- A third paper in 3ie’s internal replication series is now out – along with a response from the authors (Stefan Dercon and co-authors). The author’s response is interesting for some of the issues with such replication exercises that it raises “At the outset of this exercise, we were enthusiastic, but possibly naive participants. At its end, we find it hard to shake the feeling that an activity that began as one narrowly focused on pure replication morphed – once our original findings were confirmed (save for a very minor programming error that we willingly confess to) - into a 14 month effort to find an alternative method/structure of researching the problem that would yield different results.” (See also Berk’s posts on the previous replications).
- On the Let’s Talk Development blog, Emanuela Galasso reflects on the Chile Solidario program and how social programs can move from social protection to productive inclusion.
- From Cornell’s Economics that really matters blog – conducting fieldwork in a conflict zone in Mexico.
- In the latest issue of the American Journal of Sociology, Duncan Watts discusses common sense and sociology: “through closer attention to causal inference, experimental methods, and out-of-sample testing, it ought to be possible to improve the scientific validity of sociologists’ explanations. The bad news is that attention to these issues will do more than highlight the difference between scientific validity and understandability—it will also reveal that they are in tension with one another. Precisely because they do not have to satisfy any of the standards of causal inference or prediction, explanations that are evaluated solely on the basis of understandability can be satisfying in ways that scientifically valid explanations cannot be. Causal stories, for example, can be extremely rich and detailed precisely because the counterfactuals in question are hypothetical and so limited only by the analyst’s imagination. Models that do not have to worry about overfitting can include potentially as many features as they have observations and hence can trivially achieve the appearance of perfect within-sample prediction ... It seems inevitable, therefore, that the more rigorously a hypothesis is tested and the more data it is tested against, the weaker it must be in order to survive scrutiny”
- Going around on twitter over the past week or so – this post on “Economics has an Africa problem” – interesting post, but seems to maintain the ideas that migrants lose all their identity (e.g. scholars from Africa who move to developed countries no longer ‘count’), as well as the more tricky question of how much being from somewhere matters in being able to do research on that place, which I think depends on the research question to some extent and in particular how social the social science you are doing is and how much you think you can learn about context versus have to live it (after all the best astronomers have never been to space, the best botanists have never been plants). Thoughts welcome, as well as suggestions for African-based scholars whose work might be of interest for this blog to cover.
- New Data: AidData has just released geo-coded data at the subnational level on over 3,500 World Bank projects from 2000-2011. A short blog post describes the data.
- Call for papers: conference on corporate governance and corruption to be held in Helsinki in June.
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