This class is designed to motivate learning microeconomic concepts by showing that they can be simply and practically applied to real world data, and to give you some first-hand experience in doing so. Much of the learning will be structured in the form of empirical economic analysis problems. In addition to completing individual exercises, every student will take an individual midterm examination, an individual final, and join in two small-group analysis projects, with in-class presentation, discussion, and critique.
Group analysis projects that are assigned can be undertaken in the student group’s choice of Excel, R, or Python software. Students interested in using the Python data science software suite will be asked to take 12 hours of individual, introductory online instruction, imparting basic Python software skills. Examples worked in class and solutions posted online will use the Python data science software platform. Student groups working in Excel or R are expected to have sufficient expertise with this software to do their empirical analysis and should not expect to receive additional software-specific support.
The class does not require a previous economics course. If you do have some economics experience, you are encouraged to assist those of your peers who do not. Time will be reserved for in-class laptop “empirical analysis clinics,” to provide you with real time feedback on conceptual or data analysis issues you may encounter.
Every student will be asked to participate in two group presentations to the class. These exercises will be oral presentations of student analyses and solutions to a detailed policy analysis problem based on the particulars of a real microeconomic topic. In addition, each student will be asked to complete problem sets, to take a midterm, and a final exam. The group presentations will each count for 15% of the final grade, the problem sets for 20%, the midterm for 20%, and the final exam for 30%.