How to write an empirical research project in (labor) economics April 8, 2014

The course project The proposal (due May 12) The presentation (June 3-10) The “final” project (due June 16) (30% of the course grade)

Before the proposal Send me an email with one short paragraph explaining your idea. As soon as possible! We can meet in person to discuss it in more detail, if necessary. I will either accept it, or provide you with some feedback on how to modify it.

The proposal The proposals should not exceed one page, and should include: Title and names of the team members. The question of interest, with a brief comment about its relevance. A few references to the most closely related research papers in the literature (and a comment on how they are related). A brief description of the methodology: econometric methods, data sources. Your expected results (a couple of sentences). You should submit them in print during the lecture.

The presentations In class, conference style (very brief!). Each presenter (group) will have about 10-15 minutes to present their proposal. Not everyone in the group has to talk! This will be followed by a discussion by a different student or group (5 min.), and a few minutes for general questions and discussion. You will be evaluated on both your presentation and your discussion.

The final project Max. 8 pages (including everything!!). Example of structure: 1. Introduction (1 page) 2. Methods (1 page) 3. Data (1 page) 4. Results (2 pages) 5. Conclusions and references (1 page) Tables and figures (2 pages)

How to proceed General steps to follow: Find a topic Find a question Figure out your strategy for answering it Try to find the right data (revise your strategy accordingly) Run the analysis Write down your results Present them and get feedback Revise your paper (perhaps run some additional analysis), submit.

1. Find a topic Something that you find interesting! Where? Textbooks and papers, but not only! Newspapers, TV, your own experience… For the purpose of this course, it should have something to do with the labor market. Examples of “topics”: unions, youth unemployment, gender discrimination, international migration…

2. Find a question A specific question within your topic of interest. Ideally, it should be relevant, but also feasible You would like to be able to answer it. There may be trade-offs here! The more specific, the better. “What are the causes of high youth unemployment” is probably too broad. “What is the effect of educational reform X on youth unemployment” is better.

3. Make a plan for how to answer it Typically you will have some kind of a “model” in your mind. At least a “theoretical framework”. Maybe a standard model that we’ve seen in class that applies to your question. At the very least, you need a testable hypothesis. Ex.: Policy X could affect youth unemployment in such and such direction via such and such channel(s).

Identification strategy How will you test your hypothesis empirically? Many possibilities! “Descriptive” regressions, Lab or field experiment, Instrumental variables, Regression discontinuity design, Difference-in-differences, Propensity score matching, Etc.

Literature review At this point, you probably want to see what’s been done. Find the closest papers in the literature. Where to look? NBER, google scholar, etc. Focus on reliable sources (good econ journals, …), avoid policy reports, unpublished manuscripts, obscure think-tanks, etc. Once you find a related (recent) paper, go through its references, follow the relevant ones. Do some reading (5-10 related papers).

4. Find (or collect!) the data Be creative! Sometimes it will just be the Labor Force Survey of the country you’re interested in. Sometimes you will have to be more proactive! If it can be measured, it can be collected! Be ambitious.

Examples of common data sources US: Current Population Survey (CPS) Census NLSY PSID Spain: Encuesta de Población Activa Muestra Continua de Vidas Laborales

5. Run the analysis Once you have your plan and your data, this part is easy! Start by getting to know your data well. Study the codebooks, run a lot of descriptives statistics,… Write your code carefully, document it well. Ideally write a single program that makes it easy to replicate everything by someone else.

Robustness checks Run alternative specifications, with different subsamples, control variables, etc. Think carefully about alternative interpretations for your results, limitations, etc, and try to address them. Or at the very least, acknowledge them!

6. Write down your results First, decide on what is your main result. Make the tables and figures to support your main result. You’re ready to write the paper, explaining what you did and what you found. Be clear and concise.

7. Presentations Brief, to the point! The question and why it’s relevant. What you do and why it’s novel. What you find (or expect to find).

8. Revise and submit What am I looking for? Potential, more than actual results. A good idea. The question and identification strategy are the most important components. Some preliminary results would be nice, but are not necessary. The more advanced the project, the better. At least in terms of detail (methodology, finding the appropriate data, etc).

Replication exercise An alternative way to proceed would be to replicate an existing published paper. With the same data as the original. And then extend it in some way. Perhaps just by running additional variations to check robustness. If you detect some mistake, you could get famous! 

Background reading “From Micro Data to Causality: Forty Years of Empirical Labor Economics” (Bas van der Klaauw, IZA Discussion Paper No. 8047, March 2014).

The goal Ideally, your course project would be the seed of a paper to be published in a good journal.