Prashant Garg

Happy to share this paper. Full thread coming soon!

🚨 Tariffs on inputs = tax on downstream products.🚨

Here’s cement’s granular production network. A 50% duty on key inputs propagates costs through roads, buildings, data-centres, and renewable infrastructure.

Public data, method & our paper in thread 🧵 0/8

8/8 Explore the data, methods and paper at aipnet.io. Bonus: Here's input/output links for another related product, Tin.

7/ Our paper finds that global supply shocks on internationally traded goods have become more prevalent since 2016, particularly affecting consumer goods and processed intermediates. Here's a map of these supply shocks. Post 2024 map, when data updates, will likely be worse...

6/ Diverging strategies of U.S. vs. China in global trade. China: Importing more upstream products like Cement to build advanced domestic industries. US: Importing more downstream goods, relying on global supply chains.

5/ The pending 15% baseline tariff agreement between the US and EU may impact 12 million tonnes of transatlantic clinker, reshaping supply chains, alongside ongoing 50% US steel duties

4/ Why it matters
Cement production accounts for over 8% of global CO₂ emissions and exceeds US$1 trillion in annual construction spending. www.reuters.com/business/us-...

3/ In our paper, AI-Generated Production Networks (2024), joint with @trfetzer.com , @econopete.bsky.social and Bennet Feld, we used AI to build AIPNET, a detailed map of global production networks. It shows how critical inputs like Cement and Steel underpin industries worldwide

2/ Each node is a product. Size of node based on how "important" or "central" product is in international trade. Blue nodes are capital goods, silver are intermediates and green are final consumption good.

🚨 Tariffs on inputs = tax on downstream products.🚨

Here’s cement’s granular production network. A 50% duty on key inputs propagates costs through roads, buildings, data-centres, and renewable infrastructure.

Public data, method & our paper in thread 🧵 0/8

Find more about evolving field of metascience of economics. Network approach to metascience will lower transaction costs of effort targeting

Causal Claims in Economics We analyze over 44,000 NBER and CEPR working papers from 1980 to 2023 using a custom language model to construct knowledge graphs that map economic concepts and their relationships. We distinguish bet...

Paper by @prashantgarg.bsky.social analyzing causal claims in economics from 44,000 NBER and CEPR working papers: arxiv.org/abs/2501.06873

conducting a systematic review of climate politics literature in political science and economics using the CPF. Here we draw inspiration from the work by @prashantgarg.bsky.social and @trfetzer.com on causal claims in economics.
www.causal.claims
cepr.org/voxeu/column...

Submit to the Leibniz Open Science Day 2025 taking place on October 27 in Berlin, organized with @leventneyse.bsky.social @jrgptrs.bsky.social @dsiegfried1.bsky.social @macartan.bsky.social @wzb.bsky.social @rwi.bsky.social @diw.de @zbw-leibniz.bsky.social www.zbw.eu/de/ueber-uns... 1/2

This graph from @economist.com reveals a major pivot in #economics toward data and empirical work, with theory and modeling taking a back seat. The underlying data is open source at www.causal.claims

🏷️ @prashantgarg.bsky.social @trfetzer.com

Love it !

Can we learn about world cultures through AI? It’s promising.

Have fun, hope it recharges your stock of utils

2/ Discipline matters.
Social Sci (27%) and Arts & Human. (31%) lead the exodus; Med & Health lag (13%).
Twitter signals (followers, PageRank, posting) predict migration better than h-index or citations.
Take-away: Your Twitter presence, not your CV, determines if you jump ship.

If you read the full paper, especially the content before this excerpt, you will find the citation to best practices.

Political expression of academics on Twitter - Nature Human Behaviour An analysis of nearly 100,000 academics on Twitter reveals strong progressive stances on climate and social issues, driven by a small, vocal subset. The study highlights potential gaps between academi...

An analysis of ~100,000 academics finds that a small subset of academics generate majority of social media posts.

That vocal minority can skew how the public—and even journalists—infer “academic consensus,” potentially fueling false perceptions. www.nature.com/articles/s41...

Great idea. At minimum, we will share the ID links, if not other individual level measures.

Full data at www.academicexpression.online and raw data potentially available on filling the form

Thanks for sharing. Full thread below: bsky.app/profile/pras...

Some interesting insights here, including that people are much more influenced by *people they follow* switching platforms than by their own *followers* switching platforms.

Preprint: "Why Academics are Leaving Twitter for Bluesky" arxiv.org/abs/2505.24801

Why Academics Are Leaving Twitter for Bluesky We analyse the migration of 300,000 academic users from Twitter/X to Bluesky between 2023 and early 2025, combining rich bibliometric data, longitudinal social-media activity, and a novel cross-platfo...

Preprint: "Why Academics are Leaving Twitter for Bluesky" arxiv.org/abs/2505.24801

Capture d'écran de l'article "Why academics are leaving Twitter for Bluesky ?"

Combien de scientifiques ont quitté Twitter entre 2023 et 2025 ?

Selon cette étude, 18% des universitaires sur 300 000 étudiés ont effectué une migration vers Bluesky.

De plus, les chercheurs qui ont reconstruit leur réseau sur Bluesky restent significativement plus actifs et engagés.

1/2

It will be fun to understand it if someone’s interested to collaborate

Indeed. I already made an analogue of claims extracted from full text. This one just uses abstract for retrieval from 6x the size of dataset: all papers on top 100 journal since 1950s. So far only one stat we have: there are 20% more edges on average from full text and with a long tail.

That being said you point is still valid as it’s fine print hidden somewhere.