Nathan Lane

Niharika Singh

This course is compulsory on the MSc in Economic Policy for International Development. This course is not available as an outside option to students on other programmes.

Co-requisites:

Students must complete DV494 either before taking this course or in the same year as this course.

DV496 will build on DV494 to teach students how to develop a quantitative research project in development economics. It will cover micro-econometrics, causal analysis, data visualization and research design. It will discuss the latest research on experimental and quasi-experimental methods, and machine learning for big data analysis. The objective is to enable students (a) to critically assess the quality and validity of development economics research; and (b) to independently design their own research proposal. Students will learn about the challenges of research design, including how to generate a research question, how to select a research method, and the relative strengths and weaknesses of alternative research methods.

15 hours of lectures and 10 hours of seminars in the Winter Term.

This course has a reading week in Week 6 of Winter Term.

Presentation.

Dissertation draft.

Students will be expected to produce a draft of the empirical paper in the ST. The course will have the following formative assessments:

- Research proposal draft and in-class presentation: each student will perform at least three in-class presentation during seminar meetings in order to get peer feedback and oral feedback from the course convenors.

These two formative assessments will provide students with skills and feedback to write their research proposals and pursue their research paper projects. They will also be relevant data for course convenors to assess which areas may need strengthening.

Angrist, Joshua D., and Jörn-Steffen Pischke. Mastering Metrics: The Path from Cause to Effect. Princeton University Press, 2014.

Angrist, Joshua D., and Jörn-Steffen Pischke. Mostly harmless econometrics: An empiricist's companion. Princeton University Press, 2008.

Cunningham, Scott. Causal inference: The mixtape. Yale University Press, 2021.

Duflo, Esther, Rachel Glennerster, and Michael Kremer. 2008. “Using Randomization in Development Economics Research: A Toolkit.” Vol. 4.

T. Schultz and John Strauss, eds., Handbook of Development Economics. Amsterdam and New York: North Holland, 4

Presentation (20%).

Dissertation proposal (80%).