the simplest model that shows what I want to show about that relationship?

Oh, and a practical suggestion: I’d love it if we all stopped writing ceteris paribus and used “all else equal”. What’s with the Latin?