- Marc Bellemare on the subject of my dissertation work – using repeated cross-sections
- From Next Billion, a summary of research showing how saving leads people to generate more income by working harder
- In the Guardian, how the World Bank is nudging health and hygiene in several projects…and the defense against whether this distracts from more structural issues “Why not make all programmes as effective as possible, even if it doesn’t turn a very poor country into a Scandinavian country overnight”
- Also from the Guardian, 10 sources of data for international development research
- randtreat – a new Stata command to do random assignment that can deal with uneven numbers of observations (more details here) – this builds on an old blog post I did on the issue, and great to see some of these practical issues getting made easier for everyone.
- synth_runner – the IDB’s Development that Works blog has a post about a new Stata command to help automate use of the synthetic control method.
Over the summer I’ve been slowly working my way through the new book Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction by Guido Imbens and Don Rubin. It is an introduction in the sense that it is 600 pages and still doesn’t have room for difference-in-differences, regression discontinuity, synthetic controls, power calculations, dealing with attrition, dealing with multiple time periods, treatment spillovers, or many other topics in causal inference (they promise a volume 2). But not an introduction in that it is graduate level and I imagine would be very confusing if you had no previous exposure to causal inference. So I thought I’d share some thoughts on this book for our readers.
This is the seventh paper in our series of guest posts by graduates on the market this year.
This is a joint post with Miriam Bruhn.