- One of the best descriptions of what the productivity term A is in the production function – Growth Economics illustrates through Universal Studios’ Harry Potter attraction.
- At the CGD blog – impact of the GAVI vaccine initiative on vaccination rates in poor countries – using a country GNI per capita threshold for eligibility.
This post is co-authored with Marshall Burke.
One morning last August a number of economists, engineers, Silicon Valley players, donors, and policymakers met on the UC-Berkeley campus to discuss frontier topics in measuring development outcomes. The idea behind the event was not that economists could ask experts to create measurement tools they need, but instead that measurement scientists could tell economists about what was going on at the frontier of measuring development-related outcomes. Instead of waiting for pilot results, we decided to blog about some of these ideas and get inputs from Development Impact readers. In this series, we start with recent progress on measuring (“remote-sensing”) agricultural crop yields from space.
- Ted Miguel is teaching a course on research transparency methods in the social sciences. Berkeley is posting the lectures on YouTube. Lecture 1 is now up.
- Chris Blattman on a paper looking at how the tendency to publish null results varies by scientific field.
- In Science, Jorge Guzman and Scott Stern on predicting entrepreneurial quality
- Ben Olken’s forthcoming JEP paper on pre-analysis plans in economics: this is a very nuanced and well-written piece, discussing both pros and cons – it notes a reaction I am increasingly persuaded by, which is that RCTs don’t really seem to have a lot of data-mining problems in the first place…and also that “most of these papers are too complicated to be fully pre-specified ex-ante”…main conclusion is benefits are highest from pre-specifying just a few key primary outcomes, and for specifying heterogeneity analysis and econometric specifications – less clear for specifying causal chain/mechanisms/secondary outcomes which can too easily get too complicated/conditional.
- HBR provides an update on the working from home experiment done by Nick Bloom and co-authors. This experiment worked with China’s largest travel agency, and randomly choose workers to be allowed to work from home. They find workers are more productive when they do so. The interesting new finding is that when, at the end of the experiment, the treatment group was given a choice “half of the home-workers changed their minds and returned to the office and three quarters of the control group — who had initially all requested to work from home — decided to stay in the office” – the authors find it is the most productive workers who prefer to work from home.