https://sites.google.com/site/connermullally/home
www.nytimes.com/2024/12/07/o...
www.nytimes.com/2024/12/07/o...
Which made me think about a few other recent papers where fixed optimization costs are a key ingredient:
Some of the most important lottery anomalies from the behavioral risk literature (e.g., probability weighting and loss aversion) actually have nothing to do with risk.
They also arise in perfectly deterministic settings.
Lead article in the latest AER issue:
www.aeaweb.org/articles?id=...
Which made me think about a few other recent papers where fixed optimization costs are a key ingredient:
doi.org/10.1111/ajae...
doi.org/10.1111/ajae...
Deadline for submissions Jan 8, 2025.
Deadline for submissions Jan 8, 2025.
• don’t ask if the coefficient is different from zero. Think about the variation it can explain
• ask things you don’t know; rather than confirm something you already believe
• look for “tension”: the opposing of countervailing economic forces
• don’t ask if the coefficient is different from zero. Think about the variation it can explain
• ask things you don’t know; rather than confirm something you already believe
• look for “tension”: the opposing of countervailing economic forces
In an RCT, treated people are randomly assigned to "pods" and interact (e.g., treatment = WhatsApp group membership). The norm (e.g. Cai-Szeidl) is to cluster on indiv for control group + on pod for treatment group. Why is clustering needed if any ICC is due to treatment?
In an RCT, treated people are randomly assigned to "pods" and interact (e.g., treatment = WhatsApp group membership). The norm (e.g. Cai-Szeidl) is to cluster on indiv for control group + on pod for treatment group. Why is clustering needed if any ICC is due to treatment?
A 🧵 on a topic I find many students struggle with: "why do their 📊 look more professional than my 📊?"
It's *lots* of tiny decisions that aren't the defaults in many libraries, so let's break down 1 simple graph by @jburnmurdoch.bsky.social
🔗 www.ft.com/content/73a1...
A 🧵 on a topic I find many students struggle with: "why do their 📊 look more professional than my 📊?"
It's *lots* of tiny decisions that aren't the defaults in many libraries, so let's break down 1 simple graph by @jburnmurdoch.bsky.social
🔗 www.ft.com/content/73a1...
docs.google.com/presentation...
docs.google.com/presentation...
documents1.worldbank.org/curated/zh/8...
documents1.worldbank.org/curated/zh/8...