These are the highlights—full results and all the details are in the paper, available at @ssrn.bsky.social. Here's a link:

🔗 tinyurl.com/2nf3372a

End of the thread 🧵

(8/8)

We also analyze the obituaries with a GPT model to better understand how the deaths of the soldiers are framed and how it impacts engagement:

📌 High-grief = more engagement
📌 Nationalistic propaganda = less engagement even when high-grief
📌 Authorities mentioned = even less engagement

(7/8)

Engagement with content related to the Russia-Ukraine war also increases after the KIA reports but this is due to engagement with obituaries and personal stories of the deceased soldiers on VK rather than a surge in pro-war likes: Null effect when we remove the obituaries from the sample.

(6/8)

Main result: After the first KIA report in the municipality, engagement with patriotic/military propaganda increases, but drops for posts promoting the government or the president. Similar dynamics in comments, shares, but also views.

(5/8)

We link the relative engagement to the data on Russian war fatalities (KIA), which was kindly shared by
@en.zona.media. This allows us to check how engagement changes pre/post (first) fatality report in municipality. The plot below shows the distribution of the KIA reports dates:

(4/8)

We classify the posts from these groups with keywords/LLM, and aggregate the engagement metrics—likes, shares, views, comments—by topic. We then separately look at engagement with (i) pro-regime and (ii) patriotic propaganda, relative to neutral engagement. An example of a pro-regime post:

(3/8)

First, data: We analyze 28M+ posts from 36K+ VKontakte groups run by Russian public schools. Why schools? They are widespread, geolocatable, not targeted by bots, and mix propaganda with neutral content on school life. Below is the map of groups active on the eve of the Russian invasion:

(2/8)

200% on papers with exogenous shocks as preference should be given to internally generated variables

Thanks to Ekaterina Borisova, @nikitaz.bsky.social, @timfrye.bsky.social and Koen Schoors for being wonderful coauthors! 🧵

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On the mechanism side, we show that fear of COVID-19 leads to skepticism about others' compliance with the pandemic regulations, which is in line with the neoclassical view that free-riding concerns drive demand for regulation. No effect on risk-aversion or trust in government, though.

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Disaggregating the effect, we find that it is highest for regulations people usually oppose, like wearing medical masks, business lockdowns, or stay-home orders. The effect is still there for the other pandemic regulations, but smaller and less significant.

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Instrumenting fear of getting infected with this shock, we find:
1. A positive effect of fear on demand for COVID-19 regulations and relief policies
2. Null effect on demand for regulations that were not related to the pandemic, like censorship or housing

(4/7)

During our survey on COVID and political attitudes, a popular health TV show in Russia aired an episode saying that the probability of dying from the virus is lower than from being struck by lightning (literally). Using shift-share, we show that this episode decreased fear of the infection.

(3/7)

Fear plays a central role in classic political theories of government intervention going back to Hobbes, but the causal link between fear and demand for regulation is not straightforward. To identify this link, we exploit a natural experiment from a health TV show misreporting COVID-19 risks.

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