Recently I met a new coworker, a now-former professor who left academia. Like many, he seemed wounded by the experience. This is his first private-sector job. I have to give him respect: he left willingly. He had a plum position, and he was years away from tenure review. It’s hard to walk away from a place like that after a lifetime of striving. But he was unhappy, and he’d grown disenchanted with his research agenda, and didn’t enjoy the labor itself anymore, and it was degrading his ability to enjoy his private life; so he quit. Not everyone is brave enough to do that.
So say I have been considering a return to academia for a graduate degree that will let me operate at the level in an organization that I'd prefer to work at. Are there antipatterns that work in the other direction that I should know about beforehand?
So many thoughts on this as someone who did a diss using econometrics and now works in a non-academic role.
Every so often I get asked to do a brown bag or class on how to communicate with statistics to legislators and policy types. My primary message always is to use the simplest possible presentation--if clear descriptive statistics can do it, that’s great. If OLS is required, or even worse logistical regression, you CAN explain but you increasingly are going to have to rely on them basically trusting you. If your’re getting into the arcana of various robust regression estimators, well, friend, you’re doing that for yourself and hopefully for the correct reasons (e.g., you have a reason to believe they’re necessary like trimming outlier influence) and you shouldn’t expect your care will be even noticed by, well, anyone.
What’s far more troubling to me, though, is the reaction of academics to practitioners trying to enter into the academic debate. You will find over and over again that even though you have access to data that academics should dream of (in my cases, linked arrest and prison records by inmate and linked K-12/higher ed prep records by SSN) it doesn’t matter. You will have access to to new and untapped resources and you. Will. Not. Get. Published. Either your operational data isn’t a perfect control, or there will be dark mutterings about collider variables, or you’ve haven ‘t employed the latest hyper-specific regression technique from a journal two years ago you don’t have access to. And it won’t matter that even your summary statistics will have more real world impact than a career of academic esoterica.
I am someone who left academia, I was longsighted enough to figure oy quickly that the environment wasn't for me. However, I also had a great time in academia, I met some awesome people and I learned a lot. Anyway, my point is that while I agree on a general grounding with this post, I think it tends to generalise a bit too much. Two examples: industry people being more extroverted and having an attitude for doing things. The industry world is huge and varied and things aren't that simple - there's a lot of different situations, good and bad, everywhere. For me the main reason to leave academia was feeling that I didn't have full freedom, that my career was not really for me to shape but rather depended on relations and red tape. I definitely felt much better outside immediately, but that's not to say that there's always a good mindset overlap - culture is culture and companies can have widely different ones.
This is good advice for most people, not just those leaving academia
So say I have been considering a return to academia for a graduate degree that will let me operate at the level in an organization that I'd prefer to work at. Are there antipatterns that work in the other direction that I should know about beforehand?
So many thoughts on this as someone who did a diss using econometrics and now works in a non-academic role.
Every so often I get asked to do a brown bag or class on how to communicate with statistics to legislators and policy types. My primary message always is to use the simplest possible presentation--if clear descriptive statistics can do it, that’s great. If OLS is required, or even worse logistical regression, you CAN explain but you increasingly are going to have to rely on them basically trusting you. If your’re getting into the arcana of various robust regression estimators, well, friend, you’re doing that for yourself and hopefully for the correct reasons (e.g., you have a reason to believe they’re necessary like trimming outlier influence) and you shouldn’t expect your care will be even noticed by, well, anyone.
What’s far more troubling to me, though, is the reaction of academics to practitioners trying to enter into the academic debate. You will find over and over again that even though you have access to data that academics should dream of (in my cases, linked arrest and prison records by inmate and linked K-12/higher ed prep records by SSN) it doesn’t matter. You will have access to to new and untapped resources and you. Will. Not. Get. Published. Either your operational data isn’t a perfect control, or there will be dark mutterings about collider variables, or you’ve haven ‘t employed the latest hyper-specific regression technique from a journal two years ago you don’t have access to. And it won’t matter that even your summary statistics will have more real world impact than a career of academic esoterica.
based
I am someone who left academia, I was longsighted enough to figure oy quickly that the environment wasn't for me. However, I also had a great time in academia, I met some awesome people and I learned a lot. Anyway, my point is that while I agree on a general grounding with this post, I think it tends to generalise a bit too much. Two examples: industry people being more extroverted and having an attitude for doing things. The industry world is huge and varied and things aren't that simple - there's a lot of different situations, good and bad, everywhere. For me the main reason to leave academia was feeling that I didn't have full freedom, that my career was not really for me to shape but rather depended on relations and red tape. I definitely felt much better outside immediately, but that's not to say that there's always a good mindset overlap - culture is culture and companies can have widely different ones.