I am pretty sure this tweet is wrong on two or three levels. Can you find them all? But maybe I am wrong. Thoughts?https://twitter.com/davidshor/status/849316701862862851 …
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maskirovbot Retweeted (((David Shor)))
I am pretty sure this tweet is wrong on two or three levels. Can you find them all? But maybe I am wrong. Thoughts?https://twitter.com/davidshor/status/849316701862862851 …
maskirovbot added,
Problems off the top of my head: 1. No causal identification. No causal identification. No causal identification. No c
2. Even allowing causal interpretation for every coefficient, Female Educational Attainment's effect size is ~2.5 times larger than unions'
3. Permitting crimes against statistics and science and caring about this: collective bargaining's p > 0.05
4. I don't see a charitable interpretation of this travesty outside of me completely misunderstand that table.
5. If not that, the author is either (i) grossly incompetent, or (ii) a hack of the worst kind, blackening the good name of Data. Ugh. /fin
yeah bad tweet. also, trying to imagine a story where higher top marginal rates causally increase gender gap. not easy.
I wouldn't even be surprised if the story turned out to be true, given that unions tend to flatten wage dists. But that's not evidence.
also it would require unions to not have a gender-segregating effect between occupations
impression I've been getting in data sci interviews is econometrics emphasis on interpretation + modeling, even implicit, is a huge asset
this, especially for avoiding stupid pitfalls like the ones above, though also very much in feature engineering
Certainly makes sense! It's not like our stats curriculum is cutting-edge in other aspects
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