The purpose of this course is to cultivate economic intuition. My goal is not to teach students what to think, but rather how to think as economists. The course considers how social outcomes are shaped by the decisions of many individuals, even though each individual commands only a small fraction of the economy. Expanding upon the notion that individuals respond to incentives, students will use models to analyze and assess a variety of social phenomena. Successful students will leave the course with an intellectual framework for understanding and evaluating economic issues and policy.
This course introduces the statistical techniques that help economists learn about the world using data. Using calculus and introductory statistics, students will cultivate a working understanding of the theory underpinning regression analysis–how it works, why it works, and when it can lead us astray. As the course progresses, students will apply the insights of theory to work with and learn from actual data using
R, a statistical programming language. My goal is for students to leave the course with marketable skills in data analysis and–most importantly–a more sophisticated understanding of the notion that correlation does not necessarily imply causation.
In this course, students will explore issues in the labor market using insights from economic theory and empirical papers. In the spirit of the “issues” moniker, the course examines some of the potential causes of wage inequality, such as human capital, compensating wage differentials, discrimination, the decline of organized labor, and assortative matching. My hope is that students leave the course with a more nuanced understanding of the mechanisms that drive wage inequality in the labor market.