bauble

It's also useful for exercising and improving your skills in economic intuition. The intuition in calc/analysis isn't exactly the same as in branches of economics, but there is enough similarity so that the skills are complementary. In general, it's probably helpful to get away from the "am I specifically going to be using Theorem 5.4 on p. 125 in my career" mentality into a more broad notion of "will this help me become a better researcher."

Yeah, I don't know how much "traditional" game theory comes into play. But look at Tirole's work. He would be one of the names that come to mind in looking for an intersection. For "micro" growth models, aside from using computer modeling, you have to abstract in one way or another away from 100000 unique firms. Either you reduce to small number of firms (like 2), or assume that some underlying continuous distribution of firms (distribution in the sense of some underlying quality such as efficiency). There are examples of both in the IO and innovation literature.

AJNewey's on the right track. Here's my understanding, which admittedly is a bit shaky. Broadly, the intuition behind some of the growth and innovation literature is that growth in part is affected by incentives to invest in innovation. Game theory comes into play in analyzing these incentives and therefore in building microfoundational models of growth (of which there are many). Not all of these involve game theory though.

The variability of effort needed in a Ph.D. makes holding a PT job pretty tough. Apart from that, significant part-time work above RA/TA is sometimes frowned upon, and in some cases I think that there are formal (and low) limits imposed by the b-school or dept on how much you can work outside of the program (with RA/TA counting as "inside").

In top (20 say) programs, the workload is high. Definitely not 40 a week. There is a lot of variation week to week, so a light week might be like 40-50, but when deadlines or times come when you realize that you are under the gun, there are weeks when you are working non-stop (80 or more hours). If I had to guess an average, I'd say it is 60 or maybe more for most people.

Realistic advice? Your concern for the poor souls who might be walking into a time-bomb by going to Chicago not fully funded is touching, truly it is. May all be so fortunate to have an advisor as good-willed as you. But let me offer up a couple of alternate, economically motivated alternate theories: 1) Social welfare will increase if the ignorant masses all go into the top schools thinking that they are "the chosen one." Economics will advance faster as a result. 2) Personal welfare is an open research issue as well. In other words, even many of those who go to Chicago (or other top school) and ultimately bomb out will have higher net utility, all things considered, than if they went to podunk u. There are a number of externalities to consider.

There are probably some reasonable hypotheses here, but you know what? Who cares? No one wants to hear that the odds are better for people with stellar records coming in except those with such stellar records who are insecure or feel they are entitled to inherit a distinguished academic career. In other words, nice people like rooting for underdogs and nice guys. This should be abundantly obvious from this forum.

Beyond the signaling aspect and getting into grad schools, there is also the notion of breadth vs. depth of learning. There is likely to be a substantial tradeoff in your case since you got some B's and are worried about prospects of doing so again. Both things considered (signaling + learning), it seems like it is better to take a more manageable load and ace those classes.

I respect your enthusiasm. But two things: 1) take the mental break, as many others have suggested. 2) You'd be doing well to cover a decent fraction of any single one of those first few books in a summer. If you must do something, then my suggestion would be to skim through notes that are listed in econphd.net instead, e.g. the Nolan Miller micro is a pretty good self-read.

Top tier programs overwhelmingly favor research over teaching, so we're just splitting hairs. But the idea of entering the job market with zero teaching experience (either TA or instructor, and counting teaching experience prior to starting the Ph.D.) is a very risky proposition. You might be hard pressed to find someone (and we're of course talking b-school here) who has successfully done it.

Teaching is one of those things that research (particularly top) schools like to dismiss, but as a practical matter teaching is a requirement for junior faculty everywhere that I know of. So it strikes me as weird then that people could potentially come out on the job market with zero teaching as might happen in the future with USC grads. But then again, it is hard to imagine that a person who gives a good enough job talk to get a job can't teach.

I don't think that inflation makes such a huge difference. It's extremely difficult to say how a student would have done at school X rather than the school she attended, even with "perfect" performance data. Clearly, selection and endogeneity problems abound. So, profs know certain (well-known) schools and have their built-in adjustment factors, right or wrong. If they don't know the school, then the other factors become more important. To sum things up, grade inflation merely adds a sprinkling of uncertainty to an inherently uncertain and ambiguous assessment process.

Well, getting a good placement is pretty hard in general. And it is quite possible that age came into the equation in some fashion for your acquaintances. But the "long time horizon" argument is questionable. Profs are pretty smart, so I just doubt that they'd miss noticing that 80% (or whatever the number is) of their senior colleagues moved schools within 10 years of initial placement, so why worry about whether their research longevity is 10 or 30 years?

I've heard this argument a few times, but I've yet to be convinced by firsthand experience. Two reasons: mobility and the prospects of dialing down "research active" status substantially after tenure. Mobility tends to be higher for younger folks, and the problem is exacerbated by hotshot status. Case in point: I almost went to a school to work with a young-ish hotshot, and he is no longer there. Granted, every school would love to have the iconic scholar who will chair the department at some point and bring fame and fortune to the university for the next 30 years, but I just can't see how hires can be based very strongly on that given my above points.

Simply put, the main emphasis is on demonstrating that you will become a good researcher. You've got to convince people that the low GPA was due to "the foolishness of youth" or something like that, and that you are past that and that the "true you" is a capable researcher. Whether you articulate this in the SOP is another question, but to your original question: High GPA and GMAT signal that you are smart, which is not the same as being a good researcher, but it is one of attributes that is highly correlated with it. The best way (in my opinion) is to take hard classes that the profs know are hard and ace them. For accounting, that would be (grad level) econ and maybe some finance classes I would think. Math classes (e.g. real analysis) would also do, but accounting profs would be less familiar with those in general. Doing good research, such as in an RA capacity, is perhaps a more direct signal of research potential. Making this signal credible is a bit more of a challenge though. Short of solo-authored work in a top journal, you'd have to get a rec from a prof that knows the faculty at the place to which you are applying.

You're fine. I don't think your profile will be what holds you back from a decent chance at the top schools (top undergrad is a significant plus).

Yup, this is exactly what I meant. For Princeton, X=60, Y=22-23, and E(X)=18-19 in this example.

I'm guessing that somewhere between the two answers is correct. A department targets Y students so it admits X > Y. But, if it admits so that expected students enrolling E(X) = Y, then the problem is that too many students might accept the offer. Therefore, it admits so that E(X)

Within b-school, I think that the strongest overlap is with IS departments (the "online" part of your interest) and those with organizational theory leanings. On the latter, those might be organized within a management or perhaps strategy dept. Outside of b-school, there are some places with school of information departments (e.g., Berkeley, Michigan), and I think that some related work might be done there, though I am not intimately familiar with those departments.

The application grief isn't such a big deal in the whole scheme of things, and you won't really be one year behind. Maybe a "half" year behind or something like that, but obviously by doing some work in the extra year you'll know more than now.

I'm not talking about just the large public research schools. What I'm talking about is what the university, irrespective of private or public status, is paying you for. So at the top schools, promotions, pay raises and tenure are heavily weighted towards research productivity. This is a clear signal of where they want you to spend your time. At a teaching school, I'm guessing it is based on evaluation of teaching performance with less emphasis on research, but I don't know. Can you place at LMU (or an equivalent place) from a top 100 school? A casual and random clicking showed a number of nice backgrounds. Plenty of UCLA/USC grads, hardly the "top 100" variety. I guess I can see how a balance can be met at a small private university. Still, the path from a "math-lite," top-100 Ph.D. to balanced private university academia might be extremely rocky, as a cursory glance at LMU faculty seems to imply.

Research and teaching are fundamentally at odds. At a top school, research value is in large part endogenously driven. Good reputation, good research, the financial ability to hire faculty who have light teaching loads, and the ability to gain endowments and high tuition all go hand in hand. What are the incentives to do research at a teaching university? For the love of it? Well, there is something nice about that, but how well does it work in practice when budget pressures call for increased teaching loads? If you’re OK with research-lite and teaching heavy, obviously there is no problem. But if you are adamant about a strong focus in both, perhaps this issue warrants a closer look. Maybe your school strikes the nice balance, so maybe you’ve talked to profs that convince you this can be done. On the other hand, I know that teaching pressures are high in some non-U.S. countries because the schools there are not as well-endowed as the (top) schools in the U.S. I met a highly regarded researcher from abroad who lamented the state of affairs in his home country. Scholars, even good ones graduating from top schools in the U.S., get tenure and then check out of research. Why? Endowments and the competing pressures like I discussed above. I’m saying these things because you seem to be striving for fine lines: math lite, and strong focus on both research and teaching. This balance might be easy to achieve, but from some indicators I’ve seen (like the prof I met from abroad) hint that this is not an easy stance to take.

Yes, I shared some of your pain, and you raise an excellent concern. I wonder if the OP has now been frightened into having MWG and a yard high stack of readings on his desk :).

Completely uncontroversial ;)

I get a little antsy when people say things like "micro is a waste of time." Certainly having a deep understanding of the theories in the business field you are in will help you do good research. It forms one leg on which to stand to define a strong research agenda. However, most business fields (I am quite certain of this in management, finance, and probably also for quant marketing and accounting) have social science disciplines that form another vital leg. Distancing the business fields from these disciplines is very much going the wrong way. For instance, one of the "hot topics" in quant marketing, borrowed from relatively recent IO economics, is structural demand estimation (from a seminal paper by Berry Levinsohn and Pakes) involving a heavy dose of econometrics and a light dose of micro. Seeing things like micro as a waste of time is a quick way of foreclosing similar innovations in business fields in the future. Sure, not everybody has to be the gatekeepers of this knowledge flow, but calling it a waste of time is just a bad message. Whew. Sorry to rant on like this, and I realize that feathers might have been ruffled. But this is academia, not a country club - we should be collegial, but probing. Also, as others have already pointed out, working through micro proofs is not the gritty hard part of a Ph.D. So why not see your first year or two as the best chance to develop the disciplinary leg of your background and have fun with it? Easier said that done, but it is a healthy attitude. To the original question. I'd study something that feels the least like a chore. For some that would be math or econ, or for others that might be big papers in their field. There is plenty of opportunity to grind through the few (or many?) chore-like classes that everyone invariably has during a Ph.D. (with time constraints added in for good measure).