Just before Christmas, Taranaki District Health Board in New Zealand announced their plans to make the emergency contraceptive pill freely available through pharmacies for youth aged 12 to 24.
Berk Ozler's blog
“Everybody lies.” This is the famous refrain of Dr. Gregory House that is repeated in almost every episode of the TV show House. But, we need not need to take our guidance from an eccentric TV character: academics have been heard stating similar sentiments.
You might get a similar "DI Bump" if you submit a post on your job market paper:
After last year’s inaugural Job Market Papers (JMP) Blog Series, we were not sure this year whether to repeat it or not. However, after David got a few queries about when it’s coming up this year, it looks as if the demand is there. After some discussion among the four DI bloggers, we decided that it’s worth doing it again and see how it goes.
Our biggest worry was about the time it took for us to vet papers last year and to work with you to revise your blog (sometimes multiple rounds), so we’ll change things up a bit this year to make the process smoother for everyone involved.
- job market papers
While the U.S Presidential Debate on Tuesday night brought to the fore issues of gender equity in the U.S.
A 1994 song titled “Positive” by Spearhead goes:
“I should-a done this a long time ago
A-lot of excuses why I couldn't go
I know, these things and these things, I must know
'Cause it's better to know than to not know!
But how am I gonna live my life if I'm positive?
Is it gonna be a negative?
Reporting findings from studies in economics is changing, and likely for the better. It’s hard to not credit at least some of this improvement to the proliferation of RCTs in the field. As issues of publication bias, internal and external validity, ex-ante registration of protocols and primary data analysis plans, open data, etc. are being debated, the way we report research findings is changing.
With the increasing use of randomized and natural experiments in economics to identify causal program effects, it can sometimes be easy for the layperson to be easily confused about the population for which a parameter is being estimated. Just this morning, giving a presentation to a non-technical crowd, I could not help but go over the distinction between the average treatment effect (ATE) and the local average treatment effect (LATE). The questions these two estimands address are related yet quite different, in a way that matters not only to academics but equally to policymakers.