Author: mdmakowsky

Economic history as it’s happening is alway relative

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This is the chart that I’ve been thinking about today.

The US government has been able to borrow on the cheap for most of it’s existence, with the exception of 70s and 80s when stagflation put the clamp down. Treasury rates are soaring right now…or at least, it feels that way because for most of my adult life the United States has been viewed as arguably the safest borrower in history. What follows are in some ways the only two questions that matter for the US economy. Is the US government a reliable institution? Is economic growth going to keep pace with inflation? The answer to each question (and their subcomponents) is, of course, unknown, but the market seems to think the net of that question is going in the wrong direction.

That said, for all of the neverending parade of (sometimes unintential) nostalgia that seems to pollute the discourse, wow, 1975-1985 was not exactly macroeconomically “aspirational”.

Will AI kill the research paper?

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Will AI kill the research paper?. I don’t know, probably not. But I do know that what has constituted a research paper has changed many times before and will change many times again.

Before the the 1940’s, economics research papers were largely prose. Analytic in nature, sure, but prose. Some graphs, maybe a box. A little math, but math largely for the sake of demonstrating logical relationships. Then Samuelson hit, reframed economics as thermodynamics and differential calculus. What was previously a research paper was was now a polemic, a monograph at best. Thought experiments were out, high theory was in.

This era of high theory flourished in the 70s, the math changed, and at some point computers arrived with the possibility of data sufficiently rich and numerous you couldn’t just plot all of the observations in Figure 1. That data couldn’t stand on its own, though. To be a credible publication you really needed to bundle your analysis with some theory that generated testable predictions. Pure theory papers gave way to an era of applied imperialism as economic models found themselves applied to every quantified context under the social scientific sun.

Causal identification became a thing of interest, and we got really good at telling stories again. Specifically, stories about instrumental variables. You needed a story to convince anyone, but we told so many that some folks started to notice that these stories were often pretty weak. That, in part, turned up the heat on a credibility revolution that was already in swing, which meant now you needed even better data and you needed to defend you identification strategy to the death. What was a paper before was now an embarassment you should probably consider retracting (nb: no one retracted anything, but that doesn’t mean people were suggesting it behind their backs).

Which kept rolling in data set after data set until we woke up one day and realized you either need to go out in the world and create your own actual experiment (nothing quasi- about it) or you needed to cultivate access to better…no, better…no, the very best-est, most detailed and granular administrative data ever, preferably a universe if possible. Data so perfect as to allow for contributions unassailable in their legitimacy. Do you have friends at the Danish Census? If you want tenure you should probably start flirting with someone at the Danish Census.

So a paper was a paper. Until it wasn’t a paper anymore. Until that wasn’t a paper anymore. Until that wasn’t a paper. The Recursive Dundee Theory of Research*, if you will. They all met the criteria of a contribution, until they didn’t.

So what does this mean for AI and research papers now? Well, if we look to thermodynamics in the 40s and cheap computing power in the 90’s for analogues, then I’d say it’s going to reshape the criteria for a contribution in no small part because it lowers the cost of mediocrity. Mediocre analysis will no doubt persist, but it will shift over into blog posts and journals no one ackowledges as legitimate. Do remember, please, that mediocrity is a relative concept. The quality of blog posts and publications in scam journals will likely massively improve as what can be accomplished in an afternoon’s work is radically increased. Don’t worry, I have no intention of improving beyond my current warm bath of blogging unremarkableness, but others will likely cave in to the pressure.

What about the papers in top journals, though? The papers Tyler is presumably talking about. Will AI kill those economic research papers? Probably not, but it will likely improve it significantly. Why? For the same reason that Michael Kremer says that technology and quality of life improve with the size of the human population. More people means more ideas, and there is nothing more important to economic growth than the sheer number of ideas. And no, I do not mean ideas generated by AI’s. I mean the raw number of researchers with the capacity to make major contributions is increasing dramatically because we’re all getting research assistants. We’re all getting copy editors. We’re all getting support. That’s how AI is going to change the research paper: by giving more ideas the support they need to reach the light of publication. The bar is going to get higher for the same reason that the level of sports improve as you widen the geography they pull from. There’s someone at a directional state school who didn’t get the placement they deserved out of grad school. Sure they have to teach a 3-3 load, but they’re licking their chops right now because they don’t need an army of grad assistants. Summer is here and they’ve got everything they need to make a contribution.

Or I don’t know. Maybe AI will do all of our thinking in 50 years. Forecasting technology beyond 5 years is like forecasting weather beyond 5 days: I can’t do it and neither can you.**

*Apologies to Justin Wolfers and all my Aussie friends for a bit of cultural appropriation. I promise to put some Vegemite on toast while enjoying a flat white and explaining Aussie Rules Football to a friend within 90 days.

**Except for Neal Stephenson. That guy’s the Warren Buffet of Sci Fi forecasting. Maybe he’s the one in a billion person actually experiencing one in a billion level luck, but that doesn’t make it any less impressive.

On implicit numéraire

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Just a quick thought today. When we, economist or otherwise, talk about the opportunity cost of time, the most common default is an individual’s expected wage. This ends up becoming a sort of implicit numéraire, a unit of measurement and exchange that captures value of an individual’s time.

Now, to be clear, this is a gross reduction of the complexity of opportunity cost and decision-making, but such reductionism is a necessity when observing the world on a day to day basis. People are generally, I hope, aware of this reductionism, but also understand that cognitive tractability is a necessity for getting through life. That also means, however, that there is no shortage of traps. If you reduce decision-making to a single variable equation, you can get yourself in a lot of trouble picking the wrong variable.

Which brings me back to expected wage as a single variable numeraire revealing the opportunity cost of time. Sure, such a simple model is a great way for understanding why high income CEO’s outsource and delegate so many of their “life maintenance” tasks while I, for example, do not. That same logic, however, can be a trap when looking at decision making at the other end of the income distribution. Why wouldn’t someone making minimum wage leave work to pick up their sick kid from school or bail their cousin out of jail? Their forgone wages, their opportunity cost of time, is relatively low, right?

Actually, no, it isn’t. In fact their opportunity cost of time is exceptionally high, it’s just that you’re using the wrong numeraire. The opportunity cost of time isn’t the wages foregone, but rather the additional risk that they are taking on. It is quite common for individuals to lack the precautionary savings necessary to maintain solvency and housing stability during a dip in earnings or unexpected job loss. Nobody likes asking their boss if they can leave work for two hours on no notice when they can’t afford to risk losing an extra shift, let alone their job. The opportunity cost of their time is best measured in the marginal probability of household economic catastrophe rather the explicit wages gained or lost.

A lot of economic decision-making is easy to make sense of when you get your single-variable numeraire right, but that is easier said than done. A good rule of thumb: if someone else’s decision-making looks grossly irrational to you, you probably aren’t using the right variable.

I’ve never been more wrong

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I’ve been reading far too much forced rationalization of “maybe tariffs aren’t actually that bad” and “see, Hungary got rid of Orban after 16 years of a state media monopoly and broad authoritarianism, so global and local Democracy is perfectly healthy”, so I thought I would spend an entire post explaining why tariffs and authortarianism are bad. But then I thought it might be better to not write the most obvious post entirely redundant with everything every good economist has ever written.

Instead, let’s talk about a core element of US political economy that I got completely, 100% wrong for the last 20 years.

In my (and, I suspect, most people’s) public choice model of our democratic republic, election officials were always modeled as power and vote maximizing. It never occurred to me, until the last few years, that those outcomes might ever be mutually exclusive. There’s been no shortage of handwringing over Congress abdicating it’s authority, most notably the power of the purse, to the executive branch. For all that posting and scolding, I don’t recall anyone saying why Congress has abdicated that power.

The overly simplistic explation is that vote maximization dominates power maximization in the utility functions of most legislators. The thing about power is that it makes it that much harder to dodge responsibility and, in turn, blame. So much of politics depends on the currency of credit and blame. Abdicating power may impoverish you of credit, but it also insulates you from blame. If incumbency, name recognition, and partisan brand identity are sufficient to carry your next election, power brings more risk than opportunity. Better to hide behind gridlock while a second term executive branch does whatever it pleases.

So yeah, I never in a million years expected a whole branch of elected US representatives to become uninterested in their own political power. It was anathema to how I conceived of the individuals selected into running for office and their objective functions. But here we are. I was wrong. I wonder what I’ll be wrong about next?

A Canticle for Aadam Jacobs

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For the talk of the future of generating art, let’s not forget the task of remembering the art we’ve already made. Behold: more than 10,000 cassette recorded concerts, from as far back as 1984, recorded in community centers, church basements, taverns, all-ages clubs, and hundreds of other unsung “venue” owners who let then (and often always) unknown bands play shows for a a couple dozen attendees, all in the hopes that door money and beverages might keep the owner out of the red on a random weeknight while.

I have a couple bootlegs from concerts I attended, but it never occurred to me that I might get to listen to a 1995 Blonde Redhead show at The Empty Bottle or The Blow Pops playing 1991 show at a Milwaukee spot I’ve never heard of. These shows have always had an ephemeral quality to them, existing far more in the stories of those who claimed to be there that night than the actual direct artistic footprint.

But maybe not. Maybe the internet can and does, in fact, remember. Because while there is a lot to be absorbed from the finished product, but there is often so much more learn from the imperfect and unpolished early stages. A band before they slowed down or ventured beyond their first 3 chords, a writer still stuck in the first person, a disseratation chapter still haunted by the writing of the insecure graduate student we all were. The awkard phases when an artist (or artists) are still finding their voice. Perhaps, more than ever, we need to remember the importance of not skipping over the embarassing, exhausting, and, yes, often futile work at the beginning and middle. There are more shortcuts than ever to making a thing, but no shortcut to becoming the version of yourself that can make the thing that only you can make.

Education is a core US export

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While there is no shortage of examples of willful ignorance and outright lying in politics, the idea that blocking foreign students from attending US universities is anything other than disastrous to US students is positively enraging. The real curiousity here is whether the value of a US degree has yet dipped below the full tuition price tags that foreign students almost always pay. Beyond the billions in tuition received and tuition subsidies indirectly consumed, I couldn’t even begin to put a price on the cultural power accrued from being the global center for higher education for the last century. This administration’s capacity to find new and innovative ways to tear down US institutions is unrivaled and beyond even the grandest dreams of our most optimistic enemies.

The actual AI problem in academic economics

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There is a steady flow of takes on the impact of AI on academic economics research, whether its the example of someone writing an ostensibly legitimate, if somewhat trite, research paper with only a few hours effort to the implication that there is already no need to continue writing papers as the AIs are already better at at. Oh, what shall all the candlemakers do now that the sun has risen?

I think the idea that AI has already rendered the research paper an obselete endeavor is very wrong, almost to the point of negligence. It both vastly underestimates the quality of the median contribution provided in the 80 to 100 or so best journals and vastly overestimates the reliablity of current AI attempts at research on the margin. Putting such concerns aside for the moment, it’s still worth pondering how we can extrapolate from current AI as a tool for status quo research to forecast if it might reshape labor as an input 5 or 10 years from now. That’s far enough away that it borders on futurism and, more importantly, the kind of forecasting that I shy away from. Feel free to tell me in the comments where we are headed.

At this moment, however, we already are in the middle of a far more subtle disruption in academic research that I haven’t seen anyone write about yet. The quiet, but pronounced, uptake of AI tools in the writing of referee reports for academic journals. If you’ve submitted papers for review in the last 18 months, dollars to donuts you’ve received a referee report that has been lengthy, well-organized, with an unusual number of bullet points and headers discussing your paper, summarizing it’s contributions, and offering suggestions that on their face seem reasonable but upon a moment’s reflection are quickly realized to be entirely vapid by someone familiar with the structure of the data and relevant literature.

There is something uniquely frustrating about working on a research project for 3 to 5 years only to have judgement passed down on the basis, at least in part, of a review written by ChatGPT that is not just wrong but, well, kind of stupid. I’ve already personally had to deal with having a paper refereed via ChatGPT, rejected, and then, thanks to it being internalized by ChatGPT into their text base, it being reconfigured into a citaton hallucination cited by other papers that, to maxmize comedy, replaced 3 (including me) of the 4 authors with other (nicer? better looking?) economists. What’s most frustrating, however, is that this is hitting economics journals that do not seem to have any plan in place to deal with it. Not to suggest this is an easy problem to solve (not remotely), but it certainly should not be coming as a surprise to anyone. Let’s look at the facts:

  1. Academic economists are almost universally overcommitted.
  2. Journal referees are, for the most part, unpaid for their time.
  3. As the number of quality articles produced and submitted to journals has increased, so has the strain on the entire editorial process, including review writing.
  4. The only thing holding it together at all has been reputational incentives (i.e. nobody wants a bad reputation with the editors that are going to consider your future work) and a disciplinary sense of “civic duty”. Reputation is, of course, the load bearing mechanism here.
  5. A technology was introduced that, at the very least, pantomimes the review process well enough that it can produce a low quality fascimile of a review that, with a few sentences tossed in at the beginning and a short separate letter written directly the editor by the reviewer, can allow a task that used to take a 0.5 to 1.5 work days can now be crossed off your to-do list in less than an hour.

Is it really that hard to see what’s coming? Of course academic economists are going to be tempted to ask ChatGPT to write a review for them. There are almost no direct rewards for writing good reviews, while the costs are significant. Evaluating a genuinely new and distinct piece of research that has never been done before is hard work and takes significant time.

Now, how this is playing out across the body of journals is an open question. Here’s my best educated guess:

At the top journals, reputational concerns are the strongest, but so is the opportunity cost of everyone’s time and the competition for limited article space. Referees might not have the courage to outsource the actual decision to ChatGPT, but they’ll be awfully tempted to offload as much of the grunt work as they can. If I were an editor at a top 10-15 journal, I would expect a growing number of reports from referees who read the paper quickly (<15 minutes), then made a decision to recommend acceptance or rejection based on 1) if they knew any of the authors, 2) whether the content is a complement or a substitute for their own research, 3) whether they had seen the paper presented in person and was well-received, 4) the general bundle of status associated with the authors and the subject, and 5) whether they liked the paper (you can, in fact, have a strong opinion on paper you’ve looked at for 15 minutes. We’re all guitly of it). Having arrived at their positive or negative assessment, they then outsource the actual first draft of the the review to ChatGPT, with the instruction to write a positive or negative review. Now, given the strong reputational considerations that any credible reviewer at a top journal should have, I expect there to then be significant rewriting of the review, including that addition of the reviewer’s preferried economist gripes about identification, whether the results generalize, etc, giving an otherwise generic report some more bespoke vibes. This isn’t the real recommendation anyway, that’s the letter to the editor that goes unseen by the research authors. I don’t think most referees will have the brass to outsource that.

That’s probably not great, especially for young authors trying to break into a field. But honestly, none of those problems are new. If anything it takes a very old problem (i.e. overcommitted economist at top school asks his or her student to write a referee report rejecting an article for them) and just tweaks it slightly (i.e. overcommitted economist at top school asks ChatGPT to write a referee report rejecting an article for them, freeing up a PhD student to get back to work cleaning and analyzing their data for them). Not optimal, but hey, what is?

The real problem, I am sad to say, is the next tier down. The field journals. The second-tier general journals. The oddball and heterodox journals. The journals that used to struggle to get enough good submissions and now struggle to find anyone to referee for them. What used to be a trickle is now a deluge of higher quality research. That deluge, however, comes from authors who also constitute a referee pool that is far busier than they were before and without the same resources that come with appointment as top institutions.

I promise you, from experience, keeping a significant research agenda going during my salad days when I was teaching a 3-3 load was not easy. What happens when the 71st ranked journal that you might submit an article to one day sends a seemingly acceptable, if mediocre and slightly banal article to review? Are you really going to give it a precious work day? Or are you going to give it a once over, ask chatGPT to review it, and then give a recommendation based on a 5 minute skim? I want believe that I would never associated my professional reputation with a half-assed review, but that’s easier to say on this side of the R1 tenure fence.

Now’s the part where I smugly tell you the obvious solution and call it a night. As is often the case, however, I don’t have one. Not one that anyone is going to like, at least. Because, the only solution I have is precisely the suggestion that got Jerry Maguire fired. We could simply publish and write fewer papers. If we write fewer papers, we can review fewer papers. If we review fewer papers we can pay people to review them. If we can pay people to review them, we can hold them to higher quality standards. Editors can review the reviews. Every now and then someone suggests we get rid of anonymous reviewers, but I worry that anonymity is load bearing when it comes to the quality standards that are in many ways the hallmark of modern economics. I don’t think we can give up on quality. Quality is our comparative advantage. So maybe its time we let go of quantity. If your dean says you’ve written some good and important article, but there aren’t enough lines on your vitae, then what they’re really saying is that they don’t want research faculty, they want AI middlemen.

Don’t be an AI middleman.

The theory of the firm remains unfinished

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Why do firms exist? Transaction costs. Specialization. Returns to scale. Risk pooling. Reputation. Institutional capital. Is that everything? Probably not.

It wasn’t that long ago we were talking about the prevalence of zero marginal productivity employees within firms. Perhaps we should add low (zero?) marginal productivity employers to our list of considerations.

Graduate students rejoice, there remains more work to be done.

When do betting markets become endogenous?

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I don’t have an article or statistic to point to, just a question: what is the threshold at which a betting market outcome becomes endogenous to the existence of the betting market?

Polymarket recenttly removed its market for a nuclear detonation. The implication is straightforward: if a market exists for an outcome that a singular individual (or small group) can make manifest, then as the market becomes takes on greater volume, the maximal reward for independently producing the wagered upon outcome increases. This generates a testable hypothesis: does the predicted possibility of a positive outcome increase with market volume?

If and when the answer is yes, market volume has a positive causal relationship with market outcomes, then the welfare proposition of the market existing comes into question. I don’t care about more accurately predicting nuclear detonation if the market yielding the prediction is increasing it’s probability of occurring.

Now, do I think betting markets are increasing the probability of a nuclear explosion? Eh….probably not, or at the very least the effect should be quite modest. But there are lots of events on prediction markets that raise this possibility without wading into the waters of apocalypse-adjacent outcomes. As I discussed previously, the endogeneity of sports outcomes to betting markets is threatening the integrity of professional competition. Many of those obscure sports wagers don’t seem like thick markets, but relative to a 20 years where such wagers didn’t even exist, they are positively rippling with volume.

In a world where unscrupulous individuals are betting big on their ability to extract enough rents in public life before the world catches up with them, we would be wise not that add more profit channels for corruptuon than we are capable of credibly monitoring.