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Six Questions with Chris Udry

David McKenzie's picture
This is the first in a potential new series of posts of short interviews with development economists. Chris Udry was one of the pioneers of doing detailed fieldwork in development as a grad student and has continued to be one of the most respected leaders in the profession. While at Yale he taught David, and advised both David and Markus, and is famous for the amount of time he puts into his grad students. Most recently he has moved from Yale to Northwestern. We thought this might be a good time for him to reflect on his approach to teaching and advising, and to share his thoughts on some of the emerging issues/trends in development economics.
  1. Let’s start with your approach to teaching development economics at the graduate level. The class when you taught David in 1999 was heavy on the agricultural household model and understanding micro development through different types of market failures. Most classes would involve in-depth discussion of one or at most two papers, with a student assigned most weeks to lead this discussion. There was a lot of discussion of the empirical methods in different papers, but no replication tasks and the only empirical work was as part of a term paper. How has your approach to teaching development changed (or not) since this time?

Try as I might, I have made little progress on changing my basic approach to teaching. The papers and topics have changed, but the essence of my graduate teaching remains the in-depth discussion of a paper or two each class. I’ve tried to expand the use of problem sets, and had a number of years of replication assignments. The first was hindered by my own inadequate energy (it’s hard making up decent questions!). I found that replication exercises required too much time and effort in data cleaning by students relative to their learning gain. Students were spending too much time cleaning, merging and recreating variables and too little time thinking about the ideas in the paper. I’ll reassess assigning replication this year, because there may now be enough well-documented replication datasets and programs available. With these as a starting point, it would be possible to get quickly into substantive issues in the context of a replication.