Prashant Garg

Indeed, such network stats shine through this particularly beautifully.

Another motivation is if there’s high variance across effect sizes on similar edges.

Yup, indeed! I’m working on other disciplines—medicine and management at the moment. More to come :)

Say a review helps find all such instances of X—>Y or effects of X or determinants of Y. Then another review may do something with Y—>Z. This helps connect them, eg X—>Y—>Z.

It is one of those but helps represents the knowledge obtained from such exercises in a way that can aggregate all systematic reviews across all topics in a common framework. (It’s the ambition — not fully there yet).

Makes sense!

Is that public somewhere? I’d like to read up on it

If you can think of ways this fails (and I’m sure there are many), we’d love to hear them.

Important caveat: we’re extracting what papers say and how they support it, not judging whether the claims are true. Perhaps, AI tools could support where economics research should go next by evaluating low-quality/fragile claims.

If we’re drowning in papers, we need better ways to browse and combine evidence.
Treat this as a proof-of-concept.
The obvious next steps are things like weighting claims by effect size/uncertainty, and showing where the evidence is coming from (country, era, data).

Concretely, a “claim” is: a standardized concept → another concept, plus an evidence tag.
Think: policy → employment (DiD), education → earnings (IV), X ↔ Y (descriptive), etc.
Here's a claim graph of two landmark economics paper

Causal Claims in Economics As economics scales, a key bottleneck is representing what papers claim in a comparable, aggregable form. We introduce evidence-annotated claim graphs that map each paper into a directed network of st...

Paper: arxiv.org/abs/2501.06873
Code+data: github.com/prashgarg/Ca...
Website: www.causal.claims

It’s becoming clear the “paper” format is going to change.

We treat each result as a small, portable claim (X→Y + how it’s supported) and stitch those claims into a graph.

Major update to Paper with @trfetzer.com full code + data below. 👇

Doom loop of decline: how struggling high streets fuel far-right sympathies in UK Retail accounts for 5% of the UK economy – but its visibility gives it an outsize influence on public perception

Great to see our paper -- with @trfetzer.com and @prashantgarg.bsky.social -- on local decline in the UK featured in this Guardian piece.
www.theguardian.com/business/202...

🔗 Read the full preprint: www.researchsquare.com/article/rs-8...

Note: this replaces our earlier pre-print "The Changing Geography of Medical Research"

10/ Yes: outbreaks trigger rapid *and durable* rises in research attention.
Responses are much stronger in the 2010s than before and biggest for high-salience threats.
Capacity matters too: internet penetration, population structure, and research strength predict bigger mobilization.

9/ What about sudden shocks: Ebola, Zika, COVID?
Do countries ramp up research when health emergencies hit?
We test this using 3,134 WHO Disease Outbreak News alerts as quasi-random shocks to disease salience.

8/ In low-income countries, responsiveness growth depends heavily on these actors.
Without philanthropy, responsiveness growth would shrink by ~38%.
Without government support, by ~32%.
(And similar patterns show up in lower-middle-income settings.)

7/ Funders fund differently.
🔹 Philanthropies → neglected burdens (HIV/NTDs/nutrition)
🔹 Corporations → profitable chronic diseases (cardio, cancer, diabetes/kidney)
🔹 Governments/public → somewhere in between

6/ Even after conditioning on burden, some topics are consistently over-/under-studied:
Over: cardiovascular (+16.5%), digestive (+14.1%)
Under: nutritional deficiencies (−14.4%), maternal & neonatal (−12.4%)
So need ≠ attention (yet).

5/ The bad news: participation is still lopsided.
The Global South often appears more as a research setting than a research author.
Example: for neglected tropical diseases & malaria, Africa is 33% of research context, but only 14% of authorship.

4/ Research is getting less geographically concentrated over time, and “endemic responsiveness”
(elasticity of publications to domestic DALYs) has more than doubled since 1990.

3/ Cardio + cancer dominate papers, while respiratory infections/TB + maternal–neonatal + nutrition + many infectious diseases carry *much* higher burden than their paper ranks suggest.
but there's good news....

2/ We link a million papers (524 journals) to (i) diseases + (ii) geographic study context using LLM + (iii) author countries.
We find that the mismatch is real...

Does science follow where people are sick and does it mobilize when outbreaks hit?

@zhou-hy.bsky.social, @trfetzer.com and I answer just that in our revised paper.

1/ A short thread for highlights 👇

Thanks. Yes very related

“When researchers randomly displayed these flood risk estimates to 18M people browsing Redfin, those who saw the feature were more likely to search for homes w/ low flood risk, according to a working paper published in the Nat’l Bureau of Economic Research last Nov*.”🧪

* www.nber.org/papers/w33119

Thanks!

Cool new data set from @reubenhurst.bsky.social and coauthors: politicsatwork.org

Associated papers:
-Political segregation in the US workplace papers.ssrn.com/sol3/papers....
-VRscores: A New Measure and Dataset of Workforce Politics Using Voter Registrations
papers.ssrn.com/sol3/papers....

TOMORROW (12 November): AYEW Big Data/Machine Learning Workshop!

Join us at 9pm AEDT (10am GMT) to @prashantgarg.bsky.social (@imperialcollegeldn.bsky.social), Zhenkai (Cambridge), Saani (University of Cincinnati), Luka (@ucsandiego.bsky.social )

Sign up for Zoom link: monash.edu/business/imp...