A few more words about the "Practical Guide to Shift-Share Instruments" that we just posted (w/ @instrumenthull, @XJaravel)

We tried to make it useful to wide groups of readers: new users of shift-share IVs, experienced users, and those who didn't even know they were users (1/N)

https://twitter.com/XJaravel/status/1866173299920687398

@instrumenthull @XJaravel While @paulgp measured that 1/8th of all recent IV papers mention shift-shares, we think it's a lot more

Did you know that spillover regressions (OLS or IV) with the number or share of treated friends on the RHS, are shift-share regressions and should be analyzed as such? (2/N)

Time to report that our work with @XJaravel and @jannspiess on diff-in-diff imputation has recently been accepted @RevEconStudies!

Final draft of "Revisiting Event Study Designs: Robust and Efficient Estimation" here: arxiv.org/abs/2108.12419 Recent years produced several DiD estimators robust to heterogeneous treatment effects

They have the same estimand under very similar assumptions and are all transparent in different ways

Some of the things we like about the imputation approach?
➡️ versatility
➡️ efficiency

🧵 Do shift-share IV regressions suffer from negative weight problems?

@CdeChaisemartin and Lei recently posted a WP (Worrying Paper, one could say) arguing that way

@instrumenthull and I are more optimistic and decided to share our view in a brief note
https://t.co/J6dUwHyMWkdropbox.com/scl/fi/vi0jkwo…
We make 4 points about shift-share IVs under heterogeneous effects:

1/4) They identify a convexly weighted average of causal effects when the shocks are as-good-as-random

It's like a LATE result, and our ReStud paper w/@instrumenthull @XJaravel proves it in more generality

Here's my version of this very important point about negative weights:

You should consider the nature of treatment assignment and reasonable restrictions on treatment effects before worrying about negative weights, especially when you can’t avoid them by imputation

➡️🧵

https://twitter.com/instrumenthull/status/1682084118333358080

There are two types of weights in regressions: "ex post" (conditionally on treatment realizations) and "ex ante" (before treatments are realized)

Ex post weights are negative for some treated observations for almost every regression: some obs in the comparison group are treated

📢 I’d like to share with #TradeTwitter a 🧵 on what @XJaravel and I have learned about the unequal effects of international trade through both cost-of-living and wages in the U.S.

For those of you who have seen my JMP, this is a much-revised draft

dropbox.com/s/eiygfth61vp4… @XJaravel Let’s start with the effects on costs-of-living, which are understood less well

Who benefits more from lower prices of imports in the US?

A typical guess & prior estimates: poor consumers who buy more tradable goods, esp. from China

So trade could REDUCE (real) inequality

On the 1st anniversary of my twitter account, I'm happy to share w/the #EconTwitter community our #stata commands for event studies:

did_imputation: robust and efficient imputation estimator

event_plot: event study plots after various estimation methods

github.com/borusyak/did_i… I’ve discussed did_imputation in the 🧵 on our paper w/@XJaravel and @jannspiess

Key points: the estimator is
- robust to heterogeneous effects
- efficient under homoskedasticity
- transparent
- robust to pre-testing
- works w/controls, triple-diffs, etc

https://twitter.com/borusyak/status/1394680063384248320

🥁 I’m thrilled to announce our paper w/@XJaravel and @jannspiess, “Revisiting Event Studies: Robust and Efficient Estimation” 🥁

It’s a fully revised version of our 2017 draft that the diff-in-diff loving audience may have seen

dropbox.com/s/y92mmyndlbku… @XJaravel @jannspiess Event studies are diff-in-diff research designs with staggered adoption of treatment:

- Units in a panel get treated at different times (and stay treated forever)
- Some units may be never treated

We are interested in heterogenous or dynamic treatment effects

New WP: “Non-Random Exposure to Exogenous Shocks” (w/ @autoregress). Summary 🧵:

Papers often estimate causal effects by leveraging exogenous shocks that affect many observations jointly, to different extents

We show problems w/this & offer new solutions
dropbox.com/s/brhuxe1b1k8x… Examples:

- New railroad construction affects all regions via increased market access

- State-level policies affect different people depending on demographics

- Trade shocks affect different regions

- Shocks propagate through networks.

(More in the paper!)