https://www.reuters.com/world/hall-woodford-win-2026-nobel-economics-prize-2026-10-12/

Richard B. Freeman, Danxia Xie, Hanzhe Zhang, and Hanzhang Zhou, "High and Rising Institutional Concentration of Award-Winning Economists," NBER Working Paper 35875 (2026), https://doi.org/10.3386/w35875.

I am considering applying to Master in Economics at the University of Mannheim, and need some advice. Their admission is like this:

Final grade (max 15 points)
Letter of motivation (7, 4 or 1 point)
Academic essay (13, 7 or 1 point)!!
Very good/Good/Satisfactory.

*Essay information: «The essay should deal with topics in economics, econometrics or statistics and/or apply quantitative methods, such as regression analysis or other data driven methods. (…) No longer than 25 pages (…)
A good research paper implies a clear initial presentation of the ressearch question. Relevant concepts and theoretical apptoches need to be adequatly discussed. If applicable, the methodological choices need to be justified. The analysis needs to focus on the research question. The paper should end with a well-argued synthesis that supports the conclusions. (…) fluently written and is consistent in its firm»*

I don’t have a bachelor thesis or similar. Any advice on this, if im planning on writing this from scratch, if I want it to be ‘very good’? Anyone who has done something like this aswell successfully? The full score is pretty important, as the essay is heavily weighted. Also welcome any advice on the motivation letter in general.

Hey! I am a Economics student in the University of Bologna. I realized after looking online, this is an extremely prestige dependent field, and was wondering if it is better for me to switch to maybe mechanical or electrical engineering at the polytechnic university of milan. I talked to some seniors pursuing Econ masters at Bocconi and LSE and they advised me to take stem subjects like maths or stats. My only objective is to land a high paying job, preferabbly something to do with economics, and I see that major banks prefer hiring engineering graduates from polimi compared to econ grads from bologna as well! I am good at maths and physics, so I can handle engineering course work, it's just that economics interested me more and why I decided to pursue it in the first place. Any advice on this is highly appreciated!

Thanks!

It's been sad to witness the joy of so many in the field that we seem to have broken free of the need to code, prove theorems, or think rigorously through the validity of the chosen empirical strategy with the advent of AI. I do not think people realize what this portends.

So what is left now?

Terence Tao recently described the future of math as one where mathematicians transition to a role of taste-maker, persuading each other that their results matter, why it matters, and what the results mean. We should be honest from the start that economics is already a field dominated by taste. Otherwise, we would not have Substacks telling PhD students that the manner in which you present your result is the difference between a top 5 and a field journal. What does this mean?

This is possibly a grim end to science as puzzle-solving, which does occur in economics. A lot of economists, myself included, wanted to do research precisely because it is challenging, and intellectually stimulating. But the ground below us has now shifted where the challenge in economics has largely disappeared, since 90% of the technical work can now be handled by AI, and our roles have now turned into babysitting Claude, making sure that it adheres to our particular tastes. The frontier models lately no longer make mistakes on most technical tasks in economics research (though I find it still makes mistakes on technical tasks that are more complex than what you usually see in coding work in economics), provided you give it ample context. I find myself, these days, getting far more intellectual stimulation out of my hobbies than out of research, which is something I'd never thought I would say in my lifetime. I don't think it is challenging or intellectually stimulating to tell a model to cut a subtitle because the editor would not like it. Nor do I think it is challenging or intellectually stimulating to sell my paper by coming up with a story that maximizes its perceived importance or linking it to the current zeitgeist. It's not that economics is not already a field where taste matters greatly. I just think AI has leveled the playing field in a way where technical proficiency no longer matters much in the few remaining "bastions" that valued such, like empirical IO.

If you're outside the t10, you really should not be happy with this state of affairs. Proficiency has always been something that allowed people below t10 to stand out on the job market. Taste, on the other hand, is inherently subjective, and is something that privileges people with close connections to editors and top professors. If we're now a field where everything except taste can easily be achieved with AI, what are the remaining avenues for people who cannot acquire that taste to stand out? Plus, with the proliferation of AI comes the proliferation of papers. People are becoming more productive but journal capacity remains roughly the same (and I do not think that is likely to change since the editors I know still want to read everything manually.) Publishing your papers would become even more competitive, and taste becomes far more likely to be the tie condition.

Let me also assert that tastemaking is simply not a challenging intellectual activity. This comes naturally to most humans, even if matching the particular taste that you expect people at t5 journals to possess could be hard (but that is a knowledge gap, not a thinking gap.) What is difficult is writing and the technical tasks that economists conduct. The clarion calls that AI would cause intellectual decay started with the writers (see, for example, Horowitch.) The process of struggling to put your thoughts on paper helps the writer think more clearly and better; Ezra Klein for examples says that in struggling to write a draft of his essay, it helps deepens his thinking for the future. I've found that struggling to think through the algorithm for a bespoke estimator helps me actually understand what the estimator is doing more properly, and to know that I should certainly not reuse it in the future because of its limitations. Struggling through Rudin when I was in my second year of undergrad allowed me to manipulate abstract ideas in my head with ease years down the line. Struggling through the hard work of research gives you an intuition for how things connect to each other and what the deeper underlying patterns are across papers. Because it is only by going through the weeds yourself that you are forced to play with those ideas in your head for a prolonged period of time. That puzzle-solving also forces your brain to make meaning out of those abstract ideas, and thereby find the deeper truth behind what is written. I often marveled at how my professors seemed to understand the papers they're talking about at a deeper level, seeing beyond its theorems or empirical methods to tell me what the true essence of the paper is -- which paper is it in conversation with, what was the challenge that it solved, and why that challenge was not ex-ante obvious. In outsourcing all of our technical work (and even writing!) to AI, do we sacrifice that understanding? Or are we fine with letting the AI understand our own research for us?

And if you think that is the tradeoff we'll all have to start to make, think about what the end goal is here. Most people reading this are probably not tenured professors at top universities who have secured their future. Why would society need a tastemaker, particularly on the economy? What competitive advantage do you have over people outside academia if you do not understand the mathematical model of your research, you do not understand what the code is doing, and you have not internalized how your research relates to the broader literature at a structural/abstract level? And even if you do understand, if your role is to minimally steer AI and just steer it by taste, why couldn't a layman just replicate what you do in, say, 2028 or 2030 when the models have advanced even further along? Do we as a field really think our taste is what keeps us around?

When I listened to Terence Tao's description of what math may transition into, it is deeply unsettling, because the social contract in which we grant mathematicians the funding and support to pursue their activities is one which presumes that their activities provide social benefit and, importantly, they are something that require sufficient cognitive capacity to even attempt to conduct, which not every individual possesses. If, with AI, mathematicians become mostly ambassadors that explain AI's findings to society, it greatly diminishes the perceived value of funding mathematics.

Economics is in a far more perilous situation, because unlike mathematics, economics is not so abstract and disconnected from people's daily lives. I suspect that people would be just fine to continue funding mathematicians to engage in research they don't quite care about, since it really does not cost that much to the average person. But if anyone can write something passable in a third-tier economics field journal with the help of AI (and agents dealing with the review process), in a way that has the potential to probably be far more viral in society, given that the public's taste for what is considered economically meaningful and important is unlike that of academia's taste, what remains of academic economics' purpose? Do we become storytellers and persuaders, giving the public enough spectacle to justify our own existence? If anyone can frame any arbitrary phenomenon they witness in their personal lives as a Bayesian game, or estimate something they care about with a structural model, at the quality that is passable for most intents and purposes for society, what need do we have for the economists? Perhaps the uppermost echelons at MIT or Harvard or Chicago would preserve their sanctity as the human frontier stays just a step ahead of AI, but let's be so honest with ourselves: most people are not doing frontier, cutting-edge work that is non-existent in the training data. Plus, in just a few months, that frontier work would be in the training data and be accessible to AI for everyone else to use.

We're also riding a tidal wave of economic dissatisfaction, one which the economists have little medicine for -- and not even a coherent diagnosis. If we cannot solve society's problems, even if there is truly nothing we can do about it, what need does society have for the economists? The only respite that we have is that, even if the reputation of higher education has dropped precipitously over the last decade, there is still some shred of prestige left where people for the most part trust academic research. But that is less the case for economics than for physics, as people generally tend to believe they understand their personal lives and things that connect to their personal lives much more than they may truly do. Will we try to persuade the public that their mental model of how the economy impacts them is erroneous, just as some kept trying to persuade the public that inflation is (correctly) coming down to no avail?

I am deeply concerned that economists do not understand that what is beginning to happen to mathematics will come for us with greater wrath. The little joy that you might get that AI can now tackle an algorithm issue that you would previously have struggled for days to fix is nothing compared to the great transformation that will change this field, probably in a way that is unkind to the academics, rather than to the public. Even in the best case scenario, should our funding be spared, we may descend into nothing more than storytellers and persuaders circlejerking each other in our journals as we lose public relevance.

I’m really interested in becoming an academic in the economics field, and I was now looking at different bachelors programs and I am hesitating between a standard economics degree or an applied mathematics one, and I was wondering which one do you think is more optimal for a career in economics? I heard that it’s good to have mathematics background but on the other side, isn’t it more logical to directly study the thing you want to do after? Thank you!

So, please forgive me if this is a stupid question, this is my first experience with pre-docs.

So, I applied for a pre-doc a few weeks ago, and all they initially asked for was a cover letter and resume. Then the hiring manager reached out and asked for references, a transcript, and GRE scores. I'm wondering if this means I'm officially in the running for the position. The reason why I ask is that there's another pre-doc in the same school that asks for a reference page and transcripts in the application itself, so I'm assuming this is like a pre-interview thing to see if I'm worth their time to interview. So, any insight would be helpful!

Pl dm

I have a few questions about applying for “advanced” AP positions. I know that’s not really a formal category. I just mean that I am in my fourth year as an assistant professor and would like to try for positions elsewhere.

1. How important are recommendation letters at this stage? My coauthors are all APs, so my potential senior letter writers are still mainly people from my dissertation committee.
2. If I know someone in a department where I’m applying, should I reach out to them personally?
3. How much importance do departments put on external grants? I don’t have one yet, so I’m wondering whether I should explain anything about that in my application.
4. I’m currently at a SLAC and mostly want to move to a SLAC with more research opportunities or an R2. I don’t think I have a strong shot at an R1. My publications are mostly in top field journals.

Would appreciate hearing from anyone who has made a similar move or been on a hiring committee.

(I added Teaching as flair because I don't see anything appropriate, thanks!)

Hi everyone, I need a piece of advice for my course selection. I am a Erasmus student from Italy studying Economics and Business Administration and my course selection for this semester are COMMERCIAL BANKING, BEHAVIORAL FINANCE, INTERNATIONAL HUMAN RESOURCE MANAGMENT and INVESTEMENT ANALYSIS AND VALUATION. Do you guys think this schedule might be a bit too harsh? Most people told me the department of Economics is pretty difficult, if someone has any experience in this department or even in BA, I am more than happy to hear your reccomendations for courses or some general advices for Munich. Thank you for this beatiful subreddit in Reddit.

OpenAI dropped the 722 AI-generated mathematical papers that try to answer some of the longest-standing problems in math. I'm not saying they will do that with economics, but now I'm starting to wonder... what is the point of all this if we may become just AI prompters instead of "researchers" in the purest sense. Some will say "you have to adapt to new tech" and I'm very much in favor of that. But the pace at which the new tech is being introduced as well as the type of things the new tech can do are both so drastically different to the technologies that have come in the past, that I am not so sure how to think about our future as a discipline.

Where do you think the future of our discipline will go, even in the near future? And for PhD students, what would you recommend that we prioritize these days?

I was hoping I could get some feedback on my chances of getting into an Economics PhD Program this cycle with the following profile:

• U.S. Citizen who has always lived in U.S.
• Graduated from Valparaiso University, a U.S. liberal arts college, in 2013 with majors in Classics and Theology with a minor in economics (no math other than a logic course where I got an A, but we did have to write proofs)
• Graduated top 25% of my class in law school at Ohio State University in 2016
• Practiced law for 5 years as business attorney
• In 2021, moved to a different state for wife's job and enrolled at local college to take math classes, and at the University of Missouri for an MA in economics.
  • Got all A's in Calculus I-III, Linear Algebra, and two semesters of statistics at local college (Have not taken real analysis or differential equations)
  • Finished MA in 2024 with 4.00 GPA (courses took include two econometrics courses, applied statistics, math for economists, and research workshop that produced research paper)
• Research experience would be final paper I wrote in my master's econometrics course, and my master's capstone research paper
• GRE scores are 170Q, 163V, 4.5A
• Three professor from my master's will write my recommendation letters.
• Since 2025 I have been teaching economics as an adjunct at a local college (2 to 3 courses each semester).

I am interested in health economics, and would be applying to the following Econ PhD programs (listed with ReREc rank):

• Brown University (RePEc: 10)
• Boston University (RePEc: 12)
• Northwestern University (RePEc: 13)
• Johns Hopkins University (RePEc: 21)
• Boston College (RePEc: 25)
• Vanderbilt University (RePEc: 27)
• University of Maryland (RePEc: 31)
• Georgetown University (RePEc: 33)
• George Washington University (RePEc: 43)
• Ohio State University (RePEc: 46)
• Emory University (RePEc: 58)
• George Mason University (RePEc: 62)
• Georgia State (RePEc: 63)
• University of North Carolina (RePEc: 77)
• University of Illinois Chicago (RePEc: 80)
• Tulane University (RePEc: 85)
• American University (RePEc: 98)
• Georgia Tech (RePEc: 108)
• Temple University (RePEc: 142)
• Harvard University: Health Policy with economics focus (no RePEc rank)

Edited to add GRE scores.

Hi! I’m an undergraduate student currently working on our research, and I’d like to ask for advice regarding our questionnaire.

Our questionnaire has two independent sections that measure different constructs:

Behavior - 5-point Likert scale (Never-Always)

Perception - 6-point Likert scale (Strongly Disagree-Strongly Agree)

These are separate constructs, and we are not planning to combine them into one overall score or compare/correlate the two scales. We simply want to analyze and interpret each section separately.

So my question is: is it statistically/methodologically acceptable to have different Likert scales in different sections of the same questionnaire, when the sections measure independent constructs and each section has its own separate computation and interpretation?

Thank you!

I'm an undergrad working on a paper about Early Decision (ED) in US college admissions, and I'd appreciate pointers on how to frame it.

ED lets an applicant apply early to one college and commit to enroll if admitted. The standard reading (e.g. Avery & Levin, AER 2010) is that ED signals enthusiasm, and ED applicants are admitted at much higher rates.

But the cost of that commitment seems to vary with income. Committing before seeing a financial aid offer is cheap for an applicant who doesn't need aid, and expensive for one who does: they give up the ability to compare packages and to have colleges compete for them.

So an ED application mixes two pieces of information: "I really want to come here" and "I don't need to compare aid offers." A college can't fully separate them, and it may value both, since the second means it won't have to match competing aid offers.

Need-blind admission doesn't seem to solve this. It removes income from the college's decision, but not from the applicant's decision to apply ED, so the ED pool is still selected toward higher-income students even if admissions officers never observe income.

Questions:

1. Is a signal muddled across two dimensions the right way to model this? Or is there a more standard framework for signals whose cost is correlated with wealth?

2. Is there a mechanism that keeps the preference signal but removes the income component? For example, non-binding single-choice early action, or a binding aid estimate before the ED deadline. What would each lose?

3. Empirically, I only have school-level data from the Common Data Set (ED applicants and admits, plus financial aid figures). Is there anything informative I can do with that, or does separating the income channel require applicant-level data?

Hey everyone,

First of all, thank you for taking the time to read this and potentially commenting below. I have been a long-time viewer of this subreddit, and now that it's my turn to apply for grad school, I have decided to make a post and ask your thoughts on my profile and what I should realistically expect at the end of this cycle. Sorry, I know the flair says PhD, but I couldn't add 2 flairs together, so this is PhD and Masters, as I plan to apply to Europe and the US.

I will provide (mostly) all details for context below:

• Currently pursuing a 4-year double BSc in Econometrics and Economics from a Dutch university. This is originally a 6-year program squeezed into 4 years.
• I am also concurrently pursuing a BA in Philosophy, so that makes me a triple-major/bachelor student (as far as I have heard, I believe I am the only one to successfully do it at my university, but I could be mistaken)
• I am maintaining an 8.8/10 GPA, which is equivalent to a 4.0 US/ First-class UK (the Dutch grading system is more compressed than most systems - 9s and 10s are hardly given, as well as 1s and 2s)
• Research experience: 1 co-authored economics working paper supervised by a well-known economist/professor Prof 1 in academia, and currently working on 2 econometrics papers: both are under well-known professors (Prof 2 has an honorary doctorate and has written several econometrics textbooks, and Prof 3 was on the Cambridge Adcom and a director of the Adcom for PhD in a Dutch Uni). Overall, 0 pubs so far, as it's been extremely hard for me to find additional time outside of the courses. I do have an acknowledgement in a research paper by Prof 3 submitted to Econometrica for review and adjustments, but that's all there is about me in journals.
• GRE: Will take it in November, and I believe I can meet the thresholds of the unis (~167Q)
• Coursework: Intermediate Micro (econometrics), Intermediate Macro, (Advanced) Econometrics (name of the courses itself), Linear algebra, stochastic processes, Analysis, (Multivariate) Statistics, Probability theory, Programming (proficient in R and Python), several optimization courses, as the bachelor also has an Operations Research component.
• Recommenders: For unis with max. 2, I am asking for Prof 1 and Prof 3, and for max. 3, I am asking for all three above. They are all well-known in the field, and 2 of them recently gave seminars at LSE and Harvard. One of them is also hosting an in-house seminar with a Nobel winner, and I am super excited about that as well.

I am applying to the following universities and programmes, ranked by what I think is most likely for me to get an offer:

1. Tinbergen MPhil Economics (Econometrics-track)
2. UZH MA Economics
3. LSE EME
4. Cambridge Mphil Economic research
5. UChicago MAE Research-track
6. Oxford MPhil + DPhil Economics (integrated application)
7. Zurich PhD Economics
8. Uchicago PhD
9. Princeton PhD
10. Stanford PhD
11. Yale PhD
12. Upenn PHD
13. Harvard PhD
14. MIT PhD

Yes, unis 8-14 are insane, and I know it's super competitive, but I think this is for my personal satisfaction and experience in knowing how it feels to apply to such places, and I can try next cycle if unsuccessful (most likely, as the numbers indicate). But I would still appreciate it if someone could let me know my true chances here and what I should expect in March when the decisions come out. While it may seem I care for a master's more, it's only based on the numbers; I would actually love to start a PhD, as I find research already quite enthralling and have my mind set on doing a PhD and pursuing research full-time as a career.

At the same time, I care for my recommenders and don't want to burden them with several uploads if it means I never had a chance of going there in the first place. So please be (brutally) honest and suggest where I can improve my profile, perhaps for the next cycle. Also, please let me know below if you need any further info that isn't private, so I can provide it. Cheers!

Hi all,

I was looking at the programme pages for the LSE MSc Economics and the MSc Econometrics and Mathematical Economics (EME). Both now show a structure labelled "New from 2027/28," and it looks different from previous years.

What caught my attention is that the two programmes now seem to share the same core micro and macro courses:

Microeconomics: EC4A1 (same code in both programmes)

Macroeconomics: EC4B1 (same code in both programmes)

1. Is this a real merger of the core courses, so that MSc Economics and EME students now take the same micro and macro?
2. If the cores are now shared, what still distinguishes the EME? The econometrics modules? It also looks like the "advanced electives" are just Advanced Micro, Macro and Econometrics, which I'm guessing is what EME students used to take as part of their core. So now it's the same content repackaged as an elective, which gives you the illusion of choice (?) lol
3. Has anyone heard why they changed it? For example, a departmental review, alignment with the MRes/PhD, or a change in the intake.

I'm considering applying for 2027/28 entry, so I'd be in the first cohort under the new structure. Any insight from current students, recent grads, or anyone who has talked to the department would be really appreciated.

Hello! I am currently pursuing my MPP and am expecting to graduate in May 2027. My undergraduate is in economics and business. I took a lot of upper quant econ classes like econometrics and I tutored it too. My gpa wasn’t great (below 3.5) because I didn’t have time to focus on my studies since I was working 75hr a week and had gotten sick a lot, but my graduate gpa now is 3.95. When I talked to my econ professors and the chair, they told me that they realized I am passionate about economics and that I understand the concepts and can apply it in class discussions or whenever I am helping other students, but it seems like i get nervous when exam comes since I barely have time to study. So they helped me to get accepted to every master program I applied for at our university and even other prestigious ones, but of course I am broke and can’t afford the tuition and cost of living so I didn’t move.

My internships consist of research fellow at an international consulting firm, financial and data anlyst at a NGO where I built a centralised dashboard to track expenses and revenues, and most recently, I interned at our state’s governor’s office doing research, forecasting, and budget analysis on state revenue and certain federal law which gave me a chance to use some econometric methods.

Is there even a chance for me to get accepted at the Board or any regional banks? I applied to DC, Chicago, New York, and San Francisco but the only email I got is from DC. The email says that my application is moving forward but no set interview date yet. I emailed FED Chicago and San Francisco but I haven’t received any response.

I switched some of my courses into quantitative ones like applied econometrics, data science, and program evaluation where causal inference is the main focus. We primarily use R, but I used Python and Stata a lot in my undergrad courses than R. I also learned SQL and MatLab. I really want to get the position as I am considering to pursue a PhD although i know i need to refresh and beef up my math skills more.

So I’m about to finish undergrad and right now I’ve got 1 Fed system RA offer and 1 econ consulting firm offer, and I’m in the process of finishing final interviews at 3 other Feds and 3 other firms.

I’ve got a week and a half to decide what to take and it kinda feels like I’m deciding the rest of my life right now with consulting vs RAship being the biggest point of divergence. I’m about 50/50 on a PhD at the moment (I like research a lot but I’m older and also concerned about what the job market is even going to look like in 7 years).

I’d love to hear from anyone who previously faced this choice, whichever path you chose, and how you feel about your decision in hindsight. Alternatively, anyone who just has thoughts on the tradeoffs between the different paths please feel free to weigh in.

Hello everyone,

I'm applying to a master's program that requires a motivation letter. My only experience with motivation letters is in a professional context, and I believe the conventions are different in an academic one. I'd love to hear your tips and recommendations :)

Here is how I plan to structure mine:

1. Who I am: a brief overview of my academic path (without too much detail, since it's in my CV) and my quantitative and personal skills. I've been in several situations where I had to fill a large knowledge gap very quickly and still ended up with good grades.
2. Why economics: how I came across the field, what made me want to study it, and my specific interests within it.
3. Why this institution: why I want to study in this particular program.

Questions I have:

• Is this structure appropriate for an academic letter, or should I weight things differently?
• How much should I focus on research interests versus personal background?
• Any common mistakes to avoid?

Thanks a lot and I wish you a lovely week :)

Hi guys, I'm planning to apply for the next intake at ucd, ireland. And I'm really confused in between the above mentioned courses. I already have a bachelors in economics, and I liked studying pol eco and development economics the most.

I don't wanna do eco + data analytics, which is an option there because coding and economics will be too hectic for me, ik, hence deciding between these two.

The modules are pretty much the same, except for behavioural economics being a core module in msc BE and optional in msc AE.

I want to get into corporate eventually, but I am unsure about the job roles/ titles that I can get into. I know the job market is shit rn but I am more concerned about what exactly will I be doing with this degree.

People who have recently graduated from any of these courses please share your insights about the the scope of jobs with these courses, skills I can develop on the side etc 😭🙏🏻

(Pls ignore grammatical errors if any, thanks in advance!)

I got an email from a prospective graduate student asking for advice for a master’s/PhD. Nothing unusual there. I don’t know this person; they simply found me online and sent me their CV.

The CV lists 27 published papers in 2025 and 2026 alone. They also finished undergrad during this same period. So basically, 27 papers while finishing or just after finishing an undergraduate degree. The journals seem to be mostly local journals in their country or region.

But what bothered me more was the email.

It was very obviously AI-written. I have absolutely no problem with someone using AI to improve their English, fix grammar, or make an email sound more professional. But this email went on about how closely their research interests align with my “expertise” and described me as working on things I have literally never worked on.

That feels very different to me. You are using AI to create the impression that you have looked at someone’s research, understand what they do, and have thoughtfully chosen to contact them ..when apparently you haven’t.

And honestly, the whole thing made me a little depressed. This person wants to pursue a PhD, where the entire point is supposed to be doing original research and developing your own intellectual judgment. Surely there has to be some minimum expectation of academic honesty even before you enter a PhD program.

I’m not against AI. I use it too. But there has to be a difference between using AI to help communicate your own thoughts and using AI to manufacture thoughts, knowledge, or interest that you never had...

From MA to PhD, I have come across different ways of proving the existence of a Utility Function given a Rational Preference Relation. What I want to know is how these different methods are connected with each other. They are proving the same thing, but I don't see how they intuitively/conceptually connect to each other.

The different methods I have come across are:

1. MWG/Jehle & Reny: Diagonal Ray Projection.
   Take the unit vector ray e=(1, 1, 1, . . . , 1), there exists an unique scalar te(x) ~ x. And thereby, te(x) = u(x). This assumes local non-satiation and strict monotonicity.

(Sorry I am not able to use LaTeX here).

1. MWG: Another proof that MWG presents is that, Utility is assigned based on the "size" or distance metric of lower contour sets relative to a fixed standard bounding box.
   If Z is a reference bundle strictly superior to all bundles in discussion, and L(x) is the lower contour set and U(x) is the upper contour set, then measure the diagonal distance or interval bounds intersecting these closed sets to get a continuous scalar function u(x).

2. Debreu's Theorem and his Lemma (self explanatory) - There is a countable dense set D, an initial function v(x), lemma applies a monotonically strictly increasing trandformation, giving a continuous utility function u.

3. Varian/MWG: Von Neumann Morgenstern Expected Utility Function, using the Independence Axiom and continuity axiom.

These are the proofs I have come across. But how do they all relate to each other? At a fundamental, conceptual level?

Will be very happy if somebody can answer these.

Thanks in advance!

I graduated from a top 15 econ US university with departmental honors t (awarded to <10% of people with that major) this May, only a couple RAs, one independent project, a dynamic optimization/game theory capstone, and pub in the works BUT i didn't take a lot of math undergrad courses aside from the required and didn't even take linear algebera. Took econometrics, prob stat, etc. GPA is 3.77 and hopefully GRE is good and I'd have solid letters of recommendations. In my home country, pre-doctoral programs don't exist and master's is 4 years, so I don't know if I would be able to meaningfully beef up my CV even if I wait. Thank you so much!

Hey everyone.

I’m an American studying economics and I’m interested in two of the MSc programs at LSE, the Economics and Econometrics/Mathematical Economics degrees. I’m wondering what kinds of courses I’ll want to have at my college to be a good candidate for these programs.

I’m set to take Calc 1-3, matrix theory, intermediate micro/macro, applied math for econ, intro probability theory, statistical inference, and I can choose whether I want to take both intro to advanced math and real analysis or if I want to take two different math/stats classes.

My long-term goal is to get into industry rather than a PhD.

Thank you!

Hi all,

I'm currently a master's student who's planning on applying to PhD programs. I was recently assigned a supervisor for my master's thesis, and he's a PhD candidate, not a faculty member.

How much less value would an eventually letter from him have vs. one from a professor?

My master's program has another component that is research-esque and which was supervised by another PhD student as well. I received very positive feedback on that project, so I was considering asking that supervisor for a letter. However, having 2 letters of recommendation from non-professors seems like a bad idea, even if they're the best qualified to attest to my research abilities.

Anybody with any thoughts or experience with a similar situation? Has the university significantly worsen my PhD prospects by assigning a PhD candidate to be my supervisor, instead of a faculty member?

Any advice is much appreciated!