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
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....
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
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
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
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
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
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)
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