#demogrodon — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #demogrodon, aggregated by home.social.
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I'm at #PAA2024. If you want to meet up, let me know.
Also, I'll be giving a talk on Saturday in the last session (Migration Around the World) on how population-level educational attainment affects individual-level wages, with a special focus on nativity (domestic- vs- foreign born).
Note: This is a good paper, but I only got this slot because everyone else has to be at the airport then. So I'm a little afraid for the attendance.
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Not ready to do a full writeup of this at the moment (and I don't have a preprint up yet either) but I have some work to share!
By myself and Sagi Ramaj: Educational expansion, fields of study, and the gender gap in analytic skill usage on the job
Free to download at this link! Via RSSM. I'll get a full thread and a preprint up in early 2024.
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Manuscript word limits (or page limits) for #sociology and related journals. I spent way too long on this. I didn't finish and there were a couple I couldn't find, but hopefully this will be useful to someone else anyway.
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I'm trying to learn more about demography. A question that I had: What exactly is "critical demography"? Or "feminist demography?" What are recent examples of this type of work? I know I've heard of it. Google is only getting me so far.
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Question that I have not thought through at all and could have a really obvious answer:
If you are doing a propensity score / coarsened exact / inverse probability weighting / whatever method, is it better to use
(1) Data that's part of the same dataset to construct your weights?
(2) Data that's part of a different dataset to construct your weights?
(3) Data from either one is fine?
(4) Something else?