Very excited to see my first (and I expect, not last) paper w @ZiYangKang out in print.

Thread👇on what this paper is about + why I hope lots of folks will use it. This paper is for anyone who does an exercise like this:

1) take data on demand vis a vis a price shock
2) estimate a treatment effect
3) calculate consumer surplus to assess the policy.

The question: how sensitive are your welfare conclusions to functional form assumptions?

@RevEconStudies @SNageebAli @PennStateEcon @econ_greg @MSFTResearch @StanfordGSB @nberpubs A few years ago, I wrote a thread introducing a paper on "Voluntary Disclosure & Personalized Pricing" with @SNageebAli and @econ_greg (threadreaderapp.com/thread/1186778…).
Now that the paper has been accepted for publication, an update on what the paper is about 👇 1/n The gist: In the debate about privacy, info disclosure & price discrimination, it's important to think about the structure and verifiability of the disclosure technologies. 2/n

Very excited to share this new paper with the fantastic @ZiYangKang, out on NBER today.

Thread 👇 with an overview.

nber.org/papers/w29656 Here's the gist: a common exercise in empirical econ is to analyze the effects of a policy change by taking obs of price/quantity pairs, fitting a demand curve and integrating under it to get a measure of welfare (e.g. consumer surplus; deadweight loss). Here's an example.

Suppose you've just received a slew of PhD admissions decisions and you're trying to decide what to do. You log onto twitter and get a mountain of confusing, rancorous discourse flying in 50 diff't directions. You hesitate but reaffirm--you've made it this far; no stopping now 1/ You're excited but also anxious. Is doing a PhD actually awful? Is everything you've worked toward meaningless? Or maybe you're the exception to the rule, and your experience will be great? After all, all these people found a way eventually as far as you can tell. What to do? 2/

1/ Constructing the dashboard to explain our paper (reopenmappingproject.com) involved a lot of careful thinking about what info to display/emphasize and how. The goal of the app was to make the message, methods and results of our paper accessible. Thread👇 for more weeds. 2/ First, data limitations: we build contact matrices from Replica's synthetic population. This is amazing data (e.g. it lets us account for how long ppl spent in the same place) but:

Excited to tell you all about a new paper re COVID19 from a big team effort w/ @abhishekn, @akbarpour_, @Pietro_Tebaldi, @Simon_Mongey, Cody Cook, Aude Marzuoli, Matteo Saccarola and Hanbin Yang

reopenmappingproject.com/files/network-… Tl;dr: Heterogeneity matters when thinking about lockdown/re-opening policies. Diffs in concentrations of places where ppl encounter each other, diffs in industry, demographic (and co-morbidity) distributions, diffs in when the virus hit.

Hey #econtwitter- I'm helping put together a tip sheet re computational tools for structural IO, including notes on when some languages/solvers are better than others. I don't use python for optimization but I know lots of ppl do. Any chance y'all could lend some tips? Example 👇 A few other things that it'd be great to have a 1-liner explaining (w/ links to more):
-How to evaluate trade-offs re Analytical Derivatives vs Numerical Differentiation vs Auto Differentiation
-When to impose optimizer constraints via transformation -- e.g., mapping [0,1] -> R

Hello #econtwitter! The wonderful @SNageebAli + Greg Lewis + I have just posted our working paper.

scholar.harvard.edu/files/vasserma…

We would love your comments. Thread 👇 Motivation:

Policy debates re privacy on the internet often stress these trade-offs:
1) Firms getting user data -> better matches + service to larger market 👍
2) But lack of privacy is icky 👎
3) And facilitates "too much" price discrimination 👎

See obamawhitehouse.archives.gov/sites/default/…