Spring 2021 - 60830 - PA 393H - International Economics | LBJ School of Public Affairs | The University of Texas at Austin

Spring 2021 - 60830 - PA 393H - International Economics

International Economics-WB

Powerful new data science software tools are making policy-relevant analysis of large-scale (and often, open access and public) datasets increasingly feasible, around the world.  This class will teach key economic concepts underlying international economic policies using a suite of widely used non-proprietary, open, data science software tools applied to real world data.  The objective of this class is to teach the basic economic theory underlying various trade, technology, and industrial policies in use around the world today, sensible ways to apply these theories to frame policy choices, and use of these methods to guide analysis and visualization of actual, real world data.

By illustrating how theory and analysis can be simply and usefully applied to real world data, and giving you some first-hand experience in doing so, the class is designed to motivate learning economic concepts. In addition to other course requirements, this class will require completion of approximately 12 hours of introductory online courses covering basic skills in the Python analysis and visualization software we will be using. The online course modules assume no software experience or other prerequisites.

This class is also intended to serve as a first introduction to current generation data science software tools that can be applied to real data for practical policy purposes. Are there any concrete, practical advantages to this approach for a master’s level international economics class? Go to indeed.com (an online job recruitment site) and enter the keywords “economics” and “python” in the “what” box (leave the “where” box blank for the moment). If you actually page through these listings, you will be served additional sponsored ads from would-be employers seeking to get you to apply for their jobs on the spot. One benefit of this class is that you will be able to honestly write in both words-- ‘economics’ and ‘Python’-- when asked by a prospective employer to describe what data analytical tools you were introduced to in grad school.

The Python software tools we will be using are relatively new and very powerful, used by organizations and businesses, as well as researchers, to undertake policy-relevant analysis. The class assumes as a prerequisite only that you have previously taken an introductory microeconomics course. There are no computer programming prerequisites, but you will need to bring to class a personal computer with the Anaconda distribution of Python installed (directions to follow). Lectures will be delivered through interactive Python notebooks (Jupyter notebooks). Students will follow along class lectures using this open source software platform installed on a personal laptop computer (Windows, Mac, or Linux).

The Anaconda Python distribution should work on a computer running either Windows, or the Mac OS, or Linux. Because access to electrical outlets in the classroom may be limited, your personal computer should probably have at least a 3-hour battery life, and be fully charged before class. 

Disclaimer. You should understand that this class is likely to demand a significant investment of your time over the course of the semester. But there should be a payoff in useful things you know about, and are able to do, by the end of semester.

Every student will to participate in two formal group project 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 world economic topic. In addition, each student will be asked to complete individual problem sets, to take an individual midterm, and to submit an individual final project. Each of the group presentations will count for 15% of the final grade, the problem sets for 20%, the midterm for 20%, and the final project for 30%. Grades on oral presentations will be based on both student peer evaluations (50%) and instructor evaluations (50%).

Ph.D.
M.P.Aff
MGPS
M.P.Aff-DC
MGPS-DC
Class Schedule: 
T 2:00PM to 5:00PM
Instruction Mode: 
Internet