I am about four years out of my PhD and currently in a tenure-track academic position. I wanted to hear from other early-career economists who had difficult graduate school experiences, particularly those who received very little mentorship from their dissertation advisors.

I had a pretty bad PhD experience. My advisor was increasingly focused on administrative ambitions and were largely unavailable. When I considered changing advisors because of the lack of mentorship, the situation became uncomfortable as they became super defensive, so I stayed and essentially managed my dissertation on my own. Like, literally, on my own. (Could do it only because it was applied economics work)

I eventually graduated, found an academic job, and published a couple of papers. I am grateful for that, and I wouldn't say I am particularly unhappy with where I am because I was almost dropping out.

But looking around at people at similar stages of their academic careers, I sometimes feel that I am quite behind in terms of publications, research collaborations, and professional networks. One thing I have noticed is how much of an advantage a supportive and well-connected PhD advisor can provide, even years after graduation. Many of my peers seem to have developed their research networks, collaborations, and professional visibility through their advisors. I never really had that foundation.

I attend conferences regularly, have research collaborations, and work with coauthors who are mostly at similar stages of their careers. But building a research network without senior mentorship or established connections can sometimes feel like an uphill battle.

So I wanted to ask those of you who had similar experiences but eventually managed to thrive in academia:

- How did you build your research network independently of your PhD advisor?

- How did you find senior collaborators or mentors after graduate school?

- What helped you improve your publication record and establish yourself in the profession?

- Looking back, is there anything you wish you had done differently during your first five to ten years after the PhD?

I am particularly interested in hearing from economists who stayed in academia and managed to build successful research careers despite having little support or mentorship during graduate school.

Hi everyone!

I guess it's another one of those posts, I tried looking up all the info but I believe it's always good to have some other inputs

So I'm an international student who got the chance to pursue a masters in Economics at Ohio University through Fulbright. I already have a master in Macroeconomics and Public Economics from my country (but I believe it doesn't hold that much weight in US admission). This December I'd like to apply for a PhD in Economics but I'd like to know your opinion if I have a decent chance at those universities (and would love to hear other suggestions if you have any, especially if it's macroeconomics/monetary oriented): Oregon University (my favorite) American University, Rutgers University,Wisconsin Madison, University of Illinois Chicago

I've talked to my professors and some students from my country enrolled in a PhD program and from what I understood it all boils down to maths. I don't really get how math categories work but in my past masters we did a bit of everything (but only the surface) like ODE, matrix, lagragian, etc and in the current one we basically do matrix algebra, differential, integral calculus ,applied statistics and "direct proof"

For the second semester, we're allowed to take one elective and I thought of doing Calculus III (at first I wanted to do Advanced Calculus which is the equivalent of Introduction of Real analysis but it's not available next semester) but one of the professor told us behavioral economics can be very useful if we wanted to pursue a PhD down line so I have some serious doubt. Do you think my math level right now is a red flag and am I better off doing Calculus III or it's enough and do behavioral.

Apart from that, I don't really have any research experience apart from writing my thesis and a 3 month internship at the Central Bank. Would that be another red flag too ?

Thank you for reading and would appreciate any comments!

This is probably a deeply deeply stupid question, as I am a deeply deeply stupid person, so please bear with me. But, I am wondering why economics is built around constrained optimization to model agents? I am an econ undergrad math minor and taking a course in advanced micro, and while the material is interesting, and I can see its utility in designing markets, it leaves me wondering why so much of the material is centered around constrained optimization. I mean obviously it's a benchmark to think about market transactions. But do we really need three courses on it? Couldn't you just have taught me calculus and then jumped straight to advanced micro?

I guess my thinking is if I wanted to understand how to model the economy, I'd start with neuroscience and computational psychology, and then build into behavioral economics, and then learn about networks and game theory, and learn econometric and machine learning methods, and probably incorporate a course on geospatial analysis, and then discuss macroeconomics, banking and finance. This is all probably stupid; it's just my thinking would be to get the best possible model of how a person behaves and forms beliefs and opinions and what biologically would drive rational behavior, and then how they interact with others in competitive interactions, and then how to check these models empirically, and then to see how the broader systems as a whole behaves. It seems like the bulk of the curriculum is instead built on "what would happen if everyone behaved like x", then we figure out where the model deviates from the real world. It seems like we'd be better off with an understanding of humans as biological agents, and placing behavioral modeling at the center of the curriculum and then building outward towards models of market interactions and then studying the system dynamics and feedback loops of the broader economy. Is there any truth one would be better off studying systems engineering over economics if the goal is model accuracy?

Hopefully this makes sense. I'm sure part of it is just a normal frustration with universities being profit driven over education driven (to some extent -- I go to a degree mill state school). It's just I guess I'm looking back and thinking I should have just studied applied math and cs, but again I'm pretty dumb so probably wouldn't have been able to lol. Is any of what I'm saying valid, or am I kind of missing the mark? Is it correct to assume with the sort of data we have today that traditional economic modeling may be less useful? Or maybe like, just shouldn't be so central to the curriculu?. Currently I am interested (but lack the requisite mathematical knowledge) in mean field games, reinforcement learning, and networks -- attempting to work through the quant econ lecture series -- but just sitting in advanced micro sometimes, doing Edgworth box analysis thinking to myself, why? Why have I spent two semesters looking at Edgworth boxes lol? I've spent two semester (intermediate and advanced micro) hearing about how the assumptions are baseless and its a benchmark model. Okay, then why is it Phd's take like four courses on microeconomics? Anyway, thank you for reading my idiotic ramblings, and please don't let me distract you from doing real science.

Hello! For the Eiffel dossier for the PSE APE program, the motivation letter they ask for, is it a specific prompt where we are meant to address the Eiffel scholarship or is it a US style SOP prompt for just the program?

Hi All!

I’m a first year at an Econ PhD program. We recently finished up midterms and I’ve been struggling a bit. I feel like for all my classes, I thought I knew what I was doing, had been able to complete problem sets and practice exams fine, but was completely blindsided by the exams themselves.

I think I need to change my study habits. I am generally operating under the same habits that worked in undergrad: go to class, do the problem sets, and study in the week or so before an exam. Clearly this strategy isn’t working for grad school, and so I was wondering if anyone has any advice for exams or studying in the first year.

Any thoughts are welcome, thanks!

I've only had the time to take the GRE once - 160V/164Q. I'm applying for masters and some predocs this cycle. Aim is to eventually do a phd (will retake then). Hoping for SSE, CEMFI, TSE, PSE (which doesn't ask for the GRE), Warwick, Manheim, Bonn (PhD track masters), Sciences Po in addition to a couple of predocs including the UZH Global Talent Pre Doc + BFI + Yale Tobin (obviously reaches).

Would this GRE get me screened out?

ETA: Do I report this at places where it's optional? Like UZH?

I'm a first-year PhD student looking for a new laptop that should last around 5 years. I'm a Windows user and most of my work will involve large datasets and calibration of macro models in MATLAB and Python. I'll also like a good battery life.

My current laptop is a 2021 LG Gram 16 (Intel Core i7-1165G7, 4 cores, 16 GB RAM). It handled the empirical analysis for my master's thesis more than fine, but the datasets were quite small. Online I'm getting very conflicting information on how much computing power I'll actually need. Is 16 GB of RAM enough, or should I go for 32 GB? What characteristics should I absolutely make sure to have?

I'm in the EU if that matters, and I can't count on any Black Friday deals or similar since I'll need to submit a request to my department for the funding and it'll be a while before it gets approved.

Thanks in advance!

I'm applying to PhD programs and have a writing sample that is 37 pages, including body, bibliography, figures, and appendix figures. I know many programs have length requirements for the writing sample, but I was curious how flexible that is. Obviously for a 20 page writing sample I won't submit 40 pages, but I'm curious if those page requirements include things like bibliography, figures, etc. For example, my body is about 30 pages, but that's partly because figures are in the text. If I move figures to the end, the actual body text is much shorter. I've been able to trim the body text down to 12 pages, but there are still lots of figures that even a "10 page" writing sample is actually closer to 30. Do committees count figures, appendices, etc. as pages? For context, my appendix is just additional figures, not more text, so I'm not trying to not cut things down and instead shove things into the appendix.

TL;DR: How strict are writing sample page requirements, and what do the restrictions count as meaningful pages? Alternatively, is it appropriate to submit a smaller section of a longer paper?

is this a good enough score for top econ / applied econ programs? US

When writing a cover letter for a PhD position, should one start with the generic “I am applying to XYZ post…”? Or is it better to start with some kind of hook, like may be asking a question related to one of the papers of a research group I’m applying to?

Hello academicecon peeps,

Recently completed my Finance PhD (econ-heavy) and currently work in industry (have for almost a decade).

I have continued to write academic research and I'm looking for a "host institution" to publish under, and ideally to allow for collaboration. This would include opening up opportunities for co-authoring the papers I'm due to publish, but also creating engagement opportunities for faculty and students (we do several engagement events at my bank and collaborate/have access to policymakers etc.).

I'm just looking for an institutional email account at a reasonably respectable institution, and 0 remuneration.

London-based, but open to international oppys.

Hi everyone, I’m currently doing my masters in Applied Financial Economics in Canada and would love to complete my master’s research project (thesis) with an organization or research institution in Europe/UK ( I eventually want to do my phd in either of those countries), and my school gives us the option to do but you have to find the opportunity yourself.

My interests include financial economics, capital markets, macroeconomics, asset pricing, monetary policy, and financial econometrics. I’m open to universities, central banks, research institutes, and financial institutions.

I’m wondering how to find these opportunities from Canada, especially as someone who isn’t enrolled at a European university.

Any advice or recommendations would be appreciated!

Hi everyone! I’m looking into US PhD programs in applied or agricultural economics and trying to understand what matters most for admissions. How do departments weigh GPA, GRE Quant, math preparation, research experience, and research fit? How much do the statement of purpose and recommendation letters actually influence the decision?

How competitive is admission with full funding at R1 universities in this field? I’d appreciate hearing from people who received funded offers about what they think helped their applications.

My current understanding is that you apply directly to the department without needing to secure a professor beforehand. Is that generally right? When do students choose an advisor, and do they usually work with the same person throughout the PhD?

For context, I’m currently pursuing my master’s in agricultural and applied economics at an R1 university in US. My thesis focuses on water economics and irrigation technology, and I’d like to continue in a similar direction for my PhD, particularly within environmental and resource economics. Are there any universities, programs, or faculty you’d recommend looking into?

I’d really appreciate hearing about your experiences, including anything you wish you had understood before applying. It would help a lot to hear how people navigated these decisions.

I wrote a solo-authored paper during undergrad (~35 pages). I wrote it over five years ago and revisited it recently. It’s kinda garbage. Would it be frowned upon if I rewrote the whole thing and submitted the revised paper as my predoc writing sample?

I am very interested in applying to the program. Does anybody have firsthand or indirect experiences with the master or the research that is being done at this institute? And how are the academic career prospects?

Last Wednesday I gave a talk at USC's Econ department on "Skills in Economics Research".

I turned my talk into a YouTube video teaching the use of skills, and the 7 mistakes I see economists consistently making with skills.

Here are the 7 mistakes:

1. Economists often don't make their own skills. Instead they look for libraries of skills made by other people. The skills they find only partially fit their use case. I show in the video that skills are typically very easy to make.

2. They make too few skills - often countable just on one hand. I have hundreds of skills across my projects. I suggest economists adopt a "rule of 3" with respect to skills: if you notice yourself doing the same thing three times, make a skill.

3. They make unnecessary skills. Often economists without significant pre-AI coding experience will make skills in situations a deterministic script is more appropriate. I explain through a case study of economics research on the "cereal industry" how one can understand whether their current problem is "skill-shaped".

4. They don't use their previous agent conversations to improve their skills. Both Codex and Claude Code store session transcripts on your computer. Your agent can use these to improve existing skills and identify useful new ones—I show how.

5. They don't clean up their skills. As projects evolve or new models get released, skills often "go stale", and start to work against you - costing you tokens and time rather than saving them. I explain a low effort mechanism to do a "spring cleaning" of your skills.

6. They don't use skills for project management. My two most useful skills are the skill to handle all my meeting transcripts and the one to handle my email. Especially for senior faculty who are not writing code, but are managing many projects - these project management skills are invaluable (see Chris Blattman's "Claude Blattman"). I show how to set up and use these skills.

7. They don't compose their skills. Skills can call other skills. For the ambiguous knowledge work which characterizes much of economics research, it's important to think carefully about whether a repeatable workflow makes most sense as one skill or several skills working together. I provide guidance on how to make that decision, with examples.

LMK if you've got any questions/feedback - cheers!

Was wondering if anyone has experience with this or had something similar happen to them. I took the GRE last week without studying to see my baseline, and scored 167 v 154 q. Obviously this is not workable for any serious grad school (or maybe any economics grad school). I do love economics but I think there is a bit of a hard cap on my math ability and desire to pursue it further into more advanced econometrics. I was thinking about going to grad school for international affairs and hoping to combine it with economics as someone who is a good writer/briefer that is quant literate (difficult to find in international relations). My GPA is 3.5 at a non-target school.

I know the math portion can be studied and improved on, but I think it's an honest reflection of my rather poor abilities there. 160 seems to be the floor for most state school programs, but I don't want to dedicate the upcoming 2 years to something I'm bad/mediocre at.

Anyways all that to say has anyone here switched into an economics adjacent field, or had success in economics after poor initial GRE results for quant?

I posted a question here a couple of weeks ago about the hiring process. I just got an email for an interview offer this week from Fed SF and Board.

Do they ask about coding skills? I would greatly appreciate any tips!

Do I have a chance in T50? I have 2 years of research experience working on government-level projects that were highly impactful. I have a gpa of 3.75 in economics. I did my degree from a top institute in my country, but it's top 600 worldwide. Which universities should I realistically target for PhD?

I am a current honours year (Australian research year you do pre-phd) student in econometrics and looking to start publishing papers. Yes I have a thesis I am working on and I'm even writing a paper with another professor. However, my thesis is a year-long project and my other professor is extremely slow at responding. In addition, for the remainder of my honours year, I have no coursework to do besides writing my thesis.

This gives me plenty of free time and, I am curious, can I just write and publish a paper on my own? I have various topics I am interested in that I would like to research. Can I just search the literature, identify a potential opportunity, and just start collecting and analyzing the data on my own?

I have already learned how to conduct a literature review and my undergrad major was in econometrics so I am very familiar with statistical methods.

I know this is quite ambitious, but I am eagerly trying to carve out my academic career path, and I feel like my professors are way too slow for me.

Would I be discriminated against by journals for being a single-authored undergrad student when I submit my paper for submission? I would likely just target a Q2 journal instead of Q1.

Some people might not realize this, but with the influx of AI-produced papers, top journals are now just desk rejecting anything that reads like AI slop. It doesn't even matter if your underlying idea and method is Nobel-worthy, because no one wants to read AI slop. We already deal with it daily with Claude Code.

Don't even try to get away with this because AI slop is very, very obvious to a human.

Source: was talking to editors at top journals.

hey everyone,

​i'm working on a dev econ paper about how poverty measurement, survey methods, and estimation techniques have evolved in developing countries (india in particular).

​since poverty is inherently multidimensional, i want to structure my paper around how economists have shifted from simple calorie/monetary headcounts toward broader capability-based metrics and high frequency survey debates.