What I learned from teaching PhD-level “tool set” classes

This summer I had the pleasure to teach two PhD level modules: Introduction to R and Programming and Project Management. Both of these classes are designed to equip rising second-year PhD students with the necessary programming skills needed for independent research.

I also participated in the Teaching in Triangles program as part of my effort towards earning a Certificate of College Teaching. This program consists of pair-wise peer observations (with other PhD students who are teaching summer classes), and we get extensive feedback on the content and style of our teaching. Here are the two main lessons for me:

  • It is challenging to achieve a balance between pure lecturing and student discussions, especially in software classes. I designed the classes to contain short, in-class assignments that allow students to check their understanding right after a new concept/procedure is introduced. But when I explain the recommended solutions, I tend to lecture on and on without leaving much time for students to ask questions.
  • Students are more engaged when they feel they can contribute to the class. In the last class when I talked about programming and project management in collaborative projects, I asked students to brainstorm the good practices in this setting and emphasized that they would need to share their insights with the rest of the class. My students were super engaged and raised good points that complemented my lecture.

My experience as an instructor also makes me realize how much effort goes into course preparation. Bravos to the good teachers I encountered throughout the years! Hopefully I will become a better teacher over time.

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Surviving the transition from classes to research in your PhD

Readers of this blog might have noticed that I have not published any post since November last year. This is because I spent the past eight months struggling and surviving the transition from a student to a researcher of economics. During this transition, I have experienced many episodes of self doubt and breakdowns, and my confidence plunged so much that I did not think I would put down any meaningful thoughts. Fortunately, I was able to learn from my struggles and successfully defended my PhD prospectus a month ago (I’m a PhD candidate now!). In this post I hope to share with you what I have learned from my experience.

  • Once you have identified a research topic (not even a specific question), go talk to faculty members with relevant expertise immediately. This sounds daunting, but you should do it because 1) they will be able to point out strengths/weaknesses of your research ideas and save you time, and 2) it will teach you how to communicate with others about your ideas, which you will need to learn eventually.
  • Find at least one faculty member whom you can communicate with on a regular basis. Conducting innovative research is no small task, and you will need guidance at the beginning of this journey. Talking with faculty regularly can also help you feel that you are constantly making progress, which is more important than you think.
  • Time management is crucial to a successful transition from a student to a researcher. Without a class schedule, you can easily develop habits that reduce your productivity. What I have found to be most useful is to develop a routine: set a fixed amount of time where you go to the office and work on your research, but also leave time for social activities and rest. Make sure you have some time to rest everyday so you have something to look forward to when research is not going well (that happens a lot!).
  • Put yourself out there. Do not take negative feedback personally; accept it and improve yourself based on it. When I presented my research for the first time in my second year, I felt so bad about receiving “negative” feedback that I burst into tears in the middle of the presentation. I decided the only solution to this fear of presentation is to present more. Therefore, I presented two more times in the first semester of my third year, trying to do just a little bit better each time. Now I am proud to say that I can present my research ideas clearly and peacefully.
  • Sharpen your communication skills, in writing and in person. Good interpersonal communication skills allow you to make a stronger impression, so others are more likely to remember your research and give meaningful feedback on it. Good writing (in academic papers and in daily email correspondence) will make others understand your goals and help you achieve them. Personally, I always take clear, on-point emails and as a sign that the other person appreciates my time. This makes me want to communicate with them and help them.
  • Do not work in isolation. This is very, very, very important. Because research is highly risky and things don’t turn out the way you expect 99% of the time, you need support along the way. Make sure your office is close to your friends’, and talk to them regularly. At the minimum, they will be able to share your frustration in research. NEVER sacrifice your social life for a marginal increase in “time devoted to research” (which, as we all know, is likely to be devoted to social media).
  • Be more supportive and less judgmental of others. Research is hard, and we all know it. Instead of trashing others’ research, try to understand it and offer your colleagues constructive feedback. If you don’t understand it, maybe you can offer advice on how it can be more understandable.

P.S. I am determined to publish at least one post per week from today on. Stay tuned.

Lessons learnt on communication

The following are what I learnt from doing research and explaining my research to others.

  1. When communicating about a big decision that involves complex emotions, do it in person. Talking in person allows you to be mindful of each others’ emotions and guide the conversation to the most effective direction.
  2. When writing a professional email, make them short and to-the-point. State your requests explicitly.
  3. Acknowledge the differences in communication styles between men and women. Men are usually a lot more direct and less considerate about others’ feelings. But being considerate about others’ feelings can go too far, in which case the effectiveness of communication is compromised.
  4. When you want feedback on a particular idea, make sure you know what exact questions you have and what you need to explain so that others are on the same page. Guide your feedback around your goal, and harvest small critiques along the way.
  5. When you can ask a question by email, do not schedule a meeting. Meetings are the best for organic, open-ended discussions.

What I have been reading

  1. Angus Deaton and Nancy Cartwright on understanding and misunderstanding RCTs. A nice summary of the RCT trend in development economics and its limitations.
  2. Book review by Margaret McMillan on the making of Africa.
  3. Redding et al’s summary of recent developments in quantitative spatial economics. This area seems methodologically developed, and the approaches to model the general equilibrium can be applied to wider contexts beyond international trade.

How to read structural modeling papers in economics?

For the purpose of a research project, I have been reading a lot of literature on locational equilibrium sorting in public economics. While the topic is fascinating, it is easy to get lost in piles of papers without understanding how they unify under the same overarching theme. Since reading structural papers is likely to be a challenge for many economics PhD students, I thought it might be useful to share my thoughts on how to do it effectively.

The purpose of reading others’ papers is not to produce a thorough summary of their work but to critically assess the status quo of the literature and what you can contribute. I have found the following steps useful for achieving this goal.

Step 1: Bird’s Eye View

Start with the review papers. If the topic is well researched, it should have a summary paper (look for Journal of Economic Perspectives/Journal of Economic Literature/handbook chapters). Focus on the fundamental assumptions made in each class of models. Make a list of papers for further reading based on the bibliography of the review paper.

Step 2: Divide and Conquer

For each paper on the “to read” list, read its bare bones and understand the key message. Clearly outline the model assumptions (and whether they are made as abstraction or due to data limitation), data availability, and empirical approach.

Step 3: Say it in Your Words

After you feel you have a good understanding of the class of structural models, try to synthesize the papers by describing them in writing. Focus on how they are linked to each other, and critically assess the pros and cons of each approach.

Step 4: Make the Link to Your Research

By the end of step 3, you should have a fairly clear understanding of which approach (if any) is best suited to your own research, and how your research contributes to the existing literature.

 

My Thoughts on Presenting Preliminary Research

A few weeks ago I presented a new research project to a few faculty members. I was looking for feedback on the appropriate structural model that can be used to explain commuting and residential choice patterns in a household survey. The following is what I learned from the presentation.

  1. Be clear about what feedback you are looking for. Say it right at the beginning. When your work is in preliminary stage, there are usually concerns from all aspects — data, identification, theoretical framework, context, etc. Try to focus on the aspect that matters the most to you for now. This will make your presentation more structured and enable your audience to give more useful feedback.
  2. Know what you have to cover in your presentation and what you can skip. For example, if your purpose is to identify the right theoretical model for your research question, do not dwell on the data for too long. Address questions that are relevant, and leave the questions that stray too far from your theme “to future discussions”.
  3. When you are presenting a model, be clear about the assumptions. Which assumptions are fundamental to the workings of your model and the interpretation of your results? Which assumptions are necessary due to data limitations? Which assumptions are an abstraction and can be refined? Thinking over these questions also helps you to understand different models better.
  4. Do not put unnecessary information on your presentation slides. Slides are a form of visual aid — they make your speech more effective instead of replacing you the speaker. If you find yourself staring at a slide with too many equations thinking “it’s probably gonna be fine, I’ll just use it as reference”, then you probably should make it more concise.
  5. Anticipate your questions as much as you can. I usually make draft slides a couple days before the actual presentation, and go over the slides from an outsider’s perspective (or whoever will be at your presentation, if you know them well). If a particular line seems confusing, I revise the wording on the slide or think of alternative ways to present the same idea.

Hope this is useful.

Weekly NBER Digest 3/6/16

This is the second post in my weekly NBER digest series.

  1. Bertrand and Duflo summarize the field experiments on discrimination.Dynamics of discrimination and ways to undermine discrimination seem to be promising future research areas.
  2. Dinkleman and Mariotti investigate how circular migration from Malawi to South Africa helps to improve the human capital in origin communities. Using spatial variation in migration costs and two policy instruments, a removal of migrant quota and a ban on migration, they find that after twenty years of the shocks, “human capital is 4.8%-6.9% higher among cohorts who were eligible for schooling in communities with the easiest access in migrant jobs.”
  3. A new working paper by Hummels, Munch, and Xiang reviews the existing literature on the labor market impacts of offshoring.