Peter Hull Profile picture
Jan 1, 2024 7 tweets 3 min read Read on X
Happy New Year! Kirill @Borusyak and I have a New (short) Paper on the infamous "negative weights" issue recently raised for TWFE and other popular OLS/IV specifications



Here's an (even shorter) summary thread dropbox.com/scl/fi/gfvv9bu…
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We show that design-based specifications, which leverage assumptions on the assignment process of exogenous shocks, also have negative "ex-post" weights (i.e. ones that depend on the realized shocks)

However.... Image
Unlike w/ TWFE and other specifications that instead leverage a model for unobservables (e.g. parallel trends), these negative weights *are not a problem* for design-based IV and OLS

Why?
In design-based specifications, the estimand also has an average-effect representation with "ex ante" weights: the expectations of ex-post weights over the exogenous shocks

As it turns out, these weights are *always* convex for design-based OLS regressions Image
We prove a general version of this result that covers design-based IV. Here ex-ante weights are convex under a first-stage monotonicity condition

Importantly, this condition & the identifying mean-independence condition are weaker than those typically used to show convexity
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This is helpful for formula treatments and instruments, which combine exogenous shocks with non-random measure of exposure (i.e. )

Our mean-independence condition, in particular, builds on an earlier one we used in the shift-share paper w/ @XJaraveldropbox.com/scl/fi/4e24ujd…
We close with some important caveats, with connections to recent work by @CdeChaisemartin, @paulgp, @ArkhangelskyD, @jondr44, @pedrohcgs (and other less-online folks)

Thanks for reading! I'll be presenting this at #ASSA2024 if you'd like to see more
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More from @instrumenthull

May 6, 2024
Predictive algorithms are everywhere these days, as are concerns that they embed & perpetuate discrimination

In a new (short!) working paper with David Arnold & Will Dobbie, we develop + apply new quasi-experimental tools to address these concerns!



(🧵) dropbox.com/scl/fi/g0s83e3…Image
Consider the pretrial setting, where judges increasingly use algorithmic risk scores meant to predict a defendant's potential for pretrial misconduct

The algorithmic inputs (e.g. past criminal convictions) may embed systemic biases (in e.g. past policing / judge decisions...) Image
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But measuring + eliminating such algorithmic discrimination is hard!

There may be legitimate differences in the outcome of interest Y* (e.g., pretrial misconduct potential) across groups

Moreover, outcomes are often only selectively observed (e.g., only for released defendants) Image
Read 8 tweets
Apr 2, 2024
Regression is a tool for making comparisons

If you don't know / can't easily explain what comparisons you're trying to make, then you don't understand the regression you're running
This goes for IV too btw
Controls can play two roles in this story

1) They can determine what units you're comparing (e.g. "design-based" controls isolating clean treatment/IV contrasts)

2) They can determine what features of units are compared (e.g. fixed effects converting outcome levels to trends)
Read 6 tweets
Nov 6, 2023
Hi! I'm back long enough to tell you about some awesome @Brown_Economics JMCs I'm lucky enough to write letters for this year

It's a great cohort overall, and you should check 'em all out here: . But here are the six I know the best (in alphabetical order)economics.brown.edu/job-market-can…
First up is Tommaso Coen (), an econometrician studying robust welfare analysis in the presence of behavioral biases

His JMP shows how gains from "de-biasing" interventions can be informatively bounded w/ tools from the treatment effects literature. Neat! tommasocoen.com
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Next up, Santiago Hermo (; @santiagohermo): an applied labor/urban economist studying linkages in labor & housing markets

His JMP shows how collective bargaining shapes the response of wages/employment to economic shocks. Check out the cool shift-share! santiagohermo.github.io
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Read 8 tweets
Jul 20, 2023
Apropos of nothing, here’s a brief thread on a key point about “negative weights” in regression analysis
Consider some outcome Y_i and some randomly assigned non-negative treatment X_i >= 0

We posit a causal model of Y_i = Y0_i + beta_i*X_i, where beta_i captures heterogeneous treatment effects across different units i

We regress Y_i on X_i. What do we get?
Our friends Frisch, Waugh, and Lovell give an answer:

betahat = sum_i{Xtilde_i*Y_i} / sum_i {Xtilde_i*X_i},

where Xtilde_i is the sample-demeaned treatment X_i

Let's plug the causal model in to this formula...
Read 10 tweets
Apr 1, 2023
I'm teaching a new grad applied metrics course this spring; inspired by @paulgp, I've decided to post slides here

First, Ch. 1-3: a review of regression basics and discussions of design- & model-based ID

dropbox.com/s/8gx1oj69vz9n…

dropbox.com/s/z55anktz4anh…

dropbox.com/s/8jnlcjshek8v…
Next up, in Chapters 4-5, an overview of recent findings on "negative weights" in regression and a brief interlude on clustered standard errors

dropbox.com/s/2po53rfdc7lu…

dropbox.com/s/t2fez008rk84…
Chapters 6 and 7 have some of my favorite material: a deep dive into the mechanics of linear IV / 2SLS and IV identification (again both design- and model-based)

dropbox.com/s/7leoy0fyeum2…

dropbox.com/s/yt4ga00po1a7…
Read 5 tweets
Mar 14, 2022
Very excited about this new working paper with @aislinnbohren & @alexoimas, "Systemic Discrimination: Theory and Measurement"

dropbox.com/s/sp72pogz0lem…

We develop theoretical & empirical tools to model & measure the systemic drivers of discrimination in many settings

Summary 🧵:
Econ has long studied direct discrimination - causal effects of race/gender/etc holding all else fixed - both in theory (eg taste/statistical disc) and empirics (eg audit studies)

Other fields take a systemic view: discrimination can arise *indirectly*, thru accumulated actions
Take Griggs v Duke Power (1971): a landmark Supreme Court case

Griggs argued Duke Power's policy of requiring a high school degree for within-firm transfers discriminated against Black workers

The court agreed, noting that this requirement had no bearing on worker qualification
Read 15 tweets

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