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Weekly links October 9: the next step in transparency? Minimum wages in Africa, pirates, and more…

David McKenzie's picture
  • The next step in transparency/replication?The New Yorker on how researchers in the sciences are using videos to document each step of the process to make it easier to replicate precisely – and on how there is even a journal of visualized experiments (h/t @betsylevyp)

The infinite loop failure of replication in economics

Markus Goldstein's picture
In case you missed it, there was quite a brouhaha about worms and the replication of one particular set of results this summer (see Dave's anthology here).   I am definitely not going to wade into that debate, but there is a recent paper by Andrew Chang and Phillip Li which gives us one take on the larger issue involved:  the replication of published results.   Their conclusion is nicely captured in

What works to keep adolescent girls in school? Part 2

Berk Ozler's picture
In Part 1 of this series, we focused on increasing the returns to education for women. In this installment, we focus on the effects of removing institutional constraints.  

2. Removing Institutional Constraints
Following the World Development Report (2012), we discuss policies that can change the price of schooling under three categories: (i) direct costs; (ii) indirect costs; and (iii) opportunity costs.

Can monitoring teachers and students – with no incentive or punishment attached -- improve test scores? Yes.

David Evans's picture
Consider two challenges in global education development:
  1. Effective adult education is difficult to accomplish all over the world.
  2. Quality of education is a problem across many countries in Africa at all levels (primary, secondary, tertiary, adult).

A Review of the Imbens and Rubin Causal Inference Book

David McKenzie's picture

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.