Some time ago I wrote a piece titled "Why Study Agricultural and Applied Economics." While this was somewhat geared toward graduate study, degrees in these areas provide a great combination of quantitative and analytical skills at the undergraduate level suitable for a number of roles in industry, especially when combined with programming like R, SAS, or Python. (just think Nate Silver). Another example would be the number of financial analysts and risk management and modeling roles held by graduates holding bachelor's degrees in economics and finance or related fields. Not everyone needs to be a PhD holding rocket scientist to do complex analytical work in applied fields.
However, what are some arguments for graduate study? I bring this up because sometimes I wonder, given my role in the private sector could I have had a similar trajectory if I just skipped the time, money and energy spent in graduate school and went straight to writing code?
Perhaps. But recently I was listening to a Talking Biotech podcast with Kevin Folta discussing the movie Food Evolution. Toward the end they discussed some critiques of the film, and a common critique about research in general is bias due to conflicts of interest. Kevin States:
"I've trained for 30 years to be able to understand statistics and experimental design and interpretation...I'll decide based on the quality of the data and the experimental design....that's what we do."
Besides taking on the criticisms of science, this emphasized two important points.
1) Graduate study teaches you to understand statistics and experimental design and interpretation and this requires a new way of thinking. At the undergraduate level I learned some basics that were quite useful in terms of empirical work. In graduate school I learned what is analogous to a new language. The additional properties of estimators, proofs, and theorems taught in graduate statistics courses suddenly made the things I learned before make better sense. This background helped me to translate and interpret other people's work and learn from it, and learn new methodologies or extend others. But it was the seminars and applied research that made it come to life. Learning to 'do science' through new ways of thinking about how to solve problems through statistics and experimental design. And interpretation as Kevin says.
2) Graduate study is an extendable framework. Learning and doing statistics is a career long process. This recognizes the gulf between textbook and applied econometrics.