Martha Fiehn (PhD at UCSD):
That was a super helpful and comprehensive introduction on the overlook of pre-Doc. And now we're going to talk about applying to pre-doc with us. We have Vaishali Garga from the Boston Fed, Ashley Pandya from Chicago Booth, Alvise Scarabosio at Stanford, and Eric Zwick also at Booth. So in reverse order, maybe we can introduce ourselves. So Eric, why don't we start with you.

Eric Zwick (Professor, Chicago Booth):
Hi everybody. I am a professor in economics and finance at the business school. I've been at Booth since 2014 and have worked with, I should have counted but probably a couple dozen pre-doc over the time I've been here and I'm really looking forward to the session. Thanks very much.

Martha Fiehn (PhD at UCSD):
Great. Alvise.

Alvise Scarabosio (Research Fellow at SIEPR):
Hi everybody. I'm Alvise. I am currently a pre-doc at Stanford working for Professor Grant Miller at SPIER. Before this I did my undergrad in the Netherlands at Erasmus University and then I did a master in development economics at Yale. I started working for Professor Grant Miller about a year ago and I'm entering my second year working with them.

Martha Fiehn (PhD at UCSD):
Awesome. Ashley.

Ashley Pandya (Research Professional, Booth):
Hey everybody, my name is Ashley. I am a first year, so I only started a few months ago at Chicago Booth and I recently completed my undergrad at UC Berkeley in Econ

Martha Fiehn (PhD at UCSD):
And Ali.

Vaishali Garga (Principal Economist, Boston Fed):
Hi everyone, I'm Ali Garga. I'm a principal economist in the research department at the Boston Fed. I started here in 2019 and I've worked with multiple RAs as well, not as many as Eric and I can tell you a bit more, but we used to have a one-on-one system with RAs and now we've kind of moved into a pool system. So that increases the scope of RAs that I get to work with.

Martha Fiehn (PhD at UCSD):
Great. And then I'll introduce myself as well. My name is Matta Fee. I'm a PhD student at uc, San Diego. I did my undergrad and economics at UC Berkeley, a master's in international economic policy in Paris. Then I worked for Stephanie Che at Harvard for two years and William Nord house at Yale for a year. And the way that this is going to work, I have some questions for the PIs. I have some questions for pre-doc and then some that work for both. And whoever feels inclined or inspired to answer can answer first, and if not, I'll call on somebody or we can see how the flow works best. So the first question is going to be for a PI and we're curious what are the principles, skills, qualities and prior experience that you're looking for in applicants?

Vaishali Garga (Principal Economist, Boston Fed):
I could start

Eric Zwick (Professor, Chicago Booth):
For it again. Sorry. I guess we didn't talk about the order. Go ahead.

Vaishali Garga (Principal Economist, Boston Fed):
So at the Boston Fed, I think I can break it down into maybe a few different categories. So we look for RAs with first and foremost passion, interest and those who show some initiative and examples of this is like you read econ papers or you are thinking of ideas or you worked on some thesis projects in the past. All of those show some interest. Then we look at technical skills and these include skills either with econometric tools or methods with handling data sets like large data sets, small data sets, public data sets, if you've cleaned these, merge these or if you worked on research projects again as part of your thesis or with some professors or at some institute as an RA. Those are all very useful. And maybe thirdly, we also look at the ability to work collaboratively because that's very important for us here at the Fed because you will always almost not almost, you will always work with an economist, so you at least need that one-on-one collaboration skills and then offer projects are joined with other economists who also have RAs. So you kind of work in a team and we need people who have shown evidence of that or are inclined to engage in that kind of interaction.
Eric Zwick (Professor, Chicago Booth):
On my end, I mean a lot of overlap, a lot of correlation there, but definitely technical background is something that we focus on. Lots of math, taking hard classes and doing well, although you don't have to do perfectly. I think we tend to prefer an a b plus in a really hard math class to an A plus in some less relevant class and demonstrates that you're used to working on hard problems and getting better at doing that. We also look for some CS and programming because our lab uses a lot of large data and so some background in that is helpful but not necessary. And then on the softer side, sort of a little bit of evidence of passion and leadership skills, like some hardworking markers and sort of what kinds of activities or clubs or other things you've done aside from just taking the classes just as a way of thinking about independent work habits, discipline and so on. But those things are hard to evaluate, but those are other things that we kind of look for.

Martha Fiehn (PhD at UCSD):
Great, thank you. So both of you mentioned passion and that kind of leads into a question for all panelists, which is how important the fit is in research interests between the PI and pre-doc. So for instance, I knew I wanted to work on tax policy, I continue to want to work on tax policy, so I was very targeted in my approach, but not everybody is in that position. So how do you think about the passion? How do you think about the fit and why don't we start with Alvise?

Alvise Scarabosio (Research Fellow at SIEPR):
So my impression is that, I mean seen from an applicant side is that that is really important because compared to for instance, PhD apps where whoever is doing admission selects a multitude of people, the PI is just choosing one. And so they will want someone that has a very good fit because they'll work one-to-one for two years. So I think in general when I was applying, I was trying to look for people that I could see myself working one-to-one for two years and that the research they were doing, I looked at their recent papers or the kind of research they had done in the past and I was trying to look for something that I was like, okay, yeah, these projects look very cool to me. I could be working on these for two years. And I think I wouldn't recommend just sending applications to any available spot because most of these positions are very competitive and so they'll want to know, the PI will probably be looking for someone who's aligned with the research interest. If you've never even thought about the problems they're researching, I think you wouldn't have a good chance and you are better off just trying to focus on those positions where they're very aligned with your interests.

Martha Fiehn (PhD at UCSD):
Great, thank you. Ashley, do you want to talk about your experience?

Ashley Pandya (Research Professional, Booth):
Yeah, definitely. I can speak to it a little bit too because this is fresh in my mind. It was almost only about a year ago when I was doing my pre-doc applications and I came from IT from the position of the RA position that I had done in undergrad was one form of economic research. The thesis that I had done was entirely different and by the time that a few months later it came time to applying to pre-doc, it wasn't actually explicitly clear to me what specific sect in econ I was super interested in. I just knew that I wanted to econ research. And so the pre-doc that I applied to actually ended up being of disciplines of all sorts, but as VIS was mentioning, it had to be something, there's something about the methodology that they were using or about the research questions that they generally touch on.
That was interesting to me. And I think that passion came across in the interviews came across in the cover letters that I wrote, things like that. So I definitely think that having that passion for specifically what the researcher is doing does come across, especially if you make an effort to make that come across explicitly. But I will say that if there are, and I'm speaking just from somebody who is an applicant, not necessarily a pi, obviously if there is a position that you are interested in applying and we are going, oh God, I don't actually have that much experience during research and tax policy, I would not self-select out. I definitely don't think that that's a non-starter. If it does interest you and you can sort of generally tie it to your research experience in the past, I personally say definitely go for it.

Martha Fiehn (PhD at UCSD):
Great. And Eric, do you want to talk about how you evaluate candidates that for instance, don't have the experience in a given subfield and how you think about whether they're still a good fit?

Eric Zwick (Professor, Chicago Booth):
Yeah, we can take turn. We can alternate who goes first. So definitely, I mean I work on pretty nerdy tax stuff a lot of the time and definitely if I restricted the people that I worked with to people who had experience or interest prior to pre-doc in that there would be almost very few people that I would be looking at. So I definitely don't focus on a really tight fit based on background. It's sort of more a general openness in applied micro using those types of tools, doing some data work, maybe a bit of theory, but so sort of broad types of things that I expect the person to be doing. I want them to be looking for a job that provides those. So not a pure theorist, not a macro calibration type person. But I think the background that sort of demonstrates some skills in some interests that are broadly adjacent to public policy and economics research is sort of enough. And those people have worked really well. We do in our interview process have a period where they read one of our papers and we ask them some questions about it. And so folks who seem to enjoy that, it kind of comes through in the interview and that helps a little bit even if they've had no prior experience.

Vaishali Garga (Principal Economist, Boston Fed):
I think Eric, my thoughts on this resonate strongly with Eric's. I work in monetary economics, some of my work is super theoretical, and so no undergrad is going to have that kind of deep passion or background in monetary economics. So I think again, general passion for the field and just having some skills or tools which overlap across fields, I think that's very useful. And one other thing at the Fed is that we, as I was mentioning in the introduction, we now do a pool system of RAs. So you actually, when you apply you too broadly to the research department, which is broken down into different groups. And then once you're placed, you're placed within a group with each group has five to six economists. And so each of us also work on different topics. And so therefore we don't really need someone who's particularly tied to what I do, for example, because my colleague might work on something which is different. And so in that sense, maybe the Fed is a good opportunity for someone who's less sure, like Martha, you were saying you were super sure about tax policy and then it makes total sense that you match with a professor who works on that. But if someone's less sure with the Fed, you can kind of cost a wider net and you can also help to maybe also help to give you clarity on what you want to eventually do after the pre-doc position.

Martha Fiehn (PhD at UCSD):
That makes sense. Thank you. And I do think there's different mechanisms at different institutions. So I know at Yale also they have more of a pooling system and are very organized. At Harvard there's less of a pooling system. So as applicants, that's something that you can inform yourselves about a little bit, try and reach out to see if there's pre-doc coordinators like Steven, super helpful resource for anything at U Chicago. But also generally I would say just something to think about. Now we have a question for the pre-doc and we'll start with Ashley. And I'm curious what motivated you to look for pre-doc positions?

Ashley Pandya (Research Professional, Booth):
Yeah, definitely. So to give a quick outline of my background, I graduated from undergrad last spring and during that last year of undergrad, I hadn't thought for a singular second about applying for pre-doc. My entire focus was on applying to industry positions, corporate, maybe even public policy, but primarily industry. The idea of doing a PhD was in my head something that maybe could happen in the future, but I had not put that much hyper focus on it. And so if that's you somewhere in this panel, somewhere in the audience, I resonate with that. And it just so happened that I had gotten a position during that last year of undergrad and that position right as I was graduating ended up falling through. And so I was stuck with a few months during the summer trying to figure out, okay, do I continue to apply for other industry positions?
It's kind of a bad time for it. Nobody I know around me is really getting anything. It's also just a pain. You feel like you're shouting into the void or do I want to pivot to something else? Funnily enough, I was actually complaining to one of my former GSIs at Cal. She's lovely, and just literally just complaining like, oh my God, I'm applying to everything and nothing. I'm applying to things that I don't even really want to do and I'm still not getting them. And that's super frustrating. And she said, well, Ashley, I know that you had expressed interest in doing a PhD. At some point did you consider maybe using this cycle to apply to a pre-doc? And at that time I had known that pre-doc existed, but I didn't realize that they had this sort of newfound position of being a necessary but not necessarily a sufficient condition to being a PhD.
And so I took that August timeframe basically to start applying. And I noticed almost instantly that at least in me, there was a step change in my mood in applying to the pre-doc and there ever was in applying to any of the industry positions, I was sincerely and kind of a simple nerdy way enjoying getting these data tasks sitting down doing my homework. I was used to going through interviews with people who seemed genuinely interested to talk to me and to talk about my research interests. And it was that difference in, oh, I can feel myself really enjoying doing this long application process than I ever had before that I knew, okay, this is probably what I'm going to be wanting to do.

Martha Fiehn (PhD at UCSD):
So Ashley, it sounds like you have more of a domestic US experience and maybe you can talk about how it is from the international lens, especially since you are Yale doing your master's as well.

Alvise Scarabosio (Research Fellow at SIEPR):
Yeah, exactly. So I think I started my master's at Yale and went into it not sure whether I wanted to do a PhD or not. So I was still looking considering, oh, maybe I'll want to work in policy and things like that. And then I think one month into being at Yale, I was like, oh, I think I really like research. I had been going to seminars almost daily and felt like, oh, I think I can see myself doing this for in the future, but I didn't quite feel ready to apply for PhDs. I had just started my master's apps for PhDs for two months later, had none of my materials ready. And so I sort of pivoted and said, okay, let me look for something that I can do to bridge from my master's to my PhD. So I guess around September October, I started looking to pre-doc positions and thought that that could be a good opportunity to actually figure out and change my mind in case I didn't end up liking it. And I thought it would be a good fit to sort of go from the master's and get all my materials ready to apply for a PhD later on. And so yeah, I think wasn't just like Ashley said, I wasn't really sure at the beginning, but then I think I really enjoyed the process and going through the professor, I think I found so many people that were doing co-work and thought, yeah, I could see myself working with a lot of these and it ended up being a pretty fun process.

Martha Fiehn (PhD at UCSD):
Great, thank you. So let's get back to the sort of details. There's a few different components of an application. And so for instance, with a PhD application, an advisor told me that the standard, the statement of purpose is basically to see if you're literate. So they don't really care what you say in there. They might use it to match you with the professors you mentioned on visit day, but it's not something that you should focus a whole lot of your energy on for PhD applications. This is at least the advice I received. But so maybe I'd like to hear from Vaishal and Eric. Eric already mentioned that you're looking for really strong math classes, but how much weight do you put on each part of the application? So like the cover letter, resume coding, task writing, sample transcript, references, interview, whichever do you think applicants should prioritize the most. And I think we'll start with Vaishal first this time.

Vaishali Garga (Principal Economist, Boston Fed):
So we do the process in two stages. So in the first stage we just look at the written material and in the second stage there's an interview. There is no coding test at the Boston pad anymore. And then in the first stage, the materials we ask for are the resume, the cover letter, the transcript, or writing sample and letters of recommendation or at least two. And I think it's difficult for me to quantify each of these, but I would say that maybe the resume, the letters and the writing samples are probably the most important given the skills that I was highlighting in the first question that you asked Martha, because all of those tell us about your passion, your research interests, your technical skills, your background. I think with transcripts it's a little bit difficult to answer this because different people read it differently, but I would say on average we don't let someone fall through the cracks just because their transcript isn't as strong.
So let's say you didn't go to the top school or you didn't have a very good grade in one semester in one math course. Math courses are important for us too, but I think we don't weigh too much on that. And this is because I think for us at the Fed, given our public service mission, mentorship is kind of also very important. And so we are looking also mentor candidates who don't come from maybe the strongest background. One thing I will say is if there is some sort of either gap in your CV or you had this bad grade, which you want to explain to us, in that case, I think the cover letter is a useful thing to try to convey that to us. If the cover letter is just going to be a summary of the resume, I think it's less important because we are looking at your resume anyway, so I would spend the least time on the cover letter if I were, unless you were trying to convey information that's not in the resume.

Eric Zwick (Professor, Chicago Booth):
Yeah, I would agree with everything by Ali said when I was ordering things, I put the resume and transcript first, then the interview, then the letters and writing sample. But they're kind of in sort of a third bucket together. The coding task kind of lasts, and the fourth quartile coding task is informative. It shows that somebody doesn't really care, but then it's sort of becomes less informative. And we'll talk about the AI stuff later I guess. But on the writing sample, this is supposed to be kind of the best work you've done in college or master's. And if it's coherent, interesting, creative, that really is distinctive and if it sort of is kind of underwhelming or unfinished or some other or lots of typos or something, I think that's a bad sign.
The interview, we'll talk about that a little bit more I guess in a bit. But when we talk about mistakes and stuff, but I think presentation and preparation for the interview is quite informative and the live interview is just really valuable. You can't put too much weight on that only 30 minutes or maybe two times 30 minutes, but a lot of people that look great on paper just have trouble answering questions and that tends to indicate something. And then on the transcript, yes, not a perfect transcript. The people with the really perfect transcripts, often they just go straight into grad school and the pre-doc is kind of looking to supplement the transcript or where they're coming from. I mean, was pre-doc coming from a liberal arts college? Not like a top research university. So I kind of used the pre-doc as a way to get some more research exposure prior to grad school. But yeah, I think also we encourage folks to take classes and we help cover the cost of some of that if there are gaps in the transcript. So the pre-doc is an opportunity to fill those gaps, but if there's no math in the transcript, it can be kind of hard to overcome that in a two year pre-doc when it comes time to apply for grad school. So there's certain limits for how big those gaps can be.

Vaishali Garga (Principal Economist, Boston Fed):
Sorry, I just want to add to Eric's comment about the interview. So for us, one thing that's different is that sometimes your written material is only looked at by a couple of people on the committee, and then in the interview stage you're interviewed by everyone on the committee. So in that sense, the interview is actually quite crucial because you have made it to, you have your foot in the door because a couple of people looked at your application materials, the written stuff, but now with the interview, you really need to come through for everyone on the committee. And like Eric said, some people do look great on paper, but then in the interview they struggle a bit and we will come to that. But that I think can be quite bad in terms of making it beyond that point. And then also, yeah, we also, the Fed has a very good program for sponsoring courses at institutions in Boston. But again, again already said, unless you have some fundamental knowledge on the topic, it's difficult to do advanced courses and have them be useful.

Martha Fiehn (PhD at UCSD):
Yeah, that makes sense. Since we're already on the topic of interviews, let's hear from Alvis and Ashley about how you prepared for the interviews, what kinds of questions you are asked, and maybe if Eric and VA then want to expand a little bit also about common mistakes you've seen applicants make during any part of the process. Let's talk about that. I think Alvise first this time.

Alvise Scarabosio (Research Fellow at SIEPR):
Sure. So I speak from experience, a small sample of interviews, maybe four or five. But even this small sample, I felt like there was a lot of heterogeneity in the type of questions that I got. So it's hard to just say, oh, you'll be asked these kind of things. I think some professors might ask me about something about econometrics and try to test my understanding of econometrics while some other, I think especially from the big labs, I think my experience was more like it felt like they trusted the process that brought me to the interview. So they thought, okay, this guy is probably qualified, let me see if he is a good fit for me. And we get along working together for two years. So I think there's a variety in that sense. I think I've heard people being asked questions about coding as well or do a sort of a live coding task where they're asked, oh, can you do this and this?
That was not my experience. I think in general I would really make sure you can tell about yourself and you can describe everything you wrote in your cover letter because I think in all interviews they ask me, oh, you wrote that you've done this, you worked as a research assistant here. How was your experience? What did you do? And so try to get that understanding or ask me about my interests and my passions, what I was doing in my master's, but I would also maybe recommend trying to be prepared in econometrics especially. That was, I think I got almost in every interview I got a question about some methods or how would I approach a problem or things like that.

Ashley Pandya (Research Professional, Booth):
Yes, sorry. Yes, I will co-sign on that. I think that the approach that I noticed my interview is taking as I went through the interviews was the same mentality that I ended up undertaking, which is if I had gotten to that point, then I was qualified enough to get through that point. And so most of the questions that I was asked were fairly standard, asking me about my research interests, my research experience, some quantitative experience questions, asking about my background a little bit that didn't have to do with research, but maybe had to do with some coding or data analysis. I got questions like that. There were no gotcha questions. I never went into an interview feeling like I wasn't prepared for that. I didn't really know what they were going to ask, nor did I ever leave feeling, oh my God wasn't what I expected at all.
That was actually quite scary. Nothing like that at all. Everybody there is just trying to get to know you, at least from my experience, maybe some of the most technical questions that I got had to do with econometrics, asking questions that would probably be on the order of the difficulty that you would've already done in the data task. So again, I would say that by the time you get to the interview process, just focus on realizing that you are there because they want you to be there. You're qualified enough to be there, they know that and know what your research experience is, know why you're there specifically, are you interested in their research, their methodologies? Why is this a one-to-one fit rather than why are you just applying to pre-doc in general? I think that would help build the connection better as well. And also help you understand why am I applying to this one or am I just applying to pre-doc in general? Yeah,

Martha Fiehn (PhD at UCSD):
Yeah, that makes sense. And I do think the application process can be really informative to helping you navigate what you're interested in. I know for me at one point there was a data task and I was already bored by it and I was like, wow, if I'm already bored by the data task, I probably shouldn't continue to apply for this position. So I rescinded my application and I think that was a good choice. So yeah, let's talk about some of the common mistakes you've seen applicants make, Eric and Ali, and then maybe Elvis and Ashley can also, if there's anything you regret or anything you wish you would've done differently, talk about that. And then after that we'll talk about the use of AI during the application process. But you can tie those in if you like. And I think Eric, yeah,

Eric Zwick (Professor, Chicago Booth):
Sure. Yeah, so I mean I think in the, I'll focus most on the interview, but on the submitted materials, if it looks like it hasn't been edited or even looked at by your human eyes before being submitted and just pasted and just kind of looks messy again, typo, basic kind of stuff, that's not a good sign of care of being careful and attentive to the work product you're submitting. So in the coding task for instance, the writeup will just look at that a little bit and some folks seem to, it just looks it's sloppy and poorly organized. So in the interview you can kind of err on in a lack of enthusiasm, and I am going through the motions like Hum, this is just another interview, why am I even here? And that demonstrate that you're actually enthusiastic or excited to have this job and then you could also be a little too excited and not stop talking.
Kind of like we only have 20, 30 minutes to go through three or four or five questions and multiple people want to ask questions. And if you talk uninterrupted for 10 minutes, that's like a mistake because we can always ask you to expand, but it's hard to interrupt, especially in Zoom and say, please contract, please stop talking. And it's sort of, so I would sort of be organized, shorter answers are better than the longer ones with follow-ups, maybe practicing answers a bit before on some of these questions, I like to sort of use the writing sample here or what the people we're working on before and ask them to go through a little bit the economics, the why, what are the weaknesses, what was the key question you were asking? How did you try to answer it? What were the limitations of what you did? And try and just get to somebody's understanding and only if they have nothing in the background like that, then I'll start to think about some basic econ 1 0 1 type questions that I can ask. But even if somebody doesn't get answer immediately, we'll start to reason through it and I'll just sort see if they get really stressed by that or can comfortably just reason through it even if they don't get the answer. That's not the kiss of death at all.

Vaishali Garga (Principal Economist, Boston Fed):
Great. I had a bunch of those points also in my list, so I'll just focus on the mutually exclusive points. So like Alvise was saying, I think we also give a lot of room to candidates to guide the conversation. So the interview will often open with tell us about yourself, tell us about some experience with some dataset in the past or some research in the past. And from there we go deeper. And here I think maybe the candidates sometimes can make a mistake of either picking research projects that they think are trendier topic wise but they haven't contributed deeply to or something that they think reflects technical skills. But then ultimately when we delve deeper, it becomes clear that they were not really involved in the technical part, for example. So I really think use the question, the open-ended questions as an opportunity to guide the interviewer towards a project that you're really passionate about that you've contributed to meaningfully and that you can answer questions about. And this can very well be your writing sample. This can be something from your resume. You don't have to deviate from what you've given to us already.
The other thing is that in terms of enthusiasm, I think one thing also that happens is we ask a question and maybe the interviewee doesn't know the answer. I think it's okay to say, I don't know, but I'm happy to try to reason this with you as we go along and start from there. Sometimes they'll give either a wrong answer or they'll give a very long answer, which just then seems evasive. They're hoping we get lost and we don't catch on. But there's many people on the interview committee, we will likely catch on. People try to use jargon to try to impress. Then when you delve deeper, they truly don't maybe know and maybe jargon was not necessary anyway. And for that, I would really also emphasize that we are looking for someone with good economic intuition.
You may be the most technically gifted person, but ultimately half of research is communication and especially so in policy work. And so if you go to our website and you look at our current policy perspective issues, these are our policy briefs, you'll see how much they cater to an average person without an econ PhD. And we really want you to approach the interview that way is be intuitive and be simple if you can. And then maybe two additional points were applicants can come across as maybe disinterested, for example, if you take too long to respond to interview requests or you don't show flexibility in terms of when you're available to interview. So we understand some of these fall around exam time, but then some candidates will be like, oh, I can't meet the entire week. But we also know the exam schedule, so if you can't meet the entire week, it shows us you are maybe not as flexible or not as interested and someone who shows more interest will probably get selected before you because this is on a rolling basis. And the final thing I'll say, and this is not specific to the Fed interview, is I think just being humble in interviews is quite important and it's okay to not know things. It's okay to know the limits of what. And I think approaching it with that mentality is you're trying to find someone you would want to work with for the next couple of years and then someone who would want to work with you for the next couple of years. So those are my additional points.

Martha Fiehn (PhD at UCSD):
Great, thank you. It actually happened to me at an interview right after undergrad where I thought I communicated well that I wanted to take the job. I worked at a job force development nonprofit called Jewish Vocational Service and I didn't respond and then they were nervous that I didn't want to take the job and had to go through the whole process again. So yeah, it definitely makes sense to try and be as responsive and clear like yes, I definitely want this position. I definitely want this interview that reflects my experience in industry as well. Ashley and Elvis, do you want to add something about regrets, mistakes, they don't have to be your own, maybe they're from other applicants, maybe they're from your friends, common pitfalls or anything along those lines. And I think this time starting with Ashley,
Ashley Pandya (Research Professional, Booth):
Sure. I can speak maybe since we're talking about interviews a little bit, I would absolutely co-sign on what Ali was saying towards the end there especially that I think the move for playing these interviews, and I think maybe most job interviews in general is the interviewer is coming from a position of can I work with this person and do I want to work with this person? And so when there are these technical questions that get thrown at you or even questions about your background that maybe you're not super, super comfortable in, are you able to work through that with them reason through that with them? If there is a technical question that they ask and you genuinely are a little bit confused, start asking questions about it. Oh, so do you mean in this instance under these assumptions or do you mean that, and I think that that maybe even shows a little bit more about you, that even when you're confused about something or you're exploring uncharted territory, can you work through it?
Can you troubleshoot with them ultimately what the job is going to be as a pre-doc troubleshooting with the PIs to some degree rather than do you know everything because at some point that well is going to run dry. So I think that's both very, very helpful answers. I don't know if I necessarily have any strict regrets in my pre-doc application process. If anything, I think I maybe could have prepared myself a little bit better just having known earlier on that I was going to do a pre-doc changing up, made my research interests, I don't have the most solid quantitative background. If I'd known that I wanted to research earlier, would've focused on that. But obviously that's in the past, those are things that I wouldn't have been able to change anyway.
I think maybe something that I had begun to do in the application process that I stopped part of the way through was something we had spoken on, which is I was kind of just applying to every pre-doc to a certain degree because at some point it's like, okay, I know how to write a cover letter, my resume is what it is, my writing sample is what, these are very similar in terms of the initial push, I'm just going to apply to whatever. And then I actually started getting a lot of data tasks and interviews and that not only ended up taking a lot of time, but I ended up realizing in some of those interviews, Ooh, I'm actually having a difficult time justifying why I'm in this interview specifically because I don't even know why I'm in this interview specifically. I'm not super familiar with their research, not super passionate about it, and I could feel it coming through.
And at the same time, I think I also towards the beginning self-selected out of positions that I didn't think that I could get. So I thought why waste my time on those? And when I started to apply to some of them, some of those ones that seemed more further away out of reach ended up coming through. So I would say maybe take a little bit more time than you would think intuitively to apply to positions that you think would be a good fit or figuring out even what a good fit would look like for you. If anything, that might've been one of my regrets not doing that earlier.

Alvise Scarabosio (Research Fellow at SIEPR):
Yeah, I'll keep it short, but I think when Ali was describing some mistakes, I remember myself writing my cover letter about this research assistant job that I had and I wrote, oh, I've done this very technical analysis, whatever. And then in my first interview they asked about it and what I had done was really just sort of running the code for it and I had no clue. And I think that reflected very poorly on me. And I remember finishing that interview and deleting two sentences from my cover letter and I think in other interviews where they actually asked me about the thesis that I had done and things like that. And then I knew very well, I think that came across and then I actually knew what I was talking about and I could show that I understood the metrics and the methodology and I had it clear in my mind. So I wouldn't just try to fill in your cover letter with things that maybe you've seen once and just write all that. Yes,

Martha Fiehn (PhD at UCSD):
There we go. Great. So for the sake of time and the question I've actually been most curious about is how you're thinking about AI in the application process and if you'd like even during the pre-doc and then we'll move on to the q and a. Okay, let's see. I think Eric is maybe first or maybe in any case all panelists are welcome to answer.

Eric Zwick (Professor, Chicago Booth):
So I didn't ask AI what to say. Just to be clear, I wrote some bullet points myself with my fingers, a lot of weight on in-person interviews before AI or chat GBT. And I think the weight will just kind of increase a bit that plus the transcript, some of the hard stuff. But it's not to say that people shouldn't be using these tools, it's okay to use them. I think we need to figure out how to use them well to produce work product that we're proud of. And I think we as society are still figuring that out. It's not just about saving time. It's like can you actually produce something with quality using these tools? So I find that they're more useful as better search engines than the existing previous search engines. So instead of going into code exchange or stack exchange or whatever, I think they're pretty good at answering the questions I would've previously used Stack Exchange to answer, but when it's solve this coding task, they're going to spit out some code that doesn't run or doesn't do the right thing.
And I think that's still kind of the case, not always but kind of the case. So we'll probably test some of the code to make sure it runs and we'll probably do some correlation across to see if people are just feeding into chat and then spitting back out the same answer. And if we have a bunch of applications of the same answer as everybody else, you have to think about the equilibrium where everybody is doing this. You'll get caught by that. And that's just evidence that you're not really spending much time taking it very seriously the job application in the first place. And so when you're asked to do something that the AI tools can't do, which is going to be most of the job, probably that's not a good sign if you just want to default to that. But I don't know if we know for sure where we'll settle, but that's kind of what we're thinking about. How

Vaishali Garga (Principal Economist, Boston Fed):
I will also say that, yeah, I mean I think maybe using them as compliments is fine and we are trying to use it in research. We are trying to use it in our lives as compliments, but maybe don't use them as substitutes because it really currently what's written a hundred percent by AI and not, I think you can tell from reading what's written and also given with that we don't do a coding test. I think the use of AI in the application is quite limited at this point for us. I mean maybe you're using it in your writing sample, but we are going to ask you about that in the interview anyway, so the gaps will come through there and maybe Ashley mentioned this point of the fixed cost of the application is actually quite low. You prepare the same material and you kind of send it out.
And the place where you write to personalize I guess is the cover letter. And there you really do need to do your research. So I would say using them as compliments to maybe check for typos, improve the flow of what you're giving us, shorten bullets in your resume, all that's fine. And then just we're not formally thinking about this yet. Our meetings of the recruiting committee for the next cycle have just started and the question of AI in applications has not really come up. So I don't have any insights to offer from there, but I'm actually curious to take it back to my colleagues and see if any of them are thinking deeper about this yet. And maybe one other thing I'll say is because of the sensitivity of the work that you do at the Fed, especially when you're doing policy stuff, we are not allowed to use chat GPT on our work computers like it's banned. And so it's good if you know coding and the things that you would typically rely on chat GPT for when you come in. Otherwise you will probably be quite miserable after you come in.

Martha Fiehn (PhD at UCSD):
That makes sense. Vis or Ashley, is there anything you'd like to add? If not, we'll move on to the q and a. And there's a few questions here about international applicants and I think Vis, but interesting to hear from you in particular. There's someone who says that the Chicago Fed pre-doc is only open to US citizens and other permanent residents. Is that true for Fed pre-doc? There's a question about all else equal, is there a preference for US native or otherwise native applicants? And yeah, just in general if you have any thought, especially perhaps giving the difficulties international people might currently be facing, if you have any thoughts there from this last application cycle or in general...

Vaishali Garga (Principal Economist, Boston Fed):
Start with you. So there is unfortunately a restriction I think across the Fed, and I can speak for the Boston Fed. We're only allowed to hire US citizens. I don't even think we can hire permanent residents, but I'm not a hundred percent sure about that one, it should say on the application page. And I believe the reason for this is every time you hire an international person at the Fed, you have to make a case for why a US citizen could not be hired for the same job. And this case is harder to make for undergrad at the undergrad econ level given how many American citizens show interest in this position. So that's just a binding federal rule. So yeah, I'm an international student myself, so I sympathize with whoever is unable to apply to us because of this. But yeah, this is something we have no control over.

Martha Fiehn (PhD at UCSD):
Eric, do you want to talk about it and maybe also, I know some positions require it when there's sensitive administrative data for instance, but maybe,

Eric Zwick (Professor, Chicago Booth):
Yeah, so I mean other than when working when folks are explicitly working with data that has these kinds of requirements, which some of my work that uses admin tax data does have these requirements, there's a process that folks have to go through to basically submit the relevant information to be eligible for that job. But most of the predocs at Booth don't have those rules. And I work with predocs in some of the research centers at Booth that I also supervise that are not working on that admin data stuff. And yes, that I think from all over, but the policy is definitely above our pay grade to decide. So whatever the rules are today, I'm not sure, but definitely read the applications carefully or the job postings carefully to make sure you're eligible given how the rules have been moving around.

Martha Fiehn (PhD at UCSD):
Great. There's a question here about cold emailing. It says if there's no official posting, would it still be valuable to reach out to professors at research University asking if they have anything? I'll answer this and then you guys can say also what you think. Yes, absolutely. I did a bunch of cold applications and I got a bunch of affirmative responses. I got some no responses, but I had a really high success rate with it. I would say there you do want to be careful with match. If you can demonstrate in the email already, which should not be too long, why you're a good candidate, then they might take a look. I know for one of my PIs, I was the one who screened the incoming emails and then forwarded it to my PI if I thought, Hey, this is someone maybe worth looking for Alvise and Ashley and then Eric and Ali in that order. Do you want to talk about cold emailing?

Alvise Scarabosio (Research Fellow at SIEPR):
I don't think I've ever done it, so maybe I'll just leave the floor to everybody else.

Ashley Pandya (Research Professional, Booth):
I also don't think that I cold emailed for pre-doc application specifically. Maybe primarily because there were a lot of open positions already readily available on pre-doc dot org. It didn't necessarily feel like I was missing out on anything. I can kind of speak to though I ended up cold reaching out to other pre docs to ask their experience. I did that through LinkedIn, some network connections. I would say that is extremely helpful. I think folks are actually willing to give maybe a little bit more of a candid response about working with their pi, working in that environment, working in a pre-doc in general than you might expect. And so if there is somebody who is working at an institution or in a field that you are interested in joining, I definitely say reach out to them. Folks are generally quite warm and willing to share their experience.

Eric Zwick (Professor, Chicago Booth):
Probably, I mean for better or worse, a couple of these a week. So maybe like a hundred or more a year from either undergrads, international students or potential pre-doc. Pretty hard to process all of that. The fraction that I tend to mostly look at ones that are from the Chicago community to see if there's any part-time stuff that I can get them to do because if there's really good fit, and I'll just look me basically at the resume. So a really long email that has, I read your paper, it was really interesting, but that's low value or maybe even negative signal because usually there's no content in that. It's just, I really like your paper title X. And yeah, it's just sort of hard. That's why we have these postings for jobs and stuff. It's like, so we have a system for processing this information because sort of overwhelming to receive that much incoming. So I don't think the cold emails, I mean, I don't know, probably less than 1% success rate, but for somebody who has some connection to already, I think that increases the success rate a lot. So within the academic community that you're already in. But that of course creates an insider outsider problem. But that's why we have this application process to try and sift through the larger pool of potential people and find good matches.

Martha Fiehn (PhD at UCSD):
I imagine you don't get as many cold applications.

Vaishali Garga (Principal Economist, Boston Fed):
Yeah, no, I wish a hundred people were telling me, I read your paper titled x. I love it. No, you

Eric Zwick (Professor, Chicago Booth):
No doubt.

Vaishali Garga (Principal Economist, Boston Fed):
I don't get that. I get a few of these because we do outreach talks from the Fed, so we'll go to universities and campuses and talk about the work at the Fed and then people will be like, can we come and work with you other opportunities? And for the RA position, it kind of point us to cold email. Everything goes through that official process. I have no ability to hire anyone independently outside of that process. But maybe for people who are here, if it's a few, there is also an internship program if you're interested in a shorter term position. So those will happen in the summer or in the winter, like 12 week programs. And it's a bit different from the RA program, but there you work one-on-one with an economist and maybe for that the cold emailing could help, but that's a different position. It's like a short-term position. You kind of work on one task, one project.

Martha Fiehn (PhD at UCSD):
Thank you. Okay, so we'll do one or two more questions. There's a couple in the q and a that are asking basically, why am I not hearing back from any positions? I feel like my cover letter summarizes my skills and research properly. I use a generic cover letter for all applications with some minimal changes, but I've applied to 60 to 70 pre-doc so far and I couldn't even crack the first round. So two applicants who maybe have applied to many positions. And Ashley mentioned you applied to lots of industry positions and it was getting frustrated. What would you say? Yeah, maybe starting with Ashley and then

Ashley Pandya (Research Professional, Booth):
Yeah, I sympathize. Can you actually repeat exactly what the question was? Is it what do I do?

Martha Fiehn (PhD at UCSD):
Yeah, sort of. Sorry, I was synthesizing, trying to synthesize a couple at once, but basically why am I getting rejected? What am I doing wrong? And we don't see this individual's profile per se, but perhaps some, I don't know, words of encouragement or general advice.

Ashley Pandya (Research Professional, Booth):
Yeah, I would definitely say that it is tough. It is definitely not you speaking to people who are my current coworkers who are successful and working as a pre-doc. Lots of us apply to a lot and lots of us didn't hear back from a lot and it takes a certain amount of grit, especially since right now the pool is pretty oversaturated. So it is not just you. I would definitely suggest to get a more individualized response. Again, like I mentioned, reach out to people if you don't feel like you have a super robust advisor program maybe in your undergrad or other people that you can reach out to who would have solid answers. This pre-doc dot org is a really good resource. Ask any of us, feel free to reach out to me if you have any specific questions. Maybe I can answer it. I would also say too, if you are applying this cycle, as far as I know, very early, I don't think I started applying right around now and I don't think I had gotten a final offer until end of November or something. So it is a decently long application process. And I will also say, actually, I'm remembering this now extremely fondly, which is I think the first five responses that I had gotten maybe a month after I finished starting my initial application process. Rejection, rejection, rejection, two of which ones that I really wanted. So it'll get better at some point, I promise

Alvise Scarabosio (Research Fellow at SIEPR):
If I can add something to that. I think this is a pretty long game in a way. I think a lot of the big labs maybe have applications coming out right about now or in the fall, but some positions will open in May or something. Some PIs will find funding very late. And in my experience, my masters classmates, some of them got a job very, very late. So I think don't worry too much, even if at the end of fall you don't have an offer, I think plenty of people do and there's plenty of opportunities that continue coming out through the spring. I know in my experience here at SIEPR, I know that I think last year got like 650 applications and I think we had 20 people coming in this year. So it is highly competitive. And in terms of what you can do about your materials, I think Eric, I think mentioned multiple times, make sure it's not sloppy, you don't have typos and things like that. Make sure you're a good fit. I think seeing people doing applications, sometimes part of the application review is done by the pre-doc. So what I've seen is a lot of PI tell them like, oh, maybe if you don't have the strongest transcript, make sure these people really like what I do or show some interest in my field or in my research. So I think maybe personalizing your application to fit whoever you're applying to could be something good as well.

Martha Fiehn (PhD at UCSD):
Well, great. Thank you so much. We are out of time, but I really appreciate all the panelists coming in today and hope that it was a useful experience. So yeah, thank you so much. I'll hand it back over to Steven.

Alex (PhD at UC Berkeley):

All right, let's get started. So just to introduce myself quickly, I'm Alex and I'm a second year PhD student in the economics department at UC Berkeley. And the goal of this second panel will be to give some general tips and advice for how to succeed as a pre-doc and give you some more information on what the day-to-day responsibilities of pre-doc are and what that routine looks like so you can have a better idea of what you're getting yourself into as you're starting to apply for these positions. Hopefully with the great advice that Martha and the other panelists just gave you. So hopefully we have all of our new panelists spotlighted here. Maybe can you each just go through and give yourself, give us your name, title, institution, any other information you want to share.

Samuel (PhD student, NYU):

I can go first. I'm Sam. You can call me Samuel if you want. I'm an incoming student at NYU Econ and currently pre-doc at UChicago.

Adi (PhD student, Brown):

I'm a second year at Brown. I did my pre-doc at Wharton and I'm interested in development and behavioral economic.

Rodrigo (PhD, UC Berkeley):

Hi, I can go next. I'm Rodrigo. I'm a first year starting now PhD student at Berkeley, and I'm originally from Latin America from worldwide, and I did my pre-doc in Switzerland under the position of David Donaldson.

Chris (Assistant Professor, Booth):

Hi everyone, I'm Chris Campos. I'm an assistant professor of economics at the Booth School of Business, the University of Chicago, and I'm a labor economist and focus on the economics of education primarily.

Alex (PhD at UC Berkeley):

Awesome. Thank you guys. So I think one good place to start would be for the former pre-doc in the panel. Given that I think all of you're coming straight from a bachelor's or master's degree, when you started the pre-doc, what did it feel like to start a pre-doc coming out of school? Was it a big adjustment coming from being a student all day to having a job that is, it becomes more responsibilities than a student, but is still very much in the academic sphere, unlike the private sector? How did it feel to adjust to being a pre-doc coming right out of school?

Samuel (PhD student, NYU):

I can say for myself, just coming straight out of school, it felt really odd not to have a grade on my work, and you have to be the one that decides how well you're doing at your job, which is annoying at first. But the good news is that not everything needs to be done to perfection. That would be kind of unkind to you and inefficient for your project to be a hardcore perfectionist by every single thing. If you mess something up, the damage is not permanent. You don't have an F for the rest of your pre-doc, you can redo it and mistakes happen all the time in research. And finally you can meet with your PI and discuss how you're doing, how you can improve. Not everyone is going to provide constructive criticism right off the bat, so you want to make sure to demonstrate that you're coachable and humble. But yeah, in summary, it was weird to kind of not have grades at first, but it was important to grow in that way and definitely things you can do to, I understand how well you're doing, talk to your PI and yeah.

Adi (PhD student, Brown):

Yeah, I agree with Sam. It was an interesting experience

To not have as much feedback day to day. So trying to figure out keeping yourself honest and how good you're doing and how much you're doing as new challenge as well as keeping a schedule nine to five, it's a big adjustment just as it is for every new job, I'm sure out of school. But figuring out how to keep that schedule and communicating with your PIs is I think probably the biggest adjustment. Making sure that you're keeping them on track with what you're doing and making sure that the project is moving forward at a rate that's not killing you, but also you're still being an asset to the PI is a challenge that

Is new to the job.

Rodrigo (PhD, UC Berkeley):

So from my experience, I didn't do the pre-doc right after college, but I can say that it's kind of pretty much I would treat it as any other job. And for me, there are two things that are super important. One enters the job market and starts working and that frequent from school is that first you need to kind of get out of the mood that you're being graded all the time and that you are having to take exams all the time. So for example, if you're a PI or supervisor asks you, okay, what are we estimating in this situation and why this estimator al for example, they're not grading you, they're not trying to give you an A or a B or a C, they just want to understand what you did and you also will want to know to understand what you did as well.

I think that's the key difference. You have to stop feeling that you're being graded all the time and answer truthfully and to the best of your knowledge and kind of build up on that answer and try to seek the truth of what you're doing. And on a second point I would say you have to always seek feedback. So since you're not being graded all the time, you don't know exactly what you're doing on a day-to-day basis. So it's always good to try to seek feedback. It doesn't have to be from your pi, it can be from your colleagues, from other pre-doc, from friends that know kind of what you're doing. Always try to get a sense of feedback of your work on your decisions. I think that's always super useful and it will help you the most. I would say the feedback that you get from peers

And your supervisor will help you grow and understand things in a better way.

Alex (PhD at UC Berkeley):

Awesome. Thank you for all of that. So I think in terms of what being on the job is like and the daily routine, as we discussed in the last panel, one of the major things that PIs who supervise pred docs are looking for are a variety of coding, econometrics, technical skills. Do each of the current pred docs want to talk a bit about what your daily workflow is like, what programming languages or tools you're using the most often, and Chris, if there's any particular technical skills that you really value in a pre-doc and tell us a bit about how you typically work with your pre-doc.

Chris (Assistant Professor, Booth):

I can go ahead and start in terms of skills. I mean I think most of the applicant pool that I come across, they have some pretty decent background in Python or R, so I think that's pretty standard now. Less so Stata. So I think that's a bit more varied. Economists still use Stata a lot, so if you do have some background with Stata, that's obviously great, but Python and R are probably the main things that I particularly look out for. And having taken some CS classes is usually helpful. But with that said, I don't entirely just screen on that. I think there's a lot of learning by doing on the job and not everyone has the opportunity to take CS courses in undergrad. They may have found the path to economics a bit late, so there's still a lot of scope to contribute even if you don't come in with a strong coding background.

So yeah, those are the skills, but I think besides just technical skills, I think the thing that to me stands out the most in interviews are students or applicants just kind of can convey a strong eagerness to just learn and continue to just learn. So you have to be passionate about wanting to learn and solve problems. It's okay if you don't really have the coding skills. If you have that passion to want to learn and continuously improve your skillset, then you're going to be a great pre-doc. But if you kind of don't have that passion, it's always going to feel like work. Having to go learn this new skill is kind of this tedious kind of maybe not so fun job. But if you have a passion to want to figure out problems, answer problems or figure out solutions, this is never going to feel like a job and you're going to be naturally a good fit for this type of work.

Samuel (PhD student, NYU):

I can definitely echo what Chris is saying, obviously have pythons data or have that whatever the job description calls for, try and have that ready to go, but more importantly, know how to solve problems in general, like be a good problem solver, learn how to navigate stack overflow or coding subreddits or online coding forums. Chat GBT is helpful, but really you need to be able to patiently read through forums and independently understand people's comments and advice and then be able to take the recommended solutions and apply them to your situation. This is a skill which many people including me need to work on. In my opinion, research is a cycle of getting stuck and then trying to get unstuck. And so if you have that passion for learning, passion for wrestling with problems, I you'll get familiar with that cycle and you can make up any kind of coding deficiency just by being a very passionate problem solver.

Rodrigo (PhD, UC Berkeley):

Yeah, I'm building on what Samuel said.

I would add that learning a language or learning a software is going to be software specific. So either you learn python or data that's going to help you in a general sense, but it's going to be specific to that program. But learning how to be a pro source is going to help you across all the softwares that you can use. I think that's a generalized skill that goes beyond the specific software that you're using. And most of the time the language that you're going to be required to code on, it's going to depend on your PI was posting the job. In our case, or at least in my case, everything was built in Stata, so we stayed a lot and then we went to Python to web scrap or to art to run some simulations. But everything was mainly done in STATA. And I would add that good skill have learn to or knowing how to use Gub and glab because nowadays I think research has be reproducible and yeah, the way you can do that with our data, it's by storing all your code and files on a server or Glab or Glab that allows you to go back and review the history of your code and track down possible errors and stuff.

So I think that's also a good add-on to have, and at least in our recruiting process, we didn't put much or we didn't make much of a focus on the language per se or on the coding skills per se. Of course there's a data test and according test, but the test, what it really is looking for is for the applicant to have super attention to detail and be also aware of what you don't know. I think those two things being super attention to detail and also being aware of what you don't know are things that are going to get you far in the free job, especially the second one because of course BIS don't expect you to know everything that you're going to ask you, but they would like to know. For example, if you're comfortable with something then it's okay, but then if you're not and you are unsu still, you should always feel free to ask questions and also you should be aware that you're not what you bring us to do.

Alex (PhD at UC Berkeley):

Great. I totally agree with, sorry. Yeah, go ahead.

Adi (PhD student, Brown):

Sorry. Yeah, I agree with everything being said, I think when it comes to languages, it just depends on your pi. One of my PIs, everything was in data and the other one, everything was in R and Python. So it just kind of depends on what you're being asked for. Another skill, I think it's very important, which I think I underestimated at first is understanding, making sure everything is organized and clean and that your file structure is always as clean as possible because you have to remember this is one of eight or maybe more projects that your PI is working on. A little confusion will take a lot of time out of their day that they don't want to have to deal with. So make sure everything's super organized and file structure is always great and clear is very good. And then also just making sure that you understand the economics behind what you're doing. If you know how to run regressions but you don't actually know what they're doing, that's not very helpful. So making sure that you also are being a good economist and not just being a good coder.

Alex (PhD at UC Berkeley):

Yeah, I think that's all great advice. Particularly agree with everything that was said about how being a pre-doc involves a lot of learning by doing. I know in my two years as a pre-doc at Chicago Booth, I definitely over the first few months, but honestly the entire time I was having to solve new problems and having to figure out how to code up things that I'd never tried before that I didn't really understand until I had to start working with them very hands on. And I think being willing to admit when you don't know what you're doing and to learn and go search for new answers like Samuel mentioned is really important. So now that we've talked a bit about hard skills, I think it's also worth covering what kinds of soft skills, communication, time management, et cetera are important to having a good experience as a pre-doc. So for Chris, maybe you can tell us a bit about your management style and how you interact with your pre-doc and everyone else. It would be helpful to hear what are the other important elements of being a successful pre-doc that you think are worth mentioning and do you have any advice for managing up and building that relationship with your pi?

Chris (Assistant Professor, Booth):

Sure, I can go ahead and start. So one thing I make sure to tell all the pre-docs that have worked with me is they don't teach you how to manage people when you get a PhD. So I think it's not something that we're all naturally good at. So it's definitely something that I've been evolving with In terms of my management style, I think I've converged on something that has been working and so I communicate with my pre-doc at a minimum three times a week because we have three meetings. The first meeting is on Mondays where the purpose of that meeting is for each pre-doc to basically on their own kind of establish what they think their goals for the week should be. And the reason why I put this on them is because a lot of students when they start the pre-doc, they've always been told what to do, here's your homework assignment, you have to do this and don't really, they're not too good at just kind of working independently or kind of guiding the direction of their work.

And it's something that you definitely have to be good at when you go to the PhD program and start working on your own research. So I thought it's a good idea for them to start practicing that. Obviously in the first few weeks or the first few months, they don't really know what their goal should be, so there's definitely a lot of direction I have to give them. But over time, as they kind of adopt and are working on several projects, they kind of know how to prioritize different tasks that are forthcoming. And so it's usually what a Monday check-in is. And then we meet Tuesdays and Fridays where we'll have kind of a lengthier check-in where everyone will just kind of for 20 minutes talk about what they've worked on since we last checked in, clear out any doubts, stress, any issues that may have come up.

And then in between then we communicate via Slack. So I feel like I am actively communicating with them, definitely Slack 'EM at least once a day about something. So there is constant communication and I do try to encourage them to feel comfortable reaching out and asking me questions, especially early on where we're kind of getting to know each other and how each other works. And so I think that model is working. It's been working for a while, so I'll probably be keeping it. We also kind of use Asana and in Asana is like where they kind of maintain all of their tasks and all of the goals that they're kind of striving to achieve in a given week, but also more on the long run. They also summarize what they've done at the end of the week. And I think the end of week summary is very helpful just because in the PhD program there'll be periods of time where you're just working on your own research and maybe a month or two will pass, but you won't really have much to show for it.

And what was very helpful to me in the PhD program was kind of maintaining a research journal where even though months may have passed and I don't really have much to show for in that journal, I have a lot that kind of really shows and demonstrates that I've actually done a lot and maybe there may not be any particular results that are encouraging or that I'm happy about, but there is a lot of human capital I've obtained along the way in that research journal was very helpful to me. And so the analog to this is kind of the end of the week summary where everyone just kind of reflects on what they worked on, what they achieved, what gets punted into next week and things like that. And so over time the pred docs learn to start directing how their attention should be devoted to different tasks. Obviously if I need to intervene and say actually prioritize this, I'll do so, but that's the general flow that I've been having for the past year and a half or so.

Samuel (PhD student, NYU):

I can just say the strength of pre-doc relationship with their PI is very much proportional to the communication. Provide clear updates on what's happened and what's coming next. Be honest about what's not working because they can help you and they can help you a lot more than you think. It doesn't matter if they don't look at the data as much as you do or code in the same language as you. You'd be surprised by how much they can help.

Adi (PhD student, Brown):

Yeah, I agree. Communicating

Is incredibly important. And I think another thing that is important about building prevailing relationship is making sure that organization works. You guys are organizing tasks in a way that makes sense. I think one sort of skill I had to learn when I first started, because in undergrad I pretty much kept everything because in my brain I didn't really write things down, I didn't have a planner, but that doesn't really work when you're being a pre-doc. So like Chris was saying, they're not professionals at managing people. So trying to communicate with them to build a system that works for both of you, I think is a very important step. Try to figure out early and then continuously communicating about problems that come up or even just about your research and your ideas. I think PIs are looking for people who can contribute intellectually and improve papers, not just get them done, but improve them in some ways. So working to try to do that and having the confidence to do that

I think are both very important for building a relationship.

Alex (PhD at UC Berkeley):

Cool. Rodrigo, anything to add or should we move on?

Rodrigo (PhD, UC Berkeley):

No, I mean I didn't want to be repetitive, but maybe one thing that I remember Dave told me in my first day at the job was that you know how in industry there's this saying safety till make it. I would say in academia and in the Joe, what he said is don't fake it, you'll make it. Just never fake it it ever. And be open about, as I said before, with things that aren't going well and be open to being authentic and communicating that things aren't going well. So there's space to improve and work on it. So for example, if you find a mistake on your code that you did a month ago, don't try to cover it up. I would say, for example, if you find a mistake, no, no beforehand that your PIs are going to be glad that you find a mistake and that you can fix it now instead of you being scared about how you made that mistake and then yeah, not saying anything about it, I think that's important. And then this might be kind of a cliche advice, but just be honest and be yourself. I think people pick up on the authenticity of who you show you are and I think that's going to help build their relationship with your PI and with your peers more and more.

Alex (PhD at UC Berkeley):

Yeah, I definitely had a lot of experiences in meetings with my PIs as a pre-doc where I had to admit I set that thing up to run overnight last night, but it didn't work. Or I found a bug in something I wrote two weeks ago and they were generally very receptive to that and saying, look, we're glad you found it. Tell us these things upfront. We just want to make sure that the final results end up being right one way or another. And I think being honest about what's going well and not going well from the start, it's very important to establishing good communication with your pi. So now that we've covered a few of the most important things for pre-doc and their day-to-day routine, is there anything we've missed? So preds here, any other on the job skills that you think are really critical and for Chris, anything else that you're looking for that we haven't covered yet? What are the hallmarks of a pre-doc that make you really want to write that all important recommendation letter for them?

Chris (Assistant Professor, Booth):

I guess I can go

Ahead and start. Yeah, it's kind of very similar to what I started with in that we'll quickly be able to pick up on how passionate you are to just learn things and how curious you are about whatever it is you're working on. And just really having that curiosity and that passion to learn really is going to make you a successful. And so that kind of are passionate about what they're doing, don't really view this as a job. Some transactional kind of 40 hour a week thing are usually the pre-doc that I think we're going to be happiest to write a letter about just because they naturally enjoy doing this type of work. So that's

Adi (PhD student, Brown):

I think one thing for me that took a while

For me to figure out is don't be too nervous to show that you don't know something or don't understand something. You have to be willing to maybe look a little dumb sometimes for the sake of learning. And if you don't try to show this passion for learning, like Chris was saying, you'll never make a great impression and your letter won't be that great. I think what makes a good letter is showing that you are extremely passionate and curious about this and have the beginnings of interesting ideas. So you have to be willing to be wrong occasionally or look maybe a little silly occasionally so that you can learn from those mistakes, but also

Show that you have that passion for learning. Cool. That's super

Alex (PhD at UC Berkeley):

Helpful. So for the current pre-doc, if there's anything you want to add on what surprised you about the pre-doc experience or what you struggled with the most, that would be helpful. If not, we can move on to a couple of other things about during the pre-doc and afterwards, is there anything you would add on what challenges you faced or what obstacles you see pre-doc running into frequently?

Samuel (PhD student, NYU):

If I was going to restart, if I were to talk to myself, the version of myself two years ago when I was just starting my pre-doc, I would just say to that new pre-doc to do your best to be confident in your abilities and don't be hard on yourself. When you make a mistake, you will mess something up and that will feel embarrassing, but just do your best to be confident and let it go and that will help you a lot.

Rodrigo (PhD, UC Berkeley):

So for me, I think that the most challenging thing that I encountered doing my pre-doc was, so we had a similar but different set up as expansion. So I was working in two projects and in these two projects every week there was a PI call in which I was responsible to present what I had done during the week and show results and explain what their results looked like, that, et cetera, et cetera. So every time that I had one of those calls, I was super nervous because there were three or four super experienced PIs, well-known PhDs as professors, et cetera.

And also it was different than, I don't know, for example, presenting a thesis where you kind of are the person that knows everything and knows the most about your project. This of course is not necessarily your project, so they know better than you, but you have to be confident in what to know you're presenting and be confident on what you did was the right approach and justify why you choose that, why you choose an another thing and kind of be open to that conversation and to resume feedback. And for me, those kind of presentations were the most challenging. At the beginning I was always super afraid, but then that fear grew less and less over time. And then yeah, at the end I became comfortable with it. But yeah, when I was super comfortable, basically my preDoc was over, so I couldn't exploit that during the pre-op, but I hope that I can exploit that during grad school.

And finally, I would say that I had a great time during my predoc, I'll recommend it to anyone that's looking to do a PhD clinic on other social sciences. I would say it's a great experience and for me it was kind of how I would describe it is that you get to play to be a researcher before you actually are because you get to work on a lot of cool things, but you don't have the responsibility of those cool things. So you don't have to manage people mostly you're not responsible for mistakes it you get to play first before you actually become a researcher. So I think that's cool. And yeah, I would say at the end of the day if you are deciding or you're set on doing grad school always was kind of like a needle move or a middle step towards that.

But I will make sure that you enjoy that year or two years of your pre-doc because it's two years of your use. And yeah, don't stress it much. I would say if you do the right job from nine to six or whatever your working hours are, it's going to be probably fine and make sure to keep enjoying life outside of during those working hours as well. But outside of those working hours especially and get also the chance if you are in a program that has a lot of other predoc, get the chance to socialize with them to become friends with them because those connections, especially once you finish the predoc and start diverging into other schools all around the world, they're going to be important and you're going to then have a lot of people that are within the profession and then that are all over the world. So that's kind of cool and you start building connections super early on and I think that's also invaluable, unusually hidden asset that I think Predoc have.

Chris (Assistant Professor, Booth):

I'll add one thing on the presentations this bit in the weeds, but when you do have to present in front of your PIs or co-authors of PIs try to make some slides or something that can kind guide the discussion. But most importantly, even if you can't create some slides, look at the results before the meeting and don't present anything that you just made right before the meeting. Some of the most frustrating moments can be when someone sends you some stuff that they worked on and you open up the document and the first number you look at is clearly wrong and that means that there's some mistake. And that's usually happens when someone just sent you something in a rush, they didn't have a chance to take a look at the results. So that just ends up being a waste of time for everyone. So be comfortable saying, look, I wasn't able to get this in before the meeting, it's fine. That's much better than discussing some results that are probably going to be some nonsense. So this is my 2 cents on that.

Alex (PhD at UC Berkeley):

That was definitely something that took me some time to learn, to figure out how much information I should put in the memos or other meeting agendas that I was sending to my PIs to realize how much detail do I need to give you so that you can make an informed decision without overwhelming you with the information that's not super useful to you. And I think as a pre-doc coming straight out of school, that's something that took me a while to really calibrate for myself. So now that we've heard a lot about what the day-to-day workflow is like of being a pre-doc, I think it's also worth mentioning that there are a lot of side benefits that come with being a pre-doc, such as being able to take classes at your institution, go to seminars, get advice on grad school applications, et cetera. So would each of you want to say a bit about what those sort of side benefits and opportunities are like at your institution and how you might've taken advantage of them during your time as a pre-doc?

Adi (PhD student, Brown):

Yeah, I would like to say every seminar that you could go to, I recommend going to, even if it's not what you think is your main interest learning, I think that the main thing you get is learning what a good presentation looks like and what bad presentation looks like and learning that skill early is incredibly important for grad school. Already through my first year I've noticed how much it's helped, but it's also a great way to start thinking about where the frontier is and how you can sort of build on that. So I definitely would recommend going to as many seminars as you can fit. I mean don't go to too many and let your work slip, but probably three, four a week during lunch, something like that you could probably do without losing too much. And then classes I really valued taking and even auditing a couple of fueled courses if you're behind, if there's a couple of things on your transcript that you should improve, I needed to take a linear algebra class, so I did that. But I think taking field courses is also extremely helpful so that when you get to grad school you hit the ground running.

So I definitely try to take advantage of those things if you can. I was at

Samuel (PhD student, NYU):

UChicago, like I mentioned earlier here we can take classes and get financial support for that almost always a hundred percent paid for. So that's an option. It should be an option at other university too. I chose not to, I did a lot of school in college and I wanted a break from the classroom and that really helped me to better magnify my responsibilities of the pre-doc itself. I think the best resource available here at UChicago was the seminars you get to explore different research topics and once you, I mean I recommend going to a wide variety of seminars, find out if you're interested in econ for real or maybe you might be interested in something that's related to econ but not classically defined as econ such as accounting marketing or operations research or other topics. These are all seminars that are offered at Chicago Booth Business School, which is where I'm at. And I imagine they're offered at other business schools, other university pred docs because the hardest part of the PhD application is figuring out what you want to do in your PhD and figuring out how to phrase that in your statement of purpose. So highly, highly recommend taking advantage of the seminars and exploring your research interests through that.

Adi (PhD student, Brown):

And I can add to that,

Rodrigo (PhD, UC Berkeley):

I mean this might be obvious, but I think the biggest advantage of a predoc is getting familiarity with what research looks like on an earlier stage before you commit and five to six years to your PhD. I think that's the most advantageous thing that a predoc has, especially because for example, using my case when I started my pre-doc, I started work on a paper that was born 10 years ago and we submitted that paper and then it got rejected and then we submitted to another journal, then you got an RR and then got a secondary on hour and I left that paper, but it was to almost barely over two years that, for example, I worked on that paper. And I think getting to know that aspect of research that is kind of slow in a way that you always work on the same paper for two or three, four years, that's us being super different than for example, working in consulting or working in the private sector where the environment is much more fast paced and you get to turn on things and then those things disappear and then you work on another thing and another thing and another thing I think in research, a lot of the time the work is more repetitive and more deep and you go deeper and deeper into the same paper sometimes.

So I think getting exposure to that academic environment and how academia works in that aspect I think is super useful and it will help you decide whether you want to do that as a profession or not. So for example, I didn't have a problem with that and I like research, so I decided to pursue a PhD. But for example, one of my friends didn't like that pace and she decided not to do a PhD. So I think it's a good environment for you to get to know what research looks like and decide whether you want to pursue a PhD or not and a research career or not.

Adi (PhD student, Brown):

One other thing I would recommend actually, depending on where you are, I was lucky enough to have a pretty good relationship with the PhD students at Wharton while I was there and I would definitely recommend trying to do that if you can and even joining some reading groups. Sure that for the most part they'll be happy to have you unless there's your getup huge pre-doc group and there's hundreds of you that maybe not so much, but I'm sure that a lot of them would love to meet you and chat and sometimes they can give you different advice than your PIs. If your PIs are more senior, they might not really have the experience with some things as they are now. The market's changed quite a bit. So

I do recommend doing that if you have the opportunity.

Alex (PhD at UC Berkeley):

Thanks guys. So I think Rodrigo gave me the perfect segue into one of our final questions, which will be about coming into the pre-doc, being unsure whether a PhD is the right path for you and in general paths after a pre-doc. So as Steven mentioned in the introduction earlier, probably the most likely outcome for most pre-doc and most institutions is that they will end up going on to PhD programs in economics or related social science. But there are certainly a good number of people who go through the pre-doc to test the waters of whether they'd research and then figure out, you know what actually maybe I should have done, I can do something else after the pre-doc and not say in academia, which is a totally valid outcome. So what advice would you give to people who are not yet sure if grad school is the right for them? Do you think a pre-doc is useful in that case and can it give you helpful information about whether research is the right fit for you?

Chris (Assistant Professor, Booth):

I mean, yeah, I think for sure if you're unsure doing the pre-doc is really going to give you the clearer signal, but whether if this is for you or it's not for you, you're going to basically get into the weeds of what it's actually doing research that's at the frontier of economics. So yeah, definitely if you have slight interest on the margin, definitely do it. It could turn out that you're not interested and that's totally fine. I think there's no one looks at you any differently if you decide to do something else. If anything, we're happy for you and just want what's best for you. So

Yeah, that's my take. Yeah, definitely. And I would add that

Rodrigo (PhD, UC Berkeley):

Even if you decide not to do a PhD during the pre-doc, you pick up a lot of invaluable skills in that process as well. So for example, from my predoc, got a lot of skills that will be translatable to the private sector or to any other sector. So for example, project management, managing other hourly arrays, presenting skills. I think these type of things are really generalizable to any type of job. So yeah, you're going to spend one or two years trying to figure it out, but even if you decide not to do a PhD, I think you'll gain a lot of valuable insights and skills that will

Translate to other things that you move on to life.

Adi (PhD student, Brown):

Yeah, I think one thing I would also say is

I know some perhaps try not to infer from your preddoc experience what grad school will be like. I think if you're enjoying your pre-doc, you'll probably enjoy grad school, but sometimes you just don't click with your PIs. Sometimes the projects that you thought you'd turns out aren't actually your favorite topics. So I think seminars or and meeting with the current students and stuff can be useful for. So I mean I was lucky enough to really development so I stuck with it, but if I didn't like development, talking to other PE students and coming to other seminars would've just really helpful to sort of find what my interests actually are. But yeah, sometimes just don't click with your PIs. I think I kind of clicked with one and the other one kind of not so much. So just because you don't like your pre-doc doesn't mean you're not going to like grad school, but if you don't sitting down and coding and something like that, then

You probably won't like grad school. It is a lot of that.

Samuel (PhD student, NYU):

I have nothing to add here. I second everything that was said.

Alex (PhD at UC Berkeley):

Awesome. So that was in large part my experience as well. I went into the pre-doc trying to be very self-aware about saying, I think I'm on the fence about grad school right now and I will use this time to figure out whether I actually doing research and try to immerse myself in academia as much as possible to decide is this actually for me? I came out of it saying, you know what I do, kind of staring at my Python window all day trying to debug why the disease results don't look the way I'm expecting them to look. And I enjoyed that. I enjoyed trying to start being a more active contributor to the project I was working on and I think for most people it will give you a relatively good signal of is this career path for me and at least am I willing to commit to the five or six years of a PhD. So I think that's pretty much all of the questions I had prepared before we had to answer any questions from the q and a in which I can see there's only one or two right now. Is there anything else we've missed? Any other advice you would give to someone who's starting a pre-doc right now?

If not, that's fine by me. We've

Covered quite a lot of good suggestions already. So I see just a couple of things in the q and a, I'll try to cover those. We have one for Rodrigo. What was it like for you to apply as a Latino with the barrier of not attending a top 10 university and a language barrier and how did you overcome that in your pre-doc applications and interviews? Do you have anything specific to share about being an international applicant to pre-doc? That would be interesting?

Rodrigo (PhD, UC Berkeley):

Yeah, so I think my experience was, well, particular in the sense that I know some people applied, I know docents or yeah, I dunno, hundreds of predoc, but I did apply to a few and I think I was kind of lucky in the sense that I was chosen for the predoc by my supervisor. And yeah, I would say in general I think that experience helped me a lot in the application process for the PhD. I think without that experience I wouldn't have gotten where I'm at right now without that experience. So I think it worked wonderfully for me. And I dunno, language barriers, I would say I learned English since I was young. So I would say that understanding reading, writing English was an English field, but I think it also helped me a lot to speak parent English because even though it was in Switzerland, everything was in English and we always speak in English. And yeah, I think that also helped me a lot in not only communicating English and conveying things in a way that are more natural now, but also, yeah, it helped me a lot with applications in general for the PhD.

I dunno if that answered the question, but that's all I can see.

Alex (PhD at UC Berkeley):

I think that was very helpful. So there's also one question from the previous panel that is worth bringing back up now. So as Eric and VE mentioned in how to apply to pre-doc panel, there's some institutions that do things more as you are hired to work with a particular PI and some institutions like a lot of the fed branches that tend to allocate pre-doc or RAs as a pool who work with several economists during their one or two years as a pre-doc. So do you guys have any thoughts on contrasting what it's like to work in a more pool environment in a lab with several PIs, maybe other maybe PhD students or postdocs whom you're interacting with versus a more traditional RAship where you are working very specifically for one pi? Are there any significant differences and how do you think pre-doc can succeed in each

Of those scenarios? So yeah, mine was pretty sort of

Adi (PhD student, Brown):

Locked in. I had two PIs and I just worked with the two of them, so I don't really know what precisely what the counterfactual was like, but I thought it was quite nice to sort of build this relationship over time. But one thing I'd recommend for people in the same scenario is to not be afraid to reach out to other professors that you aren't working within the department. I'm sure for the most part they'll be happy to chat with you about whatever sort of

Interests you have that is aligned with theirs. I didn't

Samuel (PhD student, NYU):

Work in a lab, I worked for two PIs just every consistently. But I feel like the, no, the lab environments here at U Chicago seemed really cohesive and people, it didn't seem like it was overly competitive, which was one thing I was kind of thinking that it might be bad in that way, but nope, the lab that I knew really enjoyed each other, they supported each other. So if you do end up in a lab, I know that people mentioned some concerns about working in a lab, but I think it really is kind of what you make of it.

Martha (PhD, UCSD):

Alex, if you want, I can answer that question if that's

Okay. Yeah, please go ahead.

Alex (PhD at UC Berkeley):

Okay, so this is something that I really like thinking about. So I did my pre-doc, like I mentioned earlier, both at Harvard and at Yale. And the experience was extremely different because of the ways that the programs were structured. So Yale has an official program with a program director with program support, whereas Harvard, I didn't even know that HKS had other pre-doc, right? We knew that there was pre-doc at HBS, the business school because there were roommates with some of the econ pre-doc. So it was very dispersed. And that means you don't quite have the same sense of community necessarily. It also means that you have to organize yourselves better potentially. So at Yale for instance, there was a pre-doc seminar every week with food and the coordinator would find a professor to give a talk or a PhD student to practice their job market talk.

And at Harvard we kind of made our own seminar, but just in general there's significantly, there was significantly less support and that also meant that if you had an issue with your pi, it really wasn't necessarily clear who to speak to. Whereas if there's a program director that can be a lot easier. You should also pay attention in case, I don't know if this has been mentioned already, but if you have multiple offers, you should look at that structure and look at the precise benefits. So for instance, if you are an NBER pre-doc, then you do not benefit from the same tuition reduction as if you are at that institution. So again, that's kind of a structure that can make a really big difference whether you're paying 500 or $5,000 to take a real analysis.

Martha (PhD, UCSD):

Thanks. I think we've

Alex (PhD at UC Berkeley):

Given everyone a lot of good suggestions about how to position themselves to apply and now how to think about succeeding in a pre-doc and adjusting to that after probably spending most of your professional career in school before starting a pre-doc or RA position. I'm happy to wait a few seconds longer if there are any other chat questions about the pre-doc experience. Otherwise Steven, feel free to take over at any point.

Adi (PhD student, Brown):

Okay, thanks Alex. Let me stump the recording

Stephen (Booth):

Here.