From David McKenzie at the World Bank, here is one excerpt:
Data from big middle-income countries, and English-speaking Africa were most common, with no papers on the Middle East and North Africa, and very little study of the poorest places: In both samples, India, Brazil, and Colombia (and the U.S.!) were the most common countries studied, with a smattering of papers from East Asia, other South Asian countries, and Latin America, and one from Russia with nothing else on Eastern Europe and Central Asia. Of the World’s 25 poorest countries, only one (Mozambique) was the subject of study; of the five countries that contain half the World’s poor, there were papers on India and Bangladesh, but none on Nigeria, DRC or Ethiopia.
Here is another:
RCTs have far from overtaken development, difference-in-differences is the most popular identification method, yes, people still do IV, and no, no one does PSM on the job market: The pandemic may have reduced the ability of people to do some field experiments, but this year at least, only 20% of the top school sample, and only 6% of the World Bank sample were doing RCTs. More than one quarter in both cases were using difference-in-differences. RDD and IVs were used in about 10% of the papers each, and structural models were common in the World Bank sample (which has more trade and macro papers). None of the papers used propensity score matching.
The blog post is interesting throughout. Via the excellent Samir Varma.