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.

A few notes on academic presentations

For our writing and presentation class we are asked to explain a standard concept in intermediate microeconomics in a ten-minute presentation. Here are a few good practices I concluded from my own and others’ presentations.

1. Practice your script and appear confident on the stage.

2. Make sure your graphs are legible. Fonts should be large enough. Use contrasting colors that will show up clear given your background.

3. If your graphs are not legible, explain the key messages in the graph verbally or draw the graphs on the board (if they are simple).

4. Stay consistent with your notations.

5. Cite the sources to your materials, even if they come from widely used textbooks or online resources.

6. Don’t include information that you are not going to talk about in your slides.

7. It helps if you stick with the same examples and go through facts->explanation->solution for each of them in the same order throughout your presentation.

8. When you are explaining a model, start from the infrastructure (agents/players, relationships, basic assumptions, etc) and continue to the superstructure.

9. Don’t include too much information in your slides! This will make the audience overwhelmed and eventually bored.

10. Don’t read off the slides. Treasure the dynamic nature of presentations and interact with you audience.

Notes from A Guide For the Young Economist (2)

This is a continuation of my previous post on this book.

First, a few tips on cleaning up your text.

1. Delete the redundant information or excessive use of clauses to make yourself across at the least cost of words. Instead of writing “the technology of the firms in the economy is convex”, trim it to “technology is convex” because it is obvious that the firms are the adopters of technology and they exist in the economy (p80 in the book).

2. Do not be obsessed with the word “assume” (or “is/are assumed”). It’s better to state “assumptions” once and list all of them. But when you want to emphasize some aspects of your model that are different from the existing literature, you may start a separate sentence of paragraph highlighting these.

3. Stick with plain words if you can deliver your message. Instead of writing “the set of Nash Equilibrium is nonempty”, write “Nash Equilibrium exists”. The latter expression gets rid of the nerdy feel in the text and makes your writing more accessible.

Then, Thomson talks about how to present a model effectively. I have found the following most useful.

1. When you are introducing your model, go from the infrastructure to the superstructure. For example, when describing a multi-stage game, introduce and describe each of the players separately before bringing them together. Follow the logical steps of defining actors -> relationship -> concepts based on actors and relationship.

2. A good yet under appreciated (in my opinion) way to prevent ambiguity is to avoid using multiple clauses. This is especially true for non-native speakers. Adding clauses will dramatically increase your chance of making grammatical errors. Moreover, badly placed and imbalanced clauses will disorient the reader.

3. When stating a difficult definition, assist the reader by giving an informal and intuitive explanation preceding the formal explanation.

4. Use one enumeration for each object category. Combining different categories into a single list saves your time at the cost of your reader.

5. When specifying your assumptions, make sure there is at least one example satisfying them. If you cannot think of an example, then your assumptions are likely to be practically useless even though they are mathematically meaningful.

Calculating anthropometric z-scores in Stata

Anthropometric measures: height-for-age, weight-for-height, etc, are widely used in economics and sociology to assess the health condition of children relative to their peers. The procedure in Stata works as follows:

1. Download package dm0004_1, which contains commands “zanthro” and “zbmicat” for generating standardized anthropometric z-scores.

2. Download the relevant data files (the health measures for the reference populations) into your working directory.

3. Follow the instructions about the commands to produce z-scores. Note the following:

1) You can choose the reference population/standardizing scheme: US/UK/WHO. Each has different ranges of applicability and calculation algorithms. Make sure you fully understand them before choosing a particular one.

2) Add option “nocutoff” if you do not want your z-scores be truncated to [-5,5], i.e. to only keep observations within five standard deviations to the reference population.

3) Weight-for-length and weight-for-height usually restricts the range of weight and height that these statistics can be calculated. The z-scores are essentially put to missing if any of the two original measures falls out of the range.

Play with the package and have fun!

Notes from A Guide to the Young Economist (1)

My department has offered a three-week course on writing and presenting in economics, and I have found one of the reference book, A Guide to the Young Economist, very useful. This posts summarizes a few points that I have generally ignored but are important.

1. Doing research is an iterative process. This shows up in the process of getting ideas and formulating questions. Do not get frustrated when you have to revise your research question, adopt a different theoretical model, change your empirical analysis due to data limitations, etc. Try to have fun in this iterative process: remember this is essential for you to become a good researcher.

2. The same iteration process applies to writing papers. Thomson makes the following point about circulating your work: send your paper to a few people that you’re confident who would give you feedback soon.

When you start circulating your paper, it if often better to proceed sequentially, at least initially. Send it first to a few people who are likely to respond and make suggestions. … Revise your paper according to the comments you get, and send it to a few more people. You may once again get suggestions. Revise it again. … After the suggestions have dwindled to a few minor comments, send it to a wider audience.

This approach allows you to acquire the most thoughtful advice (from the people who are most likely to respond) and to impress the people whom you need to impress.

3. Adding to the previous point, always consult your adviser before circulating your work, especially your original data.

4. When you have a specific question to discuss with your adviser, write that down first. Doing so will sharpen your thinking about the question (and maybe solve it!) and will demonstrate to your adviser that you have taken the initiative to solve it. I personally think each meeting with faculty is an opportunity to present your research ideas and to present you as a person.

5. Get into the habit of coming up with research ideas right from the beginning of your program. Carry a notebook with you to seminars and conversations about research and jot down any ideas that come to mind. Reflect on them regularly and see if they are feasible for research.

More thoughts in the next few posts.

Summer readings (updating)

Summer means I’ve finally got the time to read some non-economics/technical books. I highly recommend the following:

1. Factory Girls: From Village to City in a Changing China, by Leslie Chang.

A documentary interwoven with a personal story. Life of the female migrant workers in Dongguan, Guangdong. Some of it reminds me of my early days in Hong Kong.

2. Everything I Never Told You, by Celeste Ng.

Wonderfully written story about race, cultural integration, and family. Anyone who has contemplated the question of “fitting in” versus “standing out” will find this novel deeply intriguing.

3. On the Run: Fugitive Life in a American City, by Alice Goffman.

Stories of young men in Philadelphia navigating through searches and raids.

4. The Big Truck that Went By: How the World Came to Save Haiti and Left Behind a Disaster, by Jonathan Katz.

5. Africa: Altered States, Ordinary Miracles, by Richard Dowden.

6. Globalization and Its Discontents, by Joseph Stiglitz.

A detailed, intriguing account of what international aid actually did after Haiti’s 2010 earthquake. Great writing.

5. The Road to Character, by David Brooks.

Reminds us to be humble and inspect ourselves often. An essential read.

6. 一个村庄里的中国,熊培云著。


Still updating. Check often.

World Bank 8th International Migration and Development Conference (II)

This post highlights several pieces of research that I think are original and important.

1. Zach Ward (Australian National University): Return migration, Self-Selection, and Immigration Quotas (slides, paper).

The author collected data of migrants that entered in the US on Ellis Island with their skills and plans to return. The imposition of migration quotas, an important policy change in the US in 1920, is found to have resulted in fewer returns but more unexpected stays of low-skilled individuals.

Ran Abramitzky (who does amazing work in migration and economic history) offered a few suggestions on the interpretation of the results:

1) How do we tell apart the failure in US labor market from other explanations? Do the higher skilled have higher returns to experience? Do immigrants from new source countries have less support?

2) Return immigrants became more selected after quotas. Do the quotas affect the perceived difficulty of re-entering into the US? If so, this can make staying in the US an optimal strategy. Expectations matter.

3) Look at Russian return migration (Jews versus non-Jews) on “intentions to return”.

4) Occupations are transitory. Are there more permanent measures of skill?

2. William Kerr (Harvard Business School): Heterogeneous Technology Diffusion and Ricardian Trade Patterns (slides, paper).

If technology transfer exists, there should be exhibitions in trade patterns. The author uses migration patterns as an instrumental variable for trade and assesses the level of technology diffusion resulted from trade flows.

3. Francesco Lissoni (GREThA, Bordeaux): Foreign Inventors in the US: testing for diaspora and brain gain effects (slides, paper). 

The KEINS database on academic inventors is interesting, and publicly available.


Unfortunately, I could not attend the second day of the conference because of a mistake in scheduling my flight back to China. Actually, I had to drive in heavy rain for four hours (8pm-midnight) from DC to Durham! It was quite an experience, although I probably won’t let myself end up in something like that again.