Christoffer H. Dausgaard @chdausgaard

PhD, political scientist @polscicph @uni_copenhagen. Retrospective voting, social groups, political behavior. chdausgaard.github.io Copenhagen Joined August 2015

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Thiemo Fetzer 🇪🇺🇺🇦 - same handle elsewhere @fetzert

a week ago

Its now ten years since the UK voted narrowly to Leave the European Union. I have studied how the UK got there through my research for many years now. Evidence is growing that the UK is indeed leveling up, but, by leveling down. Let me introduce the Brexit Cost project:

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Verasight @verasight_data

3 weeks ago

Congrats to @chdausgaard (Copenhagen) on a new @AJPS_Editor article, with US survey data from Verasight. Voters judge how their own group is doing, not just the national economy, and reward or punish incumbents for it. doi.org/10.1111/ajps.7…

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Juan Felipe Riaño R. @JuanFelipeRiano

4 weeks ago

🚨New NBER working paper alert 🚨 🤔2021–2024 was the worst U.S. inflation in four decades. The question: Did voters react to the rising prices, or to the shrinking paychecks? 😱We approached this paper with one major surprise: there is no official measure of inflation at the county level in the U.S. 🌟To study the relationship between inflation and electoral outcomes during, we first had to construct local inflation measures using county-level data on family budget costs and incomes. The answer? -> Check it out! 👇 🔗 nber.org/papers/w35301

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joseph francis @joefrancis505

4 weeks ago

I fear that the new cellphones-cause-low-fertility NBER working paper has found a potentially genuine divergence between the fertility rate in rural and urban counties in the US, then labelled it "iPhone." The problem is that AT&T coverage was heavily urban, resulting in an imbalance between the control and treated group. The matched Poisson regression is supposed to address that by overweighting the small number of notionally urban counties that did not have iPhone coverage. But I believe that by "urban," they mean counties in which just 33% or more of the population were in towns of 2,500+ people. It would therefore included a lot of places that are, in fact, rural. To me, it seems like there's a failure of identification. Hence, the SDiD result could reflect iPhones or a differential effect of the Panic of 2007 on rural and urban areas. We don't know. The other-carrier placebos are also uninformative because they are structurally incapable of reproducing the urban-rural confound, given how they are mapped. The paper is here: nber.org/papers/w35310.

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Yamil Ricardo Velez @YamilRVelez

4 weeks ago

Did the treatment work because it included moralizing language, as your theory predicted, or because it was packed with political references and written in an informal style? A new JASA paper by Imai and Nakamura offers a clever approach to these questions and others: leveraging generative AI models to extract and control for latent confounds. As researchers start relying more on generative AI outputs as treatments, this kind of approach serves as a useful post-hoc robustness check. But this isn't solely for AI-generated treatments. It also works for human-generated treatments, since the vast semantic information captured by these models can help adjust for features we may have trouble measuring or articulating. Exciting to see an emerging workflow that uses AI at multiple stages of the experimental design process. imai.fas.harvard.edu/research/llm/

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AEA Journals @AEAjournals

a month ago

Forthcoming in the AER: "Taxing Top Wealth: Migration Responses and their Aggregate Economic Implications" by Katrine Jakobsen, Henrik Kleven, Jonas Kolsrud, Camille Landais, and Mathilde Munoz. aeaweb.org/articles?id=10…

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Gabriel S. Lenz @GabeLenz

a month ago

Want to use agents like Codex or Claude Code to speed up academic projects? Want to tell the agents what changes to make to each figure and table and then watch the changes appear in the book, dissertation, or article? Then, you might find repo helpful. ...

Gabriel S. Lenz @GabeLenz

4 months ago

Codex has taken the pain out of finishing a data-heavy book. Here’s a paragraph about it. I use high effort for planning, medium for execution with a ChatGPT plus. Yet to hit a limit, despite talking to it many hours a day (using Wispr Flow). Academia sure is changing.

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Christoffer H. Dausgaard @chdausgaard

a month ago

@erinhengel @ben_golub Cool thanks!

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Christoffer H. Dausgaard @chdausgaard

a month ago

I mean this is just pretty obviously collider bias, right? By conditioning on Nature comms acceptance it induces a spurious negative correlation between reviewer leniency and paper quality. Very obvious that papers that *survived* tougher reviews are higher quality on average.

Jay Van Bavel, PhD @jayvanbavel

a month ago

People often complain about tough peer reviews But papers that elicited stronger criticism from reviewers and required more-extensive revisions received more citations tha did papers that drew light comments and sailed through the peer-review process. We need to embrace

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Christoffer H. Dausgaard @chdausgaard

a month ago

@erinhengel @ben_golub Yeah good point

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Christoffer H. Dausgaard @chdausgaard

a month ago

@erinhengel @ben_golub Interesting! I’d be curious to see those estimates. To be sure, collider bias could in that case still be depressing a strong relationship without flipping the sign; whereas here it flips the sign. The problem is that this correlation kind of mixes up both effects.

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Seth Lazar @sethlazar

a month ago

I have been testing it *very* extensively, and I think that, as of right now, the confidence is warranted. However I do think that people should have a clearer sense of what it's saying when it says something is 100% AI generated. It breaks the item down into chunks, generally 350-400 words, and makes a prediction about whether that chunk contains some AI. So "This paper is 100% AI generated" really means "100% of the tokens in this paper are in a chunk that we believe has AI in it".

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Joshua Gans @joshgans

a month ago

Oof this paper is a bit of a worry. nber.org/papers/w35240?…

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Christoffer H. Dausgaard @chdausgaard

a month ago

@runesqvist well 😅

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Christoffer H. Dausgaard @chdausgaard

a month ago

Excellent and thoughtful piece.

Robin McKenna @rbnmckenna86

a month ago

New Substack piece where I argue for the need to give symmetrical explanations of political beliefs--you can't explain the ones you don't like by saying they serve some psychological or social need and the ones you like by saying they just reflect the evidence. Link in the reply.

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Christoffer H. Dausgaard @chdausgaard

a month ago

@BCEgerod @sanford_gordon @hyeyoungyou @mkjustesen @fhollenbach @a_rasmussen Kæmpe tillykke Ben!! 🎉

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Hanno Hilbig @hannohilbig

a month ago

Update on my benchmark of local vs. commercial LLMs for text classification, focusing on political science applications. I compared 5 local open-weight models with 4 API models on 34 coding tasks (~147k predictions). Tasks include tweets, news, survey responses, policy texts, etc The best local LLMs are often close and sometimes perform better. Local models match or exceed API on 9/34 tasks. The average API advantage is pretty small, at 0.015 F1.

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Owen Winter @OwenWntr

2 months ago

The local elections in one chart. Labour has been squeezed between the Greens in young wards and Reform in older working-class wards

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Eashwar Nagaraj @eashwarnagaraj

2 months ago

🚨Early version of my JMP! 🚨 To what extent is the contact between corporate lobbyists and federal government officials publicly disclosed? In other words, how big is the market for "shadow lobbying"? The Lobbying Disclosure Act mandates quarterly disclosure of lobbying "contacts" subject to many caveats. Watchdogs have long complained about lacunae in the LDA, but there is little evidence of the size of shadow lobbying market. In my JMP, I use 4.5 trillion pings from 179 million smartphones spatially merged to building shapefiles and observe movement between lobbyists' offices, corporate headquarter buildings, and the federal government in Washington DC. See below: movement of lobbyists from corporate HQs to federal government buildings:

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Brandon Stewart @b_m_stewart

2 months ago

1/ New @Nature! We study how powerful institutions shape the information environment for LLMs. Commercial LLM training is opaque, so we trace a path from state-coordinated media -> training data -> model responses.