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.
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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.

 

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.

Weekly NBER Digest 2/20/16

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

1.What can we say about optimal trade policy using heterogeneous firms theory?

Costinot and coauthors use the classic Melitz (2003) model of heterogeneous firms trade theory to derive optimal tax levels at the micro (firm) level. They find that optimal import taxes discriminate against the most profitable foreign exporters, while optimal export taxes are uniform across domestic exporters.

Relative to another recent paper by Costiinot, Donaldson, Vogel and Werning (2015), the assumption of monopolistic competition (rather than perfect competition) in this paper leads to conclusions that are exactly the opposite. More generally, this paper is part of the trend in international trade research to connect traditional macro theories to micro data regularities.

2. How to evaluate the impact of international competition on firm performance?

This is not a new topic, but De Loecker and Van Biesebroeck highlights two aspects that are not well addressed in previous research. First, the impact of international trade on market power and productive efficiency should be studied in an integrated framework. Second, trade liberalization has the potential to increase competition by enlarging the relevant market, but this effect is not well understood. The discussion on the relevant market definition in the trade context is especially insightful.

Weekly NBER Digest 2/14/16

I decided to start a series of weekly blog posts on the new NBER working papers on development economics, labor economics, and international trade that I find interesting. In the past few months, I have experienced the excitement of finding an interesting research question, going through the empirical methodology to answer the question, cleaning data, and then figuring out there is not enough variation to answer my question (due to the contextual nature of the question). Now I am opening myself up to new ideas, and my NBER digests will serve this purpose as well.

1. How does taxation affect growth through corruption?

This working paper builds an endogenous growth model to examine the relationship between taxation, corruption, and economic growth. Taxes have disincentive effects on entrepreneurs, but also provide them with public infrastructure. Political corruption governs how efficient tax revenues are translated into infrastructure. The model predicts an inversed-U relationship between taxation and growth, which is consistent with data from the Longitudinal Business Database (LBD) at the US Census Bureau.

This paper is an example of combining macro modeling with micro empirical analysis to address an interesting question.

2. Are trade policies no longer important?

This working paper by Goldberg and Pavcnik describes the declining research interest in assessing the impact of trade policies and reasons for this decline, and suggests future areas of research. A lot of useful insights. As an example,

The variation in trade policy across cross-sectional units and time is only helpful for identifying the effects of trade policy in the presence of some type of friction and/or heterogeneity in exposure to policy change. … the main limitation of relying on differential exposure of economic agents to trade policy to identify its causal effects is … that this approach by its nature will generally reveal only the relative and not absolute effects of a policy change. The latter require a theoretical framework within which the relative effects can be interpreted.

What I have learned from my first academic presentation

Yesterday I presented my work on parental migration and health outcomes of children in Indonesia in the development lunch at Duke. It was my first time to present my own research in front of a (relatively) large academic audience. The presentation did not progress as planned (similar with most research initiatives), but I learned a great deal from it. Here’s a few.

  1. Talk about key facts instead of broad histories when you are introducing the context of your study. Providing a description of broad histories is easy for you as a presenter but usually makes the audience more confused about your main argument.
  2. Related to the first point, structure your presentation to focus on the key questions you are interested in answering, the strategies you use to address these questions, and where you have experienced difficulty and need advice on.
  3. In a short presentation, avoid doing a detailed literature review. You are almost guaranteed to miss some papers in the literature, and it is easy to spend a long time answering tangential questions.
  4. Know your question really, really well. Present it to different people and see if anything confuses them. If they are confused, try to diagnose the problem and clarify your question. If there are broad terms in your main question, try to narrow them down to clear-cut, specific definitions that people can directly relate to.
  5. Know when to answer questions, when to delay them, and when to politely turn them down. Always answer clarification questions, but delay questions which you are going to address later in your presentation.
  6. Practice. Practice. Practice. You cannot anticipate everything, but if you do not practice, there will be too many awkward moments.

I encourage other students to present their work early on in the PhD program to practice thinking deeply about a question and explaining it to other people. It will be painful at first, but you will get better at it over time.