The argument is that (Biden 2020 vote - Clinton 2016) vote is not normally distributed, which suggests fraud.
But:
1.) There's no particular reason for it to be normally distributed. Not every phenomena is normally distributed.
2.) Eyeballing it, it *is* normally distributed.
Oh jeez, it seems likely the "weird outliers" are entirely the result of bad data cleaning.
For example, "Pleasant Ridge" was two precincts in 2016 (691 and 670 votes for Clinton, respectively), and one precinct in 2020 (such that "Precinct 1" had 1605 votes).
Which is to say, that's where the rightmost outlier comes from - its a 914 vote difference!
The third biggest outlier seems to be from
Nov, Precinct 2" where Biden increased his vote by 568 votes compared to Clinton.
Between 2016 and 2020, Novi went from 22 districts to 25.
Anyways, this analysis fails to replicate and I'm not going to spend more Holiday Time on it. Testimony is here if someone else wants to: courtlistener.com/recap/gov.usco…
I think that lots of people misconceive how state level UI replacement rates are calculated. It's not the case that states are currently just setting replacement rates at 45% and we can just change the variable.
The new @rubinreport book has a section on the gender wage gap.
It's a very tight and concise exposition, such that it has one the highest errors-to-sentences ratios I've ever observed.
Let's walk through it. 1/18
Obviously the GWG has not been debunked by "countless" economists. I'm sure there are a couple of economists who have made claims that there is no wage gap, but I suspect that number is quite small. 2/18
That a number is "aggregate" doesn't suggest that it is "pure spin". Also, I'm not sure where Rubin is pulling the 79% from, but usually the number is derived looking at full time workers (e.g. here): americanprogress.org/issues/women/r… 3/18
I'm not following @tylercowen's argument here - I think we should expect a minimum wage and occupational licensing to have very different effects under monopsony! (1/10)