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Kaysa last won the day on June 6 2014

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  1. 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.
  2. What are you hoping to get out of these experiences?
  3. For the most part, the rankings have not changed substantially in the last 40 years
  4. 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.
  5. It falls in the same category as private sector work. It is worthless from an adcom perspective.
  6. Absolutely. Python is much easier to do then R, and its piping capabilities are really nice once you get them running.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. Well, it might. Unfortunately, we cannot answer that because you cannot tell us what it is heh heh.
  12. 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.
  13. I would first resolve whatever made you have to live in your car. Then retake.
  14. 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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