Thoughts about designing Econ PhD first year class
Published:
If I had to pick one thing the first-year PhD sequence truly taught me, it’s this: economics is about connecting dots—and I don’t mean the kind you see on problem sets. When I was in college, economics felt like a set of disconnected islands: reduced-form causal inference lived on one continent, micro theory built castles on another, macro models drifted somewhere far away in the fog, and econometrics… well, econometrics was its own planet, orbiting everything but touching nothing.
The first-year PhD courses gathered all these pieces into the same room—and suddenly, they started talking to each other. Slowly and interestingly, I realized that economics isn’t a collection of separate languages. It’s one big conversation. And learning to follow that conversation—across theory, data, policy, and back—is probably the real value of the first-year sequence.
Looking back, I think one thing that could make this journey easier for new students is more intentional interdisciplinary connections in the classroom. Not in the “here’s a random paper we won’t have time to discuss” way, but in the “here’s how this model actually lives in the real world” way.
Below are a few examples of what I wish instructors emphasized more.
Example 1: Micro Theory ≠ Just Solving Models
Take the classic topics: adverse selection and moral hazard. The first-year micro sequence often teaches them as elegant models—beautiful, clean, and completely detached from messy reality.
But it would be far more illuminating to walk students through the full circle:
- theory → data → research → policy → back to theory
For example:
Start with the basic moral hazard model.
Then show a Health IO paper trying to identify moral hazard using real insurance data.
Connect this to the econometrics: what makes moral hazard empirically identifiable? What assumptions do we lean on?
Move to policy: how do governments try to solve this? What failures or tradeoffs do they face?
And finally: how does the original model guide welfare analysis and policy design?
The same can be done with auction theory—connecting abstract bidding strategies to how economists estimate auction models in IO or how governments design real-world spectrum auctions using the exact theory taught in class.
Once you see the loop, you can never unsee it. And that’s a good thing.
Example 2: Econometrics Lives in the Real World
Econometrics is arguably the easiest place to connect with empirical papers, but we often limit ourselves to old classics. It’s more exciting—and more realistic—to show students how frontier empirical papers push the boundaries of identification.
The causal inference and counterfactual frameworks from the first-year sequence show up everywhere:
labor economics
development
IO
public finance
health
political economy
What would really help first-years is more time spent on assumptions. Not just “state the assumptions and prove the theorem,” but questions like:
Does this empirical paper actually satisfy the assumptions from lecture?
What happens if the assumptions fail?
How would you fix the problem?
Is the estimator still meaningful, or does everything fall apart?
Empirical econometrics becomes a lot more engaging—and honest—when students see that identification is rarely clean and always debatable.
Example 3: Macroeconomics and the Limits of Identification
The DSGE portion of macro is famously heavy: Bellman equations, Euler equations, Lagrangians—the usual suspects. We spend weeks solving models by hand, which is important, but ultimately incomplete.
What’s often missing is a clearer sense of what can and cannot be identified from macro data.
For example:
Which parameters in a DSGE model are actually pinned down by data?
Which ones will always be “calibrated”?
How much information do aggregate time series actually contain?
What are the trade-offs between Bayesian estimation, GMM, and indirect inference?
Why is identification in macro so challenging in the first place?
This perspective makes macro feel less like a black box and more like a field with real data limitations and modeling constraints.
Final Thought
The first-year PhD sequence is often described as a “rite of passage,” but maybe it’s better understood as an exercise in synthesis. We enter the program seeing economics as separate domains; we leave the first year realizing that the walls are thin and the doors between fields are wide open.
If instructors can help students see these connections earlier—through interdisciplinary examples, real research, and honest discussions of data and identification—the first-year experience could feel less like survival training and more like intellectual exploration.
And maybe, just maybe, the dots would connect a little sooner.