- 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.
One question that often comes up in empirical work concerns the appropriate way to calculate standard errors, and in particular the correct level of clustering. Here is a specific version of the question that someone posed, slightly paraphrased:
Many important policies are implemented at the national level. Monetary policy, fiscal policy, and many regulations are key examples. Pure time series or before-after analysis of the impacts of changes in these policies on the economy of a country will be contaminated by other changes going on in the economy. Simply trying to difference out global trends will not account for systematic differences in the growth path of the country where the reform took place from the average global growth path. This makes evaluation of the impacts of such policies difficult.