I’m part of our recruitment committee this year, which has meant looking at a lot of CVs and job market papers. In doing so, I thought it might be of interest to put together some statistics on what makes for a competitive job market candidate – which can hopefully be useful for those still in grad school, potentially help provide some relief to those currently on the market who worry about whether they have enough papers and whether they need publications or R&Rs, and help correct some misperceptions of what is needed to be successful as an economics grad student these days.
To do this, I put together two samples. The first consists of 46 candidates (35% female) who listed development as a field and were at one of 15 top U.S. programs for development. The second consists of 31 candidates (51% female) who were selected for first-round interviews with the World Bank’s Development Research Group (I have excluded those who were post-docs, or who are several years post-PhD) – with an overlap of 8 candidates on both lists (61% of our interview list were from those 15 programs, but some were in fields like macro or trade). Of course neither list by any means contains all of the leading development economists, but both seem useful samples for highlighting some patterns I found interesting.
1. Pre-docs are still the minority, but lots of people do a masters before their PhD: There has been a lot of recent talk about an increasing tendency for people to do a “pre-doc” before their PhD, where this is a year or more of research experience as a full-time RA, researcher at the Federal Reserve, consultant at an international organization, or working for an organization like IPA or JPAL. Only 37% of the top school sample, and 23% of the World Bank interview sample had done a pre-doc. However, 74% of the World Bank sample had done a separate Master’s degree before enrolling in their PhD. Doing a Master’s is particularly common for non-U.S. students, but doesn’t seem that much different from when I was applying for PhDs almost 25 years ago.
2. The modal development PhD takes 6 years. I remember one of my advisors, Peter Phillips, sitting me down before classes had even started and telling me I should plan to get out in 4 years, and 5 years being reasonably common back then (and just recently I’ve seen several people tweeting David Romer’s “Out in Five” rules). But now only 17% of the top school sample and 16% of the World Bank sample finished in 5 years, and 65% (81%) in 6 years. I’m not sure how much the pandemic delayed people, but it could have also meant those planning on finishing in 6 did not get to go on the market this year in some cases.
3. The modal job market candidate has 2 research papers, 0 publications, and 0 revise-and-resubmits. There is a perception of a bit of an arms race, where people tool up on pre-docs and Master’s degrees, then take 6+ years, and go to the market with publications or revise-and-resubmits, leading to the fear that we will become like some other social science fields, where you need to have published multiple papers to get a job. But the modal/median job market candidate in my samples have 2-3 papers on their websites, list another 2 in progress (with at most an abstract), and only 35% of the top school sample and 29% of the World Bank sample have any publications, and only around 10% an R&R.
4. Despite the rise of team production, the job market paper continues to be largely sole-authored: 65% of the top school sample, and 74% of the World Bank sample have a sole-authored job-market paper; most of the rest have only one other co-author, who is usually another graduate student; and fewer than 10% have 3 authors. None of the job market papers were co-authored with advisors.
5. 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.
6. Getting your hands on amazing admin data is behind a lot of the job market papers, field surveys were not common (perhaps due to the pandemic): Over half the job market papers in both samples were using some form of administrative data (e.g., data on students within schools, on personnel in government ministries, on banks’ lending decisions, etc.), while only around 10% collected any type of survey data themselves. The pandemic clearly made conducting surveys difficult, but this rise in the use of large administrative datasets shows a big move away from using publicly available survey datasets.
7. 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.
Hopefully this is informative to those on both the supply and demand sides of the market, and good luck to everyone on the market this year!
Join the Conversation