econslave

Many of you seem to be concerned about funding at UCSD. The standard first-year guarantee is a 25% TAship (~$8500), which also fully covers tuition and health insurance. Note that this is just what the department guarantees. In practice, of the three quarters during your first year, you will probably be a 50% TA for at least 1 and most likely 2, which means you make twice a 25% TAship would make you during those quarters. Demand for TAs is so high for the undergrad program that they need more than they guarantee, and this is not going to change. Also summer funding is usually available for first years who are interested, so you can factor in an addition $3000 probably as well.

NSF gives three full years of funding, and you have to use these within 5 years of being awarded the fellowship. You can absolutely "hold on" to the NSF for a year or two before beginning your program.

Many departments will reconsider your application once you've won the NSF (if you let them know you'd be open to attending if they were to make an offer). Even if they don't, it should be relatively easy to apply again next year with a NSF in your pocket. Most departments will admit you if you do have a NSF.

:rolleyes: I don't think this is because it's a heterodox program. I do think it's because it's a very low-ranked one. Heterodox ideology won't pose much of an issue on the job market, but heterodox methodology will find little respect when it comes to the market.

I guess it depends on the school. Chicago certainly use exams to restrict access to their "guild". However, at other schools, everybody (eventually passes). Perhaps this is because they're trying to push you through. I like to think, however, that it's an equilibrium outcome.

You could make this argument for econometrics too. If I know I want to be a game theorist, why do I care about econometrics? Therefore, I should only have to take micro theory courses. The problem is that being a good economist/researcher requires you to be able to understand, to some extent, research in other fields. If you're on a hiring committee, and they're hiring in macro, your department doesn't want their seminar to be a different language. There's also the fact that they assume students don't know their fields for sure before entering. I know some people who thought they'd like macro since macro was fun in undergrad, but now can't stand the sight of an Euler equation. The jump from undergrad to grad is huge, and many do change their minds. Just to let you know, I can't stand macro either. I don't think you need macro to be a good economist. Consider the excellent economists that have Ph.D.'s in applied (or pure) mathematics, or those with business school Ph.D.'s that never take macro and go on to be excellent researchers. Unfortunately, in a general economics program, they want to expose us to a little bit of everything, for better or for worse. I hope that after this first year is over, I think it was for the better.

I've done a lot of RA work throughout college and also full-time post-college. My experiences have varied quite a bit, and what I've found is there's no golden way to impress any professor. Every professor has a different style -- some are more hands-on, others want you to work independently and report back to them. At this point, since you've been working for this professor, you should have a sense of which style the faculty member prefers. It sounds like you are already working very hard for this professor. I would not work any harder than you already are. If you want to impress this professor more, don't work harder, work smarter. The big difference between undergraduate RA work and doing research in graduate school is that in the latter case, you need to come up with your own ideas. You are learning very useful skills right now, but you need to bring it to the next step and bring some of your own ideas to the table. Your professor may or may not be receptive to your own research ideas (of course, this depends on who you're working for). But often, professors have their own research agendas, and they are not looking to pursue someone else's ideas or looking for coauthors. You should aim to bring something new analytically to the work you do for this professor. Don't just run 20 regressions the professor asks for and bring back results. Consider possible issues in the analysis. Is there another model you might use? Are there omitted variables? Are there relevant papers that are not in the literature review? Are there other things you can find in the data that would enrich the analysis? ... These are the types of questions you should ask yourself while you're programming in Stata. Doing the tasks the professor asks for is important. But you should aim to add value to the research, and in some sense, take ownership as if it were your own.

Those who are not already in graduate school do specified a proposed program, although it often works out that people attend programs different from those they specify. For those who apply after having started graduate school, the institution is known.

There's no way to know whether evaluators know for certain whether an applicant has a university fellowship or TA-ship. But there are programs in which every person admitted has full funding. I also think that many of the evaluators know the typical funding packages offered by various university departments (for instance, Chicago is known to extend a lot of unfunded offers, and many state schools are less generous than private ones). Of course, there's no way to know for sure since it's a black box. This is just what I've gathered from people I've spoken to.

I don't think there's any reason to worry. The truth is, from what I've been told by people who have served on NSF evaluation committees, there is a lot of randomness in the selection process. In most cases, NSF fellowships simply crowd out departmental or university fellowships, so often decisions are made not completely based on merit, but on how much the applicant "needs" the funding compared to others. People I know who have applied in the past have gotten fairly disparate reviews, indicating that your fate is largely based on those reviewing your application. This is not to say that people who win an NSF are not deserving. However, if you don't, it doesn't mean you shouldn't have won, or wouldn't have won if different reviewers read your materials. Since in some ways, the NSF is a lottery, best of luck to those who applied.

If you were 100% sure you wanted to do IO or macro, I'd say go to UCLA. Otherwise, UCSD is a much better department. I'm not sure what you mean when you say UCLA is generally regarded as being better than UCSD. I do believe UCLA has more high-ranked programs than UCLA, but that isn't important when you're really only part of one program at any given school. Attend both flyouts and you will see that UCLA is in pretty bad shape right now.

Nobody will take it personally. Unless it's Harvard or MIT, most applicants a school accepts will turn down their offer. You can simply thank them for the offer, and say you've ultimately decided to accept an offer at another school. On another note (this may or may not apply to you), if you have offers that are strictly dominated by others, you might consider other fellow applicants by turning down your dominated offers. That is, if you are deciding between a couple top 10 programs, and you have some low-ranked programs that you haven't turned down, it might be nice to turn down the low-ranked one so that someone else who really wants to go there can attend the flyout. Of course, it is your right (after all, you've been accepted) to attend the flyout and hold your offer until the last minute. But I know some others who have done this, and they've told me that attending flyouts to schools they are almost 100% sure they will not attend is sort of awkward, and a waste of everyone's time. Just something to consider.

I don't have much information with which to back this up, but I get the sense Pitt is on the rise. I would certainly choose Pitt over Texas A&M.

If you're looking for the "best" ranking, it doesn't exist. Look at USNews for the top 10 (give or take a few). After that, try to find field-specific rankings (using IDEAS or the outdated econphd.net page). Outside the top 10 (or so), overall rankings are not very useful.

Attend all flyouts (except for Duke). Unless you really don't like Berkeley, you should go there.

If you are concerned about how professors will perceive you if you ask this question, you can direct the question to a current graduate student or department administrator. Attrition is a hot topic for flyouts. If you DO get to meet professors, have more substantive questions for them, and learn a little bit about them before you get there.

I think that a strong interest in academia is a necessary condition for entering a Ph.D. program in economics. Throughout your first year, your desire to enter academia will be challenged. Your ideas about what it means to be an academic economist will change, since you will witness what academic economics is really like, as opposed to what you thought it was as an undergraduate. Moreover, your expectations about what your life as an academic economist will change. For this reason, you need to have a strong interest before you enter. If you go into the program thinking you'll be fine in a private sector job, you'll take any chance to convince yourself it's time to drop out with a MA and get a private sector job. Even with a strong interest in academia, you might feel this way. When I was an undergraduate, I focused on only the positive aspects of academia -- flexible schedules, lack of the kind of politics you see in the corporate world, and what I viewed as a meritocracy. However, once you become acquainted with successful academic economists, you see some things that mirror the corporate world more than you might think: -- Many successful academic economists don't really feel like making time for graduate students. Some are quite cocky, and view graduate students as a kind of resource to advance their own agendas. -- Academia is EXTREMELY political. If you're the type who's going into academia because you don't want to network your entire career, you are in for a rude awakening. Writing excellent papers is not enough. -- Chances are, even if you are an excellent researcher, you will not get a top 20 placement. This is just supply and demand. -- The life of an assistant professor at a top research university is similar to the life of someone starting out in the private sector -- you work long hours, and still have people above you that you need to please, in some sense. -- Academics are just like any other group of people -- some nice, some not That said, your expectations might change your first year, and if you still want to enter academia, then you made the right choice. But if you enter with little to no desire to become an academic, you are probably only going to like academia even less once you're in the Ph.D. program. Just my two cents.

I don't think it will hurt you if your interests are pretty mainstream. But you should also make sure your letter-writers know your interests are more mainstream. A letter from your recommender saying, "X will be the best marxist economist academia has seen in the last 20 years" is unlikely to get you a spot in any non-heterodox program.

You should absolutely signal your location preference, but I don't think it'll make a big difference unless the location is odd. For instance, if you write that you really want to be in NY, Boston, or the Bay Area, your location preference isn't going to really help. If your preference is a more isolated location, like, say, Columbus, Ohio, or Austin, Texas, then this could help.

I like the idea of segregating profile evaluations. I sign in very rarely now because most of the admissions-related stuff is irrelevant to me. What would be more useful is information about other departments, placement, and other grad student issues -- something more like the job market rumors site, but less obnoxious.

Some types of research take longer than others. Research, in general, takes a lot longer these days. Expectations are higher and the low hanging fruit have already been picked. In addition, advising varies greatly. You can't just go to your advisor every time you have a little question. You have to try to answer as many questions as you can by yourself -- in most cases, approaching your advisor with questions you could look up the answers to yourself will make you look like a fool. Some people get out in fewer than five, but it's actually pretty rare. It's all a function of how early you're able to start. Some people come in with more preparation than others and need to devote different amounts of time on coursework. There are a dozen variables, and taking longer isn't a sign of slacking off.

I'm a little bit confused by this thread. Are we just trying to identify the non-top 50 programs that are still good for something? Or are we trying to identify these lower-ranked programs that are still good overall? Or are we saying that these programs shouldn't be non-top 50, but rather top 50?

Chicago for sure. UCSD as well. NYU.

Nobody is excited for first-year courses because nobody applies to Ph.D. program for the purpose of taking courses. That said, it doesn't sound like you're especially excited by research either, which is the point of a Ph.D. program. If that's the case, you should consider alternatives. Most people find the first-year coursework very difficult. In undergrad, it's pretty easy to drop a course and forget about it. But when you move somewhere and commit to a program for 5 years of your life, dropping is not as easy. And if you're not 100% sure you want to do it before you get in, you're only going to get less and less excited about being in your program once things start getting tough. My suggestion is to wait it out for a little while. Get your feet wet a little more (maybe audit the course instead), then apply next year if you're sure.

These three schools are not different from the rest -- they're just the only top 15 schools the OP mentioned in schools he/she is applying to. Most departments officially have no cutoff, but unofficially they do.