- Pre-registration should be a plan, not a prison – from the Center for Open Science
- the Atlantic on how female mentors help female engineering students based on a paper forthcoming in PNAS – study only has n=150 at one college, assigned to male mentors, female mentors, or no mentors: 100% of women with female mentors remained in engineering majors at the end of year 1 compared with 82% with male mentors, and 89% without mentors
- Eva Vivalt gives four reasons your study should collect priors
- Beware of small samples with changing survey methodology: On the CGD blog, Michael Clemens revisits yet again the Mariel Boatlift and impact of immigration on native wages – and shows that the CPS sample used by Borjas dramatically changed its racial composition, leading to a spurious finding in Borjas’ work. Borjas responded in a non-gracious way, arguing his results still hold up if you exclude Blacks, but then the sample sizes are around 5 people per year. David Roodman provides more analysis and discussion.
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Tyler Cowen interviews Raj Chetty, interesting throughout, and in particular the discussion of what is the special sauce in the Chetty production function:
Tyler: “If we look at your papers, they’re about topics people have already thought about. The data work is completely state of the art, but I don’t think it would be said you’re doing something other people can’t do, and yet several times a year, you come out with papers of great import that make a big splash, and the results seem to hold up. So what in fact is your competitive advantage?”
Raj: “What hopefully our contribution and scale is, is showing how you can take those large datasets and not get lost in them, and bring out the key lessons that are relevant for thinking about these classic questions.
It’s very easy — students often have this reaction, that all I need to do is get access to this big dataset, and then I’m going to be all set for my thesis. And what you end up finding is that that is often not the case. It’s very easy to write a paper that is not that good, even with cutting-edge data and modern techniques. So one of the things that I try to do — and the easiest way to see this is if you internally, within our research group, see the iterations of the papers we’ve been working on — where we start out is often very far away from the papers that people see as the finished product. We work hard to try to write a paper that ex post seems extremely simple: “Oh, it’s obvious that that’s the set of calculations you should have done.””
- Call for Papers: inaugural conference of the Asian and Australasian Society of Labour Economists, to be held in Canberra in December.
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