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.

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.

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.

Development economics in a developed country: how do poor Americans save?

This recent episode of NPR’s planet money talks about the financial lives of poor Americans. A few practices mentioned in the article, such as group lending, private lenders, and high interest rates for loans, are strikingly similar with what poor people do in developing countries in Africa and Southeast Asia.

This resemblance leads me to think whether ideas and methodologies in development economics can be (more extensively) applied in developed country settings. Classic models in development economics such as health-based poverty trap and credit constraint can be easily applied to study the causes of poverty in a developed country setting. Comparative studies of poor individuals in developed vs. developing countries can shed light on the impact of institutions, governance, and infrastructure on addressing poverty.

The data mentioned in this episode are pretty amazing — 235 poor households across America tracked over a year with high frequency financial diaries. I bet interesting research based on this data is on the way.