We know surprisingly little about how automation will unfold outside rich countries.
So we built the Global Automation Atlas: 18,000 tasks, 124 countries, and 2.3 million task-country comparisons.
🚨Thrilled to share our new paper "Causal Claims in Economics"! 🚨
@fetzert and I analysed over 44,000 economics papers using AI to create a knowledge graph of economics and map out causal relationships.
Here's what we found 🧵👇
🚨China's export bans on Gallium & Germanium 🚨
Why it matters: Gallium is central to countless downstream industries: semiconductors, aerospace, telecommunications, & more. This image shows how interconnected it is.
Public data, method & our paper in thread 🧵
My childhood dream of being covered in @TheEconomist came true.
Unfortunately, it is as part of the article titled "What the failure of a superstar student reveals about economics" 😅
Across 300,000 papers published in top 100 economics journals, more than 90% of claims use language that is causal while less than 10% are actually using causal inference methods.
Public trust in science is more important than ever. But what happens when scientists openly share their political opinions on social media?
Our updated paper with @EleAla & Francesco Capozza shows how academics’ political expression affects their credibility.
🚨 New Working Paper Update! 🚨
Delighted to share our updated version of "Causal Claims in Economics" (joint with @fetzert). We analyze 44k+ NBER & CEPR working papers (1980–2023) with AI to map causal relationships. We’ve added new results on narrative complexity, paper
🚨NEW DATA🚨
An input-output network between 5000 products.
Check our production network interactive tool and open-access data at aipnet.io
See this🧵for findings 👇
🧵 New paper uses AI to map global production networks & study recent shifts in global trade: "AI-Generated Production Networks" by @fetzert, @pjlambert, @bennetlf & @Prashant_Garg_ (1/14)
🚨 Exciting News! 🚨
Thrilled to announce our latest working paper with @fetzert:
Political Expression of Academics on Social Media
In this thread, we will highlight our key findings and contributions using a newly built dataset🧵👇
1. Elon Musk tells them what will happen
2. Staff has to agree, with a one week deadline
3. They start to realise how valuable the API was
4. They realise they made a mess.
5. Who knows what now