Reminiscing on a First Semester: Corny Advice for Surviving Grad School

Not everything about grad school will be as sunny or pretty as this Cambridge morning, but there's no reason it can't be as inspirational.

I write this post from the comforts of home during my first winter break from grad school, and as I sit here and look back on my first semester, I see nearly four months of the most intense coursework, stress, and insecurity I’ve ever known.

I went into grad school knowing I came in with fairly common yet nevertheless still worrying “disadvantages”: I’d been out of school for a couple of years, and did not have as strong a mathematical background as many of my peers. And unfortunately, I do think those “disadvantages” materialized and reared their ugly heads to some extent in my experience of this first semester.

So indeed I won’t lie and say that the first semester was ever easy or particularly pleasant; there were very few weeks or even days where I felt moderately on top of all my responsibilities, and the days where I felt like I could allow myself even a brief moment of relaxation without the guilt that I was sacrificing on precious productivity or schoolwork time were few and far in between. Most importantly, I look ahead to the second semester and I know that very little will be different. Yes, I come in with the knowledge that I am indeed capable of surviving (if not thriving) a semester of graduate school, with the veil surrounding the experience demystified. I’ll come in equipped with slightly more confidence that it’s not entirely impossible, and that I’m not the only one doubting my ability to make it through the process (to the extent there is strength in knowing you do not suffer alone).

So I wish to reflect somewhat as I prepare to go back in the next few weeks, partly from the desire to leave behind a trace of these moments I can hopefully look back on in the future as a reminder to myself of what I was feeling during similarly stressful times (hindsight bias always making the experience of one’s past always seem so easy, contributing to our perpetual feeling that we always struggle more in the present than we ever did before), and partly as a form of advice to anyone who may stumble upon these few words as they consider or experience grad school for the first time themselves.

I realize as I write this that grad school affords one very few opportunities to check in with oneself, to introspect and reflect on what one is experiencing and how one is feeling. To some extent, the whirlwind of action, classes, and responsibilities is a good thing: it keeps you moving, distracted, and with some notion of a goal to constantly keep working towards. This is of course exhausting, so I take the opportunity now to reflect mostly on how surprising I found it that whatever I was feeling in terms of insecurities, doubts, or anxieties was very much a common and shared symptom.

It’s a common part of human nature that to our eyes, everyone always seems to be doing so much better, to be finding it all so much easier. And that is still very much the case during grad school; it seems there’s always one person or multiple people able to balance working with professors, attending seminars, going to class, finishing problem sets, and also getting a social life in. And while there may be one or two people who are able to do so, I found that more likely than not, everybody else in the program was struggling through very similar emotions that, quite often, were left unsaid. It was this strange environment, where I don’t feel like anyone actually thought anybody else was doing particularly well, or where there was any shame whatsoever in sharing a particular difficulty or mental obstacle, but where nevertheless there was a very transparent veil of stress that could simply not be broken. Part of it was due, I feel, to what I’ve already mentioned: we have very little time to reflect, or even to share a brief moment of venting with any of our peers. Another non-trivial part of it was that, even though I never felt judged or looked down on (in my cohort at least, quite the contrary) there’s always the pressure to appear more competent, more prepared and more on top of things than one really is (or at least, feels).

So my first takeaway after a long preamble then would be that it’s incredibly important to verbalize and share. I found myself seeking some signal, some indication that at least someone else was feeling something at least close enough to the level of exhaustion, stress, and insecurity I was feeling. While technically in an ideal world one never wishes one’s peers to also be suffering, it’s absolutely true that there is strength in numbers, in commiseration and in a public support system. Otherwise, grad school can be an isolating place. So please, share, talk, even laugh about stress and insecurity, if that’s an effective way of addressing the issue.

While there’s something clearly undesirable and unattractive about waving off the clear mental health concerns that grad school introduces with “oh, everybody feels that way”, it’s an important first step that everybody realizes how alone they are not in whatever they are feeling.

My second takeaway would then be that, because everyone at some point or another (or really, at almost all points) feels a similar way, one should never fall into the trap of comparing oneself to others. In grad school, you are your own measuring stick, your own competition, and your own hero. While this may seem to some extent a contradiction of what I’ve already mentioned re: the importance of sharing in the common experiences and emotions, what I will say is that graduate school is also an important step in constructing your own academic or professional brand, in carving out one’s own interests or joining and contributing in the work of many others who have shared them already. As such, to a large extent, you are the master of your own destiny and (pardon the cliches), captain of your own ship. With that comes the ability to know for yourself your limits, to recognize when you need a break, and most importantly not let those moments make you feel guilty or less than others. Contrary to what your instincts, peers, or advisors may sometimes tell you implicitly or explicitly, sleep, relaxation, and leisure are not unproductive time. They are necessary moments of and means to rest, memory consolidation, and de-stressing that your brain will very much need throughout the long process. No one else can give those moments to you or make space for them. You must decide for yourself when and how often you feel you can take them, and never think that because no one else seems to be doing it, that you should not give yourself a moment’s pause or break.

During grad school, there is a constant pressure to do everything. You’ll never feel like you’re doing enough, or doing the right amount of anything. You’ll feel lazy or irresponsible for not going to enough (or, for many of us) any seminars, for not attending some of the TF’s office hours, for not reading dozens of papers, or letting your workout regimen go.

I myself struggled with never feeling satisfied with the amount of effort or time I could dedicate to anything. I’ve never considered myself a person that is particularly naturally brilliant. While I look around me and see people that can understand and grasp complicated theories or new pieces of information on a whim and on the spot, I’ve always considered myself a bit of a slow-burner. I need to ruminate, to absorb things constantly and repeatedly to truly internalize them. Unfortunately, grad school does not lend itself to any slow rumination of anything. Before she’s taken a breath, the professor has moved on to the next slide, or the lectures have moved on to a new topic, and I found it often very impossible to engage in the kind of mental processing and type of learning that I find most compatible and effective for myself. I feel that if I am able to succeed, it has been so far and still is through a Spartan level of discipline and dedication, through sinking many hours pondering on a subject until I have fully engaged with it, and unfortunately grad school quickly and effectively eliminates all of the stamina required to engage in that kind of learning, and as I’ve mentioned, solely through the very breadth, depth, and speed of it all, is not conducive to that particular style of studying.

My only response to that (if anyone reading this has felt the same way and found a particular mechanism to deal with this issue, please let me know!) would be to have faith and flexibility. Grad school is an entirely different animal to any kind of academic level most people have experienced before; it may very well be true that everyone feels like their particular style of learning and succeeding has been compromised in the difficult and novel process that is graduate-level education. So what I can say is to allow your brain to adjust, and to trust that you have gotten to this point in life so far very much because you have been able to adjust and adapt in the past many times already. I’m no evolutionary biologist, but survival is as much about the ability to adapt and change as it is about natural ability, and it is often sticking with and relying on the latter that can lead to extinction in the face of the unknown and the rapidly changing landscape.

Lastly, one final observation I commonly had through the experience was how stunningly little was required of me in terms of materials. After all, it was almost always just me and a pen or pencil. Ultimately, your brain is your instrument in grad school. Sure, you’ll eventually work with some statistical software or package, for which you may have some privileged access or fancy computer or server, and having the minds of others to bounce ideas off of and discuss difficult problem set questions with (is there such a thing as an easy problem set question?) is of the essence, but ultimately what’s working 24/7 for you is your mind. It needs rest, and most importantly, to believe in itself (pardon again the Chicken-Soup-for-the-Soul level of advice here). Grad school is an exercise in self-trust, in self-confidence; to survive you’ll need to build a support system and go for it.

Never before in my life have I felt so close to understanding what blind faith is. It may sound heretical to the religious among us or narcissistic and arrogant to many others (I hope that the many previous paragraphs in this post have convinced otherwise those among you that may feel I am either of the latter two), but I have learned nothing more forcefully in the last four months than to entirely and wholeheartedly believe and trust in myself, even when all reason would have me believe otherwise.

I still have very much to experience and learn (I’ve only been through this process for a handful of months), and the advice I may have could very well be very different in six, twelve, or twenty-four months’ time, but for now, I tell myself and anyone else who may be reading this: during graduate school, don't let your problem sets, grades, others' performance or self-imposed, arbitrary measures of progress discourage you; it's all a learning process, take it all in and trust that in the background your brain is processing and making connections. Absorb everything and let inspiration surprise you when it comes, on its own time. There will be many downs, but trust the process and carry on.

I am reminded now of one of my favorite quotes from my favorite book (I owe my Columbia education many thanks for introducing me to it), To the Lighthouse by Virginia Woolf. She writes:

“Was there no safety? No learning by heart of the ways of the world? No guide, no shelter, but all was miracle, and leaping from the pinnacle of a tower into the air? Could it be, even for elderly people, that this was life?--startling, unexpected, unknown?”

Like many other things in life both big and small, graduate school has no safety, no guide, and no shelter. It is all leaping from that pinnacle of a tower into the unexpected. What I have most learned so far in these four months is to have the confidence to take that leap. With hope, trust, and compassion in and for ourselves, we will safely land wherever our skills and ideas can most widely and profoundly impact the world, sometimes in surprising ways.

At worst, let the support system of your family, friends, peers, acquaintances, or any combination of the above, be there at the bottom to catch you if all else fails.

First Days of Grad School

This last week I finally began my first week in grad school, as math camp officially kicked off for our program.

We began the week with a review of topics most relevant to microeconomics, including Real Analysis, some fundamental results from Calculus, Optimization, correspondences, lattices, and a look at various fixed point and separating hyperplane theorems.

We then covered a couple days’ worth of material in Statistics and Econometrics, with a particular emphasis on asymptotics.

Next week we’ll finish math camp by covering topics in the macroeconomics portion, where we’ll focus on solution methods for differential equations, along with stability analysis and optimal control theory.

However, beyond all of the material covered so far and yet to cover, I have found this first week particularly inspirational due to the wide and diverse interests and backgrounds of the rest of my cohort.

Speaking with the other 35 members of the cohort, listening to their brilliant ideas, and feeling the incredible potential in the room is an enormously humbling experience, and a standard that I only dream I can live up to.

I’m excited for what’s ahead and ready to meet the challenge.

Applying to PhD Programs in Economics: Visit Days

Jumping off my last post on grad school admissions, a fun component of the entire process is each school’s visit days.

As you hopefully have the privilege to weigh competing offers in the Spring, you’ll hear arguments on both sides regarding visit days—those who think they are the most important decision factor, and those who think they don’t matter at all and in fact should not factor into our decision-making process in the first place.

Personally, I found them incredibly helpful, and while I agree that some components of these visit days should be actively discounted by the applicant when it comes to making decisions—i.e. don’t let one school’s particularly nice wine-and-dine experience outweigh more important factors like strength of potential faculty advisors, fields offered, etc.—I ultimately found an incredibly helpful level of candor from faculty, current students at each school, and prospective students who might form the cohort.

While there’s a lot of potential biases and even tricks of decision architecture that likely go into the process from both the applicants’ and the schools’ side of things (after all, on this side of the admissions game, schools are competing for the applicants they have admitted and not the other way around, and ultimately everyone is undergoing a matching process), I found the visit days really fun and helpful. I was advised most of all to enjoy the victory lap they represent, and to enjoy for once the feeling of actually being courted by the schools you spent so much time applying to.

However, to make the most of the time you’ll have at each school and make the most informed decision, I suggest you come prepared with a list of questions to ask, and as such, I have compiled below a running list of questions I kept the few weeks before visit days. Most of these questions were devised purely out of my own concerns as I began considering where to go, as well as from other online sources.

My last piece of advice though is that ultimately, the conversations you have with faculty, staff members, students, and other fellow admits that flow organically are likely to be the most informative. Definitely do not allow the need to ask all or even many of these questions get in the way of you developing a feel for each school and how you might get along in particular with the admits who may make up the rest of your cohort. After all, these are the people you’ll be spending long hours with doing problem sets, studying, socializing, and maybe even doing research and writing papers together.

For me then, getting a sense for who else was there on the admit side (and being able to see some of them at multiple schools, and in turn learn what they were thinking about as they were weighing their own choices) was probably the most critical piece of information coming out of visit days.

But to the extent the following set of questions may also be helpful, this is the non-comprehensive list of questions I came to each visit day with:

GENERAL

• What would you consider the best and worst aspects of the Department?
• What are important details about the program (i.e. requirements, examinations, papers) I should know? Are any in particular different from other schools’ typical requirements?
• (For those coming from the workforce) How have those with prior experience found going back to school after some time off for work?
• What are attrition rates like? Why do people drop out?
• What fields are particularly popular and strong in the department?
• Are any faculty members in my intended fields of study planning to leave soon? Are there any offers out for professors in those fields?
• What is the math camp like?
• How does the Department overall function? Is there a lot of politics involved in the department culture?
• How helpful is the administration staff? How nice are the facilities?
• Do you get a Master’s after some years, or if you have to drop out after a set number of years?
• (For current grad students in particular) Could you remind me what field you’re going into? Who’s advising you? Any thoughts on who might be the best advisors in that field?
• What do people typically do over the summers?

COURSEWORK

• How do you perceive the difficulty of the courses? Of the math involved?
• Are students competitive with each other? Do grades matter? Are students ever required to repeat core classes?
• I notice there are often seemingly-high grade requirements for classes. Does everybody generally do that well?
• Where do first year students study? Do they have offices?
• What are the first year classes like? Are they well taught? Do they turn out to be useful?

RESEARCH

• Do you have opportunities to present your research? How often? Who gives you feedback?
• Do faculty members co-author with students? Which faculty members?
• Do students talk to each other about or collaborate on research?
• What are the opportunities for first-year research? When do people typically start getting involved in research?
• What is access to data sources like?
• What resources are available for learning statistical coding like STATA, MATLAB, R, or other programs?

TEACHING

• What are teaching responsibilities like? What is the workload from teaching, hours and commitment-wise?

ADVISING

• How is interaction with faculty early on?
• (For faculty in particular) What research are you currently working on?
• Regarding the ratio of junior vs. senior faculty, how much attention does the more distinguished faculty give you? On the other hand, how involved can you get with more junior faculty’s research?
• How attentive are faculty advisors? How easy or hard is it for you to get face time with your advisor? Which professors are more and less available and helpful as advisors? How many hours do you meet with your advisor per week?
• How well are first and second year students integrated into the department? How/by whom are they advised before they have committees?
• Will a potential advisor be open to working with me? Will our working styles be complementary? How many students is s/he currently supervising, and what have their placements been like?
• (For those interested in more “interdisciplinary” or “inter-field” work) How would advising and research work carry on in a desired field of study that draws heavily from two separate fields and advisors?

COMMUNITY/ SOCIAL LIFE

• How do you spend your time? Do you tend to have time off often? How many nights a week do you study?
• How is community and mental health among the grad student population?
• What is the housing situation like?
• How do enjoy the town or city? What is transportation and ease of access to campus, airports, etc. like?

FUNDING

• What is the funding situation like for upper year graduate students? Do students need to do extra work beyond a standard TA or RA job to earn money?
• Is it difficult to get funding for research needs, like data, software, travel?

Applying to PhD Programs in Economics: Tips and Process

As I suggested more than a year ago, this last fall of 2016 I had the opportunity to apply to PhD programs in Economics.

In the hope that my experience may be helpful to someone out there also thinking about applying to graduate programs (in Economics in particular), in this first post after another long hiatus (who would have thought work and being an adult would be busy!) I will join the chorus of other great and helpful advice online in covering my application process, as well as some hints for maximizing one’s chances of success. These are of course by no means prescriptions, and no case—let alone mine—is representative of the overall process and of typical results, even for similar applicant profiles. Boilerplate/ disclaimer language aside, let’s dive in!

I had my sights on applying to PhD programs since at the very least late Junior year of college.  While I had long-considered pursuing a PhD as early as freshman year, it wasn’t until then that I fully made up my mind to jump into the process. So the first lesson is that it’s never too late to get one’s act together and produce a successful applicant profile, and my case is by far not on the extreme of the range of experiences and ages at which many candidates apply or start preparing. Moreover, the process will inherently be long and oftentimes stressful. I myself mulled over versions of my Statements of Purpose for months, and each component of my application (as I will cover down below) came very piecemeal, months at a time over the span of a couple of years (that being said, I have first-hand knowledge of other very successful candidates who have gone through the process of deciding to apply to submitting applications literally within November and December of the same application year, so every story is different).

By the beginning of Senior year though, and after some research over the summer, I didn’t consider my mathematics or research background particularly up to snuff, and hence decided that the best approach for me in particular would be to beef up these two components and, hopefully in the process, make closer connections with members of the faculty or research supervisors who could advise me and potentially serve as writers of my letters of recommendation.

So after a Senior year spent focusing harder on the math classes I was missing, on producing a good research paper in my Senior Seminar (which I have written about before), and registering and preparing for the GRE, I began working at Cornerstone Research, the economic consulting firm.

After a year of relatively carefree, adult bliss, the bulk of my application process began around June or July of 2016.

While I had already done a lot of work since graduation to prep for the application process (i.e. I took the GRE a month after graduation, took Analysis as a post-bacc while working at my research job, began a very early, generic draft of my SOP, and started researching schools to apply to), over the summer I dedicated myself to finally choosing schools, doing research on my fields of interest, and getting advice from people and potential recommenders.

During the later months of summer and early fall, I formally reached out to recommenders and met with them in person whenever feasible. I then finalized my school list to send my recommenders the actual invites to write their letters as early as possible, and from that, I prepared a tracker to make sure everything was submitted in a timely fashion and nothing fell through the cracks (chances are you’ll be applying to dozens of schools so this is essential, and even I realized last-minute that I was missing some particular items, such as the optional diversity statements or NYU’s optional video essay). While the shape of the tracker evolved as I completed more parts of each application, the final form had the following fields:

• School Name
• Program (Economics/ Business Economics/ Applied Economics, etc.)
• Application Deadline
• Username (for the Application itself)
• Progress Updates¹
• Application Form (i.e. the portions of each application relating to demographic info, educational or professional history, etc.)
• Transcript (U: Unofficial Okay / O: Official Required; C: Color Transcript; G: Grayscale Transcript)
• Letters of Recommendation (Number Required)
• Resume/ CV
• GRE Score
• Statement of Purpose
• Abstract of Courses
• Writing Sample
• Application Fee (dollar amount)
• Other/ Miscellaneous (e.g. Diversity Statement; Supplemental Applications; Video Essay)

¹ This section of the tracker contained checkmarks for whenever each of the listed items was a required component of the application for a given school (or the word “Optional” for when it was instead an optional component). Each checkmark/ “Optional” started off as red for each school, and as I started on each school I would change the corresponding checkmarks for whatever I worked on to yellow, and finally to green when complete (the self-fulfillment I felt when the whole tracker was marked green was beyond words).

With the tracker set up, I first took care of completing the logistical items, such as sending official GRE score reports to the list of schools, secured copies of transcripts and sent the official hard copies wherever necessary, officially sent my recommendation invites to all my recommenders, as well as formally created my online accounts in the application systems for each of the schools I was applying to, and filled in all the demographic and program of interest info. This allowed me to feel productive by marking items in my tracker from red to yellow and finally to green, and let me de-stress by finishing the particularly tedious components early, buying me more time to continue research on schools and the faculty in each one that I would like to work with (a critical portion of each statement of purpose).

Indeed, with all of the above being done by the start or so of October, I spent the rest of that month and all of November with perhaps the most time-consuming aspect: finalizing each statement of purpose by filling in the generic template (describing my background and research interests) with why I was particularly interested in each program I was applying to, and discussing the work of every professor who shared some of my interests and who I thus thought would be able to guide me.  I was officially done with all my applications by November 23^rd, about a week before the deadline for a couple of the earliest schools on December 1^st. Thus, the last several weeks to a month of the application process all I had to do was simply follow-up with my recommenders as necessary; this was great as I had a particularly busy stretch at work right from the end of November to the middle of February, and I’m sure I would have been incredibly stressed out had I also had components of my applications yet to finish (for those still in school, finishing in late November will mean wrapping up this somewhat stressful process right before the finals stretch, which I’m sure you’ll appreciate).

I also want to make special mention of applying to the NSF, which I believe was an important signal to the programs I applied to of my real interest in graduate school and research, and was an early start at helping to formulate my research interests, coaxing my recommenders’ letters to be finished early, and generally getting me in the application groove. Moreover, if you’re one of the couple of dozen Economics candidates who win the prize, the NSF could be the key to some fantastic programs and of course, great funding!

I’ll wrap up this post by providing more details about my process and results, and with some overarching hints and themes.

As an applicant, I submitted applications to PhD programs at twenty-one (yes, that’s twenty-one) programs in Economics and closely-related areas of study (i.e. Applied and Business Economics, and Finance), and if it’s any indication of both the competitiveness and absolute randomness of the process, received six favorable decisions from these programs: four fully-funded acceptances to the PhD programs I actually applied to, one acceptance to a related Master’s program (with a rejection to the PhD program itself), and one inquiry regarding my interest in admission with no first-year funding.

This brings me to my first, and possibly most valuable piece of advice (to the extent I myself can judge the value of my own guidance): the whole process is a crapshoot; the best you can do is to weave a cohesive narrative for your interest in research and the programs you’re applying to, and understand from the start that it is a fairly random process with a lot of noise, both within and across applicants’ sets of results. While history is informative, it is by no means predictive, and you should not take someone else’s success or failure in the past as a signal for your own performance, no matter how similar their background and perceived experiences to yours.

To that extent, to maintain your sanity, simply remain organized and patient. Discipline yourself through the process by maintaining steady progress with the application components, and try to shelter yourself from the barrage of websites out there where people post legitimate (and sometimes non-legitimate) results. While it may seem fruitful to know what is being released by graduate programs, it won’t in any way change what you have already submitted or affect your chances; I myself despaired during the waiting process seeing the favorable results others were getting at the beginning while I sat in radio silence, and ultimately discovered I was subjecting myself to unnecessary grief.

Regarding hints for the application process itself, remember that marginal costs to some of the application components (or entire applications to extra schools) are incredibly small compared to the expected marginal benefit that could be obtained from possibly getting in somewhere that might be a good fit. So while it might be mentally taxing or painful, or while the extra cost from an application fee or an additional GRE score report might seem expensive, now is not the time to be stingy or lazy. Work hard, spend the time no matter how painful, and spend the extra money if feasibly within your means; it could pay off handsomely in the future and in a great school outcome!

However, having said that, try not to apply to too many programs to which you may not be a great fit. My general rule for selecting schools was to ask myself: “would I really go here if this were the only school I got into?”. Yet, because I prioritized selecting a school list early in order to allow my recommenders ample time to work on their letters and forms at their leisure, I admit I probably applied to schools that I ended up discovering weren’t great fits with my background or interests during the process of writing statements of purpose.

This brings me to my other piece of advice regarding the application components: while many people say the Statement of Purpose doesn’t really matter, I would actually suggest not to sleep on the SOP.  Even though it was by far the most time-consuming component (as it was school-specific and required lots of research), I think that the time I spent and investment I made in properly tailoring each SOP to each school and expressing my research interests and fit with each particular program ultimately showed. I would suggest to not underestimate research interests as a factor in the admission decisions of each school; I personally believe that focusing on this factor and properly expressing it pushed me to the very end in many of my top schools who saw potential in the kind of work I want to do (remember that, more than anything, graduate schools are looking to prepare research professionals and to find people who could contribute during their time there to research that matches the work of their faculty).

Since most of the application components won’t vary school-by-school, the SOP is your chance to really make an application special.  For me, at least, holding basically every other component constant (which really is the case given that the rest of the application is just GPA and set of courses, letters of recommendation, GRE scores, etc.), I feel that where I ended up doing the best was where I had more detailed, impassioned Statements of Purpose that really made a case for myself at that school; why the school and I were a mutually beneficial match based on research interests and also non-academic factors.

Lastly and perhaps most importantly, at the risk of being trite, do not underestimate yourself or your potential. While some delicate calibration and an honest conversation with yourself regarding the strength of your background and application components is necessary, remember that this process is inherently self-selective. If you have a genuine and impassioned interest in pursuing research, then you should make sure apply broadly, but boldly. Remember there are people there to help and guide you (starting with the very recommenders whom you have trusted a significant portion of your overall application package to.) Ask for honest feedback, have people read your SOP, and have some confidence, with the understanding that it’s a random process that can often let down even the best of us.

And ultimately, while there’s a lot of noise, I myself was surprised with how good my top results were, and had I decided not to apply to those top schools thinking I wasn’t worth it, writing this post could be a very different process right now.

At the end of it, I come out of a lengthy and stressful process one of the particularly lucky ones. I had the privilege of debating between four great PhD programs in Economics at Harvard, Princeton, the University of Wisconsin at Madison, and the University of Maryland, each with their compelling attributes and exemplary faculty and opportunities.

I’m excited to finally begin my graduate school this coming fall at Harvard’s Department of Economics—having chosen this program due to the strength and breadth of its faculty’s research output and interests, the possibility of conducting innovative research in fields such as Cultural Economics that would be considered niche elsewhere, and the great opportunity that it represents to feel challenged, fulfilled, and welcome in an academic community that will hopefully produce an effective and creative researcher soon.

I very much intend to revive this blog by periodically posting about what I’m sure will be an exciting grad school experience and on anything else that may be on my mind (let’s hope any hiatus isn’t six years this time).

I’d like to finish this post by encouraging those who are considering graduate school in Economics to absolutely give it a shot; the preparation and process can be grueling, and the end-results inherently arrive with a lot of noise, but with discipline and luck, the pay-off should be more than rewarding.

Political Prediction Markets: The “Pricing” of Electoral Candidates as “Assets”

I wrote not too long ago about the declining accuracy of electoral polls. Yet, one remarkably reliable source for the possible outcomes of electoral contests has been political prediction markets. For example, the outcome of the 2012 US Presidential election was predicted accurately by prediction markets, despite many respected pollsters portraying a much closer race, or even a Romney win.

The fact that candidates’ chances of an electoral win are “traded” in prediction markets brings up interesting questions of “asset pricing” for these “instruments.” After all, how do you price a candidate for elections, where the price is a reflection of her odds of winning her electoral contest? What kind of information must you take in to “buy” or “sell” a candidate? In particular, two main questions arise:

• Does “politics” trade in an efficient market? Does the probability of a candidate’s win adjust quickly as expected to new information on the candidate and her campaign?
• Why are prediction markets so accurate, in particular in relation to traditional polls conducted by respected pollsters conducting surveys with tested statistical methods? Are “predictors” better informed than voters and the pollsters who survey them? What is the transmission mechanism—if any—between polls and prediction markets (since presumably polls are a large part of the bettors’ information set), and why is the results from this information set usually so different? In short, why does a mismatch exist between polls and prediction markets?

The second question to some extent already presumes the answer to the first question, in that in order for prediction markets to be accurate, “prices” for these candidates must accurately capture the chances of that candidate’s win and, in the long-run (since by definition of odds, there’s always uncertainty and the most “favored” outcome in each individual race may not actually win), be able to predict electoral outcomes with the level of probability expressed for the most favored candidate in each electoral contest (a prediction market can be only as accurate as the probabilities implied for each contest and whether they actually occur).

Of course, prediction markets are more akin to options in that they expire at either 0 or the strike—or in this case, “prediction”—price. As such, prediction market “instruments” are quite different from stocks and bonds when it comes to their payout structure (in that for the latter two, some form of payout is almost guaranteed to the investor in the form of dividends or selling the stock, or coupons or principal payment—with of course much more certainty for the bond than the equity holder). Moreover, “prices” in prediction markets invariantly move in relation to one another as they must—in total—represent the 100% certainty of one discrete event happening from a set of options. Thus, topics such as asset pricing and market efficiency can differ non-trivially between prediction markets and more “traditional” financial and capital markets.

However, attempting to apply the intuition from these more traditional markets to prediction markets, the simplest possible answer for the above questions is the factors that typically promote market efficiency. For example, the volume of “trade” may be a reason for the higher accuracy of prediction markets compared to polls: prediction markets are more frequently and continuously open to trade, with larger number of participants than the more periodic and sporadic polls with smaller sample sizes.  Moreover, there may actually be a sample selection effect. After all, people who participate in prediction markets might in fact have better information; people who participate in polls do not typically volunteer to participate in these polls, whereas investors have skin in the game and actively choose to participate in these markets, presumably because they feel confident in their beliefs/ pricing of these electoral candidates. Of course, “investors” in prediction markets are subject to the same behavioral biases as investors in more traditional markets, such as overconfidence and speculative bubbles. Also, the same question arises of what kind of different information prediction market participants face that make them good “candidate pickers,” seeing as how, presumably, polls themselves are part of their information set. Are these investors just politically more savvy and aware, i.e. they follow electoral news more closely than poll participants and, as such, have a larger information set that incorporates other factors that poll participants—in aggregate—may not?

Lastly, in order to determine whether these prediction markets actually “do” trade “efficiently,” a simple event study of sorts could be considered, where we study a cause-and-effect relationship over the period of time of a campaign between unexpected developments in a candidate’s campaign and profile, and an immediate response in that candidate’s odds of winning the elections in political prediction markets. Of course, how to accurately control for other factors in a regression model is less clear. For example, one might control for news on the candidate’s political party or opposing party that could be driving her odds in the market. But considering how factors affect one candidate individually as opposed to all candidates—and considering how these odds must invariably move in relation to each other—is less immediately obvious.