Cultivating loving kindness through meditation (1)



In the midst of busy academic work, I have started to go to the Buddhist Meditation Group’s weekly meetings on Monday evenings.

Our topic today is cultivating loving kindness — the ability to love ourselves and to deliver love and kindness to people around us. In today’s session, Sumi guided us through a meditation which consists of three steps.

As a first step, we tried to think of someone who love and care for us unconditionally. It can be a mentor, an elder, or a peer. Picture how they look and position them in a situation where they appear to be happy. Then concentrate your energy, “say” to them the following four sentences:

May you be happy.

May you be healthy.

May you be safe.

May you be peaceful. (You can change it to another sentence as you wish)

The second step is to think of a friend who really cares about and admires you. This is meant to develop loving and kindness towards yourself. Again, visualize this person, then think about something nice you have done recently (e.g. helping someone carry a heavy bag) and what it says about you. Then say the four sentences to yourself.

The third and final step is to think of a “neutral person”, someone who you don’t like or dislike. It can be the safeguard in your apartment complex, or the cashier at a restaurant you often go to. Picture that person, and wish them happy, healthy, safe, and peaceful.

At the end of the meditation, we also extended our loving kindness to everyone who was in the room and said the four sentences to all of us. This made me particularly happy — I feel blessed as a result of collective loving kindness.

Next week we are going to talk about cultivating loving kindness towards difficult people (of which there is an endless supply). I’m really looking forward to that.



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I was doing a random google search on how to do research in economics and the differences between fields, and the following two popped out.

1. How to do empirical economic research? An interview with economists Joshua Angrist, David Blau, Armin Falk, Jean-Marc Robin, and Christopher Taber. People have different styles and there’s no unified rule, but there are some nice directions to start working on a new piece of research.

2. Differences between IO and labor economics, by Aviv Nevo and Michael Whinston (written in 2010). The authors pointed out the practical reasons for the extensive structural modeling in IO. Nice reading before going to bed.

I had a lot of thoughts about the first two labor economics class and will write a reflective post later this week.




相爱的人们也只是”在黑暗中并肩行走”,所能做到的仅是各自努力追求心中的光明,并互相感受到这种努力,互相鼓励,而”不需要注视别人的脸和探视别人的心灵” 。


在最内在的精神生活中,我们每个人都是孤独的,爱并不能消除这种孤独,但正因为由己及人地领悟到了别人的孤独,我们内心才会对别人充满最诚挚的爱。我们在黑暗中并肩而行,走在各自的朝圣路上,无法知道是否在走向同一个圣地,因为我们无法向别人甚至向自己说清心中的圣地究竟是怎样的。然而,同样的朝圣热情使我们相信,也许存在着同一个圣地。 作为有灵魂的存在物,人的伟大和悲壮尽在于此了。


Matlab learning log (1)



Programming is fun and rewarding. It is similar with writing in that you reach clarity and elegance through constant revision. As a beginner in Matlab, I’m starting a learning log to record my thoughts along the way. This time, my thoughts come from homework problems for my Demand Estimation class and a dynamic programming problem in my RA work.

1. Be clear about the steps you need to take before you write down any code. If you have a model to guide your analysis, make your code as consistent with the model as possible. Sometimes a tree structure or flow chart can help you think more clearly. Once you start writing the code, it is very easy to get lost in the details (e.g. vector dimensions).

2. In a loop, have a clear idea of the relationship between variables and when and where a variable needs to be defined. For example, empty vectors/matrices to store estimates should be defined before the estimates are produced.

3. Use vectors where possible to make calculations more efficient. For someone like me who is spoiled by straight-forward “programming” in Stata, this is something I need to learn.

Learning a new programming language is like getting to know a new friend. Over time you learn about her strengths and weaknesses, and how she can complement you to make your work more productive. Starting next week I will be taking a course on entry games taught by Professor Allan Collard-Wexler. Looking forward to learning more about programming and IO theory in the next seven weeks!

Learning from writing research proposals



Now that I’m done with my PhD micro midterm and two proposals, I finally have some time to write down what I’ve learned in the past few weeks. The learning curve was pretty steep, and there were moments when I felt more torture than excitement. Many thanks to JG for help and support along the way.

I encountered major difficulties when I was writing my proposal for the public finance class. My topic was on the wage gap between rural migrants and urban local workers in China, and I found it difficult to 1) state a clearly framed question and 2) find the right conceptual framework/economic model to address it.

Stating a clearly question is not always easy. It was only by talking with my professor and fellow PhD students that I discovered I didn’t know what exactly my question was. A well framed question might be “How does policy A affect outcome B in region C?”, or “What is the level of substitution between product A and product B in market C?”, or something slightly more general than these. I found that as beginners, it’s very easy to make one of the following two mistakes (or both):

1. Thinking too much in descriptive terms but not being able to write down a clear question. This will become obvious when you are explaining your research to a colleague or even a friend who is not in the economics profession.

2. Getting too ambitious and hoping to address too many (complex) questions in one paper. This might lead to failure in finding a suitable framework/model to address all your questions. I was heading towards this direction until an upper year PhD student kindly pointed it out in our conversation.

Solutions for these problems? Sorry, I don’t really know any since I’m also struggling through this process. But the following two practices should in general be helpful:

1. Explain your research to others. Sometimes we tend to avoid talking about research with others, but then we are less likely to be aware of any lack of clarity in our research questions or any invalid assumptions we are making. It is better to “lose face” in front of a friend than to lose track of where you exactly are with your research.

2. Start simple. Find the central question you are trying to address, and write/use a model for that goal. If the question you are asking is too complex, break it down into pieces or find a simple example to illustrate. When I was writing my model, it felt like banging my head against the wall. In retrospect, however, it was because I was trying to feed too many things into a model and it confused myself.

When we are writing proposals, we should also develop a positive and proactive attitude. It can be frustrating, especially for beginners (like me!), but with challenges comes fast progress and eventually we become better. I think an open mindset and positive attitude are really important for doing a PhD in general.

A story to share at the end of this post: I ran into a third-year PhD student the other day. He asked:”How has your first year been?” I was struggling to come up with a model for my demand estimation class, so I looked at him with weary eyes and said:”Tired, it’s pretty challenging.” He asked further:”Are you bored?” “No! No way!”I shaked my head. “Well, that’s good.” He smiled. I am starting to understand the importance of maintaining the passion for doing research in doing a PhD and becoming a good researcher.

Two things learnt about identifying research topics

I was talking to a professor about my ideas for the research proposal. Last week I was telling him I’d do something related to planned obsolescence but thought it would be really hard to find credible data to evaluate the level of planned obsolescence. Although it is a fascinating and important topic (theoretically and practically), there is almost zero empirical study and therefore no empirical framework for me to directly adopt.

When I expressed my concerns, the professor straightened his face and said seriously: “You can’t have this attitude when you’re looking for research topics. I know it’s hard to find data for this, but that doesn’t mean you CAN’T do it.” Then he went on to suggest a few potential sources (most anecdotal). I think he was right about the attitude. The very essence of research is the discovery of something new. This is where excitement and frustration come from, where years of work are spent. To become a successful researcher, I need to get rid of the fear for the unknown and undefined.

The second thing I learnt is the importance of justifying why your research matters. This is important because a) you can only succeed at something you truly have passion about and interested in finding out the answer, and b) you will need to convince others that your research matters to get a job and (more importantly) to establish your credibility in your field. I’ve seen a couple upper-year students present their research and failing to tell the audience why we should care. The result is a group of semi-asleep audience and little useful feedback.

“How to build an economic model in your spare time?” by Hal Varian


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As I’m trying to build a model for my public economics research proposal, I am struggling to piece together my thoughts and find a suitable framework for my interest. Then I stumbled upon this piece of great advice from Hal Varian. I’m definitely going to put his advice to practice.

If you’re a graduate student in economics or even other disciplines, you might find it useful.

When do we need models and how to write them?

These are important questions I’ve been thinking about recently, and here are some thoughts to share. The answers are open, and I welcome your contribution of thoughts.

When do we need models? In a broad sense, I think every paper needs to have a conceptual framework to guide the analysis and explain the patterns in the data. In the preliminary stage of a piece of research, writing down the exact assumptions and predictions of the model can also help a researcher to organize her thoughts and frame her question better. That said, whether a structural model is needed really depends on the purpose of the paper. If the point of the paper is to propose a new model, then obviously a model is necessary. However, for papers which contribute in estimation methods a review of relevant models is sufficient.

What makes a good model? One of my professors gave this piece of advice: Make the parts of your paper that aren’t the point of the paper as simple as possible, and spend the richness in the part where your innovation is. It is all too easy to come up with complicated assumptions and notations in every dimension of your model, but adding complexity also sacrifices flexibility and robustness. Another piece of advice, given by the same professor, is “don’t let the perfect get in the way of the good”.

A side note: I have found writing skills to be extremely important in economics, or maybe in all research disciplines. One might not need to be an extraordinary writer to succeed in research, but failing to convey one’s ideas smoothly definitely reduces the rate at which they spread and benefit others.

What I’ve learned in my first three weeks as a PhD student



The first three weeks of the fall semester flashed by, and I feel the urge of summarizing a few things I’ve learned so far before I lose track of them. Unlike most first-year PhD students, I only need to take two of the six core courses (micro I and macro II) and am therefore taking two field classes — Demand Estimation with Prof. Jimmy Roberts and Topics in Public Econoimcs with Prof. Juan Carlos Suarez Serrato. I’m also attending lunch groups and seminars to have a better idea of what frontier research is like in different fields.

In Demand Estimation we are studying models of firm behavior such as pricing strategies, collusion vs. perfect competition, introduction of new products, etc. Horizontal and vertical product differentiation are the two conceptual frameworks often used to model the product space. In vertical product differentiation, consumers agree on the desirability of products but have different willingness or ability to pay. With horizontal product differentiation, consumer tastes vary, and product characteristics affect demand. Although I’m not an IO expert yet, I’ve learned that industry knowledge is essential for you to succeed in this field, whether you learn it from experience or from reading. An entrepreneurial spirit is valuable when it comes to finding data.

I’ve also learnt a few things from practice job market talks, seminars, and student presentations.

First, establish your research question before you address it. Clearly framing the question, positioning it in the literature, and describing its contribution are essential before going into the details.

Second, always know what you are explicitly and implicitly assuming in your model. This is especially important for people using the reduced-form approach. Not having a structural model shouldn’t be the excuse for not thinking through the underlying mechanisms.

Third, make sense of your results, in numerical and economic sense. Comparison with existing well-known results can be useful. This is especially true for policy-relevant questions.

Last but not least, interpret, or at least speculate the mechanisms behind your results if they are unexpected.

Now that I’m formally in the PhD process, I’ve come to realize that “work-life balance” is such a vacuous claim. The best time management strategy for me (at least for now) is to have a clear timeline of the things that need to be done (i.e. with a strict deadline) but also bear in mind the long run goals. Don’t work so hard to get burned out early. Instead, organize your life so that you work efficiently and deliver what you need to deliver. As JG has wisely pointed out, no one will get an award for working all day without a rest.

How to display Chinese characters in Stata in Windows 8


Researchers using Chinese data are often disappointed by the inability of Stata to display Chinese characters correctly. The solution from the most reliable source I can find online:

Most modern software (OS and applications) work with Unicode. Stata does not work with Unicode. Unicode encodes characters with 2 or more bytes. In Stata each character must be 1 byte only. You need to make sure the input CSV file is encoded in a codepage proper for your region, presumably 1252.

It’s actually simpler than that. If you’re using Windows 8 like I do, the steps are as follows:

1. Go to Control Panel->Language->Advanced Settings.

2. Click into “Apply language settings to the welcome screen, system accounts, and new user accounts”.

3. In “Administrative” tab, under “language for non-Unicode programs”, change it to Chinese (Simplified). You might need to change system locale if your computer wasn’t initially set to be “located in China”.

Note that you don’t need to change your preferred language or system display language. The above steps should also work for other languages as well. Hopefully my note can benefit other researchers.

P.S. I didn’t want my first post in the semester to be this technical, but this kind of reflects what’s on my mind.


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