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PhD in Economics Advice for Research Heavy Undergrad


Zaramemon
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Hi All! I am currently in my second year at American University in D.C. I'm graduating in 3 years in may 2022, making me a junior in credits. I am an economics major and math minor with a 3.88 GPA

 

I plan to apply for PhD in economics programs as well as some dual-degree or combined econ/public policy PhD programs. Right now I am planning on applying to top 30-60 for my target schools as well as potentially some reach schools in the top 30. I wrote a brief summary of my statistics below:

 

Math Classes: Calc 1 (A), Calc 2 (A-), Statistics (passed because of COVID grade changes), Linear (A), and Calc 3 (A). I also plan on taking Foundations of Math (Math 403) and Real Analysis, both of which I hope to get an A or A-.

 

Economics Classes: Basic Macro and Micro (A), Intermediate Macro (A-), Intermediate Micro (A), and 3 econ 3xx electives (A). I also will take both econometrics 1 and 2 as well as 2 more upper level econ electives and mathematical economics. I expect that those classes will also be mainly A or A-.

Internship Experience: I have interned at the Treasury Department, worked at the Smithsonian, and interned for a consulting company.

 

Research Experience: I have worked as a RA for a year and have co-authored a historical economics paper (and one more on the way) with my economics professor. The papers will be presented at various conferences in Spring/Summer 2021. I also am working as a RA in the mathematics department on a federally funded project that identifies malware in data documents using comp sci theory. I hope to do some sort of research position next summer as well.

 

I also have significant diversity in my background although I don't know how much that would help.

 

I would appreciate any advice on my chances for grad school or classes to take!

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Not sure what your priorities are but your profile seems primed for a full-time research position (provided that your "historical economics" paper was an empirical paper that used historical data a la cliometrics rather than a history of thought type paper) at a top 10 predoc (I am at one with a similar profile to yours). This would open the door to top 20, and even top 10 PhD programs, provided you do well and get superb letters. Of course, you may not want to spend one or two years as an RA and may want to start a PhD right away, in which case you should apply widely. You should sprinkle some top 20 schools (and maybe a few top 10s) because the expected marginal benefit far exceeds the marginal cost, even with the low probabilities of getting in.
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Not sure what your priorities are but your profile seems primed for a full-time research position (provided that your "historical economics" paper was an empirical paper that used historical data a la cliometrics rather than a history of thought type paper) at a top 10 predoc (I am at one with a similar profile to yours). This would open the door to top 20, and even top 10 PhD programs, provided you do well and get superb letters. Of course, you may not want to spend one or two years as an RA and may want to start a PhD right away, in which case you should apply widely. You should sprinkle some top 20 schools (and maybe a few top 10s) because the expected marginal benefit far exceeds the marginal cost, even with the low probabilities of getting in.

Very misleading suggestion. The OP’s profile is hardly T25 material; suggesting to “sprinkle some T20 and few T10’s” is just a waste of money. American University is unranked both in Econ and Mathematics; I would talk to your professors on the range but T27-60 seems about right. If you have the time, I would take Grad Micro/Metrics at Georgetown to improve your profile.

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God sometimes you guys give such silly advice! I personally know someone from American who got into Berkeley with a similar profile. The marginal cost of applying to another school is negligible and the benefits are large. I don't know what you consider top 25 material, but I am at a top predoc and am familiar with the profiles of students at both Stanford and Yale's predoctoral programs and they place fairly well; you guys love to claim that predocs are pure selection and zero treatment. Well then, OP has a chance!
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As for advice on applying to predoctoral programs, the fact is that you will end up applying to pretty much all of them in sight. However, you can "optimize" your placement outcomes with a few heuristics: try to pick faculty who are established scholars (with top 5s), even if they are at slightly lower ranked schools over APs at top schools. Try to pick *projects* where you have an absolute advantage in some aspect of the project, not just a comparative advantage. Such projects often involve heavy computational component, interacting with proprietary server backends (such as projects that require use of Facebook data; see the work of Michael Bailey, Johannes Stroebel and Theresa Kuchler), and use of machine learning methods where RAs quickly become indispensable. Of course, pick projects that you are actually interested in for the most part; you want to be able to write credible cover letters for these positions.

 

The tasks you will get if you hear back would depend on the type of position: reduced form/applied micro type positions usually have a Stata/R exercise where they make you clean some data, figure out some specifications, estimate these, and report back with tables and figures. I haven't seen what data tasks for structural work looks like (even though my current position is in structural); some people in my cohort were asked to submit SQL code samples and MATLAB samples from existing projects. These could include code for dynamic programming (so like value function iteration), or discrete choice demand models in the BLP style (some IO people at Yale look for this). For theory, you usually get a problem set that depends on the field you are applying to work in. Theory positions are rare.

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God sometimes you guys give such silly advice! I personally know someone from American who got into Berkeley with a similar profile. The marginal cost of applying to another school is negligible and the benefits are large. I don't know what you consider top 25 material, but I am at a top predoc and am familiar with the profiles of students at both Stanford and Yale's predoctoral programs and they place fairly well; you guys love to claim that predocs are pure selection and zero treatment. Well then, OP has a chance!

I would rather speak from a data point of view than make “I know someone who did this” arguments. Also, T10 Predocs are much harder to land than actually getting into T10 for PhD. The OP is yet to take Analysis, so there is no T10 predoc acceptance at sight. You are predicting prematurely that OP will get into a T10 predoc and that even if OP does not, he should still apply to T10. Btw, constantly mentioning you are a T10 predoc on every post will not make people consider your argument noteworthy. You actually need to make an intelligent argument.

 

Note: Looking at your post history, you claimed before it was not worth going to program outside of T30 and made some absurd claims about grad micro. I doubt you are a predoc, oh excuse me, a predoc at T10 or whatever; just a wannabe who has a lot of free time in their hands.

 

To OP: Don’t mind people like this and always check their post history on this forum. Make sure you continue to do research with your profs and ace the analysis sequence at your school. Georgetown is very close (and it is a T50 Econ School); you can surely take Grad Metrics/Micro from there to boost your rankings even more. Good luck!

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Bayes, I don't want to get drawn in an unnecessary fistfight over this but I know several predocs at top 10 who take Real Analysis for the first time as predocs. Top 10 PhDs are far far harder to get into than predocs, partly because until recently predocs were largely restricted to domestic students (MIT SEII still might be restricted to domestic student). The inelastic supply of international students, along with their vastly superior preparation in terms of coursework (they often have taken the entire PhD core), means that domestic students are forced to exploit their comparative advantage in applied research. Often, this means less focus on math and more on actually doing RA work since early in undergrad. In fact, at my predoc, most predocs are not sufficiently well prepared to take PhD level first year courses (and in fact mostly opt to take 2nd year courses). There are a large number of incentive issues too here, because often not taking PhD micro is better than taking it and not getting an A.

 

See here the people taking MATH 301 are predocs taking analysis for the first time. One person is taking grad micro for credit out of 30.

[ATTACH=CONFIG]7276[/ATTACH]

 

Also, yes, I don't believe it is worth it to go to schools outside a certain range as they just become a way to exploit cheap labour from grad students via teaching assistantships. You want to have fun teaching intro micro while taking a full load of grad courses just so you can get your meager 15,000 stipend go for it. But don't sell these idealistic pipe dreams to people, where they slave away for 5 years at a program only to get jobs they could have had with a bachelor's degree.

 

Frankly, you sound like a bitter and jealous person by insinuating than I am not a predoc; I don't have anything to prove to you or anyone else and I am just offering a pathway for people who didn't go to Ivies as undergrads to get into predoc positions. I didn't go to an Ivy or a top 15 phd granting school (I went to a top 15 LAC) and I got into a predoc just fine. If any curious person reading this really wants to verify my credentials they can do so by messaging me and I'd happily tell them who I am.

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Bayes, I don't want to get drawn in an unnecessary fistfight over this but I know several predocs at top 10 who take Real Analysis for the first time as predocs. Top 10 PhDs are far far harder to get into than predocs, partly because until recently predocs were largely restricted to domestic students (MIT SEII still might be restricted to domestic student). The inelastic supply of international students, along with their vastly superior preparation in terms of coursework (they often have taken the entire PhD core), means that domestic students are forced to exploit their comparative advantage in applied research. Often, this means less focus on math and more on actually doing RA work since early in undergrad. In fact, at my predoc, most predocs are not sufficiently well prepared to take PhD level first year courses (and in fact mostly opt to take 2nd year courses). There are a large number of incentive issues too here, because often not taking PhD micro is better than taking it and not getting an A.

 

See here the people taking MATH 301 are predocs taking analysis for the first time. One person is taking grad micro for credit out of 30.

[ATTACH=CONFIG]7276[/ATTACH]

 

Also, yes, I don't believe it is worth it to go to schools outside a certain range as they just become a way to exploit cheap labour from grad students via teaching assistantships. You want to have fun teaching intro micro while taking a full load of grad courses just so you can get your meager 15,000 stipend go for it. But don't sell these idealistic pipe dreams to people, where they slave away for 5 years at a program only to get jobs they could have had with a bachelor's degree.

 

Frankly, you sound like a bitter and jealous person by insinuating than I am not a predoc; I don't have anything to prove to you or anyone else and I am just offering a pathway for people who didn't go to Ivies as undergrads to get into predoc positions. I didn't go to an Ivy or a top 15 phd granting school (I went to a top 15 LAC) and I got into a predoc just fine. If any curious person reading this really wants to verify my credentials they can do so by messaging me and I'd happily tell them who I am.

If you are indeed a Pre-doc and instigating misinformation like this, then that is even worse..

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Bayes, I don't want to get drawn in an unnecessary fistfight over this but I know several predocs at top 10 who take Real Analysis for the first time as predocs. Top 10 PhDs are far far harder to get into than predocs, partly because until recently predocs were largely restricted to domestic students (MIT SEII still might be restricted to domestic student). The inelastic supply of international students, along with their vastly superior preparation in terms of coursework (they often have taken the entire PhD core), means that domestic students are forced to exploit their comparative advantage in applied research. Often, this means less focus on math and more on actually doing RA work since early in undergrad. In fact, at my predoc, most predocs are not sufficiently well prepared to take PhD level first year courses (and in fact mostly opt to take 2nd year courses). There are a large number of incentive issues too here, because often not taking PhD micro is better than taking it and not getting an A.

 

See here the people taking MATH 301 are predocs taking analysis for the first time. One person is taking grad micro for credit out of 30.

[ATTACH=CONFIG]7276[/ATTACH]

 

Also, yes, I don't believe it is worth it to go to schools outside a certain range as they just become a way to exploit cheap labour from grad students via teaching assistantships. You want to have fun teaching intro micro while taking a full load of grad courses just so you can get your meager 15,000 stipend go for it. But don't sell these idealistic pipe dreams to people, where they slave away for 5 years at a program only to get jobs they could have had with a bachelor's degree.

 

Frankly, you sound like a bitter and jealous person by insinuating than I am not a predoc; I don't have anything to prove to you or anyone else and I am just offering a pathway for people who didn't go to Ivies as undergrads to get into predoc positions. I didn't go to an Ivy or a top 15 phd granting school (I went to a top 15 LAC) and I got into a predoc just fine. If any curious person reading this really wants to verify my credentials they can do so by messaging me and I'd happily tell them who I am.

 

Went to the same school as where alphatrunk works (from info of the screenshot), I can confirm what he/she described regarding coursework preparation for predocs and domestic students to be accurate. However, acceptance of predocs are more about fit than coursework preparations, or other factors typically considered for grad application. It is entire possible for one to get rejected anywhere from predocs but get into top 5 PhD programs, or the other way around

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I'm not sure why this was downvoted, to be honest. As a T30 predoc, a lot of this is good advice. My predoc let me become "indispensable" precisely by mastering computational methods, which ended up leading to coauthorship on the project. But even if you don't end up becoming a coauthor, the prof can then sincerely say "the project would not have happened without Predoc X."

 

As for advice on applying to predoctoral programs, the fact is that you will end up applying to pretty much all of them in sight. However, you can "optimize" your placement outcomes with a few heuristics: try to pick faculty who are established scholars (with top 5s), even if they are at slightly lower ranked schools over APs at top schools. Try to pick *projects* where you have an absolute advantage in some aspect of the project, not just a comparative advantage. Such projects often involve heavy computational component, interacting with proprietary server backends (such as projects that require use of Facebook data; see the work of Michael Bailey, Johannes Stroebel and Theresa Kuchler), and use of machine learning methods where RAs quickly become indispensable. Of course, pick projects that you are actually interested in for the most part; you want to be able to write credible cover letters for these positions.

 

The tasks you will get if you hear back would depend on the type of position: reduced form/applied micro type positions usually have a Stata/R exercise where they make you clean some data, figure out some specifications, estimate these, and report back with tables and figures. I haven't seen what data tasks for structural work looks like (even though my current position is in structural); some people in my cohort were asked to submit SQL code samples and MATLAB samples from existing projects. These could include code for dynamic programming (so like value function iteration), or discrete choice demand models in the BLP style (some IO people at Yale look for this). For theory, you usually get a problem set that depends on the field you are applying to work in. Theory positions are rare.

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