Christoffer H. Dausgaard

Very excited that the first paper of my PhD is out in @AJPS_Editor! 🎉 I theorize and test the idea of "group-based" economic voting using experimental + observational evidence from Denmark + UK + US. 1/🧵

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:

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…

🚨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

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.

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/

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…

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. ...

@erinhengel @ben_golub Cool thanks!

@chdausgaard @ben_golub Establishing a corr between time/rounds spent in peer review and citations isn't the purpose of this paper, but you can see a clear negative correlation between time in Tables 1 and 2 (p. 12 and p. 15). (There is no or a positive correlation w no. rounds.) erinhengel.github.io/time/time.pdf

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.

@erinhengel @ben_golub Yeah good point

@chdausgaard @ben_golub Of course, in economics journals papers are very rarely rejected after receiving an R&R so it’s perhaps less likely that weak papers given R&Rs will be weeded out in subsequent rounds and more likely that they’ll be accepted.

@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.

@chdausgaard @ben_golub Actually, a reverse correlation is generally observed, at least in economics journals. (That is, accepted papers that went through more rounds and longer peer review are generally cited less than accepted papers that went through shorter peer review with fewer rounds.)

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".

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

@runesqvist well 😅

@chdausgaard My political beliefs serve my psychological need for my political beliefs to reflect the evidence.

Excellent and thoughtful piece.

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

This happy piece of news awaited this morning: I can now call myself a tenured associate professor. Very happy and proud. My sincerest gratitude to mentors who supported me along the way @sanford_gordon @hyeyoungyou @mkjustesen @fhollenbach @a_rasmussen Zoli Fazekas Jacob Hariri

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.

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

🚨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:

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.