Quantitative Analysis of Income Inequality
Units: 12
In this course, students study income inequality and learn how "big data" tools are used to understand these trends. It covers three sets of topics: 1) How has inequality evolved in recent decades and how do people choose data sources and concepts to suit their preferred narrative? 2) What are the theories that can explain the level of economic inequality and its trend? 3) What methods can we use to evaluate the effectiveness of alternative theories and policy options? Specifically, we analyze leading policy suggestions to tackle rising inequality, including free college education, minimum wages, and changing taxes on top earners. In the process, the class introduces basic methods in data science, including regression and causal inference. The structure of the class assignments follows the insight that 80% of a data analyst's job is to tell stories with data and understand existing results. The class aims to strengthen these skills and emphasizes the ability to communicate quantitative results and critically evaluate the strength and weaknesses of existing approaches. To practice these skills, students are assigned recent research papers on inequality, prepare a policy brief, evaluate the empirical methods, and prepare suggestions to improve them.
The primary objective of the course is to introduce students to the main theories and empirical methods used by economists to understand the scope and evolution of income inequality. The class will prepare students to:
- Evaluate the effectiveness of data and methods used to measure inequality.
- Understand data tools to make causal statements.
- Apply the causal inference toolkit to evaluate research and policy claims about the causes of inequality.
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