Kaysa
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What you enjoy ultimately depends on you, what you like to do, and how you perceive situations. What is ideal to one might be torture to another. Comparing academia and industry. Some individuals might view academia as a secure, rewarding career that allows them to interact with smart students, stay current in the latest advances in their field, and to pursue a research agenda that excites them and contributes to their discipline and society as a whole. These same individuals might view industry as a stressful, soul sucking endeavor where the employer compensates your youth and health for more money. On the other hand, other individuals might view academia as a stagnant job with no career advancement that requires them to babysit spoiled adult children while they pursue research that no ordinary person will ever care about, know about, or use. They might view industry, however, as an exciting adventure with unlimited career advancement, a higher salary, more perks, and access to projects and work that will impact the fabric of society. Everything depends on your perceptions and on the job you end up with. Some industry jobs are a delight and some academic jobs are a delight, and vice versa. Some important things to note is that academia is a job. You have bosses. You aren't are free as most initially believe, and the publication game can be quite unscientific and unfair to those who do not know how to navigate it properly. You should go into academia if you value researching, teaching, and learning. Most research say "love" rather than "value", but valuing what you do rather than loving it is oftentimes much better. Loving what you do can lead to profound disappointment when you receive a rejection, while valuing what you do can soften that blow and even steel your resolve. Enjoying committee work helps a lot too. You should give academia a second thought should you dislike the above, dislike working on long term projects, and dislike rejection. You might also want to give it a second thought should you be overly passionate about academia. Passion is sometimes a blessing and sometimes a curse depending on how it affects how you react to adversity. Another alternative is to think about the negatives in academia and see whether you are okay with them. Enjoying the positives is great, but being able to not let the negatives eat away at you is oftentimes more important.
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What are you hoping to get out of these experiences?
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For the most part, the rankings have not changed substantially in the last 40 years
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This is not entirely true masstech. We take a couple charity cases every year and sometimes they pass. Also, elitism is not unique to economics. It is especially bad in economics but we do not hold the monopoly in it. Everything you described in your post happens in every other profession on earth. If you think otherwise, you're oblivious to reality.
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It falls in the same category as private sector work. It is worthless from an adcom perspective.
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Absolutely. Python is much easier to do then R, and its piping capabilities are really nice once you get them running.
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Python is most similar to R. I recommend python because it is fast, easy to read, and is the best at scraping data. Scraping data is in demand because it is a great way to construct a novel data set. Matlab is very niche. I never used it, and most of my colleagues don't use it either. Stata is easy. You can pick up the basics in a couple hours, and in most cases that it all you will ever need to do most economic reasearch. However, in some cases stata makes simple tasks complicated and complicated tasks very laborious. R is harder to learn than stata, but it has an impressive statistical library. Unfortunately, that library also has many bugs and quality control issues are prevalent. R is also slow and difficult to read. I quit using R because I could not read my old code for past empirical projects. Python is harder to learn than stata, but it is still not that hard to learn. It's documentation and online resources are excellent, unlike stata. It's statistical library is good and growing. I like python because it is very easy to read. You can go back to old projects and pick up from where you left off quickly. It is also the best at scraping data, which is important right now. A lot of novel data sets are starting to come from scraping websites or other sources. I will note that R is easier when it comes to running any analysis, but python is way easier when it comes to data cleaning. Most projects are 90% data cleaning, which is why I really like python.
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Chateu's advice is spot on. Leolin's intentions might be in the right place but leolin's advice is wrong. OP's profile is solid. Landing an RA would be great as long as OP's mentor actually provides good guidance on how to do research, or is able to write OP a solid recommendation letter. Getting such an RA is easier said than done though. Learning python often helps.
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If there is a correlation, it is negative. The problem with test taking as an indicator for success in research is that tests are taken under an environment that is almost entirely structured. Students know the problem, its framing, and most material around the topic. In research, the environment is completely unstructured. The problem is largely unknown and the material needed to study it is typically unclear and scattered. In this research you need to have to know how to frame the question, your approach, and your execution. In my experience, most excellent test takers are terrible at this because they have no training in the area. Some poor test takers are terrible at taking tests because they reject the structure provided to them when taking tests, and instead develop their own. Ironically, this makes them awful at taking tests but better at taking on research projects.
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As startz said, things vary considerable across departments. However, from what I know about department budgets, and this is by no means comprehensive, fellowships do not always pay for things like tuition and other fees. The problem then is that adding on another student, even if they can pay their living expenses, can eat away tens of thousands of dollars in tuition for the department of their award does not cover tuition.
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Well, it might. Unfortunately, we cannot answer that because you cannot tell us what it is heh heh.
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It is not that helpful. If the award covered tuition expenses than it would be helpful, but living expenses is not that attractive because most students cover their living expenses with activities that generate the department money, such as teaching.
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I could use some help understanding what it is you might enjoy and not enjoy about a career. Answering the following questions would help me give you more informed advice. First, are you interested in academics or industry? Where do you want to live? Urban? Rural? Suburban? No preference? How much money do you want to make? Are you okay with under 50K? 50K-100K, 100K - 200K or more? How much stress are you okay handling every day? Do you want low stress? Are you okay with a some stress? Or can you thrive in high stress? How much politics do you want to stomach? How important is work-life balance? Raising a family and having time to spend with that family? In all honesty, everyone wants to work in a collaborative, enthusiastic environment, but that is job specific. Every discipline has jobs that meet this requirement. Albeit they are exceedingly rare. Furthermore, even though some disciplines might have such jobs, these jobs might also carry with them other characteristics that you might not be able to stomach. Hence, it is good to know your preferences upfront.
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No, it does not.
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Hmm, this is what I thought. Most tests contain questions that are nearly identical to what was presented in class or in the problem sets, and a smaller set of questions similar to the former but that challenge the intuition or assumptions underlying the economic theory. In addition to the above advice, make it a habit to understand the underlying assumptions, mechanics, and intuition behind the theory, models, and questions presented to you. Be sure to ask yourself "What would happen if I changed this/removed this/or added this". This is the hardest but best way to learn, and it will ingrain in you the right way to approach and question research. Make sure to visit professors during office hours and to only pose these kinds of questions after you have given them much thought (never make it a habit to regularly pose these questions without putting in any effort). Once you understand what is going on, present them to your working group members. Chances are they will come up with questions, and this will be a good chance for you to see how much you actually know. Don't worry about blowing a test. I got the lowest score on many exams. No one remembers. You aren't measured by your failures. You are measured by your greatest accomplishments.
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Could you describe the questions in your midterm relative to what was presented in class and in your problem sets, and also which questions gave you the most difficulty?
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No, they do not.
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Yes. MATLAB definitely does some things much better, but there is so much overlap between these three languages that it makes me question whether your time might be better spent on another activity rather than mastering MATLAB.
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Hmm did you tell data camp that you are a student? I know some students that get data camp for free because of their student status. If you are using R for econometrics, I would read ISLR. The book is free, does an excellent job covering the linear regression and logistic regression, and most importantly, it contains code that shows the reader how to use R to run these regressions. It really makes things easy. If your econometrics course is going to use R for data cleaning, I would recommend learning Hadley’s tidyverse plus piping. It makes data cleaning much easier to do than base R. Matlab tends to be used mostly by economists that need to code their own empirics. I would not learn it unless you absolutely have to because you can usually do the same thing in R or Python, or even better, someone might have already coded it and deposited in github or cran
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You are right. PhDs lead research at think tanks while MAs do the research. However, those who lead research also tend to be the more established experts in their field rather than fresh PhDs. I know several very prominent health economists in their mid 40's, and even they aren't "established" enough to garner interest at most think tanks.That said, you are at a think tank right now. Maybe your colleagues could give you more efficient guidance than us.
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R has a steep learning curve, but Datacamp flattens that curve. Datacamp is not free, but they give students a steep discount and you only need 2 months to learn the ropes. That said, I would recommend learning Python over R.
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For years 1 and 2, I tended to study 6 - 8 hours every day and took either Saturday or Sunday off (this does not include class time) In year 3, I worked roughly 8 hours every day and took either Saturday or Sunday off. In year 4, I worked roughly 10 hours every day during the first half of the year. In the second half, I worked 6 hours and networked another hour or so. In year 5, I worked 2 - 4 hours a day and spent the rest socializing.
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I believe it is most important to understand what our goal is when we give advice, and whether we are well qualified to give that particular type of advice. My goal is give guidance that results in the right people pursuing academia, and when I mean the right people, I mean those individuals who are not only more likely to thrive in academia but who are also more likely to enjoy a career in academia over any other possible career path. That is why my advice tends to be harsh. I don't want people to pursue academia when I am pretty sure other avenues would provide them with a much more enriching experience. I have been around long enough to notice what traits tend to thrive and enjoy particular environments, and more importantly, what happens when people with certain traits try to force themselves to succeed in environments that are a poor match.
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It doesn't work in your favor. Most adcoms would rather work on their research than review your application. Some are going to toss your application out before they even get to your other materials. Others might skim your LORs, but they are probably going to be looking at sections that discuss your research potential rather than your grades. A couple are going to go through your application with a fine tooth comb, and in that case chateau's advice is valuable. It is an unfair yet all too common reality that doesn't just stop at new phd admissions. This behavior continues in the job market,when your papers are being refereed, grant applications, etc etc.
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How to address a spectrum of math grades that I have (A+ to failing)
Kaysa replied to broccoli's topic in PhD in Economics
You might get lucky and sneak through the cracks in one or two schools in the top 30 - 50, but that doesn't mean you should go. Your inconsistent grades tell me that you aren't cut out for graduate school. How are you going to justify your scattered grades? Were you caught off guard? Did an unexpected life event occur? Was the teacher plain unfair? All of these happen in graduate school, sometimes at the same time. All an adcom is going to think is that if you were unable to handle these problems in the past than you aren't going to handle them in graduate school.