EconLit

Uncertainty Increases Profits?

Zachary BartschLeave a comment

Most people have an intuition that uncertainty can harm economic outcomes. Baker, Bloom, & Davis (2016) and Bloom (2009) demonstrated that industrial production and manufacturing decline in the face of policy uncertainty. The typical mechanism that people suggest is that uncertainty about the future causes people to engage in precautionary saving, resulting in fewer sales.

The theory continues that firms consequently decrease production as demand for their output declines. Firms aren’t interested in causing the quantities supplied and demanded to be equal. Rather, they don’t want to produce too many goods that don’t get sold or don’t get sold at an adequate markup. Production is costly.  A related theory is that more persistent or longer-run uncertainty can also depress investment, since the riskier future increases the tail risk of losses.

Rather than make a risky investment, one could instead just hold off and wait for some of that uncertainty to get resolved. There’s tradeoffs to this, of course. As future costs and benefits become clearer, they also get priced-in to asset values. So, there is an optimization problem. The possible downside outcome is big and uncertain. If the risk of the investment gets resolved and the downside outcome is still too likely or harmful, then a project manager did the ex-post ‘right thing’ by waiting.

But, if the downside risk disappears or is found to be very small, then waiting to invest in the project incurs an economic cost. Either 1) the profitable project and its associated profits will occur later and less valuably, or 2) other firms also resolve their uncertainty and bid up the price of the project’s inputs. Invest too early, and the downside is large and uncertain. Invest too late, and you may lose the potential upside partially or entirely.

But can uncertainty systematically increase profits?

Walter Oi said yes.

Continue reading →

The Journal of Healthcare Finance Is Back

James BaileyLeave a comment

Most academic journals are run by big for-profit publishing companies, and most of the rest are run by universities or big academic societies. The Journal of Healthcare Finance was an extreme outlier from this norm, run single-handedly by Editor-In-Chief James Unland since 1994. It was the rare journal that was free both for readers and authors.

I loved the idea of having a single person truly in charge and accountable without being slowed by a complex bureaucracy. But eventually a single person will want to, or have to, move on. Having an institution run a journal can ease this process, though an individual can still try to find their own successor.

In this case, The Journal of Healthcare Finance had been on hiatus since its Editor-In-Chief stepped back, with its last issue published in 2023. Their old website domain had expired- not a great look for anyone who published there and was going up for a job or tenure.

But now it is officially back at a new domain, with the single Editor-In-Chief replaced by a full editorial team, and accepting submissions again with the hope of releasing a new issue this year.

Selfishly, I’m happy to see this both because it means they will continue hosting my past publication, and to have a potential outlet for my future work. I recommend that other health economists and health services researchers give it a try, though as of now I have no personal experience with the new editorial team.

Do NBA Teams Play Worse In Back-To-Back Games?

James BaileyLeave a comment

The conventional wisdom is that the NBA regular season has too many games. Teams play worse because they are tired, or injured, or resting their stars so they can be ready to actually play hard in the playoffs.

New research shows that the conventional wisdom is…. probably right. In particular, teams play worse by many measures when they have to play two days in a row. That’s what Max Aicardi and I found in a paper published today, “Running on Empty: How Back-to-Backs Impact Pace and the Four Factors of Basketball Success“:

Teams on the second night of a back-to-back shoot less efficiently (lower eFG%), grab fewer offensive rebounds, and play at a slower pace. On defense, they allow opponents to shoot more efficiently, force fewer turnovers, and give up more free throw attempts and second-chance opportunities. Turnover percentage and offensive free throw rate did not change significantly, consistent with our conceptual framework’s distinction between effort-dependent and execution-dependent metrics. While not every metric changed significantly, the overall pattern is clear: second-night back-to-back scheduling is associated with a measurable decline in team performance

The effect sizes here tend to be small, around 0.5-2%, but they are statistically significant given that we studied over 20,000 games, and practically significant given how close NBA games are.

Max had the idea for this paper and wrote the first draft as a student in my Economics Senior Capstone class in 2025. After he graduated, I joined the paper as a coauthor to get it ready for journals. We share the data and code for the paper here.

Video on You Wouldn’t Steal a Car

Joy Buchanan2 Comments

The brilliant content creator economist Matt Hill has posted a video “How Piracy Accidentally Created AI” to the @EconNerds channel on YouTube.

The video is so funny (and smart!) that I encourage you to sit back and watch it all the way through. Around minute 2, he gets to the topic of online piracy.

The 4 minute mark is where I am featured to explain my paper with Bart Wilson: You Wouldn’t Steal a Car: Moral Intuition for Intellectual Property (SSRN link)

The @EconNerds channel on YouTube has over 100 engaging videos like this to help you learn economics. You can also find Econ Nerds updates on X/Twitter

I summarized our findings in a previous blog “Summary of You Wouldn’t Steal a Car,” but Matt’s video is more fun and quite technically accurate, so now everyone can just watch it.

Joy Explains how to integrate AI into a statistics class

Joy BuchananLeave a comment

I have put a working paper on SSRN describing three ways I incorporated AI into my Business Statistics 200-level class for undergraduates at Samford University in Spring 2026. Read the paper at the link.

Connecting Classroom Econometrics and Excel Training with Large Language Models (SSRN)

Abstract
Economics education almost always includes a component of statistics. Most undergraduate economics curricula require an upper-division econometrics course, and many economists teach data analysis. These courses help students become conversant with major contributions in economics research, but they can be challenging to teach. On its own, the mathematics of regression, error minimization, and statistical software may not feel exciting to students, especially compared with the intuitive economic reasoning that often draws them into the discipline. At the same time, students are increasingly interested in artificial intelligence and large language models. This note describes three practical teaching exercises designed to connect ordinary least squares, Excel training, and LLMs. The goal is to use students’ curiosity about AI to motivate classic statistical reasoning and practical spreadsheet skills.

The image is from ChatGPT 5.5 Thinking mode and it took over a minute to generate. The fact that their laptop screen is pointing away from them is funny (unrealistic). The portrayal of Tom Holland and Zendaya is good, which is what the audience cares about. So, this seems like a case of AI hallucinating up the thing that people want.

I posted back in February about the LLM Telephone game: Telephone Classroom Game for Teaching Large Language Models

Citation:
Buchanan, Joy, “Connecting Classroom Econometrics and Excel Training with Large Language Models” (May 27, 2026). Available at SSRN: https://ssrn.com/abstract=6839039

Note: I have been posting my papers to SSRN for a long time as a way to distribute them faster and more widely. I have heard rumors that SSRN might stop working, for my purposes. If anyone has suggestions for what I should do about the papers I have up there, please let me know! Or, if you are readying this post-summer-2026 and want a copy, send me a message at my Samford email so I can send you a copy.

The Welfare-Productivity Tradeoff in US-China Trade

James BaileyLeave a comment

Who benefits from trade between the US and China? If China subsidizes their exporting industries, should the US see this as a threat that undermines our industries, or thank China for lowering prices for US consumers? Does it matter that China runs a persistent trade surplus (exporting more than they import), while the US runs a persistent trade deficit?

Everyone has a take on these questions, but the answers I hear even among economists rarely draw from the leading modern models in the international trade literature. Krugman (1980) (10k citations) shows how large home markets matter for industries with increasing returns to scale. In a simple increasing returns model, unlike with Econ 101 comparative advantage, temporary subsidies can permanently flip which country an industry efficiently operates in.

Melitz (2003) (20k citations) extends the Krugman model to include firm-level productivity differences. Rubini (2014) extends the Melitz model to include innovation. Now Xiao (2025) has extended the Rubini model to include unbalanced trade, then calibrated the model with data from the US and China. Now that the mathematical models are able to incorporate more and more features of the real world, what do they show?

China’s trade surplus and the US trade deficit have tradeoffs. Specifically, China’s trade surplus leads them to be more productive than they otherwise would be, but have lower welfare, because so much of the fruit of their production is enjoyed by other countries. Conversely the US trade deficit leads us to produce less than we otherwise would, but to have higher welfare thanks to consumers enjoying the cheaper foreign goods.

In one sense this recapitulates some of the same debates people had without the math. Some people like trade because it benefits US consumers and overall present-day US wellbeing. Some don’t like it because it harms US manufacturing and our resiliency in any potential future conflict.

One advantage of the models is that it puts numbers on the tradeoffs. In this case, the welfare benefit to the US may be small relative to China’s welfare loss and relative to both countries’ productivity changes:

the average productivity increase caused by trade surplus ranges from 1.2 percentage points to 5.46 percentage points when the innovation cost changes. These results explain China’s long-term export promotion policies and align with its new policy goal of developing “new productivity forces”. I also identify a negative effect on China’s trade partners’ productivity (namely, the US), of between -2.74 percentage points and -5.89 percentage points. This comes at a welfare cost, equivalent to between 3 percentage points and 5.7 percentage points of consumption units. Correspondingly, China’s cheaper goods increase welfare in the US by between 0.26 percentage points and 1.22 percentage points

In addition to the big complex model, Xiao’s paper shares nice background on the sheer size of Chinese export subsidies, noting that they account for 2/3 of all manufacturing subsidies in G20 countries, and that export tax rebates are almost 2/3 as large as Chinese net exports. In short, China’s trade surplus is not simply driven by differing preferences and production capabilities across countries, but is largely driven by deliberate policy choices.

P.S. The paper’s author, Aochen Xiao, is on the econ job market.

arXiv will ban authors who submit papers with LLM mistakes

Joy BuchananLeave a comment

In the world of academic preprints, arXiv has long been the go-to platform for researchers to share work quickly. But with the explosion of generative AI tools, the repository is drawing a line in the sand.

On May 14, 2026, arXiv moderator Thomas Dietterich announced a clarified enforcement policy. If a submission contains incontrovertible evidence that authors didn’t properly check LLM-generated content, all listed authors face serious consequences.

What counts as “Incontrovertible Evidence”? The policy targets clear signs of unchecked AI output, including:

  • Hallucinated or fake references
  • Meta-comments left by the model (e.g., “Here is a 200-word summary; would you like me to make any changes?” or placeholder instructions like “fill in the real numbers from your experiments”)
  • Other obvious errors, plagiarized text, biased content, or misleading claims generated by AI

arXiv’s Code of Conduct already holds every author fully responsible for the entire paper’s contents.

The Penalty

  • One-year ban from submitting new papers to arXiv.
  • After the ban, future submissions must first be accepted at a reputable peer-reviewed venue before arXiv will host them.

At first researchers discussing the policy online seemed happy about the one-year ban, but when I pointed out that it is essentially a ban for life to use it at a pre-print venue, some people became nervous.

Why now? arXiv has been overwhelmed by low-effort “AI slop.” These papers are marked by fabricated citations and shallow summaries. This erodes trust in the entire preprint ecosystem.

In response to the complaints (someone like me would be worried that I’ll somehow let an error slip through and then be banned for life from posting working papers), Scientific Director Steinn Sigurðsson shared:

on the whole @arxiv flap about hallucinated references etc

you don’t see the stuff we reject… some of it is really really egregious

the decision to impose additional consequences is largely to throttle that stuff so n00bs and bad actors don’t trash us trying repeatedly

This is the problem that we face with every internet forum. A few bad actors ruin it for good people.

In 2022 I wrote Content moderation strategy

Elon Musk buying Twitter is the big news this week. He wants to enhance free speech on the site and, according to him, make it more open and fun. Some fans are hoping that he will make the content moderation and ban policy more transparent. Maybe that’s possible. 

If no one can be banned, then bad actors will bring the whole platform down. Inevitably, good people get caught in the net, and it’s devastating to be locked out of a platform where your peers are sharing.

However, if you want to be taken seriously by tech folk then ask for a system that is possible. A substantially better experience might be incompatible with the site being free to users.

Part of the problem that I don’t hear people talking about is that a free platform is not easily compatible with good customer service.

For some not-fake work and citations: Buchanan et al. (2024) provided early clear evidence that a mark of LLM-written work is fake citations. And, Buchanan and Hickman (2024) show that certain framings can prompt people to be more suspicious of AI-generated writing, such that they are pushed toward doing a fact-check before believing all claims.

Buchanan, Joy, and William Hickman. “Do people trust humans more than ChatGPT?.” Journal of Behavioral and Experimental Economics 112 (2024): 102239.

Buchanan, Joy, Stephen Hill, and Olga Shapoval. “ChatGPT hallucinates non-existent citations: Evidence from economics.” The American Economist 69.1 (2024): 80-87.

Most Published Research Findings Are Directionally Correct

James BaileyLeave a comment

As a new quick rule of thumb inspired by the Nature papers, you could do worse than “cut estimated effect sizes in half”. If a published paper says that a college degree raises wages 100%, then chances are the degree really does raise wages, but more like 40–50%. In 2005, John Ioannidis said that “most published research findings are false”. By 2026, we seem to have improved to “most published research findings are exaggerated.”

That’s the conclusion of my piece out today at Econlog: “Is Economics Finally Becoming Trustworthy?“

There’s plenty of both good and bad news for economics and the social sciences in both my piece and the Nature special issue it describes. It’s kind of like the Our World in Data motto:

In short, our attempt to replicate hundreds of papers showed that published social science results shouldn’t be trusted precisely today, but they seem to be getting more reliable over time, and they are much more reliable than chance. Economics and political science look the best, though we are still very far from perfect:

You can read the full piece here.

EWED cited in Top Demography Journal

Joy Buchanan3 Comments

We’ve been cited in top newspapers, such as The Financial Times, before, but this might be a first. Our blog has been cited in Demography, a top-ranked journal in the field of demographics and population studies.

The internet is fun sometimes, and that is why we are here (almost) every day. Jeremy’s work is mostly about wealth, and this paper is mostly about income:

Has Generational Progress Stalled? Income Growth Over Five Generations of Americans

I was able to download the PDF directly from the journal website linked above, so it must be open-access. Instead of trying to restate all of their finding here, I’ll just quote:

At ages 36–40, Millennials’ mean net worth was about $95,000 higher than that of Generation X. Their home equity was $30,000 higher and non­hous­ing wealth was about $65,000 higher. Thus, although homeownership among Millennials has declined, home values have increased enough among those who own homes to increase mean home equity, while their nonhousing wealth has grown as well. Our find­ings of gen­er­a­tional increases in wealth echo those pre­viously found by Horpedahl (2021, 2024).

Horpedahl, J. (2021, Sep­tem­ber 1). Who is the wealth­i­est gen­er­a­tion? Economist Writing Every Day. Retrieved from https://economistwritingeveryday.com/2021/09/01/who-is-the-wealthiest-generation/

Horpedahl, J. (2024, Jan­u­ary 24). Young peo­ple have a lot more wealth than we thought. Economist Writing Every Day. Retrieved from https://economistwritingeveryday.com/2024/01/24/young-people-have-a-lot-more-wealth-than-we-thought/

Perhaps people will forget why this finding was such a big deal in 2021. It was the opposite of what many were saying!

Should Practicing Economists Read Tyler’s New Marginalism Book

Joy BuchananLeave a comment

Tyler Cowen’s new (free online) book entitled The Marginal Revolution: Rise and Decline, and the Pending AI Revolution is going to be “interesting,” but should you read it?

Mike Makowsky explained that Academic economists are overcommitted

If you are already struggling to meet your deadlines for referee reports you owe to editors, should you take the time? If you don’t have time to indulge your curiosity about the 18th century and dead thinkers, right in the middle of the semester, should you look at it now or maybe browse it over the summer?

I think it’s worth going straight to the last chapter right now.

“Chapter 4: Why Marginalism Will Dwindle, and What Will Replace It?“

It was written for you and released quickly for this moment. Tyler does not personally have to worry about his job, but you might.

This link will take you straight to an in-browser e-reader https://tylercowen.com/marginal-revolution-generative-book/app/

Or you can download the PDF at https://tylercowen.com/wp-content/uploads/2026/03/TheMarginalRevolution-Tyler_Cowen.pdf

You might face mental resistance to reading this chapter, because you don’t want to hear the message. If that’s you, then it’s especially useful to read this chapter. He’s not correct about everything. Develop your counter argument, to go forth and save marginalism. You can only do that if you understand and name the threats. This is more about methods/professions and less about ideology than you might think from the title.

Here are some quotes that stood out to me

The ties of empirical work in economics to economic theory are evolving, and in particular the explicit ties to intuitive microeconomic reasoning, and marginalist thinking, are being cut. In much of traditional econometrics, the emphasis is on testing pre-existing models…

in machine learning, we let the algorithm build the “theory” for us, noting it may have tens of millions of variables and thus not count as a theory…

So much for prediction, what about hypothesis generation? Well, there is a new approach to that too, using machine learning.

A lot of economists do not regularly describe what they actually do for work. Yes, we are saving the world by writing papers, but what exactly do you do? Do you generate hypotheses? Is that what you are teaching your students to do?

It’s not fun to think of how the econ profession might need to reposition, but we owe it to students. Who better to work on this than tenured professors? 

I think the case for undergraduates students to major in economics is strong. I also think the case for doing 4 years of college is strong for students who want to learn.

Last summer I wrote: Students still need to learn principles

If economics is “more interesting” than hard science, then it might serve to scoop up good thinkers at the undergraduate level and get them doing something more technical than what they would end up doing in a humanities program. When I graduated from college, the fact that most econ student had accidentally learned to code was a benefit to them.

College graduate humans ought to be able to read and pass the Turing Test if they are going to be effective complements to AI.

Economists championing marginalism for students, today, write: For Gen Z, Economics May Be the Key to Success in the New AI World

Let me plug Mike as well for thinking about what research econs do in 2026: The actual AI problem in academic economics “Oh, what shall all the candlemakers do now that the sun has risen?” made me laugh.