Bad news for those who have relied on NIH for doing human subjects training. They have stopped offering free training, and are currently designing a new training that will cost and be available November 6. Twitter is recommending either the Global Health Training Centre free online course or the FHI Research Ethics Training Curriculum ...
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- Bad news for those who have relied on NIH for doing human subjects training. They have stopped offering free training, and are currently designing a new training that will cost $40 and be available November 6. Twitter is recommending either the Global Health Training Centre free online course or the FHI Research Ethics Training Curriculum as possible alternatives if you need something now, or want something that is free.
- Andrew Gelman offers a nice reminder of why asking people to do post-hoc power calculations based on estimated effect sizes is a bad idea.
- This week the Declare Design team ask whether blocking (stratifying) can actually increase your standard errors? Answer is yes, but hard. See also my AEJ applied paper with Miriam Bruhn on this.
- On the Econ that Matters blog, Chris Barrett and John Hoddinott look at the state of development economics, as seen from over 600 NEUDC submissions: “the quality of the work is remarkably high”, “suggestive evidence of an evolution in the field away from certain topics. There were virtually no “pure theory” papers, although the best papers often contained a short theoretical or conceptual model to motivate the empirical work. There were few submissions in macroeconomics... surprisingly little on trade ...Despite the profound long-term effects of climate change on developing countries, we received relatively few submissions on this topic” – also most work is on Sub-Saharan Africa and South Asia, and very few papers use IV or matching.
- Josh Blumenstock has a nice summary comment in Nature on using big data for development. Good discussion of some of the findings/use cases so far, but also of the importance of realizing that predicting levels is not the same as capturing changes, and that model fit may change quite quickly, especially if people learn the algorithms. E.g. “With colleagues, I have been working on interactive tools to provide real-time visualizations of population poverty and vulnerability. By benchmarking predictions with multiple rounds of survey data (including responses to questions about income, health and employment status), we’ve seen that the accuracy of our maps degrades quickly, sometimes within just a few months”...” when people become aware of the fact that their personal data are being monitored to make decisions — for instance, about who gets humanitarian aid or who is eligible for a loan — they are inevitably incentivized to game the system. GiveDirectly, a non-profit organization in Africa and the United States that enables direct cash transfers to people living in poverty around the world, initially used satellite imagery to target aid to households with thatched roofs. But people soon caught on, to the point at which some would pretend to live in a thatched structure adjacent to their main iron-roofed house to become eligible for the aid.”
- Free pdf download of an introduction to statistical learning with applications in R – with R code and datasets for all the examples in the book – good intro to machine learning.
- I’ve been enjoying catching up on the short episodes in Planet Money’s new podcast The Indicator. In particular, I liked this the episode why people can’t get work done at work: “the shared calendar is one of the worst inventions in modern software history... When everyone can see everyone else's time and you can see which blocks people have available, it becomes really tempting to take blocks from people... usually the kinds of things that you put on a work calendar, whether it's shared or not, are not the things that have to do with actually getting your job done... So like, it's a meeting, or it's an HR thing. It's usually not, hey, from, like, you know, 10 a.m. to 3 p.m. I'm going to be quietly working on the thing I'm working on. That space you just leave open. [The calendar is] sort of like a chronicle of distractions”
- Following up on my post on descriptive papers in development, this new NBER paper by Bento and Restuccia has some of the more interesting descriptive facts I have seen for a while – they detail average manufacturing and service establishment sizes for over 90 countries, and show a strong positive relationship.
- The Development Research Group is recruiting on the junior market: JOE listing