What are the list of practical stats/metrics/coding tools that you'd suggest an incoming student to learn?
I am always heartened by all of the optimism and excitement that so many on this forum show every year around this time. I was in your shoes 6 years ago, trying to suss out how management (STR/OB) academia actually worked. I relied on posts on Urch, even though I was highly intertwined with management academia prior to my PhD. Looking back, I had both good and bad times in my program. Upon graduation, I'll start as a data scientist at a financial institution. Here's my retrospective take for folks considering this path (starting with a healthy dose of tough love).
This career path is much more challenging and less appealing than most prospective students realize. First, the norm for program length has decidedly gone from 5 to 6 years at high-ranked R1s over my time in the program, and I imagine that 6 years of PhD + postdoc may become the norm by the time you graduate. Nowadays, when going into this profession, you have to be willing to move across the country, delay having a family (if you want one), and delay solid financial stability for 7-8 years. Moreover, you must be *uncommonly certain* that these preferences won't change as you grow older. For most people, they will. When I began, I thought that I'd be happy as a professor at any institution and any location as long as I was doing research work that engaged my mind and was useful in some way. Through the six years of my PhD, I got married, had a kid, and watched from afar as my partner's parents began to develop health issues. In short, my preferences changed pretty dramatically. If you do well on the academic job market, you'll get a handful of flyouts, one or two offers from schools from anywhere across the US/world. It's rare that the schools that give you offers will jive super well with your preferences. For example, many top schools (e.g., Berkeley) are located in HCOL areas with wildly expensive property values, so many new Haas faculty live in subsidized apartments for a good chunk of time pre-tenure.
While our field is doing better than most PhD fields on many dimensions, you're still doing a PhD and that comes with challenges. There will be constant, low-level stress that you're not doing enough to hit the moving target of getting a job, huge cultural pressure from professors to stay in academia at all costs, and (at least at my school) you will quickly lose contact with students who drop out and professors who don't get tenure. Unlike most private sector jobs, it's very hard to "turn off" at the end of the day or over the weekend. This combination of factors generally leads to mental health challenges. I'd estimate about 50%+ of the folks in my highly visible R1 program periodically experienced mental health issues due to these pressures (including myself). There's a good chance that you would face similar issues too. Usually, these issues manifest in moderate to severe depressive episodes that span 2-6 months, often crop up around the start of the 3rd year, and reappear every so often thereafter. The PhDs in my group were uncommonly open about these issues, and I'm very thankful that I had a supportive network when I struggled. In general, this is a big issue among PhDs that research is just beginning to address (New study says graduate students' mental health is a "crisis").
On a systemic level, remember that advice and opinions from professors reflect survivorship bias (i.e., you don't hear from the folks that aren't in academia anymore). There's not much (if any) positive growth in b-school enrollments any more, especially compared to the growth period 1980-2010. Tenure lines in STR/OB, while still relatively commonplace compared to other PhD fields, are slowly shrinking at many schools as older professors retire and are replaced by cheaper adjuncts. I've gotten the sense that year-over-year demand for rookie PhDs in management academia has marginally but noticeably slowed over my time in the program, while tenure requirements are also getting marginally but noticeably tougher. I'd guess that around 25% of graduating PhDs in STR/OB don't stay on the academic track (at least at highly visible R1 schools, which is my reference set). Some of these "leavers" can't get an academic job they like, and some realize that they don't want to continue to make sacrifices that the academic arena requires.
To add a silver lining, however, I'm happy I did a PhD. I recognize that I was privileged enough to not worry too much about providing financially for a family, parents, etc. I was also well-connected and "culturally ingratiated" in academia prior to starting my PhD; there are huge barriers to entry in this field for those without cultural or financial capital. I learned a lot about myself, managing my time, how to do good research, and the ability and necessity to set my own course in work and life. Unlike some horror stories online, I loved the other folks in my cohort and program, and our culture was quite cordial. If I had to do it again, I probably would, but I would have tried to graduate ASAP, learned as many practical stats/metrics/coding tools as possible, and spent less time worrying about management theory (which oftentimes isn't geared towards actual managers and sometimes, in my opinion, verges on fluffy and poorly-developed philosophical rambling).
If you're in any way put off by this message, you should consider similar jobs that require brainpower and research skills (data science, statisticians, UX, etc.). These jobs will give you a good salary, are less competitive, are more location-flexible, allow you to gain marketable skills more rapidly, and don't require a long, low-paid training period. If you want to teach, an alternative is to get a masters and adjunct on the side so that you can align incentives with university administration, who generally want to minimize expensive tenure lines subject to constraints from accreditation bodies (AACSB).
The PhD journey is wickedly tough. Experiences and outcomes differ (wildly and often randomly) from person to person. For the vast majority of folks that think they'd be interested in this path, they should take a hard look at their preferences, desires, and life goals. If you're not at least 90% sold on this path, you should consider something like what I prescribe above. However, if you are part of that 10% that could only see yourself as a full-time business school researcher, then you should go for it. Good luck!
Many folks in both fields have tried to migrate to R since it is free, widely used in other parts of academia (stats, engineering, etc.), and is more of a general purpose statistically-focused coding language. However, outside certain niches (e.g., social networks), there's been a lot of path dependence on the standard packages. Partly, this is due to both packages being nicely optimized for point-and-click psychology and economics work, and partly, this is due to the fact that most professors older than 40 are only comfortable with the standard packages, so switching to R would act as a barrier to collaboration between advisors and advisees.
It is becoming increasingly likely that you'll use Python in some aspect of your research. Python is a high-level, object-oriented programming language that is hugely flexible and is one of (if not the most) used programming languages in the world. Python is well suited for data cleaning, big data prediction tasks (e.g., sentiment analysis, cluster analysis, etc.) and certain do-it-yourself experimental work (oTree).
As you go down this list, the learning curve gets a lot steeper. For example, being productive in Python requires a lot more familiarity with computer science than STATA. That being said, the power, flexibility, and real-world value of these items goes up as you go down the list. I'd recommend getting really good at your field's "standard". If you want to give yourself insurance for the non-academic job market, I'd teach myself R and/or Python over the summers (as well as SQL, which companies use to extract data from large databases).
Did you feel like you got to create your own research stream or did it feel like you were highly skilled, low cost labor that contributed to your advisor's work? Kind of get the feeling this is how it goes at places like Haas where the faculty is made up of rockstars with already well established, highly productive lines of inquiry.
Interaction with faculty varied widely based on their view of training. I worked with faculty that mostly saw me as cheap labor, those that saw me as a way to advance pet projects that they didn’t have time for, etc. One reason why folks work with high level faculty is that they hold a lot of proprietary, journal-tested data. It is increasingly getting *very hard* to get original data to answer original questions since (1) US and similar countries’ companies are starting to realize the value of privacy, (2) these companies have data scientists to do 90% of the analysis they need. Many very successful students either use data that their advisors give them or have connections to data sources from countries with lax norms around privacy. If you do end up going the PhD route, one of (if not the) toughest challenges is trying to “thread the needle” to find advisors that are interested in your own personal development, willing to share data, are willing to make connections for you, have sway among hiring committees, are enjoyable to work with, and that treat you like a colleague. My tendency was to work with folks that satisfied the latter two qualities, which I think was a mistake.
Your relationships with advisors inject a ton of randomness into the process and have a strong impact on your outcome. In your 1st year, it’s worth asking other students to be honest about their relationships with their professors on the dimensions I describe before getting involved. While this may be intimidating and socially risky, remember that about 10 people outside of academia actually think about these professors as “rockstars” (which is a term I think that academics use to overemphasize hierarchy and market their brand... rightly or wrongly it discourages collaboration). They are just people too, and put their pants on one leg at a time.
yeah I gotchu. What's crazy is I wonder if it's not even the faculty's fault. Under such immense pressure—with increasingly mounting tenure requirements—and probably having had to perform their own indentured servitude during their PhD, they most likely think it's just par for the course to expect the same diligence from the rising generation. I think it's just a fcked system without the ability to provide space and growth for the aspirations of the proletariat it requires. Do you think a path still exists, if any, to pursue "the life of the mind"—the romantic, intellectual trajectory of deep thinking that allows one to earnestly pursue the ideas he/she finds most meaningful, and then teach such to further the reciprocal mentor-student arrangement—?
Why Are Associate Professors Some of the Unhappiest People in Academe? - The Chronicle of Higher Education
While it can be "sick" in that it's tough to be fired, you get a nice salary for life, etc., it really depends on the person and situation. Like anything in life, you need to work hard, play to your strengths and interests, catch a few lucky breaks, and always be grateful for what you have!
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