Zhen Ye

Assistant Professor of Finance at Texas Tech University

[Alex Imas @alexolegimas]

A few (personal) thoughts on reading empirical AI papers on the economy.

Economists have gotten used to reading papers with super clean identification, arguing about the validity of an instrument, making sure parallel trend assumptions are satisfied. This is what gets you into a top journal, and it is *very* important research (no question here). But it also takes years and sometimes decades to get these types of papers right---people often don't find a good instrument to answer a specific causal question decades after the natural experiment. We will eventually have this type of research for AI as well, and it is absolutely necessary.

But right we also need signals *right now*, even if they are noisier than what we are used to. We need papers where we can trust that researchers did their best methodologically, while at the same time acknowledging that the space is moving way too fast to wait for perfect identification. This will allow us to accumulate enough signals, coming at the same question using different angles, for example, to say "yes, X is likely happening in the economy".

The AI exposure and early career hiring papers are a good example of this. There is no silver bullet paper with super clean identification. But at this point we have several independent teams reaching the same general conclusion, enough where we can say "there seems to be a slow down in AI-exposed, early career hiring."

[Alex Imas @alexolegimas]

This is exactly how it’s been for me. I don’t think I’ve ever come up with an idea or developed it conceptually when given time to think. It’s always been while ostensibly doing something else (errands, preparing for class, zoning out during meetings).

When I have a full day to work, I can execute but nothing new ever comes. Something about the surrounding noise, friction and context of doing anything other than “coming up with ideas” is what lets the associations finally click.

[Jesús Fernández-Villaverde @JesusFerna7026]

Let me explain why I believe modern economics is such a powerful tool for understanding the world. I’ll do this by discussing a great paper by Simone Cerreia-Vioglio, @UncertainLars, Fabio Maccheroni, and Massimo Marinacci, “Making Decisions Under Model Misspecification,” published in the Review of Economic Studies a few months ago.

Imagine I want to drive from UC San Diego to UCLA, but I’ve never driven that route before. I need to build a “model of the world” to guide me, which we usually call a map. Maps are simplified representations of reality. They can’t include every detail if they’re to be useful. Borges, in his short story On Exactitude in Science, makes this point beautifully. (In practice, I don’t draw the map myself—I use an app—but someone still had to make it.)

Because maps simplify, I can’t fully rely on them. Maybe last night’s storm knocked down a tree and closed a street, or there’s construction and the ramp off the highway in LA is shut down.

This uncertainty matters. Suppose I’m driving to UCLA for an important talk at 11 a.m. If the ramp is closed, I might need 15 extra minutes. When should I set my alarm to arrive on time, while still getting enough sleep to give a good talk?

The problem is that I can’t assign precise probabilities to all these contingencies. How likely is the fallen tree? Or new roadwork? Even the best traffic apps can’t capture every disruption, and some might happen after I’ve already left.

In economic terms, my “model of the world” (the map) is misspecified—and no matter how hard I try, I can’t fully fix that.

But sitting down and crying about misspecification doesn’t answer my basic question: when do I set the alarm? Too early, and I’m exhausted. Too late, and I’m late.

Simone and his co-authors offer a way to think about this. They start from the idea that we often hold several structured models of an economic phenomenon, grounded in theory. For example, a central bank might use a standard New Keynesian model and a search-and-matching model of money.

Yet, aware that each model is misspecified by design, the bank adds a protective belt of unstructured models—statistical constructs that help it gauge the consequences of misspecification.

The beauty of the paper is that it provides an axiomatic foundation for this protective belt (and even generalizes it to include a Bayesian approach). It shows that if a decision-maker’s preferences meet certain conditions —reflecting both rational and behavioral features— then those preferences can be represented by an augmented utility function that formally accounts for misspecification.

Crucially, we don’t assume that augmented utility function; we derive it. We start with general, plausible properties of preferences and prove that they imply such a representation.

That’s real progress. Instead of writing endless critiques of expected utility or rational expectations (as many have done for decades, with little to show), we now have a formal way to reason about misspecification—precise definitions, clear boundaries of validity, and awareness of what we still don’t know.

Take, for instance, a brilliant Penn graduate student on the market, Alfonso Maselli

https://t.co/rl2gu95V7t

His job-market paper pushes this frontier further. He studies cases where a decision-maker not only faces model misspecification but is also unsure which model best fits the data and can’t assign probabilities to them—what we call model ambiguity. In my example, the central bank is unsure whether the New Keynesian or the search-and-matching model fits better, and it worries that both might be incorrect.

If you read Simone et al. or Alfonso’s paper, you’ll see how misguided—and, frankly, cartoonish—many of the recent criticisms of economics on X have been.

First: the idea that economists don’t understand math or have “physics envy.” The math in these papers is subtle and advanced—utterly different from what physicists do (neither better nor worse, just distinct). An engineer transitioning into economics would find these tools unfamiliar.

Second: claims of ideological bias are unfounded. I have no idea about the political views of the authors, and I’d be surprised if anyone could infer them from the analysis—beyond vague guesses about typical academics.

Third: This has almost nothing to do with what one learns as an undergraduate, or even in first-year graduate school. If your knowledge of economics stops at an intro textbook, it’s best not to pontificate on the field’s frontiers.

Fourth: Is this science? Debating that word’s boundaries is pointless; every definition of “science” breaks down somewhere.

The Germans solved this long ago with the idea of Wissenschaft—the systematic pursuit of knowledge, whether of nature, society, or the humanities. By that measure, modern mainstream economics is clearly a Wissenschaft: a disciplined, cumulative, and highly useful effort to understand how the world works. Simone and his co-authors have demonstrated that beyond any reasonable doubt.

[Abhijeet Singh @singhabhi]

Gosh, the pressure of (claiming to) publish in top-5 Econ journals even extends to the Chancellor of the UK Treasury!

Who among us hasn't wished for an extra publication in the JPE? 😂

@Econ_4_Everyone this is great publicity

[Zhen Ye @ZhenYe18]

@mnavarrete42 Congratulations!!

[Michael Ewens @startupecon]

Amazing opportunity for PhD students interested in entrepreneurship (and attending part of the @nberpubs Summer Institute):

https://t.co/uJ13h5j58B

Application due: February 15th.  Thank you to @robinson_fuqua and @JorgeGuzmanCBS for organizing.

[Vincent Geloso @VincentGeloso]

The 2024 Nobel in Economics needs to go Arnold Harberger

[Zhen Ye @ZhenYe18]

@varley_ma @UKGattonCollege Congratulations!!

[Zhen Ye @ZhenYe18]

@mcecibustamante Thank you so much, Cecilia!

[Zhen Ye @ZhenYe18]

@BPellegrino_CBS @UofMaryland @SmithSchool @Columbia Congratulations!

[Jason Abaluck @Jabaluck]

One response I haven't seen yet: your existential crisis is probably correct, your research is probably wrong or ill-motivated, and you should fix this by asking better questions, getting better data, or finding a better identification strategy (probably all three).

[Zhen Ye @ZhenYe18]

@PaulineCarry @PrincetonEcon @BeckerFriedman @UChicago @HilaryHoynes @rolandrathelot @Schoefer_B Congratulations Pauline!!

[Zhen Ye @ZhenYe18]

@_seulakim @penn_state Congratulations!！

[Chris Blattman @cblatts]

This advice comes a little late for a few of you, but hopefully in plenty of time for future PhD applicants. The advice I give my own students and RAs:
https://t.co/zvWOK2sbQJ

[Zhen Ye @ZhenYe18]

@AdrienMatray Hope you get better soon!

[Jeffrey Wooldridge @jmwooldridge]

I didn't quite get the simulations reported in the nonlinear DiD folder to match up with my Stata presentation, but they're close. I need a better organizational system ....

https://t.co/pxcSQg2r9V

[Alex Edmans @aedmans]

Greater information disclosure is often seen as unambiguously good. But it can discourage investors from doing their own research, as they rely on company disclosures. This makes the stock price less informative and inhibits managerial learning. In the RF: https://t.co/fZ6wMzedug

[Mariassunta Giannetti @ProfMariassunta]

a beautiful piece

[Matt Marx @marxmatt]

new dataset!